Microvascular plasticity in the healthy and diseased mouse cortex

by

Patrick Reeson B.Sc., University of Calgary, 2005

A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of

DOCTOR OF PHILOSPHY

in the Division of Medical Sciences (Neuroscience)

 Patrick Reeson, 2018 University of Victoria

All rights reserved. This dissertation may not be reproduced in whole or in part, by photocopy or other means, without the permission of the author. ii

Supervisory Committee

Microvascular plasticity in the healthy and diseased mouse cortex

by

Patrick Reeson B.Sc., University of Calgary, 2005

Supervisory Committee

Dr. Craig E. Brown, Division of Medical Sciences Supervisor

Dr. Patrick C. Nahirney, Division of Medical Sciences Departmental Member

Dr. Bob Chow, Department of Biology Outside Member

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Abstract

The brain relies on a properly functioning vasculature system to deliver oxygen and nutrients and remove metabolic waste. However as in all biological systems, the brain is sometimes challenged by small or large-scale failures in the vascular system, which threaten the neuronal networks they support. Cerebral capillaries are uniquely prone to spontaneous obstructions, randomly stopping flow on a moment to moment basis. While not surprising given that capillaries are narrow, low pressure tubes that pass relatively large and adherent cells and debris, the ultimate outcomes of these obstructions are unknown. The vascular response to these events could have profound effects on brain health, as these random events accumulate over time. Similarly, while much research has studied the neural and vascular responses to large vessel obstructions (ischemic stroke), how common comorbidities which also afflict the vasculature, like diabetes, alters vascular plasticity and in turn neuronal rewiring and functional recovery, is not understood. This dissertation furthers our understanding of how microvascular plasticity, in response to either small or larger interruptions to blood flow, affect brain health.

In the first aim I examined the fates of cortical capillaries in the mouse somatosensory cortex to either spontaneous or experimentally induced obstructions. Using in vivo 2 photon imaging of cortical blood flow, I found that ~0.12% of cortical capillaries become obstructed each day. Tracking natural or microsphere induced obstructions in anesthetized or awake mice revealed that most capillaries recanalize. Remarkably, 30% of all obstructed capillaries failed to recanalize and were pruned by 21 days. This loss was not compensated for by any angiogenic sprouting in any imaging area. Using this iv information, I was able to predict capillary loss over time that closely matched experimental estimates. From a mechanistic perspective, endothelium specific genetic knockdown or pharmacological inhibition of VEGF-R2 signaling was a critical factor in promoting capillary re-canalization and mitigating subsequent pruning. Thus, this work reveals the incidence, mechanism and long-term outcome of capillary obstructions and contributes to our understanding of age related capillary rarefaction.

In the second aim, I examined the vascular adaptions that accompany large scale disruptions to the cerebral blood flow in the form of ischemic stroke. Using a mouse model of type 1 diabetes, I revealed that ischemic stroke leads to an abnormal and persistent increase in Vascular Endothelial Growth Factor Receptor 2 (VEGF-R2) expression in peri-infarct vascular networks. Correlating with this, BBB permeability was markedly increased in diabetic mice which could not be prevented with insulin treatment after stroke. Imaging of capillary ultrastructure revealed that BBB permeability was associated with an increase in endothelial transcytosis rather than a loss of tight junctions.

Pharmacological inhibition or endothelial-specific knockdown of VEGF-R2 after stroke attenuated BBB permeability, loss of synaptic structure in peri-infarct regions, and improved recovery of forepaw function. However, the beneficial effects of VEGF-R2 inhibition on stroke recovery were restricted to diabetic mice and appeared to worsen

BBB permeability in non-diabetic mice. These results showed that aberrant VEGF signaling and BBB dysfunction after stroke plays a crucial role in limiting functional recovery in an experimental model of diabetes.

Overall this dissertation demonstrates that the structure, integrity, and function of mature cerebrovascular networks undergoes substantial changes in the face of both small v and large scale vascular insults. Furthermore, I have revealed a critical role for endothelial VEGF-R2 signalling in mediating many of these vascular changes, which in turn, had important consequences for brain aging and the recovery of function after stroke. vi

Table of Contents

Supervisory Committee ...... ii Abstract ...... iii Table of Contents ...... vi List of Abbreviations ...... ix List of Figures ...... x Acknowledgments...... xii Dedication ...... xiv Chapter 1 ...... 1 1.0 Introduction ...... 1 1.1 On the paradox of cerebral blood flow ...... 5 1.2 The anatomy of the cerebral blood supply ...... 7 1.3 The structure of cerebral blood vessels ...... 10 1.4 The Blood-Brain Barrier ...... 12 1.4.1 Endothelial Tight Junctions ...... 14 1.4.2 BBB Endothelial transporters ...... 16 1.4.3 BBB Endothelial transcytosis ...... 16 1.4.4 Formation and Regulation of the BBB ...... 17 1.5 The nature of blood flow in cortical capillaries ...... 20 1.6 The effects of shear stress on endothelial cell function ...... 23 1.7 VEFG-R2 is a master regulator of Endothelial cell function ...... 24 1.8 Vascular plasticity in the developing and adult brain ...... 28 1.9 Vascular obstruction in the healthy brain ...... 30 1.10 Capillary loss with aging ...... 31 1.11 Stroke ...... 33 1.12 Stroke in diabetics ...... 38 1.13 Rationale and aims ...... 44 Bibliography ...... 47 Chapter 2 VEGF signaling regulates the fate of obstructed capillaries in mouse cortex . 72 2.1 Abstract ...... 72 2.2 Introduction ...... 73 2.3 Results ...... 74 2.3.1 Superficial and lower order cortical capillaries are prone to obstruction ...... 74 2.3.2 Fates of obstructed cortical capillaries ...... 79 2.3.3 Impact of capillary pruning on local blood flow ...... 92 2.3.2 Lower capillary density in aged mice is predicted by obstruction and pruning rates ...... 100 2.3.3 VEGF-R2 signaling dictates capillary recanalization ...... 106 2.4 Discussion ...... 115 2.4.1 Microsphere Model of obstruction...... 116 2.4.2 Vascular remodelling in the mature brain ...... 119 vii

2.4.3 Mechanisms of recanalization...... 120 2.4.4 Microvascular obstructions and cognitive impairment ...... 122 2.5 Materials and Methods ...... 123 2.5.1 Animals ...... 123 2.5.2 Cardiovascular measurements ...... 124 2.5.3 Cranial window surgeries ...... 124 2.5.4 Microsphere model of capillary obstruction ...... 125 2.5.5 In vivo imaging ...... 125 2.5.6 Analysis of vascular structure and flow ...... 127 2.5.7 Estimation of capillary numbers ...... 128 2.5.8 Recanalization rates and capillary fates ...... 129 2.5.9 Aged capillary density measurements ...... 130 2.5.10 Modelling capillary loss over time ...... 131 2.5.11 Stimulating or blocking VEGF signalling in vivo ...... 131 2.5.12 Microsphere density analysis ...... 132 2.5.13 Analysis of endothelial cell regression ...... 133 2.5.14 DiI coating of microspheres ...... 133 2.5.15 Phosphorylated VEGF-R2 immunohistochemistry and analysis ...... 134 2.5.16 Western blotting ...... 135 2.5.17 Statistics ...... 136 Bibliography ...... 137 Chapter 3 Delayed inhibition of VEGF signaling after stroke attenuates blood brain barrier breakdown and improves functional recovery in a co-morbidity dependent manner ...... 146 3.1 Abstract ...... 146 3.2 Introduction ...... 147 3.3 Results ...... 149 3.3.1 Animal model of diabetes and ischemic stroke ...... 149 3.3.2 Aberrant expression of VEGFR2 in the diabetic peri-infarct cortex ...... 150 3.3.3 Diabetes exacerbates blood-brain barrier disruption after stroke ...... 154 3.3.4 Loss of BBB integrity is mediated primarily by an increase in endothelial transcytosis ...... 159 3.3.5 Inhibition of VEGF-R2 signalling attenuates stroke induced BBB permeability in diabetic animals ...... 163 3.3.6 VEGF-R2 inhibition in diabetic mice prevents dendritic spine loss and improves functional recovery after stroke ...... 168 3.4 Discussion ...... 175 3.5 Conclusion ...... 181 3.5 Materials and methods ...... 181 3.5.1 Animals ...... 181 3.5.2 Induction of hyperglycemia and stroke ...... 182 3.5.3 VEGF-R2 inhibition...... 183 3.5.4 Analysis of vessel permeability and branching ...... 183 3.5.5 Western blotting ...... 185 3.5.6 Immunohistochemistry ...... 186 3.5.7 Dendritic spine density ...... 188 viii

3.5.8 Measurement of infarct volume ...... 189 3.5.9 Electron microscopy ...... 189 3.5.10 Behavioral assessment of forepaw sensory-motor function ...... 191 3.5.11 Statistics ...... 192 Bibliography ...... 171 Discussion ...... 210 4.1 Summary ...... 210 4.2 Spontaneous cortical capillary obstructions and models ...... 216 4.3 Pruning and angiogenesis ...... 223 4.4 Mechanisms of recanalization ...... 226 4.6 Diabetics and stroke ...... 230 4.7 Conclusion ...... 232 Bibliography ...... 234 Appendix ...... 276 Appendix 1 Supplementary code 1 and 2 ...... 276

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List of Abbreviations

α-SMA – Alpha Smooth Muscle Actin ACA – Anterior Cerebral Artery AGE – Advance Glycation End products BBB – Blood-Brain Barrier CBF – Cerebral Blood Flow ECM – Extra-Cellular Matrix GFP – Green Fluorescent Protein MCA – Middle Cerebral Artery MCAO – Middle cerebral Artery Occlusion NO – Nitric Oxide NVC – Neuro-vascular Coupling NVU – Neuro-vascular Unit PCA – Posterior Cerebral Artery ROS – Reactive Oxygen Species T1D – Type 1 Diabetes T2D – Type 2 Diabetes TJC – Tight Junctional Complex TPA – Tissue Plasminogen Activator WSS – Wall Shear Stress VCI – Vascular Cognitive Impairment VEGF – Vascular Endothelial Growth Factor VEGF-R1 – VEGF Receptor 1 VEGF-R2 – VEGF Receptor 2 VSD – Voltage Sensitive Dye

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List of Figures Figure 1. Blood supply to the brain...... 8 Figure 2. Major cerebral artery territories and functional lobes ...... 9 Figure 3. Structure of the cortical vasculature...... 10 Figure 4. The Blood Brain Barrier...... 13 Figure 5. Vascular Endothelial Growth Factor Receptor 2 (VEGF-R2) Signalling...... 20 Figure 6. Mouse photothrombotic model of ischemic stroke...... 34 Figure 7 Cortical capillaries are prone to spontaneous obstruction...... 77 Figure 8 Work flow and validation of automated estimates of vessel density...... 81 Figure 9 Fluorescent microspheres as a model of spontaneous naturally occurring capillary obstructions...... 83 Figure 10 . Microsphere based obstruction and pruning did not induce a microglial response or cell death...... 85 Figure 11 Microsphere obstructions are distributed across major cerebral vascular territories...... 86 Figure 12 Fates of obstructed cortical capillaries...... 90 Figure 13 Capillary pruning does not alter adjoining capillary position...... 91 Figure 14 Additional examples of capillary recanalization and pruning...... 93 Figure 15 Recanalization correlates with obstruction location but not Local blood flow. 95 Figure 16 Blood flow in recanalized capillaries does not predict later pruning...... 96 Figure 17 Obstructed capillaries that recanalized had a higher risk for subsequent obstruction...... 97 Figure 18 Capillary pruning leads to altered blood flow in adjacent connected capillaries...... 99 Figure 19 Lower capillary density in aged mice is predicted by obstruction and pruning rates...... 103 Figure 20 Modelling capillary loss over time...... 105 Figure 21 VEGF-R2 signaling dictates capillary recanalization...... 109 Figure 22 Vascular specific knockdown of VEGF-R2 does not affect cardiovascular health or blood flow...... 113 Figure 23 Inhibition of VEGF-R2 signaling with SU5416 did not affect cardiovascular health or blood flow...... 115 Figure 24 Experimental outline...... 150 Figure 25 Diabetes amplifies the upregulation of VEGF-R2 after stroke...... 152 Figure 26 Diabetes exacerbates the loss of BBB integrity 3 days after stroke...... 156 Figure 27 Excessive BBB permeability in diabetic mice is not related to peri-infarct angiogenesis...... 158 Figure 28 Loss of BBB integrity at 3 days is due primarily to increased transcytosis and not tight junctional complex disassembly...... 162 Figure 29 Inhibiting VEGF-R2 signalling attenuates stroke induced BBB permeability in diabetic mice...... 166 Figure 30 SU5416 treatment does not affect infarct volume...... 167 Figure 31 Inhibiting VEGF-R2 signalling mitigates excessive spine loss in peri-infarct cortex in diabetic mice...... 171 xi

Figure 32 Inhibiting VEGF-R2 signalling improves functional recovery in diabetic mice...... 175 Figure 33 Summary of obstructed cortical capillary fates and the effects of VEGR-R2 inhibition...... 211 Figure 34 Summary of changes to peri-infarct blood vessels and dendritic structure after stroke...... 216

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Acknowledgments I would like to acknowledge, as an uninvited settler to these lands, the Songhees, Esquimalt and WSÁNEĆ peoples. It was upon their lands this research was performed and this dissertation was written.

First and foremost, I would like to acknowledge my twin sister Kristina, who’s love, kindness and support sustained me. And her husband Mark, the hardest working and most honorable man I know. You have built a warm, loving, and endlessly entertaining family. To have been welcomed into this haven has meant more to me than I will ever be able to express.

To Emma, Lizzie, and Teddy, you have kept me young and mindful of the astonishing joy in curiosity and the power of imagination. More importantly your love has given me strength and hope when I have needed it. Science is meaningless unless done in the service of others. I was lucky enough to love and be loved in return by three of the most extraordinary human beings to ever be. If I can spend my life making your world just a little better, if I can inspire you, help you find your passion in life, it will all have been worth it.

To my brothers Michael and Andrew, your friendship, love, and mentorship (as only older brothers can) has been a profound force in shaping my life. You have shown me different perspectives and exposed me to vast worlds of art, literature, philosophy, music, and politics. I officially forgive you for tying me up and pouring hot sauce down my throat until I vomited.

To my Mum and Dad, without your support and love this PhD would not have been possible. Our family life was not always perfect, but for better or worse I am the person I am because of your love and guidance, and I am grateful.

To my friends, if I only I could reach back in time to the shy little boy of my youth, who would eat his lunch hiding in stairwells alone, and tell him what stupendous people I would one day be honored to call my friends. To Amanda, Lena, Alex, Abdul, Scott, Ben, Aaron, Christine, and Essie. There is so much you have done for me, so many little moments of kindness, compassion, support, and friendship, in it’s totality, it is truly staggering. If I were to be judged by the company I keep, my value would be grossly overestimated. And a special gratitude to Amanda and Lena, who have always done a better job at loving me than I have myself, and it has made all the difference.

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I have had the privileged of being mentored by a few extraordinary scientists. To my supervisor Craig who gave me ever opportunity to try, to succeed, and to fail. You started my career in Neuroscience, gave me the tools and the knowledge to start down this extraordinary path, and the trust and guidance to follow my own directions. For that I am eternally in your debt. To my committee members Bob Chow and Pat Nahirney, I thank you for the years of guidance. To my external Grant Gordon, for many pieces of great advice over the years. To Kerry Delaney, your mentorship and support has always been an honor. You seem to think I’m not entirely an idiot, I find this very encouraging. To my fellow members of the Brown Lab, Andrew, Angela, Akram, Kim, Essie, Emily, Mohammed, and Ben, you made a few benches in MSB a joyous place to work. And of special mention, to Abdul, your friendship and support, your intellect, humor, creativity, and passion is amazing, your inquisitive and extraordinary mind is an example I aspire to. And to the volunteers who labor I shamelessly exploited and took credit for, thank you Natalie, Jessie, Charmaine, Kevin, and Patrick. To my first mentor in science, who will likely never read this, but to Dr. Benjamin Silverberg, who in a high school biology class challenged me to be exceptional. Your belief that I was capable of something more than I aspired to, that the lofty goal of discovering something new about the world around me was within my grasp, allowed me to stumble upon the extraordinary joy of science and started me towards my true passion in life.

To the many support staff at the University of Victoria, who’s contribution to our success is criminally under appreciated. You are the bedrock of this institution. I am deeply grateful for the help of our DMS graduate secretaries Karen and fellow Dr. Who aficionado Erin, Lab Mangers Evelyn, and Sara, and especially to Facilities Manager Robin, who’s opera recommendations were spot on, and who’s many engrossing early morning political debates I have greatly enjoyed.

And lastly to the many dogs I have known and loved, this life would suck hard without you, to Charlie, Scooter, Duffy, Maggie and Sita. xiv

Dedication

To Emma, Lizzie, and Teddy.

I will always be more honored to have been called Pat-Pat than

any other title I may ever earn.

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Chapter 1

1.0 Introduction

All complex systems depend on the infrastructure that feeds in raw materials and removes refuse. The brain is no exception, it’s function relies on an encompassing transportation network that delivers a constant influx of oxygen and nutrients, while removing metabolic waste. The human brain contains ~650 km of blood vessels, the majority of which are capillaries (Cipolla 2009; Schmid, Barrett, et al. 2017). However, to date the focus on the cerebral vasculature has been somewhat myopically centered on large vessels, such as the major cerebral arteries. For example one of the most widely used textbook on neuroscience barely mentions capillaries (Kandel 2013) (the vasculature itself is relegated to an appendix). However, almost all the exchange of gases and macromolecules occurs at the level of capillaries (Cipolla 2009). A more complete understanding of this relationship, between the vascular and neuronal systems, requires a renewed focus on the principle functional unit of the cerebral vasculature, the capillary.

Plasticity is the ability for networks to change either structurally or functionally over time. In the brain it has traditionally been considered in the context of neural circuits, however most biological systems exhibit some level of plasticity in response to environmental cues. While larger vessels are relatively fixed (structurally) throughout life, capillaries display remarkable plasticity in development, and possibly into adulthood

(Harb et al. 2013; Chen et al. 2012; Whiteus, Freitas, and Grutzendler 2014). Thus, capillaries are both the main site of exchange, and the primary substrate for vascular plasticity in the brain. How cerebral vascular networks evolve over time is likely

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dominated by changes at the level of capillaries. The relationship between the structure and function of neural circuits and the vasculature is reciprocal, neural activity shapes the development of vascular networks (Whiteus, Freitas, and Grutzendler 2014) and locally influences vascular function by Neural Vascular Coupling (NVC) (Attwell et al. 2010;

Roy and Sherrington 1890). Conversely, dysfunction of the vascular system quickly impacts neural structure and function (Iadecola 2004; Iadecola 2013; Brown, Wong, and

Murphy 2008). A critical question is therefore to what extent can the cerebral vasculature respond and adapt to challenges. In otherwise healthy animals, cerebral capillaries are lost with aging (Brown and Thore 2011; Riddle, Sonntag, and Lichtenwalner 2003), yet mechanistically why this should be the case is not clear. Given that anatomically age- related changes to neuronal or synaptic density does not strongly correlate with cognitive decline, while cerebral capillary rarefaction does, it has been speculated that capillary loss may be a key driver of age related declines in brain function and some forms of vascular dementia (Riddle, Sonntag, and Lichtenwalner 2003; Brown and Thore 2011;

Iadecola 2013). Likewise, after ischemic stroke the vascular system plays a role in supporting adaptive neural plasticity to partially restore lost brain function (Brown et al.

2007; Murphy and Corbett 2009). A compromised vascular system could however impair recovery, particularly in diseased states that injure the vasculature, such as diabetes.

Plasticity occurs in response to a changing environment, in the adult cortex this could be changes to blood flow and/or endothelial cell health. In the cerebral vasculature, the microcirculation is uniquely prone to spontaneous stalls and obstructions (Santisakultarm et al. 2012; Kleinfeld et al. 1998; Villringer et al. 1994; Erdener et al. 2017;

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Santisakultarm et al. 2014). Spontaneous obstructions, while unpredictable and often transient, happen with enough frequency to be observed in the rodent cortex (Erdener et al. 2017; Kleinfeld et al. 1998; Santisakultarm et al. 2014; Villringer et al. 1994). Some obstructions persist, halting blood flow for hours, and potentially longer (Santisakultarm et al. 2014; Erdener et al. 2017). Despite the potential impact on vascular function, the long-term fates of these obstructions are unknown (Erdener et al. 2017; Santisakultarm et al. 2014). How the microvasculature adapts to these obstructions could over time reshape vascular trees, with functional consequences. For example, if the microcirculation maintains a developmental like capacity for angiogenesis, these obstructions would be benign. Likewise, if all capillaries have similar mechanisms of recanalizing, such as angiophagy seen in large capillaries and penetrating arterioles (Lam et al. 2010), then obstructions may only briefly interrupt blood flow. However, if some capillaries are unable to recanalize, then the accumulation of spontaneous obstructions over years could lead to changes in cerebral perfusion and cognitive performance.

In the disease afflicted brain, the challenges facing the vasculature are even greater. For example, vascular endothelial cells in diabetics are bathed in hyperglycemic blood. These cells, due to the nature of their glucose transporters, are unable to regulate the influx of sugar into the cell (Lu et al. 2013; Benarroch 2014). The constant influx of glucose drives endothelial cells into metabolic overdrive, altering cell biochemistry and signalling pathways (Brownlee 2001; Tomlinson 1999; Spitaler and Graier 2002; Pieper

1997; Pieper and Peltier 1995). The end result of glucose overload is the compromise of endothelial function. The diabetic brain is also highly prone to obstructions, leading to ischemic strokes at much higher rates than euglycemic counterparts (Baird et al. 2002).

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Following stroke, the brain undergoes adaptive rewiring in the surviving adjacent brain tissue, partially restoring lost brain functions (Murphy and Corbett 2009; Brown et al.

2009; Brown et al. 2007). This process is however dependent on the surviving vascular network in the same tissue. Diabetes significantly worsens prognosis for functional recovery following stroke (Baird et al. 2002; Megherbi et al. 2003; Wei, Heeley, Wang,

Huang, Wong, Li, Heritier, Arima, and Anderson 2010). Impaired functional recovery after stroke has also been shown in rodent models of type 1 diabetes, which correlated with a failure for surviving peri-infarct (adjacent to infarct) circuits to rewire (Sweetnam et al. 2012). The underlying mechanism behind this diminished recovery is however unknown, but likely involves the intersection of an impaired vasculature with rewiring cortical circuits.

The molecular signalling pathways that regulate the various manifestations of microvascular plasticity remain incompletely mapped, yet if elucidated, could offer a promising substrate for improving cerebral vascular health. Vascular Endothelial Growth

Factor (VEGF) signalling through its canonical receptor, VEGF-R2 (Ferrara and Henzel

1989; Ferrara, Gerber, and LeCouter 2003; Olsson et al. 2006), is a probable starting point. VEGF-R2 signalling is a master regulator of endothelial function and feeds into most major signalling pathways (Olsson et al. 2006). VEGF-R2 plays a key role in sensing shear stress (Tzima et al. 2005) and is upregulated after stroke (Hermann and

Zechariah 2009) , so it is ideally situated to initiate vascular changes following a spontaneous obstruction or after ischemic stroke.

Given the vital role the microvasculature plays in maintaining brain health and supporting recovery following injury, the following thesis will examine the intersection

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of the vasculature and the brain in 2 aims. First, I will investigate microvascular obstructions, their prevalence in mouse cortex, microvascular recanalization strategies, and structural plasticity following these sub-ischemic obstructions. The first aim will also look at what effects microvascular obstructions have on the mature vascular network over years, and what endothelial signaling pathways influence can be exploited to improve recanalization. The second aim will explore how the microcirculation in the diabetic brain reacts to focal ischemic stroke in the somatosensory cortex, and how an impaired diabetic vasculature affects circuit remapping and recovery of lost brain functions. Both aims will focus on the role of VEGF-R2 in regulating endothelial cell function and response to small or large vascular insults. VEGF-R2 is well established as a master regulator of most endothelial signaling pathways, and a strong component of the vascular response to large ischemic events. However, how VEGF-R2 signaling relates to sub- ischemic events, or within the diabetic brain will be addressed by both aims.

1.1 On the paradox of cerebral blood flow

The brain consumes vastly more energy than any other organ, ~20% of resting oxygen consumption (despite being ~2% of body weight), at a rate of around 75mL /

100g / min (Cipolla 2009). The metabolic demands of brain tissue fluctuate spatially and temporally with neural activity, leading to corresponding increases in blood flow (Roy and Sherrington 1890; Attwell et al. 2010). Neuronal signalling is energetically expensive, an increase in activity of one action potential / cortical neuron raises oxygen consumption by 3% of resting rate (Attwell and Laughlin 2001). Despite this range of energetic requirements, and the fine spatial and temporal requirements of neural signalling (and thus fluctuating blood flow), neurons paradoxically lack any significant

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energy stores and the cerebral vasculature is fully perfused, without any reserve capacity beyond vessel dilation (Iadecola 2013). While a system of redundancies such as collaterals and pial surface vascular loops exist to shield neurons against large changes in flow, there are no safeguards once blood flow enters the cortex (Shih et al. 2013). This is the fundamental paradox of the cerebral blood supply, the tissue with one of the greatest ranges of energy consumption, and arguably the highest sensitivity to local hemodynamic changes (to maintain fidelity of neural signaling) lacks nearly any safety net beyond the pial vasculature to interruptions of blood flow. Why this paradox exist is a mystery, but given it is ubiquitous across mammalian brains, it likely reflects a fundamental compromise between the different demands and limitations of the nervous and vascular systems. This susceptibility also reflects the evolutionary forces that shaped the brain

(such as being contained in the skull), which in turn shaped the structure and function of the vasculature. This is even more remarkable given that brain function is nearly synonymous with life, thus maintaining proper blood flow to the brain is literally a matter of survival.

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1.2 The anatomy of the cerebral blood supply

Cerebral blood flow starts with oxygenated blood returning from the lungs to the left atrium and then to the left ventricle of the heart. Oxygenated blood leaves the ascending aorta and travels up the two major pairs of arteries supplying the brain, the Internal carotid arteries (supply the anterior brain) and vertebral arteries (supplying the brainstem and posterior brain) (Kandel 2013; Cipolla 2009) (Figure 1). The carotid arteries supply approximately 80% of the blood supply to the brain. The first major safeguard in maintaining perfusion of the brain is the Circle of Willis, the network of bilateral communicating arteries that connect the major cerebral arteries fed by either the Internal carotid arteries or vertebral arteries (Figure 1) (Cipolla 2009). Thus, when one of the main feeding arteries is interrupted, the Circle of Willis provides collateral flow between the anterior and the posterior circulation along the floor of the cerebral vault, providing blood to tissue that would otherwise become ischemic (Figure 1). The Internal carotid arteries supply the Anterior cerebral circulation. These large arteries are the medial branches of the common carotid arteries in the neck which enter the skull (Cipolla 2009).

The internal carotid artery branches into 2 of the 3 major cerebral arteries, the anterior cerebral artery (ACA) and continues to form the middle cerebral artery (MCA) (Kandel

2013) (Figure 1). The Vertebral arteries are smaller arteries that branch from the subclavian arteries. Within the cranium the two vertebral arteries fuse into the basilar artery and supply blood to structures in the brainstem and cerebellum through pontine and cerebellar arteries (Cipolla 2009). The basilar artery then bifurcates into the Posterior

Cerebral Artery (PCA) (Cipolla 2009) (Figure 1). The blood flow to cortical regions is through the three major cerebral arteries, ACA, MCA, PCA (Figure 2) (Kandel 2013).

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Figure 1. Blood supply to the brain.

Diagram showing the major routes of blood from the heart into

brain as well as the Circle of Willis, the network of collateral connections between major cerebral arteries to provide rerouted

perfusion in the case of a major obstruction

Cortical blood flow starts with the surface network of pial vessels that form an interconnected honeycomb like mesh of vessels running parallel to the cortical surface

(Blinder et al. 2013; Shih et al. 2015) (Figure 3). From surface

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Figure 2. Major cerebral artery territories and functional lobes

The three major cerebral arteries supply blood to distinct regions of the cortex. Distinct vascular territories mean occlusions of a major cerebral artery leads to distinct functional deficits, as beyond the Circle of Willis

there is minimal collateral connections between the 3 major cerebral arteries.

pial arteries, branches perpendicular to the surface, penetrating arterioles, travel straight down through most layers of the cortex (Shih et al. 2015; Blinder et al. 2013). Capillaries

(3-8 μm in diameter) branch off from penetrating arterioles, and branch many times before reaching the venous side, often with interconnected loops forming a mesh like lattice (Figure 3) (Blinder et al. 2013; Scallan, Huxley, and Korthuis 2010). The capillary bed feeds into penetrating venules which mirror penetrating arterioles and direct deoxygenated blood out of the cortex towards the pial network of interconnected surface venules. The venous drainage of the cerebrum can be separated into two subdivisions, superficial and deep. The superficial system is made up of the dural venous sinuses, whose walls are formed from the dura mater as opposed to veins (Cipolla 2009). The major dural sinus is the superior sagittal sinus, which flows along the surface of the

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cortex, between the hemispheres, anterior to posterior. At the posterior base of the cerebrum the superior sagittal sinus bifurcates into two transverse sinuses which travel laterally and go on to form the two jugular veins (Cipolla 2009). The deep venous drainage comes from traditional veins inside deep structures of the brain, which join behind the midbrain to form the vein of Galen.

Figure 3. Structure of the cortical vasculature.

Branching off the major cerebral arteries, the surface of the cortex is covered in a honeycomb like mesh of pial arteries, which are highly interconnected allowing robust redistribution of flow if obstructed. Surface arteries give rise to

penetrating arterials the branch perpendicular to the pial surface and dive down most layers of the cortex. Off penetrating arterials is the highly interconnected network of capillaries. Scalebars (x,y,z) 50 μm.

1.3 The structure of cerebral blood vessels

One of the most important concepts in cerebrovascular research to emerge in the last 20 years is that of the Neuro-Vascular Unit (NVU), the idea that neurons and blood vessels act as an integrated functional unit to regulate cerebral blood flow (CBF)

(Iadecola 2013). The NVU is comprised of the endothelial cells that form the luminal lining of the blood vessel, contractile cells around the endothelial cells, smooth muscle

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cells (SMC) or pericytes, and astrocytes that envelope blood vessels with end feet and signal increases in neural activity (and thus energetic demands), and along with neurons to the NVU (Iadecola 2017; Gordon et al. 2008; Gordon, Mulligan, and MacVicar 2007).

As penetrating arterioles enter the cortex the Virchow-Robin space surrounds them, this then gives way to an encompassing sheath of astrocyte end foot process, the glial limitans

(Iadecola 2013). Penetrating arterioles are wrapped in α smooth muscle actin (α-SMA) positive smooth muscle cells which give way to (α-SMA) positive ensheathing pericytes in the first 1-4 branches off the penetrating arteriole (Grant et al. 2017). Higher order

(more branches away from the arteriole) capillaries are more sparsely covered by α-SMA negative pericytes (Grant et al. 2017; Hill et al. 2015). The exact relationship between the different types of mural cells and how they relate to active dilation or constriction of blood vessels, let alone the proper nomenclature, is still hotly debated (Attwell et al.

2016). While some studies have suggested that capillaries initiate vessel dilation during neurovascular coupling (NVC) (Hall, Reynell, Gesslein, Hamilton, Mishra, Sutherland,

O/'Farrell, et al. 2014; Mishra et al. 2016), others have suggested this only occurs in α-

SMA positive mural cells (defined as pericytes by some but not all) (Hill et al. 2015).

Endothelial cells themselves play a critical role in conducting NVC responses across the vascular tree (Longden et al. 2017; Chen et al. 2014). Endothelial cells are electrically coupled through gap junctions, as well as to mural cells like pericytes (Cuevas et al.

1984; Komarova et al. 2017; Iadecola 2013), and conduct calcium waves likely vital to spatial and temporal propagation of NVC (Longden, Hill-Eubanks, and Nelson 2015).

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1.4 The Blood-Brain Barrier

Perhaps the most critical structural feature of the NVU is the Blood-Brain Barrier

(BBB) (Figure 4). The BBB is the property of cerebral blood vessels to restrict or filter the passive movement of most molecules in the blood from entering the brain (Figure

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Figure 4. The Blood Brain Barrier. The blood brain barrier (BBB) separates the brain from the global circulation. A) The BBB was first identified as the propensity for vascular dyes to be excluded from entering the brain unlike other organs, as shown with Evans blue dye. Injected i.v. through the tail vein and circulating for 30 min, after removing the blood through cardiac perfusion, the brain is devoid of any Evans blue staining while non-barrier organs such as skin and liver are stained by Evans blue (that permeated from blood into the tissue). B) There are two major routes through the BBB. Paracellular, through the clefts between endothelial cells or Transcellular, through vesicles moving through the endothelial cells themselves. C) The cells of the Neurovascular Unit (NVU), comprised of endothelial cells, mural cells, and astrocyte end feet. Endothelial cells are tightly bound by Tight Junctional Complexes (TJCs) and surrounded by the extracellular matrix. And Neurons (not shown). D) Electron micrograph of a cortical capillary. The endothelium is noticeably sparsely pockmarked with vesicles and the insert shows the connection of two leaflets of endothelial cells, bound by TJCs, creating a strong barrier to paracellular movement. E) Diagram of TJC structure. Right panel shows how TJCs are arranged along the sides of the two endothelial cell membranes. Left panel shows the major proteins that comprise each TJC (Claudins, Occludins and JAMs) and accessory proteins that link TJC to the cytoskeleton and regulate TJC assembly (Zona Occludens).

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4A-B). The BBB regulates the delicate balance of ions, proteins and sugars required for proper brain function (e.g. Insulin, glucose, albumin etc.) and prevents potentially harmful constituents of the blood, such as high concentrations of glutamate, from entering the brain (Abbott et al. 2010; Ballabh, Braun, and Nedergaard 2004; Dyrna et al.

2013; Hawkins and Davis 2005). The BBB is however not an intrinsic property of brain endothelial cells but rather induced and maintained by cells of the NVU (Figure 4C)

(Armulik et al. 2010; Daneman et al. 2010). Functionally the BBB is not one single property but several factors, including tight junctions between endothelial cells, the spectrum and polarity of endothelial cell transporters and channels expressed, and the suppression of transcytosis (Obermeier, Daneman, and Ransohoff 2013; Andreone et al.

2017; Brightman and Reese 1969; Reese and Karnovsky 1967). The importance of the

BBB for brain health can easily be seen by the extent of damage to neural circuits that occurs when the BBB fails. BBB disruption correlates with inflammation, cell death, loss of synaptic structure and disruption to normal brain function (Chen et al. 2009; Tomkins et al. 2007; Shinnou et al. 1998; Hawkins and Davis 2005; Zlokovic 2008). Not surprisingly BBB disruption is associated with many pathological brain states, notably

Alzheimer’s and dementia (Attems and Jellinger 2014; Bell and Zlokovic 2009; Claudio

1996; Davies and Hardy 1988; Masters and Beyreuther 1988; Taguchi 2009; Iadecola

2004; Iadecola 2013). BBB disruption is also a significant driver of secondary injury following ischemic stroke, which is reviewed in section 1.11.

1.4.1 Endothelial Tight Junctions

The first barrier is the Tight Junctional Complexes (TJC), the most apical structure, that connect endothelial cells together forming seals as close as 4 Å (Figure 4C-D)

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(Anderson and Van Itallie 2009). Endothelial TJCs are considered the core structure of the BBB, forming a physical and charge selective barrier to paracellular diffusion

(Figure 4B) (Bauer et al. 2014). TJC are multi protein complexes that also act as signaling complexes, scaffolding to cluster membrane components and as anchors to the cytoskeleton (Figure 4E) (Dejana 2004; Bauer et al. 2014; Cipolla 2009). TJC are dynamic, capable of rapidly assembly and disassembly, and in turn regulate many cell functions including polarity, proliferation, and gene expression (Bauer et al. 2014;

González-Mariscal, Tapia, and Chamorro 2008; Dejana 2004). The main backbone of

TJCs are three integral membrane proteins, claudin, occludin and junction adhesion molecule (JAM) (Figure 4E) (Cipolla 2009; Luissint et al. 2012). Claudins are 22 kDa phosphoproteins that bind other endothelial claudins on adjacent cells to form the primary seal of TJC (Luissint et al. 2012; Cipolla 2009; Furuse, Sasaki, and Tsukita 1999).

Occludins are 65 kDa proteins with 4 trans membrane domains that bind adjacent occludins and / or claudins (Cipolla 2009; Bauer et al. 2014). Lastly JAMs, a 40 kDa membrane protein, bind adjacent JAMs to form the last component of the TJC seal

(Bauer et al. 2014). All three transmembrane proteins have cytoplasmic ends that associate with accessory proteins, the Zona Occludens (ZO), which link the TJC to the actin cytoskeleton (Figure 4E) (Bauer et al. 2014; Luissint et al. 2012). ZOs connect TJC with the cytoskeleton and are regulated by their phosphorylation states (Abbott et al.

2010). Therefore, ZOs provide both structural stability and a substrate for the regulation of TJC permeability through their association with other TJC proteins.

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1.4.2 BBB endothelial transporters

Another feature of the BBB is the selective expression of luminal and abluminal transporter in barrier endothelial cells. The BBB freely passes oxygen, carbon dioxide and small lipophilic molecules, however everything else must be transported in or out of the brain (Cipolla 2009; Abbott et al. 2010). Vital hydrophilic molecules are passively moved down concentration gradients through carrier-mediated transport (facilitated diffusion), such as glucose, lactose, amino acids, and vitamins (Zlokovic 2008; Masaki

2007). Other substances, either larger, or with weaker concentration gradients

(insufficient to drive diffusion), or strictly regulated signalling molecules, are moved across the BBB through receptor mediated transport (Cipolla 2009; Zlokovic 2008;

Abbott et al. 2010). This includes proteins, chemokines, cytokines, growth factors and insulin to name a few. Lastly, as the BBB helps maintain the delicate microenvironment of the brain, moving things out of the brain is equally important. Efflux out of the brain is primarily done by an ATP dependent class of transports, the ATP-Binding Cassette

(ABC) transporter super family (Zlokovic 2008; Cipolla 2009; Masaki 2007). ABC transporters facilitate the removal of potentially toxic materials out of the brain, such as metabolites and certain chemical compounds.

1.4.3 BBB endothelial transcytosis

There are 2 major routes across a vascular wall, paracellular movement (between endothelial cells) or transcellular / transcytotic movement (through endothelial cells)

(Figure 4B). A down regulation of endothelial cell transcytosis is an important pillar of the BBB (Abbott et al. 2010; Andreone et al. 2017; Villegas and Broadwell 1993). Under normal conditions endothelial cells in the BBB have low levels of vesicular transport

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across the endothelium, as well as a near complete absence of larger pore like structures of vesicles such as vesiculo-vacuolar organelles (VVOs) and trans-endothelial pores

(Figure 4D) (Olsson et al. 2006). Endothelial cells of the BBB have relatively low numbers of pinocytotic vesicles, clathrin-coated vesicles and caveolae compared to peripheral vasculature (Elizabeth 2012). However, some transport does occur through these vesicles, the most numerous being clathrin-coated vesicles and, caveolae (formed from lipid raft domains) as well as larger fluid engulfing macropinocytotic vesicles

(Elizabeth 2012; Abbott et al. 2010; Villegas and Broadwell 1993). Transcytosis can also be divided between non-specific bulk fluid transport or Receptor Mediated Transcytosis, in which vesicular associated receptors give ligand specificity for molecules such as insulin, transferrin, and lipoproteins (Elizabeth 2012; Villegas and Broadwell 1993).

Plasmalemmal vesicles can be identified in endothelial ultrastructure as ~50 nm omega shaped invagination on the cell membrane, the majority of which are open to the extracellular environment (Predescu, Predescu, and Malik 2007; Komarova et al. 2017).

Plasmalemmal vesicles are particularly sensitive to the local hemodynamic environment through caveolae, and plasmalemmal vesicles are often colocalized with the insulin receptor, suggesting they could be involved in the pathogenesis of diabetes (Predescu,

Predescu, and Malik 2007).

1.4.4 Formation and regulation of the BBB

The BBB is a product of many cells creating a unique environment. Brain endothelial cells quickly lose BBB properties when transplanted outside the brain, while endothelial cells transplanted into the brain develop BBB properties (Abbott et al. 2010).

However, determining the exact timing of the development of the BBB has been

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challenging (Blanchette and Daneman 2015; Obermeier, Daneman, and Ransohoff 2013).

In the mammalian brain angiogenesis precedes barrier development by a few days embryonically, however some BBB features are present in invading endothelial cells

(Obermeier, Daneman, and Ransohoff 2013). The BBB then matures after angiogenesis occurs in the brain, with a down regulation of fenestrations and transcytosis in endothelial cells being one of the last features of the BBB to develop (Obermeier,

Daneman, and Ransohoff 2013; Andreone et al. 2017). While the initial angiogenic sprouting of endothelial cells to form nascent blood vessels is highly dependent on classical angiogenic signals, such as VEGF and Wnt (Obermeier, Daneman, and

Ransohoff 2013; Felmeden, Blann, and Lip 2003; Carmeliet and Jain 2011), the maturation of the BBB is a multistep and multi cellular process. The association of both astrocytes and pericytes with newly formed vessels is required for full BBB development

(Daneman et al. 2010; Obermeier, Daneman, and Ransohoff 2013). Once formed, the

BBB requires continued association with, and signalling from, associated cells to be maintained, and can be rapidly opened, particularly in cases of injury, inflammation or disease (Obermeier, Daneman, and Ransohoff 2013). A major regulator of BBB permeability in the brain is VEGF signaling through VEGF-R2 (Figure 5), and depending on the circumstance, VEGF can affect paracellular or transcellular permeability (Cipolla 2009; Olsson et al. 2006; Fischer et al. 2002). VEGF signaling can promote paracellular permeability by promoting cytoskeleton stress fiber contraction which decrease cell-cell contact and increase intercellular junctional spaces (Lum and

Malik 1994). In endothelial cells, VEGF can promote the disassembly of TJC through altering the phosphorylation state of ZO-1 (Weis and Cheresh 2005). However, most

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research indicates high VEGF signalling increases transcytosis, fenestrations and VVOs in endothelial cells (Feng et al. 1999; Kamba et al. 2006; Liu et al. 1999; Weis and

Cheresh 2005; Esser et al. 1998; Bates and Harper 2002; Roberts and Palade 1995). For example VEGF application to isolated blood vessels increased permeability to albumin 3 to 4 fold, which suggested a 2.5 fold increase in transcellular transport (Wu et al. 1996).

While VEGF signalling in vitro has been shown to interact with almost all known endothelial cell signaling pathways (Figure 5), PLC, PLA, AKT, PI3K, MEK, FAK,

Calcium influxes, and eNOS have all been implicated in increasing transcytosis (Bates and Harper 2002).

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Figure 5. Vascular Endothelial Growth Factor Receptor 2 (VEGF-R2)

Signalling. VEGF-R2 is the major receptor for VEGF-A and a member of the Receptor

Tyrosine Kinase (RTK) superfamily. Like most RTK binding of the ligand to the extracellular immunoglobulin like domains, or physical force, causes the receptor to dimerize where each half phosphorylates the other in the Kinase Regulatory Domain, which leads to phosphorylation of tyrosine residues downstream, The interplay between these distinct mechanisms of permeability and the web of notably Y-1175. Phosphorylation of these c-terminal tyrosine sites allows secondary messenger proteins to interact with the receptor and activate signal downstreamcascades. VEGFsignaling-R2 from intersects VEGF receptors with most is not major clear, signal nor is transductionit clear if and pathways,when each including MAPK, AKT, PKC and Src. route dominates in any given instance of pathological BBB disruption.

1.5 The nature of blood flow in cortical capillaries

Capillaries are inherently narrow high resistance tubes that must pass large and adherent cells and plasma constituents. Blood flow through the cerebral vasculature can be approximated by Ohm’s law in that flow is proportional to the difference in inflow and outflow pressure (ΔP) divided by the resistance (Cipolla 2009), and by Poiseuille’s law that flow is proportional to ΔP, and thus vessel length (L), blood viscosity (μ), volumetric flow rate (Q) and vessel radius (r) to the fourth power (ΔP = (8μLQ)/πr4 ) (Sumpio

1993).Flow in the cerebral capillaries is generally laminar with a blunted parabolic cross section profile and also free of inertia effects (Hirsch et al. 2012). Blood is not a pure liquid, but a suspension of cells and flow depends on both vascular geometry and serum constituents. Thus the distribution of RBC along different capillaries define each segments’ contribution to total vascular resistance (Hirsch et al. 2012). Several phenomena of flow, including plasma skimming, RBC migration to the luminal center,

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unequal distribution of RBCs at bifurcations, and vascular topology affect RBC distribution (Hirsch et al. 2012). Thus, the major determinants of blood flow are luminal diameter, pressure gradients and RBC distribution. While RBC velocities are relatively consistent across larger penetrating arterioles (~ 10 mm/ sec). Flow across microvessels have significantly greater variation, ranging from 0.01 to 10 mm/sec, across several orders of magnitude (Shih et al. 2013). This despite a much smaller range of diameters compared to arterioles (Shih et al. 2013; Blinder et al. 2013). Therefore, in the cerebral capillary bed, while changes in diameter are determinants to flow, pressure gradients and

RBC density are likely equally important. The capillary bed is also the largest source of hemodynamic resistance in the brain, and capillaries experience the largest pressure differential across any vessel type in the cortex (Gould et al. 2016). The capillary network can be regarded as a 3 dimensional mesh with a width of approximately 50 to 75 μm wide

(average capillary length) (Schmid, Barrett, et al. 2017). The architecture is both homogenous and highly interconnected, with substantial branching and interconnected loops, making any unifying topological order difficult to extract (Schmid, Barrett, et al.

2017; Blinder et al. 2013; Hirsch et al. 2012). However, just as vascular topology influences RBC distribution along different capillary paths, RBCs may play a role in molding the development of vascular topology. Computer modeling of simple capillary networks predict frequent branches of very slow or stalled RBCs (Obrist et al. 2010), which can significantly impair RBC oxygen delivery, suggesting developing capillary networks are structurally refined to increase efficiency (Hirsch et al. 2012).

Alongside the surprising heterogeneity in average RBC transit time (Jespersen and Østergaard 2012), an RBC moving through the cortical capillary bed, from an

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penetrating arteriole to a penetrating venule, has a number of possible paths (on average

8) to choose from (Schmid, Barrett, et al. 2017). However, computer modelling suggest that for most capillary starting points (>50%) there is a preferred RBC path (of the ~8) that is chosen over 50% of the time (Schmid, Tsai, et al. 2017). Further, it has been suggested that the preferential path is most often “in-plane”, that the capillary bed is primarily designed to facilitate horizontal RBC movement (Schmid, Tsai, et al. 2017).

The capillary network also displays remarkable input redundancy, with ~63% of all capillary paths being fed by more than 1 penetrating arteriole (Schmid, Barrett, et al.

2017; Guibert et al. 2012). Functionally, for permeable substances, diffusion across the

BBB in capillaries is through the forces of hydrostatic pressure, concentration gradients, and osmotic pressures. It has been suggested that the unique capillary tree structure is to maximize exchange while allowing for a resting baseline that increases the dynamic range of flow rates during NVC (Schmid, Tsai, et al. 2017; Jespersen and Østergaard

2012). However, this structural / functional arrangement has potential drawbacks, namely a vulnerability to flow stalling, reversals, and obstructions. As well excessively high flow through one capillary branch can generate hemodynamic steal, leading to abnormally high RBC transit through one path at the expense of others, leading to overall lower tissue oxygenation (Jespersen and Østergaard 2012). Maximally efficient delivery of oxygen by RBCs likely requires for each capillary (based on length and morphology) a

“goldilocks” range of RBC velocity, with either abnormally fast or slow RBC transit times reducing the efficiency of gas exchange (Schmid, Barrett, et al. 2017). It is speculated that aberrant capillary flow rates and reduced CBF may play significant roles in numerous pathological states such as ageing, diabetes, stroke, and Alzheimer’s disease

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(Jespersen and Østergaard 2012; Girouard and Iadecola 2006; Iadecola 2004; Iadecola

2013).

1.6 The effects of shear stress on endothelial cell function

Like all cells, endothelial cells sense their external environment and are shaped by it. A major source of environmental signals to endothelial cells are the forces exerted by the flow of blood on the vessel wall, indicating a functional vascular segment connected to the global circulation. The primary force applied by flowing blood to the luminal endothelial surface is wall shear stress (WSS). WSS stress influences many aspects of endothelial cells, from polarity, cytoskeleton structure, intracellular signalling pathways, and gene expression (Wragg et al. 2014; Chiu and Chien 2011; Zhou, Li, and Chien

2014). The start of flow, and application of WSS to endothelial cells, is a critical developmental step in forming a mature blood vessel (Zhou, Li, and Chien 2014; Wragg et al. 2014). Much like the initial over production and subsequent pruning of synapses in development, initially the vascular system is overly redundant and contains capillary segments with little to no blood flow, or turbulent and highly variable flow (Obrist et al.

2010; Chen et al. 2012). In the developing zebrafish this initial excess of capillary segments is followed by the refinement of the network by an orderly pruning of capillary segments (Chen et al. 2012; Franco et al. 2015). Pruning is however not random, low flow segments are specifically removed (Chen et al. 2012). Vessel pruning is achieved by a coordinated migration of endothelial cells into adjacent vascular branches, not through cell death (Chen et al. 2012; Franco et al. 2015; Kochhan et al. 2013; Lenard et al. 2015).

Segment pruning could also be induced in functioning (normal flow) segments by stopping blood flow through the injection of polystyrene microspheres (obstructing flow

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in a single segment) (Chen et al. 2012). Thus, vessel pruning wasn’t likely due to any pre-existing state of the vascular segment, such as signalling gradients or ectopic factors, but was the effect of the loss of flow. Therefore, in development there exist a coordinated program for endothelial cells in which the loss of flow leads to the collapse of the vessel segment and endothelial cells migration into adjacent segments, presumably improving the overall efficiency of the vascular tree. In the adult vasculature WSS continues to provide signalling to endothelial cells, supporting a stable quiescent state (Zhou, Li, and

Chien 2014). Turbulent flow, such as at bifurcations, lead to altered gene expression, abnormal endothelial cells physiology, and higher risk for plagues and disease (Chiu and

Chien 2011). However, the effects of the complete loss of WSS in a mature capillary is essentially unknown. While in vitro models can provide some clues, they exist independent of the effects of the larger vascular system and associated NVU. Meanwhile in vivo studies of the loss of flow has been restricted to that of many vessels simultaneously, associated with much larger events such stroke, or when isolated to a single vessel, in much larger arterioles and capillaries (8-20 μm) (Shih et al. 2013; Lam et al. 2010; Grutzendler et al. 2014). In both cases it is difficult to separate the effects of the lose of flow and the accompanying hypoxia.

1.7 VEFG-R2 is a master regulator of endothelial cell function

As early as the 1800s many, including famed anatomist Rudolf Virchow, had observed the capacity for tumours to induce the growth of new blood vessels (Ferrara

2002). By the 1940s it was widely speculated that tumours secreted a soluble factor that initiated angiogenesis, and at the same time researchers proposed some soluble “factor x” was also responsible for pathological neovascularization of the retina in diabetic

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retinopathy (Ferrara 2002). By 1971 Judah Folkman had made the now famous hypothesis that inhibiting angiogenesis could cure cancer, specifically by identifying the mysterious soluble protein he called Tumour Angiogenesis Factor. While cancer labs around the world hunted this angiogenic factor, in an unrelated lab Donald Senger in

1983 discovered a secreted protein that could induce hyperpermeability in blood vessels, which he named Vascular Permeability Factor (VPF) (Ferrara 2002). In 1989 Napoleone

Ferrara discovered a powerful endothelial cell mitogen that he named Vascular

Endothelial Growth Factor (VEGF), which would be found to be the same VPF discovered in 1983 (Ferrara 2002). Thus, the history of the discovery of VEGF highlights the duality of its nature, both a critical growth factor and a powerful inducer of vascular permeability (~50,000x more potent than histamine). Further sequencing of the VEGF gene would also reveal a surprising degree of homology across all mammals, cementing

VEGF’s place as a “master” vascular endothelial cell regulator. The VEGF family is comprised of 5 secreted, dimeric glycoproteins, around 40 kDa (Olsson et al. 2006).

VEGF-A is both the most abundant and functionally relevant to vascular endothelial cells and is by convention (and throughout here) simply referred to as VEGF. VEGF-A however has several isoforms, differing in length, specifically in the heparan binding domain, thus affecting solubility in the extracellular matrix (ECM) (Olsson et al. 2006;

Ferrara, Gerber, and LeCouter 2003). Thus VEGF120 (mouse variant, human VEGFs are all 1 amino acid longer) has weak binding affinity to Heparan Sulfate Proteoglycans

(HSPGs) and high solubility (Olsson et al. 2006). The VEGFs then increase in size and decrease in solubility from VEGF144, VEGF164, VEGF188 and VEGF205 (Olsson et al.

2006; Ferrara, Gerber, and LeCouter 2003). Larger VEGF isoforms (188 and 205) are

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exclusively found bound to the ECM and require proteolytic cleavage to be freed, while the most common isoform VEGF165 is still soluble (1993; Ferrara, Gerber, and LeCouter

2003). Thus, the varying solubility of VEGFs allow for complex angiogenic patterning imbedded and releasable from the ECM, as well as secretable and diffusible forms for establishing angiogenic gradients. While each isoform plays an important role in development, in the adult vasculature VEGF164 is by far the most abundant, and thus by convention, unless otherwise stated, referred to in this work as VEGF (meaning VEGF-

A164).

Shortly after the discovery of VEGF/VPF, its receptors were identified. VEGF receptors are members of the Receptor Tyrosine Kinase (RTK) super family, with 7 immunoglobulin like domains extracellularly and a split tyrosine kinase domain and c- terminus intracellularly (Olsson et al. 2006). Dimerization of the receptor (through ligand binding or force) leads to activation of the receptor through auto phosphorylation in the kinase regulatory domain and further phosphorylation of tyrosine residues in the c terminal tail to facilitate second messenger interactions (Olsson et al. 2006; Ferrara,

Gerber, and LeCouter 2003). There are 3 main receptors for VEGF identified, VEGF-R1,

VEGF-R2 and VEGF-R3, each varying in affinity, kinase activity, and functional roles

(Ferrara, Gerber, and LeCouter 2003). While VEGF-3 is mainly associated with lymphatic tissue, VEGF-R1 and VEGF-R2 are the primary vascular VEGF receptors, however with a unique dichotomy. While VEGF-R1 has a strong affinity for VEGF, it has low kinase activity, conversely VEGF-R2 has lower affinity for VEGF but much higher kinase activity (Ferrara, Gerber, and LeCouter 2003). This has lead to the general hypothesis that VEGF-R1 acts as a VEGF sink, while VEGF-R2 is the primary receptor

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for all or most endothelial VEGF signal transduction (Figure 5). Supporting this notion, while both VEGF and VEGF-R2 knockout mice are embryonically lethal due to lack of vascular development (Shalaby et al. 1995), VEGF-R1 knockouts are lethal due to a pathological over growth of blood vessels (Fong et al. 1995; Kearney et al. 2002). In the developing brain both autocrine (from endothelial cells) and paracrine sources (mostly neurons and astrocytes) of VEGF are required for vascular development and homeostasis

(Lee et al. 2007; Haigh et al. 2003). Furthermore, a substantial degree of the regulation of

VEGF signalling occurs at the level of the receptor, rather than the ligand. VEGF-R2 surface expression is strictly controlled, with rapid endocytosis and inactivation being critical for accurate responses to local VEGF gradients (Olsson et al. 2006; Nakayama et al. 2013). The main phosphorylation site of activated VEGF-R2 is Tyr1175 (in mice) which leads to downstream activation of PLC, MAPK cascades, PKC, AKT and eNOS

(Figure 5) (Olsson et al. 2006; Ferrara, Gerber, and LeCouter 2003).

Interestingly while normally considered pro-angiogenic, VEGF is upregulated by

WSS (Zhou, Li, and Chien 2014). Downstream of VEGF signalling and calcium influx, eNOS activation is crucial for EC alignment, polarity, and cytoskeleton regulation through regulation the Rho family GTPases (Cdc42, Rho and Rac) (Kuchan, Jo, and

Frangos 1994; Tzima 2006). Specifically, WSS has been shown to lead to activation of

AKT (also activated by VEGF through VEGF-R2), AKT phosphorylates eNOS at Ser

1177 increasing NO production (Dimmeler et al. 1999). An important discovery was of an integrin WSS sensing complex linked with VEGF-R2 (Tzima et al. 2005; Wang et al.

2002). WSS forces the dimerization and activation of VEGF-R2, independent of ligand binding (Jin et al. 2003; Tzima et al. 2005). This provides a direct link between the

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physical force of blood flow to VEGF-R2 and in turn to a plethora of signalling pathways such as PI3K, AKT and eNOS. Therefore, the loss of WSS would be expected to have a significant impact on endothelial VEGF-R2 signaling, specifically a decrease in luminal

VEGF-R2 activation. The loss of basal levels of activated VEGF-R2 and downstream

ATK, eNOS and PLC (and many others, Figure 5) likely shifts endothelial cells out of quiescence and alters intracellular calcium, cytoskeletal rearrangements, and potentially initiating larger scale structural plasticity.

1.8 Vascular plasticity in the developing and adult brain

Since the beginnings of human anatomy, the structural similarities and spatial correlations between the vascular and nervous systems has been apparent, and it is now clear that their development is tightly interwoven (Gelfand, Hong, and Gu 2009;

Carmeliet 2003). Early neural development involves an over production of synapses followed by a period of refinement through synaptic pruning, and this similar architectural paradigm is also present in the cerebral vasculature. Early pre-and post natal cortical vasculature show extensive turnover, both angiogenic sprouting and vessel pruning (Harb et al. 2013; Chen et al. 2012; Franco et al. 2015; Phng et al. 2009). Initial angiogenesis and vascular topology is driven by VEGF / Notch signaling and guidance cues such as Semaphorin and Plexins (Kur et al. 2016). Blood flow then dictates subsequent refinement of vasculature through pruning, specifically stable WSS signals a functional vascular branch, turbulent or no WSS indicating a non-functioning vascular segment requiring pruning (Chen et al. 2012; Franco et al. 2015; Kochhan et al. 2013;

Lenard et al. 2015). Early vascular plasticity is not limited to VEGF signalling or flow related cues. Recent studies have shown that altering synaptic activity in the developing

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cortex though sensory stimulation or deprivation, can adjust capillary density, (Whiteus,

Freitas, and Grutzendler 2014; Lacoste et al. 2014; Lacoste and Gu 2015). Thus, the early postnatal cerebral capillary bed displays a high degree of plasticity in response to angiogenic signals, shear stress and local neural activity.

Like most developmental processes in the brain, there appears to be a critical period for extensive endothelial plasticity. For example, repeated exposure to hypoxic conditions (i.e. 10% Oxygen in ambient air) significantly increased the rate of angiogenesis in mice under 1.5 months of age, but had little impact in mice >3 months

(Harb et al. 2013). Furthermore, capillary pruning was unaffected by hypoxia exposure.

Of note, the authors reported a low level of capillary pruning that persisted under normal conditions even into old age. These findings are consistent with a study from our own lab where adult cortical capillaries were repeatedly imaged in vivo for weeks after focal ischemic stroke (Tennant and Brown 2013). Contrary to current dogma that stroke serves as a powerful stimulant of angiogenesis, Tennant and Brown found little to no evidence of capillary sprouting in the peri-infarct cortex (Note: focusing on vessels below the cortical surface). In fact, the only common structural event observed after stroke was the regression and elimination of capillary segments after stroke. Both in vivo time lapse imaging studies indicate that the most dominant form of vascular plasticity in the adult brain is vessel pruning. However, what events were responsible for continued pruning throughout postnatal life or following stroke, is unclear (Harb et al. 2013).

Another form of vascular plasticity that was only recently recognized, is called angiophagy (Lam et al. 2010; Grutzendler et al. 2014). Microvessels (8 - 20 μm) occluded by emboli (protein, cholesterol or induced with i.v. injected microspheres)

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were shown to recanalize by endothelial protrusions enveloping the emboli and removing the obstruction through the vascular wall, allowing for the return of blood flow (Lam et al. 2010). This phenomenon, was triggered by local hypoxia and tissue injury, and suppressed as a function of age (Lam et al. 2010). Therefore, while the adult vasculature can prune capillary segments or engulfing/extruding emboli that cause hypoxia, large scale remodelling of the vascular network is generally limited after 2 months of age.

1.9 Vascular obstruction in the healthy brain

Spontaneous capillary obstructions created by stalled RBC, leukocytes, proteinaceous and cholesterol debris (Santisakultarm et al. 2014; Erdener et al. 2017) represent one plausible explanation for ongoing capillary pruning in the adult brain. This propensity for cortical capillaries to stall or become obstructed has been observed for several decades now, and while the majority of obstructions are transient, both the frequency of these obstructions and their ultimate fates of the persistent obstructions are unknown

(Santisakultarm et al. 2014; Erdener et al. 2017; Kleinfeld et al. 1998; Villringer et al.

1994). How obstructions in the smallest capillaries (~ 4μm) are resolved is a mystery that raises important questions such as to what extend are mature cortical capillaries capable of remodelling to restore blood flow (such as angiophagy or sprouting) and how do these obstructions shape the evolution of the cortical vasculature from maturity into senescence?

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1.10 Capillary loss with aging

It is generally accepted that across most mammalian species including humans, there is a significant decline in cortical capillary density associated with aging, on the order of

10-30% (Brown and Thore 2011; Riddle, Sonntag, and Lichtenwalner 2003; Hinds and

McNelly 1982; Casey and Feldman 1982, 1985). A common correlate of capillary loss in the brain is the presence of string vessels, the basement membrane remnants of a pruned capillary (Brown and Thore 2011; Iadecola 2013). String vessels have been observed in the brain as early as 1918 and were even proposed to be capillaries pruned due to lack of blood flow by Ramon Y Cajal in 1925 (Brown and Thore 2011). String vessels are abundant in new born brains, suggesting they are the product of normal vascular remodelling, and elevated in cases of dementia and Alzheimer’s disease (Brown and

Thore 2011). This increase in vascular remodelling has also been shown in several mouse lines of Alzheimer’s (Gama Sosa et al. 2010) and following ischemia (Reinecke et al.

1962).

Along with correlative evidence of greater vascular remodeling (string vessels) in aged animals and human cases of dementia, direct measurements of capillary density have shown age related capillary rarefaction. A 2005 review found 22 studies confirming capillary loss in elderly human cortex (Riddle, Sonntag, and Lichtenwalner 2003).

Capillary loss is also exacerbated in cases of cognitive decline and dementia (Bell and

Ball 1981; Buée et al. 1994; Buee, Hof, and Delacourte 1997) and importantly correlated with clinical scores for dementia and cognitive impairment (Bailey et al. 2004). In humans a strong relationship between reduced CBF and cognitive deficits has been shown across a number of studies and conditions (Iadecola 2013; Alosco et al. 2013;

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Justin, Turek, and Hakim 2013; Marshall 2012). Surprisingly sub-ischemic reductions in

CBF (causing no cell death or infarct) are still sufficient to detectably reduce cognitive function in humans (Balestrini et al. 2013; Cheng et al. 2012; Marshall 2012;

Stefansdottir et al. 2013). Overall the clinical evidence strongly supports the notion that even small reductions in CBF lead to a suppression of brain activity (Iadecola 2013)

The link between CBF and cognitive performance has also been tested and demonstrated in animal models. Using common models of lowered CBF in rodents, several studies have shown immediate impairments in memory and learning behavior, particularly in spatial memory tests like the radial arm maze and Morris water maze (Ni et al. 1994; Ohta et al. 1997; Nanri et al. 1998). Even after sufficient training on memory tasks, subsequent experimental reductions in CBF caused a slow degeneration of performance and cognitive ability (Ni et al. 1994; Pappas B et al. 2006; L. Bennett et al.

1998). While it makes sense that lowering blood flow impairs brain function, it could also be true that dementia and Alzheimer’s pathologies are equally responsible for causing capillary loss independently or after cognitive decline. While capillary loss is certainly not the sole cause of dementia, several studies have shown that CBF declines before symptom onset, and correlates well with the degree of cognitive impairment and spatially with Tau pathologies (Schuff et al. 2009; Pakrasi and O'Brien 2005; Johnson et al. 2005).

Collectively both human and animal studies have shown that aging is associated with significant capillary loss and that reductions in CBF, even sub-ischemic reductions, are strongly correlated in humans and rodents with impaired cognitive performance and various forms of dementia.

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1.11 Stroke

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Figure 6. Mouse photothrombotic model of ischemic stroke.

Using i.v. injected light activated Rose Bengal dye and targeted green light creates occlusions of blood vessels in the primary somatosensory cortex. A)

Using vascular dye Evans blue (normally impermeable to the BBB) the region of BBB disruption in the peri infarct cortex can be visualized 3 days after stroke as a blue ring around the infarct (white asterisk). B) In histology the same region can be imaged showing Evans blue in the blood vessels (red) as well as an extravascular haze around vessels near the infract, due to BBB disruption. Scale bar 100 μm C) The infarct can also be visualized using mice

that express GFP in layer 5 pyramidal neurons, the sharp border of dead cells (infarct) contrast with the surviving neurons (green) which are critical to functional recovery. Scale bar 50 μm D) A major substrate of the neural plasticity following stroke in the peri-infarct cortex is the apical dendritic spines

(insert, white arrow), the majority of which contain functional synapses, and facilitate cortical-cortical connections by synapsing with layer 2/3 neurons.

Scale bar 5 μm

Ischemic stroke is caused by the occlusion of a large cerebral artery leading to significant cell death in the downstream vascular territories. Ischemic stroke, untreated, ultimately leads to a volume of cell death in the brain (infarct) and the loss of the corresponding function related to that neural territory. Overall ischemic stroke is one of the leading causes of disability in adults (Adamson, Beswick, and Ebrahim 2004). As acute ischemic stroke care and treatment has improved the number of stroke mortalities has decreased, but accordingly the number of stroke survivors has increased. Therefore, understanding and treating stroke related disability, and maximizing quality of life through restorative measures has become a significant focus of stroke research.

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The extent to which a person can regain neurological functions lost in stroke depends both on the extent of injury and how well surviving, adjacent (peri-infarct) tissue reorganizes (Figure 6) (Carmichael 2006; Murphy and Corbett 2009; Brown et al. 2009;

Nudo and Milliken 1996; Nudo et al. 1996). The innate ability for neural circuits to remap lost functions onto new substrates requires fine scale remodelling of synapses through structural plasticity of both axon terminals and their primary targets, dendritic spines (Brown et al. 2007; Brown, Wong, and Murphy 2008; Winship and Murphy 2008;

Tennant et al. 2017). Studies using rodent models of stroke with in vivo imaging found the peri-infarct region undergoes enhanced turnover of dendritic spines in layer 2/3 of layer 5 pyramidal neurons in the weeks following stroke (compared to sham controls or distant cortical regions) (Figure 6 C-D) (Brown et al. 2009; Brown et al. 2007; Mostany et al. 2010). After stroke, peri-infarct neurons first experience significant loss of dendritic spines followed by a period of spine formation and recovery to pre-stroke spine densities

(Brown et al. 2007) Enhanced remodelling of L5 dendritic spines correlated spatially and temporally with both cortical map remodelling, and recovery of sensory paw function in the corresponding somatosensory cortex (Brown et al. 2009; Brown et al. 2007; Mostany et al. 2010). Supporting the fundamental role the peri-infarct cortex plays in recovery, silencing or lesioning this region eliminates functional recovery (Sweetnam et al. 2012;

Werhahn Konrad et al. 2003; Castro-Alamancos and Borrell 1995). Collectively, these studies strongly support the idea that peri-infarct synaptic structural plasticity underlies functional recovery.

Much less is known about the role of the peri-infarct vasculature in supporting or promoting plasticity and functional recovery, especially in the context of a common

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stroke comorbidity, diabetes. While some studies have suggested there are structural shifts in the peri-infarct vasculature (Brown et al. 2007), the extent of angiogenesis after stroke remains controversial, with many studies failing to find compelling evidence for significant post stroke sprouting of nascent vasculature (Mostany et al. 2010; Tennant and Brown 2013). In contrast, it is now well established that stroke induces BBB permeability in the surviving peri-infarct region (Figure 6A-B) (Sandoval and Witt 2008;

Ballabh, Braun, and Nedergaard 2004; Nahirney, Reeson, and Brown 2015; del Zoppo and Mabuchi 2003). Edema is considered the most significant secondary injury following stroke (del Zoppo and Mabuchi 2003; Schlaug et al. 1997; Heo, Han, and Lee 2005).

Edema putatively comes in 2 waves, first in the hours after stroke, and then a second wave 48-72 hours later (however this concept is not universally accepted, and does not imply normal BBB integrity in-between waves), characterized by significant BBB permeability in the peri-infarct vasculature (Sandoval and Witt 2008; Kuroiwa et al.

1985; Rosenberg, Estrada, and Dencoff 1998; Knowland et al. 2014). Loss of BBB integrity not only disrupts the balance of various ionic species in the brain parenchyma, but allows potentially toxic elements in the blood to enter, such as high concentrations of glutamate and iron, and can lead to cellular dysfunction and tissue damage, but not necessarily cell death (Chen et al. 2009; Zhang and Murphy 2007; Rosidi et al. 2011;

Cianchetti et al. 2013). While the existence and impact of BBB permeability after stroke is established, the mechanism behind it remains debated.

Because the BBB is comprised of many structural features, several routes exist for permeability, namely paracellular (through disrupted TJC) or transcellular (transcytosis)

(Figure 4B). Which path dominates post ischemic BBB disruption is contested. The

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standard model of BBB disruption after stroke is that TJCs (Figure 4D-E) are disassembled allowing passage between endothelial cells (Sandoval and Witt 2008). This model has been supported by biochemical or immunohistochemical studies looking at

TJC protein levels following stroke (Fischer et al. 2002; Willis, Meske, and Davis 2010;

Fernández-López et al. 2012; Knowland et al. 2014). However due to the complex structure of TJCs, and limitations of many biochemical assays, changes in TJC protein expression can not be assumed to directly correlate with disassembly. Furthermore, several ultrastructural studies have failed to find any direct visual evidence for TJC disassembly in the surviving peri-infarct vasculature (Lossinsky and Shivers 2004;

Jackman et al. 2013; Krueger et al. 2013; Nahirney, Reeson, and Brown 2015). Rather many of these studies reported a significant increase in peri -infarct capillary endothelial cell caveolae like vesicles and vacuoles, suggesting an increase in transcytosis was mostly responsible for delayed permeability in the peri infarct BBB.

In addition to the structural mechanism of BBB permeability, there is also considerable interest in understanding the molecular cues that induce changes in the

BBB. An obvious suspect is VEGF signalling, which was originally described as a vascular permeability factor (Ferrara 2002). Several studies have shown that VEGF expression or signalling is upregulated after stroke (Gu et al. 2001; Zhang et al. 2002;

Zhang et al. 2000). An upregulation of endothelial VEGF signalling could cause edema after stroke, given many studies have established that VEGF induces vascular permeability, particularly through increasing transcytosis (Feng et al. 1999; Zhao et al.

2011; Argaw et al. 2012; Chapouly et al. 2015; Esser et al. 1998; Weis and Cheresh

2005; Zhang et al. 2002). However, some studies have linked VEGF induced

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permeability to changes to TJCs, especially through ZO-1 regulation (Fischer et al. 2002;

Fernández-López et al. 2012). In complete contrast, several studies have suggested that increased VEGF signaling after stroke is beneficial, possibly through increasing cell survival or inducing angiogenesis (Zhang et al. 2000; Marti et al. 2000; Rosenstein et al.

1998). Given the degree of discordance around the effect and benefits of VEGF following stroke, it is unsurprising that studies modulating VEGF signaling after stroke have shown mixed results (Hermann and Zechariah 2009) with many conflicting conclusions to the benefits of either increasing or depressing VEGF signaling after stroke

(Manoonkitiwongsa, Schultz, and Lyden 2011; Manoonkitiwongsa et al. 2004; Greenberg and Jin 2013; Shimotake et al. 2010). While some discrepancies may be explained by experimental differences such as animal and stroke models, it is also obvious that the magnitude and timing of VEGF signalling dictate it’s effects.

1.12 Stroke in diabetics

A significant health epidemic has emerged in the 21st century, where diseases of food excess has overtaken illness from malnutrition in terms of global health impact. A major component of the new health epidemic is diabetes, which afflicts up to 7% of the

Canadian population. The fundamental feature of diabetes is a lack of insulin signaling, either from deficiencies in production Type 1 Diabetes (T1D), or sensitivity to insulin,

Type 2 Diabetes (T2D). Despite the different etiologies, T1D and T2D share many common elements, including the primary feature of diabetes, chronically elevated blood glucose levels (Deshpande, Harris-Hayes, and Schootman 2008). Even with the advancement of insulin therapy by Sir Fredrick Banting almost 100 years ago, insulin therapy is unable to completely prevent vascular complications, and up to a third of

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diabetics are either unaware or have poor glucose control (Go et al. 2014). With new projections indicating that 1 in 3 children will develop diabetes or prediabetes in their lifetime, this disease represents a major public health crisis that shows little sign of slowing down.

To understand why diabetes has such a profound impact on brain health, it is helpful to think about how blood glucose is normally regulated and what can happen when this homeostasis is lost. Normally a spike in circulating glucose (for example after a meal) leads to insulin release from the pancreas (beta cells). Insulin binds to its cognate receptor and triggers muscle and fat cells to translocate the GLUT-4 glucose transporter to the cell membrane, allowing for rapid uptake of glucose from the blood and into cells for metabolism or storage (Klip et al. 2014). In diabetics, this fine control of blood glucose is lost, leading to chronically high amounts of glucose persisting in the blood. While most excess glucose is removed through the kidneys, many cells are unable to regulate glucose influx, leaving them exposed to hyperglycemia. Endothelial cells are uniquely vulnerable, both being first in contact with hyperglycemic blood, and by the fact endothelial cells express GLUT-1 glucose transporters, which are concentration dependent (Lu et al. 2013;

Benarroch 2014). Thus, endothelial cells essentially have the gates wide open, and are unable to stop the constant waves of glucose from pouring into the cell. This pathological influx of glucose alters endothelial cell metabolism and biochemistry as the cell works in overdrive to process and clear the excess glucose (Brownlee 2001). Higher levels of intracellular glucose causes abnormal protein glycosylation, increased Reactive Oxygen

Species (ROS) production, and numerous changes to signalling pathways (Purves et al.

2001; Tomlinson 1999; Spitaler and Graier 2002). Intracellular hyperglycemia increases

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the production of Advance Glycosylation End Products (AGE), which disrupts NO production (Pieper 1997; Pieper and Peltier 1995). Overall hyperglycemia perturbs the expression and function of nearly every endothelial signal transduction pathway and cofactor, including but not limited to MAPK, PKC, RAGE, ERK and JNK (Tomlinson

1999; Haneda et al. 1995; Haneda et al. 1997; Purves et al. 2001; Igarashi et al. 1999;

Wang, Chen, and Liu 2014). The pervasive disruption of endothelial cells, awash in glucose, spares no organ in the diabetic body. Diabetic injury and dysfunction afflicts the retina, kidneys, heart and of course the brain (Brownlee 2001; Benarroch 2014; Lecrux and Hamel 2011; Banks, Jaspan, and Kastin 1997). Collectively the progressive injury to the endothelium and altered cellular biochemistry leads to vessel walls becoming thicker and more adherent, increasing the accumulation of plaques and risk of clots (Vinik and

Flemmer 2002). Compounding these effects, vessels in diabetics show impaired tone and dilation, limiting their ability to compensate for narrowing lumen (Jaap et al. 1994;

Arrick et al. 2011; Bardal et al. 2006). Critically diabetes undermines the integrity of the

BBB (Ballabh, Braun, and Nedergaard 2004; Starr et al. 2003), with the potential to amplify injury after vascular insults in the brain. Diabetes not only alters vascular biology, but also increases inhibition of TPA, reducing natural clot busting defenses

(Won et al. 2011; Small et al. 1989; Yarmolinsky et al. 2016) All these changes to the vascular endothelium in diabetes pre-disposes them to ischemic stroke.

An emergent concept in pre-clinical stroke research with animal models is to study the intersection of stroke with the myriad of diseases that are also present and interact with an ischemic event in humans. These comorbidities are important puzzle pieces in understand the bigger picture of both the risk of, and recovery from stroke for each

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person. Diabetes (T1D or T2D) is a common comorbidity of stroke as it confers a significant increase in the risk of an ischemic event (Baird et al. 2002). Diabetics who suffer a stroke also face the grim prospect of diminished prognosis for recovering sensory, motor, and cognitive function compared to non-diabetic (euglycemic) patients

(Megherbi et al. 2003; Jia et al. 2011; Kamouchi et al. 2011). This reduced capacity for recovery has been replicated in rodent models of diabetes and stroke, allowing for investigation of the mechanisms behind the impaired adaptation (Ergul et al. 2014; Li et al. 2013; Sweetnam et al. 2012).

The simplest explanation would be that diabetics suffer larger strokes, leading to greater initial deficits and less remaining tissue to remap. While a meta study of animal studies suggested that diabetic animals tend to have larger infarcts (MacDougall and Muir

2011), several caveats must be considered. Most studies initiated the stroke within hours to days of inducing hyperglycemia (MacDougall and Muir 2011; Danielle and Eric

1988). In this period neural and vascular networks may be acutely out of homeostasis due to the sudden change in glycemic levels. Furthermore, this short duration doesn’t accurately mimic the more common human progression of disease and avoids any slow adaptations to hyperglycemia that could ameliorate ischemic damage. Studies looking at longer durations of hyperglycemia (in T1D models) prior to stroke (> 2 weeks) have mixed results. Studies using larger stroke models (greater tissue loss) with a Middle

Cerebral Artery Occlusion (MCAO) model showed a trend to larger infarcts compared to controls (MacDougall and Muir 2011). But not all MCAO studies have confirmed this, with several finding no difference in infarct size with diabetic rodents (Ye et al. 2011;

Slivka 1991). Studies with long durations of hyperglycemia and smaller infarcts using the

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photothrombotic model have failed to find an affect of hyperglycemia on infarct size

(Sweetnam et al. 2012; Tennant and Brown 2013). Therefore, it is conceivable different models or sizes of infarct have different susceptibilities to hyperglycemia. Interestingly studies with models of T2D where hyperglycemia develops gradually (Goto-Kakizaki rat) also found no effect or smaller infarcts compared to euglycemic controls (Li et al. 2013), while T2D studies using the obese db/db mouse found larger infarcts (Kumari et al.

2007). What is significant is that a study which found no affect of T1D on infarct size still found significant impairments in the recovery of lost sensory and motor function

(Sweetnam et al. 2012). This study also showed that after photothrombotic stroke in the forelimb somatosensory cortex of diabetic and control mice, the failure of diabetic mice to spontaneously regain use of the impaired limb (compared to non diabetic controls) was in parallel with a failure of peri-infarct tissue to regain responsiveness to forepaw stimulation (visualized by VSD imaging) (Sweetnam et al. 2012). Importantly, treating diabetic mice with insulin (after weeks of hyperglycemia) did not improve recovery following stroke. The fact that impaired recovery in diabetics occurred without an increase in infarct size but did occur in conjunction with impaired peri-infarct remapping, suggest the underlying mechanism exist at least somewhat independent of infarct size and within the surviving peri-infarct.

While some studies have found T2D rodents have abnormal angiogenesis following stroke (Ergul et al. 2014; Prakash et al. 2013; Li et al. 2010), studies of T1D rodents have been less supportive. For example one study found an increase in arterial density in the diabetic peri-infarct, however this occurred without endothelial proliferation (measured by BrdU labelling) (Ye et al. 2011). Another study found an

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increase in vessel density in the peri-infarct region, but this increase was largely attributable to an increase in vessel width (Tennant and Brown 2013). Furthermore, this study used time lapse in vivo imaging and failed to observe significant sprouting of new vascular segments, further supporting the notion that the increase in vascular density is due to vessel dilation after stroke (Tennant and Brown 2013; Shih et al. 2009). The consensus failure to observe significant peri-infarct angiogenic vessel sprouting after stroke between longitudinal in vivo imaging studies in diabetic mice agrees with the similar lack of evidence for large scale angiogenesis in non-diabetic imaging studies

(Mostany et al. 2010; Harb et al. 2013).

Given diabetes is often characterized as a disease of “leaky blood vessels”

(Rossini et al. 1977; Vinik and Flemmer 2002), another hypothesis is that diabetes might exacerbate BBB disruption following stroke, a well established source of secondary injury in the peri-infarct cortex (Sandoval and Witt 2008). Several studies have shown that diabetes alone can impair the integrity of the BBB (Banks, Jaspan, and Kastin 1997;

Hawkins et al. 2007; Huber 2008; Starr et al. 2003; Taylor et al. 2015). Likewise given the strong connection between VEGF signaling and BBB permeability, it is notable that diabetes alone perturbs VEGF expression and signalling in both the peripheral and cerebral vasculature (Cooper et al. 1999; Lim, Lip, and Blann 2005; Prakash et al. 2012;

Taylor et al. 2015; Warren et al. 2014; Zhao et al. 2012; Vinik and Flemmer 2002;

Howangyin and Silvestre 2014). Interestingly, diabetes seems to directly perturb the cellular regulation of VEGF-R2, rather than the VEGF ligand. Hyperglycemia was shown to be sufficient to alter surface expression of VEGF-R2, and lead to excessive ligand-independent activation of VEGF-R2 (Warren et al. 2014). Following stroke

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several studies have shown an increase in VEGF and/or VEGF-R2 expression in the peri-infarct, with VEGF spiking in 6-48hrs, and VEGF-R2 at 2-14 days (Hermann and

Zechariah 2009; Zhang et al. 2002; Marti et al. 2000). Collectively this suggest a promising mechanistic hypothesis, specifically that diabetes could lead to aberrant

VEGF signaling and increased BBB permeability after stroke. The net effect of such dysregulation could explain the failure of peri-infarct cortical circuits to rewire and facilitate functional recovery.

1.13 Rationale and aims

It is evident from an extensive body of evidence from human and animal studies that the cerebral vasculature is vital in maintaining brain health and in supporting adaptive rewiring and functional recovery following ischemic events. Given that cortical capillaries are prone to persistent spontaneous obstructions which could alter cerebral blood flow; and that one of the most common vascular diseases, diabetes, predisposes people to stroke and undermines functional recovery, this dissertation will have two aims.

First, I will use in vivo 2-photon microscopy to image capillary blood flow in the living mouse somatosensory cortex (through chronically implanted cranial window) to measure the natural rates of spontaneous cortical capillary obstructions. Then, if obstructions are in fact persistent over hours or days, I will follow spontaneously or experimentally obstructed capillaries (through i.v. injection of 4 μm microspheres) over weeks to learn their ultimate fates. Specifically, I will answer if all or any obstructed capillaries recanalize, and if so how? And, what is the fate of capillaries that never restore blood flow? Lastly, I will use a combination of in vivo imaging and histology to determine the role of endothelial VEGF-R2 signaling, a known sensor for changes in

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blood flow, in dictating capillary fate. VEGF-R2 signalling will be measured by activated

(phosphorylated) VEGF-R2 immunohistochemistry, and VEGF-R2 signaling either increased through VEGF infusion or reduced by endothelial specific inducible genetic knock down, or pharmacological inhibition (SU5416).

The second aim will evaluate the effects of diabetes on BBB integrity, synaptic loss and functional recovery following a photothrombotic ischemic stroke in the mouse forepaw somatosensory cortex (S1FL). Type 1 Diabetes will be induced using the well established Streptozotocin (STZ) model, and stroke induced after 4 weeks of uncontrolled hyperglycemia. Blood-brain barrier integrity will be quantified by measuring leakage of circulating dyes into the brain in both diabetic and control mice. Further, any BBB permeability will be followed up with Electron microscopy using electron dense tracers

(HRP or nanogold) to determine the route of plasma extravagation through the BBB.

Synaptic loss and functional recovery will be measured by evaluating peri-infarct spine densities following stroke or sham procedure, and sensor-motor behavioral tasks, adhesive tape and ladder rung test. Finally given the well accepted role of VEGF-R2 signalling in vascular permeability, VEGF-R2 levels will be measured after stroke or sham using western blots and immunohistochemistry. If in fact VEGF-R2 is aberrantly over expressed in the diabetic peri-infarct vasculature, I will use the same parallel genetic and pharmacological approaches to inhibit VEGF-R2 signaling, and attempt to rescue excessive BBB permeability, synaptic loss and functional recovery of forepaw use in diabetic mice.

This dissertation will first show that microvascular plasticity, governed to a large extent by VEGF-R2 signaling, molds the adult cerebrovascular structure over an animal’s

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lifetime through the compounding effects of spontaneous capillary obstructions.

Secondly, I will show that diabetes, by causing to pathological over expression of dysregulation of vascular VEGF-R2 after stroke in the diabetic brain, leads to greater

BBB disruption within thein peri-infarct regions which in turn exacerbates, in turn causing exacerbated synaptic loss and ultimately impaired functional recovery of lost somatosensory function. Importantly, these deficits, all of which can be partially rescued by reducing endothelial VEGF-R2 signaling. In toto, this dissertation will highlight the fundamental, and inseparable role the vasculature, and endothelial plasticity, play in brain function, during health and illness.

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Chapter 2 VEGF signaling regulates the fate of obstructed capillaries in mouse cortex

Accepted by eLIFE March 23rd, 2018

This work was done with assistance by Kevin Choi, who helped conducted blinded capillary and microsphere counts, as well as Emily White who ran the western blot in Figure 22A, and the assistance of volunteers Natalie Pollock and Jesse Spooner. Patrick Reeson and Craig E. Brown wrote the manuscript.

2.1 Abstract Cortical capillaries are prone to obstruction, which over time, could have a major impact on brain angioarchitecture and function. The mechanisms that govern the removal of these obstructions and what long-term fate awaits obstructed capillaries, remains a mystery. We estimate that ~0.12% of mouse cortical capillaries are obstructed each day

(lasting >20min), preferentially in superficial layers and lower order branches. Tracking natural or microsphere induced obstructions revealed that 75-80% of capillaries recanalized within 24 hours. Remarkably, 30% of all obstructed capillaries were pruned by 21 days, including some that had regained flow. Pruning involved regression of endothelial cells, which was not compensated for by sprouting. Using this information, we predicted capillary loss with aging that closely matched experimental estimates.

VEGF-R2 signaling was a critical factor in dictating capillary recanalization and subsequent pruning. Our studies reveal the incidence, mechanism and long-term outcome of capillary obstructions which can also explain age-related capillary rarefaction.

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2.2 Introduction

The brain is an energetically demanding organ that contains kilometers of capillaries to meet this need. Capillaries are the smallest vessels in the brain and serve as the primary site of nutrient and gas exchange (Attwell et al. 2010). These capillary networks are critical for maintaining proper brain function since decrements in cognition that occur with aging and certain neurodegenerative diseases (like Alzheimer’s disease) are associated with the loss of brain capillaries (Brown and Thore 2011; Iadecola 2013;

Riddle, Sonntag, and Lichtenwalner 2003). From the earliest in vivo imaging studies of cerebral blood flow over 2 decades ago, the susceptibility of capillaries to obstruction, even in healthy animals has been noted (Kleinfeld et al. 1998; Santisakultarm et al. 2014;

Villringer et al. 1994). A recent study used Optical Coherence Tomography to estimate that over 9 minutes, up to 7.5% of capillaries experienced a stall (Erdener et al. 2017).

This is not surprising since capillaries are inherently narrow (~3-5µm diameter) high resistance tubes that must pass relatively large and adherent components in the blood (red blood cells, leukocytes, cholesterol, fibrin etc.). Cortical capillaries also experience the largest drop in pressure across the cerebral vasculature (Gould et al. 2016). However, since the initial in vivo observations, no study has comprehensively followed the long- term outcome of capillary obstructions and what mechanisms dictate their fate. This leaves open many questions that are critical to our understanding of how mature microvascular networks change. For example, if obstructions are almost always cleared or compensated for with collateral sprouting, then the impact of these obstructions could be minimal. If however, obstructions lead to the pruning of vessel segments, then it is conceivable that the accumulation of these events over a time could drive the progressive

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rarefaction of cerebrovascular networks commonly found with aging (Mann et al. 1986;

Klein and Michel 1977; Hinds and McNelly 1982; Casey and Feldman 1985; Buchweitz-

Milton and Weiss 1987; Jucker, Bättig, and Meier-Ruge 1990; Amenta, Cavallotti, et al.

1995) and certain neurodegenerative diseases (Tong and Hamel 2015; Iadecola 2013;

Brown and Thore 2011).

In order to understand what ultimately becomes of obstructed cortical capillaries and the mechanisms that regulate their fate, we employed in vivo time-lapse imaging to identify and follow spontaneous, naturally occurring obstructions in mouse cortex. Due to the unpredictable and sparse nature of these obstructions, we also developed and validated an experimental model of inducing capillary obstructions with fluorescent microspheres. Although we find that the majority of capillary obstructions are cleared, almost one third of obstructed capillaries are pruned without compensatory angiogenesis.

Based on this information and the relative incidence of naturally occurring obstructions, we were able to model capillary loss with aging. Furthermore, we show that VEGF-R2 signaling, a known sensor of shear stress, is critical for mediating clearance of obstructions and preventing capillary pruning. Our findings shed new light on the long- term fates of clogged cortical capillaries and the mechanisms that dictate this process.

2.3 Results

2.3.1 Superficial and lower order cortical capillaries are prone to obstruction

We first examined how frequent and what parts of the vascular tree were prone to spontaneous obstructions in the mouse somatosensory cortex. We imaged cortical vascular networks (~300µm in depth) in both lightly anesthetized (2% isoflurane for

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induction, 1% for maintenance) and awake Tek-GFP mice injected with Rhodamine B to visualize blood flow. We could identify flowing and non-flowing capillaries by the presence or absence of streaking red blood cells (RBCs) (Figure 7A). Spontaneous naturally occurring obstructions typically presented with a gap in capillary lumen fluorescence (mean lumen diameter of 3.85±1.25µm) created by stalled cells or debris

(Santisakultarm et al. 2014) (Figure 7A, insert, red arrow). Our analysis was restricted to capillary segments lacking flow for longer than 20 min to exclude transient obstructions that affect ~1-7% of capillaries as previously reported (Santisakultarm et al.

2014; Erdener et al. 2017). Unbiased sampling of cortical vasculature in 16 mice for 2 hours revealed that longer-lasting obstructions (>20 min) were relatively rare, affecting 2 in 20,334 capillaries (~0.118% capillaries obstructed per day, Figure 8)(Reeson 2018).

While these spontaneous obstructions were rare, 10 of the 16 mice had non-flowing capillaries segments present at the start of imaging, indicating spontaneous obstructions were not unique to a few mice (persisted > 20 min, 20 obstructed capillaries in 10 animals, range of 1-4 per mouse). To confirm this, we injected mice with 1µm microspheres coated in lipophilic dye DiI solution. Microspheres of this size freely circulate in the blood but become stalled in spontaneously obstructed capillaries, allowing the DiI to leech into the endothelium, creating an indelible stamp of the spontaneous obstruction. Quantifying post mortem DiI labelled capillaries 3 hours after injection we found 3.69±0.97 obstructed capillaries per mm3 of cortex. Based on estimates of ~20,000 capillaries per mm3 (Blinder et al. 2013; Tsai et al. 2009) this gives a rate of 0.14% spontaneous obstructions per day, nearly identical to our in vivo estimate. Furthermore, the rate of obstructions did not differ between awake and isoflurane anesthetized mice (%

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volume obstructed t(14)=1.09, p=0.29, % length obstructed t(14)=1.79, p=0.10, n(isoflurane)=

11, n(awake)= 5 mice) thereby ruling out spontaneous obstructions as purely artifacts of anesthetics.

Due to the rare and spontaneous nature of capillary obstructions, we experimentally modelled these events by injecting 4µm diameter fluorescent microspheres (i.v.; Figure 9A, B see methods). Although microspheres accumulated in peripheral organs, particularly the liver (Figure 9C), injections did not induce any significant changes in body weight, cardiovascular function, haematological chemistry or hematocrit (Figure 9 D-J), nor did it lead to inflammatory microglia responses or cell death in either the cortex or peripheral organs (Figure 10). In the brain, injection of microspheres was sufficient to obstruct 3.67% (95% CI 2.5–4.8%) of cortical capillaries

(~30min after injection). Although obstructions were spread across the cortex (in an anterior to posterior manner) and thus affected all major cerebral vascular territories

(Figure 11), they occurred preferentially in more superficial layers (Figure 12B) and at lower branch order capillaries (Figure 7C). These observations were noted in both spontaneously occurring and microsphere induced capillary obstructions (Figure 7C, D).

Therefore, the risk of obstruction is significantly higher in a subset of superficial and lower arteriole branch order cerebral capillaries.

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Figure 7 Cortical capillaries are prone to spontaneous obstruction.

A) Side projection from in vivo 2-photon imaging stack (0.027mm3) showing pial surface at the top with labelled plasma (magenta). Insert shows a flowing (Note streaking pattern in line scans caused by RBC movement) and obstructed capillary (no streaks). Red arrow indicates occluding debris/cells. Scale bara are

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50µm and 15µm for inset. B) Distribution of microsphere obstructed capillaries as it relates to depth from the pial surface determined by confocal imaging from post-mortem brain sections. Red bars indicate the relative amount of microsphere obstructions for each depth expressed as a fraction of total obstructions. Black line indicates fraction of total capillaries by depth (error bars are 95% CI) as well as raw numbers of capillaries / mm3 by depth are provided in parentheses. Note 0.04 obstructions occurred below 1000 μm from cortical surface but are not shown. C) Distribution showing spontaneous and microsphere obstructed capillaries expressed as a function of arteriole branch order and relative to the distribution of all capillaries (n(all) =3 mice, 285 capillaries; n(spont.) =5 mice, 21 obstructions; n(micro.) =5 mice, 59 obstructions).

Note that lower order capillaries are more susceptible to obstruction. Inset illustrates branch order which started with the penetrating arteriole (0 order branch). Scale bar 15µm. D) Mean branch order of spontaneous or microsphere induced capillary obstructions (1 way ANOVA F(2,360)=32.36, p<0.0001, all capillaries compared to spontaneous obstructions unpaired t-test t(302)=4.180, p<0.0001, or microsphere obstructions unpaired t-test t(342)=6.95, p<0.0001; spontaneous vs microsphere obstructions t(76)=0.46, p=0.65). ***p<0.001, n.s. = not significant. Error bars are S.E.M.

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2.3.2 Fates of obstructed cortical capillaries

We next assessed capillary recanalization rates and their fates using in vivo time lapse imaging. Imaging stacks were focused on regions where we could find natural/spontaneous or microsphere induced obstructions to boost sampling rates. Over the 21-day imaging period, we found different outcomes for obstructed capillaries. First, all capillaries that did not recanalize were pruned away in a step wise fashion (Franco et al. 2015). Pruning progressed with a pinched endothelial segment at one end followed by the retraction of that segment (Figure 12A), leaving the remaining segment connected to the adjacent, flowing capillary. Retraction of vessel segments was associated with an increase in endothelial cell nuclei (unpaired t-test t(29)=6.625, p < 0.0001) around pruned branch points (Figure 12B), suggesting that endothelial cells regress and integrate into adjacent capillaries, reminiscent of endothelial regression found in development (Chen et

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al. 2012; Franco et al. 2015). During pruning we occasionally found that the adjacent

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Figure 8 Work flow and validation of automated estimates of vessel density.

A) To provide an automated, unbiased approach for estimating vessel densities, we wrote and validated a custom macro in FIJI (Schindelin et al. 2012). In vivo imaging stacks of dye filled cortical vessels (see maximum z-projection in top left and side view projection of image stack in top right) were manually split into sub-stacks of 10 images (2µm z-steps between images). Sub stacks were then each maximally projected in the z plane, and each automatically thresholded using the built in ImageJ threshold function Triangle (Zack, Rogers, and Latt 1977), which was determined to best capture vascular signal through trial and error. The area and fractional vascular volume of vascular signal (number of black pixels after thresholding) was measured for each sub-stack image projection after applying a median filter (radius of 2 pixels) to eliminate speckling. Thresholded sub-stacks were then binarized and skeletonized using built in functions (Arganda-Carreras et al. 2010) and total vascular length was taken as the number of skeleton pixels. From the vascular length, average vessel width (w = A/L) was also calculated. B-C) Graphs show that both the fractional vascular volume (v/v) and the estimated number of capillaries per imaging stack 3 (0.02 mm volume) were sensitive to the volume of images projected. For fractional vascular volume, projecting 20µm sub-stacks led to estimates of ~0.01% vascular volume which closely matches published data (Blinder et al. 2013; Schmid, Barrett, et al. 2017). As for capillary number, we validated 20µm sub-stack z projections by comparing automated estimates with those derived from blinded manual counts (data from 4 mice, 2 imaging areas per animal). D) Box and whisker plots (+ is mean) showing close agreement between manual and automated estimates of capillary number per imaging stack (paired t-test t(3)=0.33, p=0.76). Error bars are S.E.M.

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Figure 9 Fluorescent microspheres as a model of spontaneous naturally occurring capillary obstructions. Given the relative rarity of spontaneous microvascular obstructions, and the experimental challenges of capturing and following a spontaneous event we developed an inducible model of capillary obstructions that allowed us control over the timing of obstruction and the ability to standardize, with low variability, the number of obstructions between experimental groups A) Image of fluorescently labelled (580/605 nm excite/emit) 4µm diameter polystyrene microspheres. B) Quantification of microspheres in blood after i.v. injection. By 30min, microspheres were reduced to 11% of starting concentration and undetectable by 60min (n=5 mice per timepoint). C) Presence of microspheres in other organs at 4, 15 and 21 days after injection (n=3-14 mice). D-J) To rule out any systematic effects on the cardiovascular system, we assessed body weight (D), tissue oxygenation

(measured at thigh, E), systolic and diastolic blood pressure (F, G), heart rate (H), and breath rate (I). All parameters were unaffected by microsphere injection (n=4 mice, 1 Way ANOVA, all p > 0.05) J) No effect of microsphere injection on blood gases, electrolytes, glucose, or hematocrit (n=3 sham and 4 mice per time point). Error bars are S.E.M.

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Figure 10 . Microsphere based obstruction and pruning did not induce a microglial response or cell death.

A) In vivo time lapse imaging of microglia (green) and blood flow (labelled with 4% Rhodamine dextran) in CX3CR1+/GFP mice. Microglia were not responsive to the capillary obstruction at any time point, in any mice (n=6 mice, 2-3 areas per mouse), even when the segment was pruned between 6-10 days. Inset shows microglial accumulation following laser ablation as a positive control. Scale bars 25µm. B) Fluorojade C staining in the cortex, liver or lung 24 hours after injection did not reveal any detectable cell degeneration or death (n=3 mice). A positive control illustrating cell death following ischemia is shown for comparison. Sections were stained with FJC as previously described (Reeson et al. 2015). Scale bar 100µm.

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Figure 11 Microsphere obstructions are distributed across major

cerebral vascular territories. A) Representative images of cortical obstructions (see blue arrowheads) 4 days after microsphere injection in coronal sections ordered relative to bregma. Scale bar 250µm. B) Average microsphere density (microspheres /mm3) as a function of time after injection and position relative to bregma. There were no significant main effects of position relative to bregma at any time studied (+ 4 days:

F(10,22)=0.17, p=0.99, + 10 days: F(10,22)=0.77, p=0.65, + 31 days: F(10,33)=0.26, p=0.98). Error bars are S.E.M.

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or pruning.

For capillaries obstructed with microspheres, almost all recanalized by extruding the obstruction back into the circulation (Figure 7C, D), while only a small fraction (2%) recanalized by extruding the microsphere through the endothelial wall into the parenchyma, commonly referred to as angiophagy (Figure 12E; Figure 14A).

However, we should note that we were unable to measure rates of angiophagy associated with spontaneous, naturally occurring emboli, which likely would have influenced the route of recanalization (washout vs angiophagy). Within the first 24h, 75-80% of capillaries (stalled >20min at time 0) recanalized, which did not differ significantly between spontaneous and microsphere induced obstructions (Figure 13D; effect of time:

F(4,40)=55.29, p<0.001, effect of obstruction type: F(4,40)=0.11, p=0.75). The only vascular factor that predicted recanalization was proximity of the obstruction to the nearest flowing capillary (Figure 15A, B). By contrast, capillary branch order, blood flow velocity or lumen diameter in upstream and downstream capillaries was not predictive of recanalization (Figure 15C-G). Interestingly by 21 days, 30% of all obstructed capillaries (at time 0) had been eliminated. While one would have expected 20-25% based on recanalization rates in the first 48h, we discovered that a subset of capillaries regained blood flow only to be pruned at a later time point (Figure 13F, G, Figure 14A and Figure 15B). This phenomenon could not be predicted by changes in blood flow velocity, width, or flux in the recanalized primary capillary, or connected (secondary) capillaries (Figure 16). However, by inducing a second wave of microsphere obstructions (microsphere injections 4 days apart), we noticed that the probability of experiencing an obstruction was greater in capillaries that were previously obstructed and

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regained flow (Figure 17). Therefore, it is possible that delayed pruning of capillaries in ones that initially regained blood flow, could be explained by a second spontaneous obstruction that was missed in the days between imaging (Erdener et al. 2017). In summary (Figure 13H), by following capillaries for 21 days after an obstruction, we determined there was a 69.8% chance the capillary would be intact and flowing either by extruding the emboli back into circulation (110/162 capillaries) or through angiophagy

(3/162 capillaries). There was a 30.2% chance of being pruned (49/162 capillaries), with no evidence of compensatory sprouting. Importantly, the likelihood of capillary pruning did not differ significantly between obstructions that occurred spontaneously versus those that were induced with microspheres (unpaired t-test t(19)=0.72, p=0.48).

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Figure 12 Fates of obstructed cortical capillaries.

A) Longitudinal imaging of a spontaneously obstructed capillary (time 0) that failed to recanalize and was subsequently pruned over 31 days. The endothelium is shown in green (Tek-GFP) and blood plasma in magenta. Asterisk shows location of linescan. B)

Top: Confocal images show endothelial cell nuclei at a branching point adjacent to a presumptively obstructed and pruned capillary segment. Bottom: Pruning of capillaries was associated with a local (within 10µm) increase in endothelial cell nucleus density

(n=4 mice, unpaired t(29)=6.625, ****p<0.0001). Average 4.6 capillaries per mouse, range

3-6. C) Time lapse images of spontaneous or microsphere induced capillary obstructions, both of which were cleared within 48h. D) Clearance of spontaneous

(black) or microsphere induced (blue) capillary obstructions over 48h as percent of obstructions at time 0. Recanalization rates were not significantly different between the two types of obstruction (2 way ANOVA, Main effect of group: F(1,40)=0.06, p=0.99; n(spont.)=5 mice, 36 obstructions, n(micro.)=4 mice, 35 obstructions). E) Example of a capillary that recanalized by extruding the obstruction through the vessel wall, also known as angiophagy. Note the return of blood flow at +10 days and the displacement of the microsphere in side image projections. F) Time lapse images reveal that some capillaries regain blood flow only to be eliminated at a later time. G) Time course of all capillaries that failed to recanalize and were pruned (square) compared to those that recanalized and were later pruned (circle). H) Summary of microsphere obstructed capillary fates 21 days after injection (n=14 mice, 162 obstructions). Numbers in parentheses indicate how many capillaries out of 162 total capillaries underwent pruning, angiophagy or recanalized by extrusion. Average 11 capillaries per mouse, range 6-26.

Scale bars 10µm. Error bars are S.E.M.

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Figure 13 Capillary pruning does not alter adjoining capillary position. A) Example of a capillary being pruned following recanalization. At time 0 the capillary is obstructed by a microsphere (red, blue arrowhead, asterisk shows position of linescan). Dashed line shows distance between secondary connected capillaries measured. By +21 days the capillary was pruned, dashed line shows again the distance between previously connected capillaries measured. B) Inter capillary distance for control (no obstruction or pruning, still connected) or pruned vessels at time 0 and +21 days. There was no significant difference between timepoints for control (paired t-test, t(18)= 1.76, p=0.1) and pruned capillaries (paired t-test, t(32)= 1.86, p=0.07). Error bars are S.E.M.

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2.3.3 Impact of capillary pruning on local blood flow

From a hemodynamic perspective, capillary pruning led to a transient increase in blood flow velocity and flux (flux estimates volume of blood flowing per second through a vessel) in adjacent capillaries, particularly those upstream of the pruned segment

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Figure 14 Additional examples of capillary recanalization and pruning.

A) Recanalization through Angiophagy. At time 0 a microsphere (blue arrow head) obstructs flow in the capillary (insert shows plasma with no RBC streaks).

At +4 days and +15 days the microsphere has been displaced adjacent to the capillary, and blood flow has returned (insert shows plasma with RBC streaks).

Scale bar = 15μm. B) Additional examples of capillaries that recanalized but were still subsequently pruned. In both cases at time 0 the capillary (white arrow, GFP labelled endothelium shown) is obstructed with a microsphere (red, blue arrowhead). By 4 days the microsphere has been extruded back into the circulation and flow has been restored (magenta plasma, inserts show RBC streaks). By +10 days (top) or +15 days (bottom) the flowing capillary has been pruned (dashed line, red arrow). Both Scale bars 15μm.

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Figure 15 Recanalization correlates with obstruction location but not

Local blood flow. A) Diagram of an occluded capillary and nomenclature. Measurements were recorded at time 0 and related with capillary outcome (recanalized or pruned) determined at the following imaging session B) Box and whisker plots (+ indicates mean) showing distance to nearest flowing capillary (closest branch point) at time 0 for obstructed capillaries that either recanalized or pruned

(unpaired t-test t(40)=4.228, p<0.0001, n=10 mice) C) Primary vessel length for recanalized or pruned capillaries (unpaired t-test t(43)=0.21, p=0.82, n=10 mice).

D) Primary vessel width for recanalized or pruned capillaries (unpaired t(39)=0.96, p=0.33, n=10 mice). E) RBC velocity for capillaries upstream or downstream of

obstructed vessel (1 way ANOVA, F(3,24)=0.90, p=0.45). F) Plot of branching order of spontaneous and microsphere obstructed capillaries versus depth from the pial surface. Branching order was invariant by depth (spontaneous 2 obstructions: n=18 capillaries, R =0.098, F(1,17)=1.85, p=0.19; microsphere 2 -5 obstructions: n=58 capillaries; R =5.7 x 10 , test of non zero slope: F(1,57)=0.003, p=0.95). Dashed lines are 95% CI G) Obstructed capillary branch order was not different between recanalized and pruned capillaries (n=10 and 7 obstructions per group; unpaired t(15)=0.91, p=0.37). *** p < 0.001, n.s.= not significant, p > 0.05. Error bars are S.E.M.

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Figure 16 Blood flow in recanalized capillaries does not predict later pruning.

A) Diagram summarizing the timing and location of measurements in primary and adjacent secondary capillaries. B) Box and whisker plots (+ is

mean) for RBC velocity (left), vessel width (middle) and RBC flux (right) for primary capillaries (n=4-10 vessels, 1-Way ANOVA all p>0.05). C) RBC velocity (left), vessel width (middle) and RBC flux (right) for secondary capillaries (n=6-15 vessels, 1 Way ANOVA all p>0.05). n.s.= not

significant. Error bars are S.E.M.

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Figure 17 Obstructed capillaries that recanalized had a higher risk for subsequent obstruction.

A) Cartoon summarizing experiment to determine if capillaries that were obstructed but regained blood flow were more likely to become obstructed following a second injection of microspheres, relative to naïve unobstructed capillaries. B) Box and whisker plot (plus sign is mean) show that the risk of obstruction in previously obstructed capillaries was over double that of naïve capillaries (n=5 mice, 1-2 imaging areas. unpaired t-test t (10)=6.38, p=0.0007). C) Graph shows that both injections obstructed an equivalent number of capillaries. Average 1523 capillaries per mouse, range 739-2368. (Unpaired t(10)=0.42, p=0.68). ***p<0.001, n.s. = not significant. Error bars are S.E.M.

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(Figure 18A-E). By contrast. RBC velocity, vessel width and flux in control capillaries such as those from vehicle injected mice (without microspheres), capillaries distant to obstructions or those adjacent to recanalized capillaries were stable across imaging timepoints (Figure 18B-E). These results show that capillary pruning induces a relatively long-lasting (over several days) perturbation of blood flow in connected capillary networks.

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Figure 18 Capillary pruning leads to altered blood flow in adjacent connected capillaries.

A) Diagram summarizing measurements taken from secondary/adjacent (2⁰) capillaries. B) Plots show normalized RBC velocity from individual secondary capillaries after the primary segment recanalized (blue) or was pruned (solid or dashed red). C-E) Graphs show mean normalized secondary RBC velocity (C;

Main effect of Group F(4,151)=10.27, p<0.0001), diameter (D; Main effect of group

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F(4,118)=2.31, p=0.08) and RBC flux (E; Main effect of group F(4,68)=3.53, p=0.03) in the 5 groups of capillaries studied. Note that “vehicle injected” refers to capillaries tracked from mice injected with just vehicle solution and no microspheres (green) and “distant vessels” refers to capillaries that were distant to any microsphere obstructed capillaries (black) (defined as minimum of 2 branching points away from obstruction). n=14 mice, 6-20 vessels per group.

Average 11 capillaries per mouse, range 4-20. Individual time points followed up with unpaired t-test. *p<0.05 compared to vehicle injected, **p<0.01, ***p<0.001.

Error bars are S.E.M.

2.3.2 Lower capillary density in aged mice is predicted by obstruction and pruning rates

Next, we attempted to predict age-related changes in cortical capillary density based on our experimental estimates of spontaneous capillary obstructions (~0.118% capillaries obstructed per day) and the likelihood of pruning (30% of all obstructed capillaries). We first replicated well established reports of an age-related reduction in capillary density (Brown and Thore 2011; Hinds and McNelly 1982; Casey and Feldman

1985; Buchweitz-Milton and Weiss 1987; Jucker, Bättig, and Meier-Ruge 1990; Jucker and Meier-Ruge 1989; Klein and Michel 1977; Riddle, Sonntag, and Lichtenwalner

2003; Amenta, Cavallotti, et al. 1995; Amenta, Ferrante, et al. 1995) by comparing vascular networks in young adult and aged Tek-GFP mice (Figure 19A, B; 3-4 vs. 15-18 month-old mice, respectively; 6 and 7 mice per group, >49,000 capillaries). Based on our analysis, we found a 10±8% loss in capillary density over the 13.5-month age difference

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(Figure 19C). For theoretical predictions, we created a hypothetical population of

100,000 capillaries, each assigned a branch order based on our experimentally determined distribution (Reeson 2018). We then assigned each branch order a risk of obstruction, based again on our experimental distribution (Figure 7C). For every 2h window the number of capillaries lost for each branch order was simply the number of capillaries x risk of obstruction for that branch order x risk of pruning (30%) (Figure 20).

Comparing the fraction of vessel loss predicted by our model over the course of ~ 13.5 months (~405 days, with day 0 representing young adult mouse in Figure 19C), we found close agreement between experimentally observed and predicted capillary loss

(predicted=88% capillaries remaining vs. measured=90±8%; Unpaired t-test to hypothetical mean 0.88, t(6)=0.68, p=0.52). The predicted loss matched some, but not all, published estimates of age related capillary loss in rats (Figure 20) Thus, spontaneous capillary obstruction and subsequent pruning without compensatory sprouting, can account for age related loss in capillary density.

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Figure 19 Lower capillary density in aged mice is predicted by obstruction and pruning rates.

A) Representative confocal images of Evans blue filled blood vessels in somatosensory cortex of young (3-4 month) and aged (15-18 month) mice. Scale bar 200µm. B) Box and whisker plot of capillary density (+ denotes mean) across all cortical layers for 3-4 and 15-18 month old Tek-GFP mice (n=6-7 mice; unpaired t(11)=2.47, p=0.03). Error bars are S.E.M. C) Box and whisker plot of normalized cortical capillary density for 3-4 or 15-

18 month old Tek-GFP mice (n(3-4month)= 6, n(16-18month)= 7 mice, unpaired t(11)=2.47, p=0.03). Error bars are S.E.M. D) Predicted capillary loss over time (blue line) based on measured rates of spontaneous obstructions and pruning (see methods, Figure 2.14).

Red data points and mean (±SEM) represent measured capillary loss in 15-18 month old aged mice normalized to 3-4 month old mice. Predicted capillary loss closely matched experimentally measured loss in aged mice (unpaired t-test, p=0.52).

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Figure 20 Modelling capillary loss over time.

We modeled a hypothetical set of 100,000 capillaries, applying the proportional risk of obstruction (by branch order) and global risk (30%) of subsequent pruning every 2 hours. A) Plot showing the relative risk of capillary obstruction decreased over time as at-risk capillaries were lost, leaving a higher proportion of less frequently obstructed capillaries (usually of higher branch orders). Note that this ignores possible changes in branch order due to pruning as capillaries were modelled independently with fixed branch orders. B) Relative fraction of each capillary branch order over time. Over 10,000 hours the fraction of frequently obstructed capillaries were reduced while the number of rarely obstructed capillaries stayed constant and thus comprised a greater fraction over time. C) Comparing our predicted and experimentally derived estimate of capillary loss to previous aging studies in rodents.

Note that some previous studies used histochemical markers to label vasculature such as alkaline-phosphatase, which might decrease in aged animals’ independent of actual vessel less, leading to over-estimates of capillary loss. Data shown was calculated from published numbers in each study, ±S.D. when available.

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2.3.3 VEGF-R2 signaling dictates capillary recanalization

Since the maintenance of capillary network density is important for brain health

(Iadecola 2013), our next goal was to uncover a molecular mechanism dictating capillary recanalization. We focused on Vascular Endothelial Growth Factor Receptor 2 (VEGF-

R2) signalling given that it is a critical regulator of endothelial cell function and is very sensitive to changes in hemodynamic shear stress (Olsson et al. 2006; Tzima et al. 2005).

To assess relative levels of VEGF-R2 signalling within individual capillaries that were obstructed vs. those that recanalized, we injected 4µm microspheres coated in a DiI solution and examined brains 3 hours after injection. As mentioned previously, lipophilic

DiI leaches into endothelial cells at the site of the obstruction, therefore leaving an indelible stamp of where the obstruction occurred, even in recanalized capillaries (Figure

21A). Consistent with the hypothesis that VEGF-R2 signalling is sensitive to changes in blood flow (Jin et al. 2003), obstructed capillaries (30min after microsphere injection) had significantly lower colocalization with pVEGF-R2 labelled capillaries (sham

13.41±2.4 % compared to obstructed 3.85±1.3%, unpaired t-test t(30) = 3.86, p = 0.0006, n(sham)=3 mice, 16 vessels, n(obstructed)=4 mice, 16 vessels). Three hours after microsphere injection when capillaries have begun to recanalize, we identified 2 distinct populations of recanalized capillaries based on pVEGF-R2 expression co-localization with DiI labelled endothelium (Figure 21B, insert). Recanalized capillaries exhibited either significantly lower or higher pVEGF-R2 expression, than capillaries that were still obstructed by microspheres or unobstructed control capillaries (Figure 21B).

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Although immunohistochemical analysis clearly indicated that VEGF-R2 signalling was altered in recanalized capillaries, its precise role remained ambiguous. Therefore, we utilized gain of function and knockdown experiments to better define the role of VEGF-

R2 signalling in capillary recanalization. First, we stimulated VEGF-R2 signalling using our previously validated protocol (Taylor et al. 2015) of injecting 25ng VEGF-A (i.c.v.) just prior to injection of microspheres. VEGF treatment significantly increased obstruction density 24h after microsphere injection compared to vehicle injected mice

(Figure 21C, unpaired t-test, t(8)=3.282, p=0.01, only cortex contralateral to injection site was analyzed). Based on the fact that reduced levels of vascular pVEGF-R2 correlated with recanalization in some but not all capillaries, and that increasing VEGF-R2 signaling lowered recanalization rates, we then asked whether reducing VEGF-R2 signaling could improve recanalization rates. For this we employed two approaches

(Figure 21D), an endothelial specific inducible knockdown of VEGF-R2 signalling (Tek

Cre-ERT2 crossed with floxed Kdr (VEGF-R2) line) or pharmacological inhibition of

VEGF-R2 with the blood brain barrier permeable, small molecule inhibitor SU5416

(Annie et al. 1999; Mendel et al. 2000; Reeson et al. 2015). Both of these approaches significantly reduced VEGF-R2 signalling as indicated by lower immunohistochemical staining for pVEGF-R2 (Figure 21E; tamoxifen vs vehicle: unpaired t-test, t(6)=3.32, p=0.01; SU5416 vs vehicle: unpaired t-test t(6)=3.17, p=0.02) or western blot detection of

VEGF-R2 protein levels in the cortex (Figure 22A).

The impact of reduced VEGF-R2 signalling on capillary recanalization was assessed with in vivo time lapse imaging (Figure 21F, H) or post-mortem estimates of microsphere density at 4 and 21 days after microsphere injection (Figure 21G, I). For

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both types of analysis, rates of capillary recanalization were significantly enhanced when

VEGF-R2 signalling was reduced with VEGF-R2 knockdown (Figure 21F, G) or pharmacological inhibition (Figure 21H, I). Not surprisingly, improved recanalization was associated with a significant reduction in the number of pruned capillaries at 21 days

(Fig. 21F, H). The effects of VEGF-R2 knockdown or inhibition on recanalization rates were not explained by any global cardiovascular changes, as we found no differences in heart rate, breath rate, blood pressure or tissue oxygenation between treated and control groups (Figure 22-23). Further, these effects could not be explained by significant differences in the initial density of microspheres injected (microsphere density 30min

3 post-injection: vehicle vs. SU5416 = 652±224 versus 562±200 microspheres/mm , t(6) =

3 0.25, p = 0.8; vehicle vs Tamoxifen: 509±234 versus 328±70 microspheres/mm , t(10) =

2.0, p =0.07). Lastly, VEGF-R2 knockdown or inhibition did not significantly alter RBC velocity in either control, recanalized or adjacent vessels (Figure 22-23). In conclusion, these experiments indicate that VEGF-R2 signalling plays a critical role in dictating capillary recanalization.

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Figure 21 VEGF-R2 signaling dictates capillary recanalization.

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A) Confocal images show phosphorylated VEGF-R2 (Y1175) immunolabeling in an obstructed capillary (top row, see DiI coated microsphere) and one that recanalized

(bottom row, note DiI labeling of GFP labeled endothelium without presence of microsphere). B) Histogram shows % pVEGF-R2 colocalization with endothelium at the site of an obstructed or recanalized capillary 3 hours after microsphere injection. Sham capillaries were measured from mice injected with vehicle solution but without microspheres. Inset shows the distribution of pVEGF-R2 colocalization values for all recanalized capillaries. Based on this distribution we used a 20% cut off (dotted line) to separate the 2 distinct populations. Average 8 capillaries per mouse, range 5-11. Note that recanalized capillaries exhibit significantly higher or lower pVEGF-R2 colocalization. n=83 vessels total with 3 sham and 7 injected mice, 1 way ANOVA F(3,77)=22.93, p<0.0001, unpaired t-test to compare groups. *p< 0.05, ****p< 0.0001. C) Box and whisker plots (+ denotes mean) show normalized density of microspheres in the cortex of saline and VEGF injected (i.c.v.) mice 24h after microsphere injection. n=5 mice per group, unpaired t-test. *p< 0.05. Microsphere density: Vehicle 7.97±1.3 per mm3 versus

VEGF injected 22.27±2.1 per mm3. D) Summary and timeline of VEGF-R2 knockdown or inhibition experiments. E) Left: Immunolabelling for pVEGF-R2 in Tek-GFP mice shows reduced vascular expression 3h after injection of 50 mg/kg SU5416. Right:

Quantification of vascular pVEGF-R2 in Tamoxifen treated TekCreERT2 X Kdr+/fl mice (4 mice per group; unpaired t-test, t(6)=3.32, p=0.01) or Tek-GFP mice injected with

SU5416 (4 mice per group; unpaired t-test t(6)=3.17, p=0.02). F) Capillary fates 21 days after obstruction in vehicle or tamoxifen treated Tek CreERT2 X Kdr+/fl mice based on in vivo time lapse imaging (n=6 mice per group, vehicle=75 capillaries, Tamoxifen=81 capillaries, Average 10 capillaries per mouse, range 5-16). Unpaired t-test to compare % recanalized, t(10)=3.88, p=0.003.** p< 0.01. G) Left: Representative images of cortical microspheres in coronal brain sections from vehicle or Tamoxifen injected Tek CreERT2

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X Kdr+/fl mice 21 days after microsphere injection. Scale bar 500µm. Right: Normalized density of microspheres in the cortex of vehicle and Tamoxifen injected mice at 4 and 21 days after microsphere injection (n=4-9 mice; unpaired t-test at 4 days t(6)=4.43, p=0.004; and 21 days t(12)=2.74, p=0.017. + is mean. *p< 0.05, **p< 0.01). Microsphere densities ( /mm3) at +4 days: Vehicle 16.2±1.5, Tamoxifen injected 10.3±5, +21 days:

Vehicle 6.38±3, Tamoxifen injected 1.5±0.4. H) In vivo determination of capillary fates after 21 days in vehicle or SU5416 injected Tek-GFP mice (n=4 mice per group at 4 days, n=5 for vehicle and n=9 for tamoxifen injected at 21 days, vehicle=84 capillaries;

SU5416=90 capillaries; Average 20 capillaries per mouse, range 16-30, unpaired t(7)=2.73, p=0.03). I) Left: Images show cortical microspheres in brain sections from vehicle and SU5416 treated Tek-GFP mice 21 days after microsphere injection. Scale bar 500µm. Right: Normalized density of microspheres in the cortex of vehicle or

SU5416 injected mice at 4 and 21 days (n=4 mice per group; unpaired t-test at 4 days

3 t(6)=6.97, p=0.004, and 21 days t(6)=2.92, p=0.02) Microsphere densities ( /mm ) at +4 days: Vehicle 69.2±1.4, SU5416 injected 31.94±4.2, +21 days: Vehicle 21.59±5.3,

SU5416 injected 12.21±1.33. *p< 0.05, **p< 0.01, and ***p<0.001 compared to vehicle injected. Error bars are S.E.M.

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Figure 22 Vascular specific knockdown of VEGF-R2 does not affect cardiovascular health or blood flow. A) Left: Representative western blot of cortical VEGF-R2 levels in Tek CreERT2 X Kdr +/fl mice 4 days after the final injection of vehicle (Vh) or Tamoxifen (Tam). Right: Quantification of VEGF-R2 normalized to vehicle with β-Actin as a loading control (unpaired t-test t(8)=2.91, p=0.02; n=5 mice per group). B-E) Measurements of heart rate (B), blood pressure (C), oxygen saturation (D) and breath rate (E) from control and Tamoxifen treated Tek CreERT2 X Kdr+/fl mice. Since there was no main effect of time in any of these cardiovascular measurements, data collected at 8 and 21 days were pooled (n=4 mice per group; 1-way RM ANOVA p>0.05 for all measurements). F-H) Plots show RBC velocity in Tamoxifen or vehicle treated Tek CreERT2 X Kdr+/fl mice in 3 types of capillaries: distant capillaries (F, ie. more than 2 branching points away from an obstructed capillary; Repeated Measures 2 way ANOVA: F(1,12)=4.44, p=0.06),

recanalized capillaries (G; Repeated Measures 2 way ANOVA: F(1,12)=0.27, p=0.61) and capillaries adjacent (secondary) to one that was recanalized (H;

Repeated Measures 2 way ANOVA: F(3,26)=1.61, p=0.21). 4 mice per group, 8-17 vessels per group. *p<0.05. n.s. = not significant. Error bars are S.E.M.

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Figure 23 Inhibition of VEGF-R2 signaling with SU5416 did not affect cardiovascular health or blood flow.

A) Schematic of VEGF-R2 dimer with phosphorylation sites shown and special emphasis on those inhibited by SU5416 (Olsson et al. 2006; Mendel et al. 2000).

B-F) SU5416 did not significantly alter oxygen saturation, breath rate, blood pressure, RBC velocity, or heart rate when measured 90 minutes after injection

(n=4-9 mice; all p>0.05 based on paired t-tests). G: Capillary width was not significantly different when comparing baseline values to 90 mins post-SU5416 injection. H-J) Plots show RBC velocity in SU5416 or vehicle injected mice in distant capillaries (H; more than 2 branching points from an obstructed capillary; Main effect of Group based on Repeated Measures 2 way ANOVA: F(1,15)=0.31, p=0.59), recanalized capillaries (I; Main effect of Group based on Repeated

Measures 2 way ANOVA: F(1,16)=0.00, p=0.95) and those adjacent to a recanalized capillary (J; Main effect of Group based on Repeated Measures 2 way ANOVA: F(3,75)=0.77, p=0.52). Four mice per group, 8-11 vessels per group. Error bars are S.E.M.

2.4 Discussion

Here we used in vivo 2-photon imaging to characterize the inherent risk of cortical

capillary obstruction in adult mice. We found that on average ~1 in 10,000 capillaries

will become obstructed for longer than 20 min within a 2h window. However, the risk of

obstruction was not equally distributed as superficial capillaries of lower arteriole branch

orders (distribution peaks at the 3rd arteriole branch order) were at highest risk (Figure

7C). While this distribution of obstructed capillaries (as a function of arteriole branch

order) bears similarity to a recent study (Erdener et al. 2017), we did not quantify risk

relative to venous branch order which may have a higher risk for obstruction (Erdener et

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al. 2017). The absolute number of obstructed cortical capillaries might seem quite small, however it is important to keep in mind that the mouse cortex occupies ~180mm3 (Badea,

Ali-Sharief, and Johnson 2007) with an estimated 3.6 million capillaries (Figure 19)

(Blinder et al. 2013; Schmid, Barrett, et al. 2017). Therefore, at any given time, one could expect a few hundred obstructed capillaries in the mouse cortex. We would argue that over time, this small fraction of obstructed capillaries could significantly impact cerebrovascular density and blood flow. For example, our data show that ~30% of obstructed capillaries were eventually pruned with no compensatory sprouting. Over a year, we would predict a 12% reduction in capillary density which closely approximates our experimentally determined estimate of vessel loss, as well as others (Harb et al. 2013;

Buchweitz-Milton and Weiss 1987; Casey and Feldman 1985; Klein and Michel 1977;

Hinds and McNelly 1982). In addition to these anatomical changes, we also show that capillary pruning locally perturbs blood flow and flux in adjacent capillary segments for several days. This supports previous experimental work (Schaffer et al. 2006) showing that capillary occlusion alters blood flow in branches up and downstream of the occlusion. Other work has shown through modelling that microvessel loss can lead to abnormalities in local perfusion (Jespersen and Østergaard 2012). Thus, despite the highly interconnected nature of the capillary bed, even a small number of pruning events can significantly alter local blood flow and capillary density.

2.4.1 Microsphere Model of obstruction

Previous studies have injected relatively large (10-100µm) microspheres in adult rodents to model multiple small ischemic strokes (Fukatsu et al. 2002; Silasi et al. 2015;

Rapp et al. 2008; Roos and Ericsson 1999; Sato et al. 2009) or to identify novel

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mechanism of vessel recanalization (Lam et al. 2010; Grutzendler et al. 2014). Here we used small 4µm diameter microspheres to selectively obstruct capillaries in an attempt to model spontaneous, naturally occurring capillary obstructions in the brain. Taking this approach provided several experimental advantages. Due to their small size, microspheres can be injected through the tail vein instead of through more invasive routes, such as the internal carotid artery which is believed to be required for emboli larger than 10µm to reach the brain. The non-invasive nature of these injections facilitates repeated injections and minimizes exposure to anesthetics needed for injections into the carotid. The small size of the microspheres allows them to be dispersed throughout the brain. While we did not find any differences in the anterior to posterior distribution of microspheres in dorsal cortex (from -2mm to +1.5mm relative to bregma), this doesn’t take into account regional differences that may exist in more anterior, posterior and lateral cortical regions. Since regional vulnerability to capillary obstruction has relevance to pathophysiology, future work will be needed to characterize the risk of obstruction and pruning across the cortical mantle or subcortical regions. We have also exhaustively validated this model of obstruction. First, we have established that our dosage of microsphere injection did not cause detectable changes in global cardiovascular parameters and blood chemistry (Figure 9). Thus, while microspheres do obstruct vessels in other organs, they do not induce cell death, or organ dysfunction. A future question will be to study capillary obstructions in other organ systems to see how the risk of obstructions, and ultimate fates vary in different systems. Angiophagy for example was first described in brain vasculature but turned out to be a universal property of most vessels (Grutzendler et al. 2014). Second, how the brain deals with microsphere

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obstructions yielded similarities with those that occur spontaneously. For example, rates of capillary recanalization and eventual pruning of obstructed capillaries were similar between the two types of obstructions (microsphere vs natural emboli). A third advantage is that microspheres can be coated with lipophilic dye DiI, thereby allowing us to tag obstructed vessels, even after they had recanalized. This method will be useful for furthering our understanding of the signalling events that occur and differentiate recanalized capillaries versus those that remain obstructed.

However, there are important caveats that should be noted with the microsphere model. Foremost is the fact that the composition (polystyrene microsphere vs. fibrin, blood cells) and size of the obstruction likely affects the route of recanalization. While we found low rates of angiophagy with microsphere obstructions, this likely would not be the case with larger and more natural emboli (such as cholesterol or fibrin based clots).

For example, Lam et al. showed different rates of washout versus angiophagy between natural emboli compared to microspheres (Lam et al. 2010). A second caveat that could not be avoided was that we were unable to inject microspheres without the use of anesthesia. Anesthetics such as isoflurane have pleiotropic effects that influence synaptic transmission, blood flow and ischemic cell death (Santisakultarm et al. 2016; Yamakura and Harris 2000; Seto et al. 2014). Therefore, we cannot completely rule out a possible effect of even brief isoflurane exposure (in awake imaging or histology experiments) on microsphere obstruction density, as others have reported anesthetic effects on capillary stalling (Erdener et al. 2017; Santisakultarm et al. 2016).

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2.4.2 Vascular remodelling in the mature brain

Our finding that a failure to remove obstructions inevitably leads to capillary pruning suggests that the accumulation of obstructions over time is at least partially responsible for the loss of microvascular density in aging reported by us and others

(Figure 19) (Harb et al. 2013; Klein and Michel 1977; Hinds and McNelly 1982; Casey and Feldman 1985; Buchweitz-Milton and Weiss 1987; Jucker, Bättig, and Meier-Ruge

1990; Amenta, Cavallotti, et al. 1995; Amenta, Ferrante, et al. 1995) (see (Riddle,

Sonntag, and Lichtenwalner 2003) for review, but also (Knox, Yates, and Chen 1980;

Hughes and Lantos 1987) for studies failing to find age related losses in capillaries). The fact that vessel pruning was not compensated for with sprouting (angiogenesis) is significant, but should not be viewed as completely surprising since other in vivo longitudinal imaging studies of cortical microcirculation have found scant evidence for angiogenesis (at least below the cortical surface) in the mature brain (Harb et al. 2013;

Reeson et al. 2015; Tennant and Brown 2013; Mostany et al. 2010). Furthermore, since our obstructions targeted a small fraction of capillaries and therefore did not lead to hypoxia or cell death, the hypoxia related signals needed to trigger angiogenesis would not be present.

Our estimate of capillary loss in the aged mouse cortex, while closely matching our predicted loss and many other studies, would seem conservative to some prior reports

(Figure 20). Discrepancies in empirical estimates of capillary loss with age may reflect different methodologies for visualizing capillaries. Our measurements were based on intravenous injection of fluorescent dyes therefore labelling flowing capillaries in the brain with very high signal to noise. However, this could slightly overestimate the density

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of patent capillaries since injected plasma dyes also filled stalled capillaries given that we never found a Tek-GFP labelled capillary without fluorescent dye present in it.

Importantly, our Evans blue based estimates of vessel density in young adult mice closely matches other published studies (Schmid, Barrett, et al. 2017; Tsai et al. 2009), particularly those by the Kleinfeld lab (Blinder et al. 2013; Tsai et al. 2009). In contrast, many previous studies in rats have used histochemical staining for alkaline phosphatase

(Jucker, Bättig, and Meier-Ruge 1990; Jucker and Meier-Ruge 1989; Amenta, Cavallotti, et al. 1995; Amenta, Ferrante, et al. 1995) or electron microscopy (Casey and Feldman

1985) to estimate vessel density. The caveat of using alkaline phosphatase staining is that it assumes ubiquitous vascular expression that does not vary with age (independent of vessel loss), while electron microscopy studies are constrained by very low sample sizes.

Another obvious factor underlying discrepancies in capillary loss could be the experimental animal studied since most previous studies used rats. While we are confident that our predictions are well suited for mice, it would be interesting to know if it could be applied to other larger and longer-lived species. Future studies, perhaps replicating our approaches for estimating capillary obstruction and clearance rates, as well as capillary density in aging animals would be necessary to extend our predictions.

2.4.3 Mechanisms of recanalization

Historically, the main mechanism of recanalizing a cerebral microvessel was believed to be through Tissue Plasminogen Activator (TPA), which was provided by microvascular endothelial cells (del Zoppo and Mabuchi 2003). However, previous work has shown in larger vessels the window for TPA mediated recanalization is short

(Grutzendler et al. 2014; Lam et al. 2010), and many types of obstructions are not

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susceptible to thrombolytics. The recent discovery of angiophagy (Grutzendler et al.

2014; Lam et al. 2010) in penetrating arterioles and capillaries suggested different strategies exist for clearing obstructions. Our finding of an angiophagy-like process where endothelial cells engulf and extrude emboli through the vessel wall replicates previous findings of the Grutzendler lab (Lam et al. 2010; Grutzendler et al. 2014).

However, we cannot rule out the possibility that leukocytes (which were not labelled in our study) could have engulfed microspheres and transported them across the capillary wall. Due to the low incidence (2%) of angiophagy-like events in our microsphere model, we could not extensively characterize this process. Further, the manner and frequency of angiophagy-like events is likely influenced by the specific size of emboli and its composition. For example, larger occlusions (> 4 µm) would have more severe consequences such as hypoxia and cell death (48) which can trigger major structural changes in the vascular endothelium through hypoxia inducible factors. An intriguing question that has not been resolved is what mechanisms mediate the dislodging and extrusion of obstructions back into the circulation. Although we know that VEGF-R2 signalling plays a critical role in recanalization, we do not know the precise mechanisms through which it acts. For example, VEGF-R2 is coupled to nitric oxide and other signalling pathways (Jin et al. 2003) that could mediate a change in vessel tone or diameter in the obstructed capillary. However, a recent study has questioned the role of

NO as a major capillary dilator (Mishra et al. 2016), thus other pathways may be involved. We should also note that heamodynamic forces from up or downstream capillaries are not likely a critical factor since measured levels of blood flow or diameter in these capillaries did not precede or predict capillary recanalization (Figure 15).

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Another possibility that will require further study is whether pericytes facilitate recanalization or pruning. In particular, smooth muscle actin positive pericytes, which are found at precapillary arterioles and lower branch order capillaries, have been shown to regulate capillary flow (Hill et al. 2015). Furthermore, ischemic stroke leads to prolonged pericyte constriction which can limit blood flow in capillaries (Hill et al. 2015; Hall,

Reynell, Gesslein, Hamilton, Mishra, Sutherland, O’Farrell, et al. 2014). Therefore, it is possible that pericytes may contribute to capillary recanalization by regulating microvessel tone or vasomotion. Pericytes could also play a significant role in vessel pruning since they are hotspots of matrix metalloprotease activity associated with BBB disruption, (Underly et al. 2016) which is needed for remodeling of the extracellular matrix and basement membrane around capillaries. The only variable that differentiated recanalization successes versus failure was distance to the nearest flowing vessel, which suggest at least a more complex interplay between vessel tone and heamodynamic forces.

The precise mechanisms by which vessel tone and blood flow intersect to dislodge and extrude persistent emboli will require further study.

2.4.4 Microvascular obstructions and cognitive impairment

Although not the focus of the present study, it is very likely that the incidence of capillary obstructions and the rate of recanalization and pruning could be strongly influenced by disease states. Indeed, a recent study (Santisakultarm et al. 2014) showed that rates of capillary obstruction were significantly elevated in blood disorders with excessive production of platelets and RBCs. It is now accepted that there is a strong vascular component to many types of dementia (Tong and Hamel 2015; Iadecola 2013).

While the umbrella of Vascular Cognitive Impairment (VCI) encompasses many different

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vascular pathologies, microvessel loss is an important one (Riddle, Sonntag, and

Lichtenwalner 2003; Brown and Thore 2011; Tong and Hamel 2015; Langdon et al.

2017). Our study offers at least one mechanism for the loss of microvessels in the healthy aging brain, therefore it is tempting to speculate that similar mechanisms could be at work for known risk factors of dementia such as diabetes or hyperlipidemia. To our knowledge, no study has tracked capillary obstructions and their long-term fates in animal models of VCI. In this sense, we anticipate our study will provide a useful framework for understanding microcirculatory changes that accompany, and possibly underlie cognitive impairment. Furthermore, since we have shown that VEGF-R2 plays a critical role in dictating capillary recanalization, future studies could apply this knowledge to ameliorate disturbances in brain circulation and function/cognition in neurological conditions with a known vascular connection.

2.5 Materials and Methods

2.5.1 Animals

We used 2-4 month-old, and 16-18 month old Tek-GFP mice (The Jackson Laboratory,

003658), or Tek-CreERT2 mice (referred to as Tie2-CreERT2 in Chapter 3) (Forde et al.

2002) (EMMA 00715) bred with Kdrfl/fl line (referred to as VEGF-R2fl/fl in Chapter 3)

(Hooper et al. 2009) to generate Tek-CreERT2 X Kdr+/fl mice. Male mice were used in all experiments except for determining capillary density in 3-4 versus 16-18-month mice

(Figure 19), in which retired breeders (male and female) were compared to gender matched 3-4-month-old Tek-GFP mice. No differences in capillary density were found by sex, so data were pooled (3-4-month male versus female Unpaired t(4)=0.69, p=0.52; 16-

18-month males versus females Unpaired t(5)=1.36, p = 0.23). Offspring were genotyped

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using the following primers for floxed Kdr (WT: 5’-TGG-AGA-GCA-AGG-CGC-TGC-

TAG-C-3’ and Flx: 5’-CCC-CCT-GAA-CCT-GAA-ACA-TA-3’, and common: 5’-CTT-

TCC-ACT-CCT-GCC-TAC-CTA-G-3’) and Cre (5’-CGA-GTG-ATG-AGG-TTC-GCA-

AG-3’ and 5’-TGA-GTG-AAC-GAA-CCT-GGT-CG-3’). Mice were housed under 12h light/dark cycle and given ad libitum access to water and laboratory diet. All experiments were conducted according to the guidelines set by the Canadian Council of Animal Care,

ARRIVE, and approved by the University of Victoria Animal Care Committee, protocol

2016-016(2).

2.5.2 Cardiovascular measurements

Blood pressure was measured using a commercially available system (Kent

Scientific Mouse CODA). Mice were lightly anesthetized (1% isoflurane) and placed on a heating pad with body temperature maintained at 37℃. Tissue oxygenation (% 02 saturation), heart rate (beats/min), and breathing rate (breaths/min) were measured with the commercially available Starr Mouse Ox system (Starr Life Sciences Corp.). Values for each time point were taken as the average of at least 5min of continuous recording.

Blood chemistry was measured using Abaxis VetScan i-STAT system (with CG8+ cartridges). To obtain blood (~100μL) for analysis, mice were anaesthetised with isoflurane and rapidly decapitated.

2.5.3 Cranial window surgeries

Mice were anesthetized with isoflurane (2% induction, 1.3% maintenance) in medical grade air and fitted into a custom-made surgical stage with body temperature maintained at 37°C. A 0.03mL bolus of 2% dexamethasone was given intramuscularly to reduce inflammation associated with the procedure. Following a midline incision and

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retraction of the scalp, a custom metal ring (outer diameter 11.3mm, inner 7.0mm, height

1.5mm) was affixed to the skull with cyanoacrylate glue. Using a high-speed dental drill, a 4mm diameter craniotomy was made overlying the somatosensory cortex. Cold

HEPES-buffered artificial cerebrospinal fluid (ACSF) was applied to the skull intermittently during the drilling procedure to keep the brain cool and reduce inflammation. The dura was left intact and a 5mm coverslip (no. 1 thickness) was placed over the brain and secured to the surrounding skull with cyanoacrylate glue and dental cement. The surrounding skin was then secured to the edges of the metal ring with cyanoacrylate glue. Mice were allowed to recover under a heat lamp before being returned to their home cage. After 4 weeks of recovery, mice that showed significant loss of clarity to the imaging window were excluded from the study.

2.5.4 Microsphere model of capillary obstruction

For inducing capillary obstructions, 20μL of microspheres (4μm diameter; 2% solids; peak emission 605nm; Life Technologies FluoSpheres sulfate, F8858) were mixed with 100μL of fluorescent dye or saline and injected in the tail vein. For comparing obstruction clearance rates between experimental groups (e.g. mice with treated VEGF-

R2 inhibitor or vehicle), a master solution of microspheres was first made, sonicated

(3min) and then aliquoted into separate injection doses which were then assigned to mice randomly. Groups were always run in parallel and balanced between controls and experimental conditions.

2.5.5 In vivo imaging

Mice were lightly anesthetized with 1% isoflurane with body temperature maintained at 37°C. Mice were given an intravenous injection of 4% Rhodamine B

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dextran (Sigma, R9379, average molecular weight 70,000, in 0.9% saline) or FITC dextran (Sigma, 46945, average molecular weight 70,000, in 0.9% saline). The cerebral vasculature was imaged through the cranial window using an Olympus FV1000MPE multiphoton laser scanning microscope equipped with a mode-locked Ti:Sapphire laser

(Spectral Physics) tuned to either 850 or 910nm (for Rhodamine B or FITC dextran, respectively). Laser power delivered by each wavelength ranged from 15 to 50mW at the back aperture depending on imaging depth. Images were acquired in a stack (2µm z-step) with either a 40x Olympus IR-LUMPlanFl (NA=0.8, 0.20µm/pixel) or 20x Olympus

XLUPlanFl water-immersion objective (NA=0.95, 0.62µm/pixel), using Olympus

Fluoview FV10-ASW software. Emitted light was separated by a dichroic filter (552 nm) and then directed through a bandpass filter (either 495-540nm or 558-706nm).

For awake imaging, mice were habituated to imaging in three sessions; 1 session per day for 3-5 days before the start of the experiment. For each session, mice were very briefly anesthetized, then swaddled in a cotton wrap and restrained in a custom plastic rodent restraint device, modified to allow the head to be fixed in a custom stage for imaging. Mice were allowed to wake up in the restraint device. The duration of restraint was gradually increased from 5 to 30min over the 3 habituation sessions. Any mouse that showed abnormal or prolonged signs of distress during habituation or imaging sessions were removed from the study (~25% of mice). In general, awake-imaging sessions lasted

20-60min.

Imaging areas were chosen based on the clarity of the window and were located near the center of the cranial window, with penetrating arterioles (identified from surface pial vasculature) in the field of view. For determining the rate of spontaneous capillary

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obstructions that occur in a 2h period, multiple areas were randomly selected and imaged every 10-20min (~50-350µm below pial surface). For longitudinal imaging of spontaneous or microsphere induced capillary obstructions, 2-4 regions were imaged based on suitable density of occlusions and imaging clarity. Any obstructed capillary with more than 1 microsphere lodged, or less than 2 branching points from another obstruction were excluded. In no cases did we observe larger vessels (> ~4 μm) obstructed by aggregates of multiple microspheres.

2.5.6 Analysis of vascular structure and flow

Blood flow velocity was estimated from a series of 3 line scans conducted on capillaries (≤8µm in diameter) with a 30sec waiting period between each scan. In each brain region, 2-3 microsphere-obstructed capillaries were selected for line scans (to confirm absence of blood flow) and 1-2 adjacent flowing segments. In all cases 1-2 capillaries in each region that were both flowing and at least 2 branching points away from any obstruction were used as within animal control vessels. All analysis for blood flow velocity, vessel lumen diameter, and RBC flux was conducted by two separate independent researchers blinded to experimental condition. To measure RBC velocity, two blinded researchers chose 3 equally spaced apart RBC streaks from each line scan

(each linescan was on average 30.83±1 ms per scan, 2 μm/pixel) and using built in functions of Olympus Fluoview software measured the inverse slope (Δ time/ Δ distance). This was repeated for all 3 linescans done for a single vessel at each timepoint

(30 seconds apart). Therefore, each single vessel RBC velocity at each timepoint was the average of 18 independent blinded measurements (2 experimenters’ x 3 linescans x 3

RBC slopes). Any curvilinear slopes were excluded. Capillary diameter was measured at

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Full Width at Half Maximum (FWHM) from a Gaussian fitted intensity profile (John

Lim, IMB. March 2011) drawn perpendicular to the capillary. RBC Flux was calculated assuming laminar flow, as F = π/8*v*d2 (Schaffer et al. 2006). Direction of flow was determined from the direction of the slope of RBCs in line scans (upwards or downwards) at all time points. Branching orders were determined for capillaries branching off single penetrating arterioles in each region, and each capillary that could unambiguously be assigned a branch order was included. Obstructed branch orders were determined working backwards from the obstructed capillary to a penetrating arteriole that could be followed to the pial surface and identified as an arteriole.

2.5.7 Estimation of capillary numbers

Capillary numbers were estimated using a custom written macro in either ImageJ or FIJI (Schindelin et al. 2012) (see Figure 9, Appendix 1 – supplement Code1)(Reeson

2018). Imaging stacks of fluorescently labelled vasculature (rhodamine or FITC dextran) were split into sub-stacks of 10 images (2µm z step). Sub-stacks were each maximally projected in the z plane, and automatically thresholded using ImageJ function Triangle

(Zack, Rogers, and Latt 1977), which best identified vascular signal. The total area (of each image) and percent vascular signal (number of pixels after thresholding) was measured after applying a median filter (radius 1 pixels) to eliminate speckling. The area and vascular signal were then converted from pixels to µm and from 2D to 3D manually.

The fractional vascular volume (v/v) and total vascular volume (µm3) was then calculated. Thresholded sub-stacks were skeletonized (Arganda-Carreras et al. 2010) to create single pixel linear segments and total vascular length was taken as the total number of skeletonized pixels. From vascular length and area measurements, average vessel

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width was calculated. Since capillaries constitute the majority of the cortical vasculature

(Blinder et al. 2013), we estimated the total number of capillaries by dividing the total vascular volume (µm3) with the average volume of a single capillary, assuming an average radius of 2µm and length of 75µm (Blinder et al. 2013) (average capillary volume: 942.48µm3). We should note that flattening sub stacks and then re-projecting across the volume overestimates vascular volume (square vessels rather than cylinders) and underestimates vascular length (loss of length in z direction). Therefore, to validate our estimates we first created a sample set of imaging data (4 mice, 2 imaging areas per animal) and created 2 independent and blinded manual measurements of vascular length

(tracing vasculature by hand in each sub stack) and capillary number (manual counts).

Automated estimates of vascular volume were in agreement with and published data

(Tsai et al. 2009). Likewise, the automated estimate of capillary numbers agreed with manual counts (paired t test, t(3)=0.33, p=0.76).

2.5.8 Recanalization rates and capillary fates

For estimating recanalization rates within the first 48h (Figure 12D), 3-4 regions per mouse were imaged from time 0 (~30min after injection of microspheres) and then 6,

12, 24 and 48h later. Each imaging session was limited to 40min total with mice regaining consciousness and returned to their home cage between each session.

Recanalization rates were calculated as % of remaining obstructions at each time point for each animal. For long-term assessment of capillary fates, obstructed capillaries

(microsphere induced or spontaneous; awake or anesthetized) were imaged at time 0 and then at 2, 4, 10, 15 and 21 days with a small subset of mice imaged up to 31 days. All apparent instances of recanalization were confirmed with vessel line scans. Vessel

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pruning was directly determined in Tek-GFP mice by noting the retraction of GFP labelled endothelium or indirectly in Tek CRE ERT2 Kdr+/flx mice by the complete the loss of labelled plasma (FITC dextran) in a fully pruned segment.

2.5.9 Aged capillary density measurements

Male and female Tek-GFP retired breeders (16-18 months old mice) and gender matched 3-4-month-old Tek-GFP mice were anesthetized with isoflurane and intravenously administered 100µL of 2% Evans blue in 0.9% saline (Sigma, E2129).

Evans blue was allowed to circulate for 30min and then mice were decapitated, and the brain fixed in 4% paraformaldehyde (PFA) in 0.1M phosphate buffered saline (PBS) overnight at 4°C. Brains were sectioned at 100µm on a Leica vibratome (T1000) and every 3rd section was immediately mounted on charged slide and coverslipped with

Fluoromount G (ThermoFisher, 00-4958-02). Aged and young mouse brains were processed and imaged in parallel. All sections were imaged immediately after cutting to minimize the possible leaching out of Evans blue dye that can occur over a 24h period.

Evans blue was excited using a 635nm laser and collected using an Olympus confocal microscope with a 10x objective (NA 0.40) and a Cy5 emitter filter (670 –720 nm).

Confocal image stacks were collected in 2µm z-steps at a pixel resolution of

1.035µm/pixel. All imaging parameters, including gain and offset, were kept consistent throughout. Three images of the somatosensory cortex were captured for each animal.

For manual capillary counts, two independent blinded researchers z projected (maximal intensity) the middle 24µm of each stack to minimize capillaries crossing over each other in the projection. Fluorescence levels between different animals were normalized by setting the scale of each image to 40% of the maximum pixel intensity of the brightest

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vessels. For a segment to be counted as a capillary, it must have been clearly visible and project at least 5µm from another segment. Vessels with diameters greater than 8µm were excluded.

2.5.10 Modelling capillary loss over time

Capillary loss over time was modelled by custom written software in Matlab

R2017a. Distribution of branch orders measured experimentally was applied to a theoretical set of 100,000 capillaries (without structural relationships between them). The risk of obstruction was (Figure 19, Appendix1 –supplement Code2)(Reeson 2018), applied to each branch order based on the distribution of obstructed capillaries by branch orders (sum adding to observed rate). For each 2h cycle, the number of capillaries in each branch order was multiplied by its assigned risk to give the number of obstructed capillaries in each branch order. The number of obstructed capillaries was then multiplied by the overall risk of pruning (0.30) to calculate the number of pruned capillaries for each branch order, which was then subtracted from the number of vessels in each branch order category. Importantly, the risk of obstruction for each branch order was assumed constant

(overall risk, therefore only changed as the distribution of branching orders varied).

Individual capillary branch orders (initially assigned) were also fixed and not adjusted by any “up stream” pruning events (capillaries were modeled without structural relationships between them).

2.5.11 Stimulating or blocking VEGF signalling in vivo

Cage littermates were randomly selected to either control or VEGF-R2 knockdown/inhibition experimental groups. Cre recombinase activity was induced before microsphere or sham injection by 5 daily intraperitoneal injections of 125mg/kg of

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tamoxifen (Sigma, T5648, dissolved in corn oil, Sigma, C8267), while vehicle injected mice received corn oil alone (Monvoisin et al. 2006; Sörensen, Adams, and Gossler 2009;

Reeson et al. 2015). In Tek-GFP mice, VEGF-R2 activity was inhibited by sub-cutaneous injections of SU5416 every 72h (Annie et al. 1999; Mendel et al. 2000) (Tocris, 50mg/kg,

0.5% w/v carboxy methyl cellulose, 0.9% sodium chloride, 0.4% polysorbate, 0.9% benzyl alcohol in dH2O).

For stimulating VEGF signalling in the brain, mice were injected with 1µL of

25ng of recombinant mouse VEGF165 protein (Sigma V4512) in artificial cerebrospinal fluid (ACSF) or ACSF alone into the left lateral ventricle (2mm lateral, 0.5mm posterior of bregma). Twenty minutes after i.c.v injections, 20µL of microspheres in 100µL of

0.9% saline was injected into the tail vein. One day after microsphere injection, mice were killed, brains were extracted and fixed overnight in preparation for microsphere density analysis. Only the right hemisphere was used for analysis to avoid confounding effects of the injection site.

2.5.12 Microsphere density analysis

Coronal brain sections (100µm thick) from microsphere injected mice were imaged on an Olympus BX51 microscope with a 4x UPlanFLN objective (N.A. 0.13,

456.69 pixels/mm, 1.15 x 0.87mm) using a Cy3 filter set on an Olympus DP73 digital camera using CellSens software. Images were taken of (every 3rd section) from the most anterior and medial sections of the cortex from +1.70mm to -2.70mm from bregma

(Franklin and Paxinos 2008). An experimenter blinded to condition counted the number of microspheres within cortical regions of interest to estimate microsphere density. To limit variability in total numbers of microspheres injected, mice were run in balanced

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groups of control and experimental conditions and received injections from the same diluted stock of microspheres. To compare between cohorts each experimental density of a cohort was normalized to the average density of that cohort’s control animals.

Normalized ratios were then averaged across cohorts (typically each experiment consisted of 2-4 cohorts).

2.5.13 Analysis of endothelial cell regression

To determine endothelial cell (EC) density around pruned vessels, brains of Tek-

GFP mice (21 days after microsphere or saline injection) were immersion fixed overnight in 4% PFA at 4°C. Brains were then sectioned at 100µm on a vibratome. Every 3rd section was incubated in Hoechst 33342 (20mM stock, 1:10,000 dilution,

Thermoscientific 62249) in 0.1M PBS for 20min, washed and mounted on charged slides.

Hoechst, GFP and fluorescent microspheres were sequentially excited using 405, 488 and

543nm laser lines, respectively. Sections were imaged using an Olympus confocal microscope with a 20x objective (NA 0.75). Image stacks were collected in 2µm z-steps at a pixel resolution of 0.31µm/pixel. An EC was identified and included in the analysis if the Hoechst positive nucleus showed complete colocalization with endothelial GFP signal. All EC within a 100µm radius of the microsphere were counted.

2.5.14 DiI coating of microspheres

To coat microspheres (1 or 4µm diameter), an equal volume of microsphere stock solution was added to DiI solution (30mg DiI dissolved in 5mL 100% EtOH) and sonicated for ~1h and stirred overnight. The ethanol was then allowed to evaporate overnight at 37°C and the DiI coated beads were reconstituted in 0.9% saline. Solution

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was further sonicated for ~1h to disperse microspheres. Sham injected controls had an equivalent solution of DiI (no microspheres) evaporated and reconstituted in saline.

2.5.15 Phosphorylated VEGF-R2 immunohistochemistry and analysis

Brains from Tek-GFP mice were immersion fixed overnight in 4%PFA and then overnight in 30% sucrose before being cut at 50µm on a freezing microtome. Free floating sections were incubated in pVEGF-R2 antibody (1:300 dilution, Cell Signalling

19A10 Rabbit mAb #2478) in 0.1M PBS for 18h, washed and then incubated in Cy5 conjugated secondary antibody (1:400; Invitrogen, A10523) for 4h. Confocal image stacks were collected with a 20x objective (NA 0.75) in 2µm z-steps at a pixel resolution of 0.31µm/pixel.

To assess vascular pVEGF-R2 signal, all 3 imaging channels (GFP labeled endothelium, orange-red DiI/microsphere and far red/Cy5 labeled pVEGF-R2) were split and maximally projected (40µm). A median filter (radius=2) was run on each image projection and a threshold was applied (Triangle threshold for GFP and Yen/Moments threshold for DiI/microsphere or pVEGF-2). Tek-GFP signal pixels were inverted to create a vascular mask which was then subtracted from the pVEGF-R2 signal to isolate only vascular pVEGF-R2 labelling. For estimating pVEGF-R2 in recanalized (DiI with no microsphere) or obstructed (DiI + microsphere) capillaries, an ROI was drawn 15µm on either side of a microsphere or center of DiI labelled capillary and signal pixels were measured. The % coverage of pVEGF-R2 along capillaries was determined by dividing pVEGF-R2 signal pixels by GFP labeled vascular pixels multiplied by 100. Sham injected mice received DiI treated saline without microspheres.

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2.5.16 Western blotting

Brains were rapidly removed and the cortex from one hemisphere was dissected and placed immediately in chilled lysis buffer (2mL/100mg tissue, CelLytic MT Cell

Lysis Reagent for mammalian tissues. Sigma, C322810), and 1× Halt™ Proteinase

Inhibitor Cocktail and 1× Halt™ Phosphatase Inhibitor Cocktail). Samples were sonicated and centrifuged at 14,000 rpm for 15min at 4°C. Supernatant was then removed and used for gel electrophoresis. The total protein content of the samples was measured with a BCA protein assay kit (Pierce, #23225, 562nm absorbance). Twenty micrograms of protein were loaded per well and separated on a 8% SDS polyacrylamide gel followed by transfer to PVDF membranes (Bio-Rad Cat# 162-0177) at 40V in transfer buffer (25 mM tris, 192 mM glycine, 20% v/v methanol) overnight at 4°C. Membranes were blocked for 5h at room temperature with 5% (w/v) bovine serum albumin (BSA, Sigma,

A7906) in tris buffered saline containing Tween 20 (TBST) at room temperature and incubated overnight at 4°C with the following primary antibodies: anti-VEGF-

R2(1:1000, Cell Signalling, CS2479s) and anti- β-actin (1:2000) as a loading control

(Sigma A-5441) diluted in TBST. Goat anti-rabbit IgG-HRP (Cell Signalling, 7074S) and goat anti-mouse IgG-HRP (KPL, 04-18-15) were used as secondary antibodies. Blots were washed in TBST and incubated with the HRP-conjugated antibody (1:1000) in

TBST for 1h at room temperature. Blots were developed by enhanced chemiluminescence (BioRad Clarity™ Western ECL Substrate, Cat# 107-5061) and imaged with a G:BOX Chemi-XR5 (Syngene) gel doc system. Western blot images were processed and quantified by densitometric analysis using Genesys software (version

1.5.3.0, Syngene) and Image Studio Lite (version 5.2, LI-COR Biosciences). Levels of

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VEGF-R2 were first normalized to the levels of the β-actin loading control and then calculated as fold change of vehicle-injected mice.

2.5.17 Statistics

Statistical analysis of the data was conducted in GraphPad Prism 5. Relevant 2 tailed independent or paired t-tests were used to follow up significant 1 way or 2 way

ANOVAs. In some cases, a priori t-tests were used to compare experimental groups. A repeated measure ANOVA was used for blood flow analysis. Outliers were detected using GraphPad Prism Grubbs test, with an alpha value of 0.05. Two capillaries (from different mice) were identified as outliers and were excluded from analysis in Fig. 3.

Sample sizes for each experiment were based on comparable n values used for similar experiments in the literature. All n’s were based on biological replicates. Data are presented as mean ± standard error of the mean (SEM) unless otherwise stated.

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Chapter 3 Delayed inhibition of VEGF signaling after stroke attenuates blood brain barrier breakdown and improves functional recovery in a co-morbidity dependent manner

Published : Reeson, Patrick, Kelly A. Tennant, Kim Gerrow, Josh Wang, Sammy Weiser Novak, Kelsey Thompson, Krista-Linn Lockhart, Andrew Holmes, Patrick C. Nahirney, and Craig E. Brown. 2015. 'Delayed Inhibition of VEGF Signaling after Stroke Attenuates Blood–Brain Barrier Breakdown and Improves Functional Recovery in a Comorbidity-Dependent Manner', The Journal of Neuroscience, 35: 5128-43. (43 citations as of April 2 2018) This work was done with assistance by Kelly Tennant, Kelsey Thompson and Krista- Linn Lockheart who ran and analyzed behavioral experiments in Figure 3.9. Kim Gerrow and Josh Wang ran western blots in Figure 3.2 and 3.6 B. Sammy Novak and Patrick C. Nahirney assisted with electron microscopy in Figure 3.5, Images in Figure 3.5 A-D and I were taken by Patrick C. Nahirney. Patrick Reeson and Craig E. Brown wrote the manuscript.

3.1 Abstract

Diabetes is a common comorbidity in stroke patients and a strong predictor of poor functional outcome. To provide a more mechanistic understanding of this clinically relevant problem, we focused on how diabetes affects blood brain barrier (BBB) function after stroke. Since the BBB can be compromised for days after stroke and thus further exacerbate ischemic injury, manipulating its function presents a unique opportunity for enhancing stroke recovery long after the window for thrombolytics has passed. Using a mouse model of type 1 diabetes, we discovered that ischemic stroke leads to an abnormal and persistent increase in vascular endothelial growth factor receptor 2 (VEGF-R2) expression in peri-infarct vascular networks. Correlating with this, BBB permeability was

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markedly increased in diabetic mice which could not be prevented with insulin treatment after stroke. Imaging of capillary ultrastructure revealed that BBB permeability was associated with an increase in endothelial transcytosis rather than a loss of tight junctions.

Pharmacological inhibition (initiated 2.5 days post-stroke) or vascular-specific knockdown of VEGF-R2 after stroke attenuated BBB permeability, loss of synaptic structure in peri-infarct regions, and improved recovery of forepaw function. However, the beneficial effects of VEGF-R2 inhibition on stroke recovery were restricted to diabetic mice, and appeared to worsen BBB permeability in non-diabetic mice.

Collectively, these results suggest that aberrant VEGF signaling and BBB dysfunction after stroke plays a crucial role in limiting functional recovery in an experimental model of diabetes. Furthermore, our data highlight the need to develop more personalized stroke treatments for a heterogeneous clinical population.

3.2 Introduction

Diabetes is a major risk factor for ischemic stroke (Iemolo et al. 2002; Wei,

Heeley, Wang, Huang, Wong, Li, Heritier, Arima, Anderson, et al. 2010) and lowers the prognosis for recovering function and independence in daily life (Toni et al. 1992; Kruyt et al. 2008). Currently, the molecular mechanisms underlying this significant clinical problem remain unclear. However, recent experimental studies have traced the diminished prospect for recovery to abnormalities in peri-infarct cortical plasticity

(Sweetnam et al. 2012), neovascularisation (Li et al. 2013; Prakash et al. 2013) and blood flow (Tennant and Brown 2013). Unfortunately, restoring euglycemic conditions after stroke has not been sufficient to prevent these maladaptive neuronal/vascular responses or normalize functional recovery (Sweetnam et al. 2012; Prakash et al. 2013; Tennant and

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Brown 2013). Therefore, further mechanistic studies are needed if this common stroke co-morbidity is to be adequately treated.

The blood-brain barrier (BBB) plays an important role in maintaining normal brain function in healthy and diseased states (Zlokovic 2008). Following ischemic stroke, the BBB breaks down in the infarct core and in peri-infarct regions (del Zoppo and

Mabuchi 2003; Ballabh, Braun, and Nedergaard 2004). This breakdown typically occurs in two distinct phases; an early phase that peaks within the first few hours and then a second wave of permeability that crests 2-3 days later (Sandoval and Witt 2008). As a result of increased BBB permeability, toxic constituents from the blood plasma can enter into the brain parenchyma, causing irreversible damage to neural circuits (Chen et al.

2009). For diabetics, it is conceivable that the extent of BBB breakdown may be much more severe after stroke (Berger and Hakim 1986); especially since tissue repair mechanisms and endothelial function are already compromised (Hawkins et al. 2007; Li et al. 2010). Whether BBB disruption and subsequent damage to peri-infarct circuits is exacerbated by stroke in an animal model of type 1 diabetes, has not been examined.

Vascular endothelial growth factor (VEGF) plays a pivotal role in regulating vascular function and is upregulated under ischemic conditions (Zhang et al. 2002; Stowe et al. 2007; Gu et al. 2001). By binding to the receptor tyrosine kinase, VEGF Receptor 2

(VEGF-R2) (Terman and Dougher-Vermazen 1992), VEGF is well known to induce

BBB permeability (Feng et al. 1999; Zhao et al. 2011). Given the multifaceted effects of

VEGF on BBB integrity and endothelial function, many pre-clinical studies have either blocked or promoted VEGF signalling after stroke (Hermann and Zechariah 2009).

Unfortunately, the outcomes of these studies have been quite variable (Greenberg and Jin

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2013; Manoonkitiwongsa, Schultz, and Lyden 2011), suggesting that too much or too little VEGF signalling, or even the timing of VEGF therapies could significantly affect stroke outcome. Here we characterized the spatial and temporal progression of VEGF signalling proteins and resultant loss of BBB integrity after stroke. We focused on the

“late” phase of BBB breakdown (≥3 days after stroke) in this pre-clinical study since it represents a time frame that would be maximally inclusive for stroke treatment. Our experiments reveal that hyperglycemia augments VEGF signalling after stroke, thereby exacerbating BBB permeability, synaptic damage and limiting functional recovery.

3.3 Results

3.3.1 Animal model of diabetes and ischemic stroke

In order to evaluate the effects of chronic hyperglycemia on BBB integrity after stroke (Figure 23A), mice were subjected to photothrombotic stroke in the right primary forelimb somatosensory cortex 4 weeks after STZ (diabetic) or vehicle (non-diabetic) injection. Mice injected with STZ had significantly elevated blood glucose levels (Figure

23B, p < 0.001 for all comparisons) and lower body weight (Fig 1C, t(30) = 3.142, p =

0.0038) than non-diabetic controls. The mortality rate for all experiments and groups was

5.8%.

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Figure 24 Experimental outline.

(A) Timeline of experiments assessing the effect of diabetes on cerebrovascular integrity following stroke. (B) Average fasted blood glucose

(mM/L) for each experimental group at each time point. (C) Mouse body weight (grams) in each group averaged across all time points. ** p < 0.01.

3.3.2 Aberrant expression of VEGFR2 in the diabetic peri-infarct cortex

To determine if hyperglycemia altered VEGF signalling after stroke, Western

blots were used to measure protein levels in the peri-infarct cortex at 3, 7 and 28 days

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recovery. Since we did not detect any differences in protein expression between sham

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Figure 25 Diabetes amplifies the upregulation of VEGF-R2 after stroke.

(A) Representative Western blot (cropped) from tissue 3 days after stroke and quantification of VEGF and its receptors 3, 7 and 28 days after stroke in peri- infarct (PI) cortex (normalized to β-actin) relative to undamaged contralateral (CL) hemisphere (normalized to β-actin) (n= 5-7 mice per group). Immunohistochemical staining for VEGF-R2 in the contralateral hemisphere (B) and peri-infarct cortex (C). Note that VEGF-R2 expression co-localizes with Isolectin B4 labelled vessels in the peri-infarct cortex (D, see white arrows). Scale bar 200µm. (E) Quantification of VEGF-R2 expression in peri-infarct or contralateral cortex (n = 4-6 mice per group). *p < 0.05, **p < 0.01, *** p < 0.0001 comparing non-diabetics to diabetics, # p < 0.05; ## p < 0.01; ### p < 0.001 comparing contralateral to peri-infarct. Note western blots run by Josh Wang and Kim Gerrow.

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stroke mice and the undamaged contralateral hemisphere (p > 0.05 for all comparisons), protein levels were first normalized to β-actin and then a ratio was calculated with peri- infarct cortex as the numerator and contralateral hemisphere as the denominator. At 3, 7 or 28 days after stroke, there was no change in VEGF or VEGF-R1 protein expression in the peri-infarct cortex relative to the contralateral control hemisphere in either non- diabetic or diabetic mice (p > 0.05 for all comparisons, Figure 25A). However, VEGF-

R2 expression in the peri-infarct cortex was significantly increased (relative to the contralateral hemisphere) in both non-diabetic and diabetic mice (Figure 25A). In particular, diabetic mice exhibited significantly greater VEGF-R2 expression in the peri- infarct cortex than non-diabetic mice at 3 (t(8) = 3.403, p = 0.0093) and 28 days (t(12) =

2.775, p = 0.017) of stroke recovery (Fig. 2A). Immunohistochemical staining revealed that VEGF-R2 (unlike VEGF-R1, data not shown) was highly expressed in peri-infarct regions compared to the opposite control hemisphere (compare Figure 25B and C) and co-localized with isolectin B4 labeled vessels (Figure 25D). Analysis of VEGF-R2 labeled vessels indicated a general trend for higher expression levels in diabetic mice, which peaked 3 days after stroke and progressively declined over the 28 day recovery period (Figure 25E). Interestingly, we found very little expression in peri-infarct regions

1 day after stroke, suggesting that any role VEGF-R2 signalling plays in BBB permeability, likely occurs at later time points (eg. ≥3 days recovery). These results show that hyperglycemia amplifies the upregulation of VEGF-R2 expression on peri-infarct blood vessels after stroke.

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3.3.3 Diabetes exacerbates blood-brain barrier disruption after stroke

Since VEGF is a potent inducer of vascular permeability under ischemic conditions, we next quantified changes in the integrity of the BBB at different epochs of stroke recovery (3, 7 and 28 days) using Evans blue dye (Wolman et al. 1981). As expected in both diabetic and non-diabetic mice, Evans blue was largely confined to the vasculature in the non-ischemic contralateral hemisphere (left panel; Figure 26A) or in both hemispheres of sham stroke controls. Of the little dye that was detected in extra- vascular spaces in the non-ischemic hemisphere, we did not detect any significant differences between groups at any time point (F(7,35) = 1.159, p = 0.357). In the ischemic

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Figure 26 Diabetes exacerbates the loss of BBB integrity 3 days after stroke.

(A) Representative images of Evans blue fluorescence in diabetic and non- diabetic brains after stroke. In the undamaged contralateral hemisphere, fluorescence was restricted to blood vessels (left panel). After stroke, extravascular dye fluorescence was evident in peri-infarct cortex, particularly in

diabetic mice. Scale bar 200µm. (B) Representative image showing isolated extravascular fluorescence signal (after subtracting vascular signal, see methods) and areas where fluorescence intensity measurements were made. Each line is 800µm. (C) Plot profiles show normalized extravascular fluorescence

in non-diabetic (blue) and diabetic (red) peri-infarct cortex, plotted by distance from infarct and by depth from pial surface at 3, 7, and 28 days after stroke (n= 5-

7 mice per group). *p < 0.05; **p < 0.01; ***p < 0.001.

hemisphere, Evans blue was easily detected in extra-vascular spaces in both groups, especially peri-infarct cortical regions proximal to the ischemic border (right panel;

Figure 26A). In order to understand the spatial breadth of dye extravasation, we quantified extra-vascular fluorescence both as a function of distance from the infarct border and depth from the cortical surface (Figure 26B). At 3 days, but not 7 or 28 days recovery, our statistical analysis revealed a significant interaction between experimental group and dye extravasation as a function of distance at each cortical depth (p <0.001 for each depth). As shown in Figure 3A and C, diabetic mice exhibited significantly greater dye extravasation in superficial, middle and deep layers of the peri-infarct cortex at 3 days recovery (Main effect of diabetes: Superficial F(1,64) = 38.95, p < 0.0001, Middle

F(1,64) = 57.72, p < 0.0001, Deep F(1,64) = 83.62, p < 0.0001). At all three depths, dye extravasation decreased as a function of distance from the infarct border (Main effect of

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distance: Superficial F(7,64) = 38.94, p < 0.0001, Middle F(7,64) = 9.22, p < 0.0001, Deep

F(7,64) = 16.46, p < 0.0001). A subset of diabetic mice were treated with insulin to determine if controlling blood glucose levels immediately after stroke would prevent excessive BBB permeability (blood glucose before vs. after insulin: 24.10 ± 4.1 mM/L vs. 7.113 ±1.5 mM/L, t(11) = 4.566, p = 0.008). However, insulin treatment did not reduce

BBB permeability 3 days after stroke as insulin treated diabetics had significantly greater dye extravasation compared to non-diabetics (Superficial F(1,56) = 172.6, p < 0.0001,

Middle F(1,56) = 95.18, p < 0.0001, Deep F(1,56) = 42.93, p < 0.0001).

Seven and 28 days following stroke (Figure 26C), the BBB was still permeable at all three depths in peri-infarct cortex (Main effect of Distance: Superficial F(7,39) = 7.48, p

< 0.001, Middle F(7,39) = 6.94, p < 0.001, Deep F(7,39) = 7.24, p < 0.001). However, peri- infarct dye extravasation was relatively equal between non-diabetic and diabetic mice at these later time points (p values > 0.05).

Since angiogenesis has been reported following stroke (Prakash et al. 2013; Ergul et al. 2014) and newly formed blood vessels lack a fully developed BBB (Rigau et al.

2007), we then examined vascular branching density (a surrogate measure of vessel remodelling) in peri-infarct cortex (Figure 27A) where considerable BBB permeability was observed. In general, branching density in the control contralateral hemisphere was slightly increased, but not significantly in diabetic relative to non-diabetic mice (t(5) =

2.15, p = 0.08). Stroke tended to reduce branching density in peri-infarct cortex (Figure

27B), although these trends did not reach statistical significance for both non-diabetic

(F(3,25) = 0.9717, p = 0.42 ) and diabetic mice (F(3,27) = 2.761, p = 0.06). This data suggests that increased permeability to Evans blue dye in the diabetic peri-infarct cortex is not due

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to excessive sprouting of new vessels after stroke (Tennant and Brown 2013; Mostany et al. 2010).

Figure 27 Excessive BBB permeability in diabetic mice is not related to peri- infarct angiogenesis.

(A) Representative example of the peri-infarct region where vessel branching density was quantified (red box, 400 x 800 µm). Scale bar 100µm. Inset shows skeletonized vessels and examples of identified branching points in red. (B) Branching points per mm3 showed that there was no significant change in the peri-infarct cortex compared to the contralateral hemisphere, although there was a trend towards lower branching density in diabetics.

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3.3.4 Loss of BBB integrity is mediated primarily by an increase in endothelial transcytosis

Loss of BBB integrity after stroke can occur through several distinct mechanisms.

For example, some studies have suggested that endothelial cells lose the tight junction complexes that normally prevent paracellular movement between cells (Fischer et al.

2002; Willis, Meske, and Davis 2010) whereas others indicate that tight junctions remain intact (Krueger et al. 2013). In order to elucidate possible cellular mechanisms for BBB disruption, we imaged the ultrastructure of microvessels (< 10 µm diameter) in the peri- infarct cortex 3 days after stroke (Figure 28A). In the contralateral control hemisphere

(Fig. 5B), microvessels appeared normal, with intact tight junctions (Figure 28B, arrowhead in inset) and a generally thin endothelium with few vesicular/vacuolar compartments in the cytoplasm. In the peri-infarct cortex, endothelial tight junctions were generally intact for both non-diabetic and diabetic mice (Figure 28C, see inset arrowheads; N = 4 mice, 24 vessels ranging from 50 - 500µm from infarct border). To account for the possibility that BBB permeability occurred through transient openings of tight junctions rather than full disassembly, we intravenously injected 5 nm colloidal gold

(in saline) one hour prior to euthanasia. However, in all vessels examined, we did not detect any gold particles trapped within tight junction structures.

One clue regarding the mechanism of dye extravasation was that endothelial cells in peri-infarct microvessels appeared swollen with a marked increase in caveolae-like vesicles, sometimes forming an assembly line from luminal to abluminal sides of the

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endothelium (Figure 28D). To test the hypothesis that BBB disruption could be mediated by an increase in endothelial transcytosis, we imaged peri-infarct vessels reacted for HRP

(2mg/g, i.v.). HRP is widely used for imaging the ultrastructure of the BBB because it binds albumin, is taken up by transcytotic vesicles, and easily forms an electron dense precipitate (see Fig. 5E) when reacted with 3,3’ diaminobenzadine (Banks and Broadwell

1994; Villegas and Broadwell 1993). In the contralateral control hemisphere (Figure

28E, F), relatively little HRP was detected within the vascular endothelium and beyond.

By contrast, there was a profound increase in the amount of blood-borne HRP detected within peri-infarct vascular endothelial cells (Figure 28G), especially for diabetic mice

(Figure 28H). To confirm that this enhanced transcytotic transport was not specific to just albumin transport, we injected 5 nm colloid gold with the HRP (Figure 28I).

Colloidal gold was easily detected alongside HRP within vesicles (Figure 28I, arrows), and importantly, gold was found both within the endothelium, underlying basement membrane, adjacent glial cells and undefined

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Figure 28 Loss of BBB integrity at 3 days is due primarily to increased transcytosis and not tight junctional complex disassembly.

(A) Representative Toluidine blue stained section of embedded tissue. Scale bar 250µm. Inset shows example of region suitable for electron microscopy. Scale bar

100µm. Inset box shows suitable vessels (arrows) for imaging. Scale bar = 10 µm. (B) Electron micrograph showing a capillary in the undamaged contralateral hemisphere of a non-diabetic mouse, 3 days after stroke. Insets show intact tight junctions (see arrowhead) and generally thin endothelium. Scale bar 2µm. (C) Peri-infarct capillary 3 days after stroke. Insets show tight junctions of the capillary are intact (arrowheads).

Scale bar 2µm. (D) Peri-infarct capillary 3 days after stroke. Inset shows thickened endothelium and large increase in caveolae-like vesicles. Scale bar 2µm. (E) Bright field image of coronal section from HRP injected mouse, reacted with 3,3’ diaminobenzidine, generating electron dense brown reaction product around the infarct. Scale bar 1.0mm. (F) Capillary in the contralateral control hemisphere showing little HRP staining 3 days after stroke. (G-H) Representative electron micrographs showing increased HRP reaction product in the endothelium of capillaries in the peri- infarct cortex of non-diabetic and diabetic mice. Scale bar 0.5µm for images in F-H. (I) Diabetic peri-infarct capillary 3 days after stroke and higher magnification image of boxed region showing 5nm gold particles (arrows) present in vesicles within the endothelium, basement membrane (BM) and adjacent glial cells. Scale bar 1µm, Scale bar for inset 100nm. Note images A)-D) and I) taken by Patrick C. Nahirney.

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extravascular tissue (Figure 28I, arrows). These experiments reveal that enhanced BBB permeability in mice 3 days after stroke was associated with an increase in endothelial transcytosis without ultrastructural evidence for widespread opening of tight junctions.

3.3.5 Inhibition of VEGF-R2 signalling attenuates stroke induced BBB permeability in diabetic animals

To directly assess the role of VEGF-R2 in vascular permeability after stroke, we crossed tamoxifen-inducible Tie2-CreERT2 mice (Forde et al. 2002) with a floxed VEGF-

R2 mouse line (Hooper et al. 2009). The resulting Tie2-Cre ERT2 / VEGF-R2 flx/+ mice allowed us to specifically reduce VEGF-R2 expression in the vasculature of adult mice, avoiding the complications of embryonic knock down. As shown in Figure 29A, we first confirmed both the vascular specificity of Cre expression with immunohistochemistry, and that our tamoxifen dosing regimen was sufficient to induce Cre recombinase activity using a floxed tdTomato reporter line (Madisen et al. 2010). We then confirmed through

Western blots that tamoxifen administration significantly reduced VEGF-R2 protein expression in the peri-infarct cortex after stroke (Figure 29B, t(4) = 3.09, p = 0.037).

Vascular specific knock down of VEGF-R2 significantly reduced Evans blue extravasation in diabetic mice (Figure 29E, F, F(1,64) = 33.00, p < 0.0001), while, to our surprise, increased BBB permeability in non-diabetic mice (Figure 29F, F(1,56) = 19.06, p

< 0.0001).

Since a Cre-lox approach is not well suited for translation to the clinic, we then determined if pharmacological inhibition of VEGF-R2 with a clinically tested and safe drug (SU5416) could ameliorate BBB disruption. SU5416 is well known to inhibit

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VEGF-R2 dependent tyrosine kinase auto-phosphorylation (Annie et al. 1999; Mendel et al. 2000), is capable of crossing the BBB, and has a relatively long half-life of 72 hr

(Giles et al. 2003), thereby making it well suited as a stroke therapy. As shown in Figure

29C, stroke (with vehicle infusion) induced a significant increase in phosphorylated

VEGF-R2 (pVEGF-R2) expression in peri-infarct cortex at 3 days recovery (t(3) = 3.162, p = 0.05). In accordance with our previous protein quantification experiments, hyperglycemia was associated with significantly greater expression of pVEGF-R2 in peri-infarct cortex relative to non-diabetic mice (Figure 29C, D; t(6) = 5.52, p = 0.0015).

A single dose of 50mg/kg SU5416 (s.c.) was sufficient to significantly reduce the expression of pVEGF-R2 in diabetic peri-infarct cortex (Figure 29C, D; t(5) = 2.78, p =

0.039). This reduction in pVEGF-R2 signalling with SU5416 treatment was accompanied by a significant reduction in the extravasation of Evans blue dye in the peri-infarct cortex

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Figure 29 Inhibiting VEGF-R2 signalling attenuates stroke induced BBB permeability in diabetic mice.

(A) Immunohistochemical staining for Cre recombinase and Tamoxifen induced tdTomato expression in the cortical vasculature. Scale bar = 50 µm. (B)

Representative western blots showing VEGF-R2 levels 3 days after stroke in Tamoxifen or vehicle-injected Tie2-Cre- ERT2 / VEGF-R2 fl/+ mice. Note that

Tamoxifen injections reduced the stroke induced increase in VEGF-R2 expression. (C) Representative western blots (cropped) showing that a single injection of SU5416 (50 mg/kg) reduces the expression of phosphorylated

VEGF-R2 (pVEGF-R2) in peri-infarct cortex 3 days after stroke. (D) Quantification of pVEGF-R2 levels 3 days after stroke in SU5416 or vehicle-

injected non-diabetic and diabetic mice. Note that stroke leads to a significant elevation in pVEGF-R2 expression levels in vehicle injected diabetic mice, which can be reversed with SU5416 (n = 3-5 mice per group). *p < 0.05, ***p < 0.001. (E) Representative confocal images showing greater extravasation of Evans blue dye in vehicle-treated diabetic mice compared to VEGF-R2 fl/+ or those injected with SU5416. Scale bars 200µm. (F) Plots show normalized extravascular dye fluorescence for vehicle, VEGF-R2 fl/+ or SU5416-treated diabetic and non-diabetic mice (n = 4-7 mice per group). Vehicle to SU5416 treated *p < 0.05; **p < 0.01; ***p < 0.001. Vehicle to VEG F-R2 fl/+ †p < 0.05; ††p < 0.01; †††p < 0.001. Note western blot run by Kim Gerrow.

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of diabetic mice (Figure 29E, F; F(1,88) = 13.85, p = 0.0003). Consistent with our inducible VEGF-R2 knockdown experiments, SU5416 treatment exacerbated BBB permeability in non-diabetic mice (Figure 29F; F(1,88) = 12.37, p = 0.0007). There were no significant differences in infarct volume between SU5416 or vehicle treatment groups

(Figure 30A, B; F(3,17) = 0.04, p = 0.989). This argues against the possibility that reduced

BBB permeability in SU5416 treated diabetic mice was secondary to a reduction in ischemic cell death in the infarct core. It should be noted that SU5416 does have some off target effects, notably modulation of the immune system (Mezrich et al. 2012) which may also play a role in vascular permeability. However, the strikingly similar results yielded with SU5416 to that with genetic knockdown of VEGF-R2 suggests that SU5416 improves BBB integrity primarily through attenuation of VEGF-R2 signalling.

Figure 30 SU5416 treatment does not affect infarct volume. (A) Representative series of coronal brain sections (300 µm apart) stained with cresyl violet 3 days after stroke. Scale bar 1.0mm. (B) No differences in infarct volume (mm3) for vehicle or SU5416-treated non-diabetic or diabetic mice 3 days after stroke (n= 5-7 mice per group).

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3.3.6 VEGF-R2 inhibition in diabetic mice prevents dendritic spine loss and improves functional recovery after stroke

We hypothesized that impaired functional recovery in diabetic mice (Sweetnam et al. 2012) could be mediated, in part, by the excessive VEGF-R2 dependent disruption of

BBB and resultant damage to neighbouring neural circuits after stroke. Therefore, non- diabetic and diabetic mice were treated with SU5416 (50mg/kg) or vehicle beginning 2.5 days after stroke (see Figure 31A). This relatively late intervention time was selected because our Western blot, immunohistochemistry and Evans blue experiments suggested that the BBB at 2.5 - 3 days recovery would be particularly sensitive to VEGF-R2 inhibition. To assess synaptic damage after stroke, we quantified dendritic spine density in layer 2/3 in sham operates or the peri-infarct cortex (Figure 31B, C) at 7 days recovery when previous stroke studies have reported significant spine loss (Brown et al.

2007; Brown et al. 2009). For mice subjected to sham stroke surgery, we did not find a significant difference in spine density between non-diabetic and diabetic mice (9.95 ±0.3 vs. 8.91 ±0.4 spines/10 µm; t(7) = 1.96, p = 0.090). Further, we did not find any difference in spine density between sham operates and the hemisphere contralateral to stroke

(p>0.05 for comparisons in diabetic and non-diabetic mice). In stroke affected mice, we first examined spine density as a function of distance from the infarct border and found a significant effect of distance (Figure 31D, non-diabetic R2 = 0.3897, p < 0.001; diabetic

R2 = 0.3593, p < 0.001). Spine loss was most evident within a 300 µm wide zone adjacent to the infarct, therefore subsequent analyses were restricted to this particular zone. As expected, stroke in vehicle-treated mice was associated with a significant loss of

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dendritic spines (Figure 31C, E) in both non-diabetic (t(11) = 2.513, p = 0.029) and diabetic mice t(10) = 4.042, p = 0.0025). In accordance with the idea that increased BBB permeability could exacerbate synaptic damage, vehicle-injected diabetic mice had significantly lower spine density after stroke than vehicle-injected non-diabetic mice

(Figure 31C, E, t(14) = 3.584, p = 0.003). However, this excessive loss of spines could be mitigated with SU5416 treatment in diabetic mice (Figure 31C, E; diabetic stroke vehicle vs. SU5416 = 6.44±0.4 vs.: 8.35±0.8 spines/10 µm, t(10) = 2.267, p = 0.0047). We also noted that SU5416 treatment in mice not subjected to stroke (i.e. shams) had no effect on spine density (non-diabetic: t(5) = 1.893, p= 0.117, diabetic: t(7) = 0.200, p =

0.847). Since as mentioned previously SU5416 may have off-target effects which could influence spine density, we injected Tie2-Cre ERT2 / VEGF-R2 flx/+ mice with AAV-GFP

2 weeks prior to Tamoxifen injections and stroke to assess spine density in mice with vessel specific knockdown of VEGF-R2. Spine density in the contralateral control hemisphere of non-diabetic and diabetic Tie2-Cre ERT2 / VEGF-R2 flx/+ mice was not

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Figure 31 Inhibiting VEGF-R2 signalling mitigates excessive spine loss in

peri-infarct cortex in diabetic mice.

(A) Experimental timeline. Mice received SU5416 injections 2.5 days after stroke and every 3 days afterwards for 6 weeks. (B) Confocal image of peri-infarct

cortex showing Evans blue filled vessels (red) and YFP-labelled neurons (green). Spines on the apical dendrites of layer 5 neurons in superficial peri-infarct cortex

were imaged and analyzed. Scale bar 10µm. (C) Representative examples of dendritic spines in the peri-infarct cortex. Scale bar 5µm (D) Spine density plotted as function of distance from the infarct border, note the clear effect of distance on spine density, beyond 300 µm from the infarct spine densities return to normal. (E) Quantification of spine density in peri-infarct cortex 7 days after stroke. Note that diabetes leads to greater spine loss after stroke which can be prevented with SU5416 (n= 4-9 mice per group). *p < 0.05; **p < 0.01.

significantly different from the spine density reported in non-diabetic (t(3) = 1.736, p =

0.18) or diabetic (t(6) = 2.179, p = 0.07) YFP-H line shams. This result confirmed that our viral approach did not lead to significantly different spine densities in the absence of stroke. Spine density in the peri-infarct cortex of non-diabetic Tie2-Cre ERT2 / VEGF-R2 flx/+ mice was significantly reduced compared to the contralateral control hemisphere (t(3)

= 3.309, p = 0.04). Furthermore, the spine density in the peri-infarct cortex of non- diabetic Tie2-Cre ERT2 / VEGF-R2 flx/+ mice was indistinguishable from those of the non-diabetic mice treated with SU5416 (t(5) = 0.4031, p = 0.7). In the peri-infarct cortex of diabetic Tie2-Cre ERT2 / VEGF-R2 flx/+ mice, spine densities were not significantly

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different from the contralateral control hemisphere (t(4) = 0.84, p = 0.44) or SU5416 treated diabetic mice (t(6) = 1.272, p = 0.25). Therefore, in line with our BBB permeability results using this genetic knockdown of VEGF signalling, we have further confirmed that inhibition of VEGF-R2 activation after stroke rescues spine loss in the diabetic peri-infarct cortex, but failed to improve spine loss in non-diabetics. These experiments reveal that excessive BBB permeability in hyperglycemic mice exacerbates the loss of dendritic spines after stroke, which can be prevented by inhibiting VEGF-R2 signalling.

If SU5416 treatment can attenuate BBB permeability and spine loss in diabetic mice, does this translate into improved recovery of forepaw function? Our behavioural tests indicated that stroke induced a marked deficit in forepaw sensory function in all treatment groups, indicated by an increased latency to remove tape from the impaired

(left) forepaw (Figure 32A, B; F(5,200) = 10.074, p < 0.001). Consistent with previous work (Sweetnam et al. 2012), the stroke affected left paw of vehicle-injected diabetic mice was significantly more impaired than in non-diabetic mice (Figure 32B; F(1,15) =

7.45, p < 0.05), especially from testing week 3 onwards. Treatment of diabetic mice with

SU5416 for 6 weeks after stroke significantly reduced tape removal latencies for the affected paw (Figure 32B; F(1,17) = 5.11, p < 0.05) to levels that were comparable to vehicle-treated non-diabetic mice. Interestingly, the beneficial effects of VEGF-R2 inhibition were restricted to diabetic mice, as tape removal latencies in non-diabetic mice treated with SU5416 were similar to, or slightly worse than their vehicle-injected counterparts (F(1,16) = 0.16, p = 0.69). In the right (unaffected) paw, tape removal latencies did not differ significantly between treatment groups (Figure 32B, p > 0.05 for

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all comparisons). Testing of motor coordination and function of the forepaw with the horizontal ladder test revealed a stubborn and persistent motor deficit after stroke (Figure

32C, D). This deficit was evident in all treatment groups (Figure 32D) by a significant decrease in the percent of correct forepaw placements (F(5,200) = 81.107, p < 0.001) and an increase in partial (incorrect) placements (F(5,200) = 61.604, p < 0.001). SU5416 treatment had no effect on performance in this task in either diabetic or non-diabetic mice (diabetic state x treatment group x testing week interaction for correct placements: F(5,200) = 1.283, p = 0.273; partial placements: F(5,200) = 1.751, p = 0.125). In summary, SU5416 treatment was effective in promoting the recovery of sensory (but not motor) forepaw function, but only in diabetic mice.

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Figure 32 Inhibiting VEGF-R2 signalling improves functional recovery in

diabetic mice.

(A) Sample image frames showing trial initiation and removal of adhesive tape from the forepaws (black arrow). (B) Plots showing the change in time (Δ

Latency) it took mice to remove adhesive tape from the left (impaired) and right (unimpaired) forepaw (n = 7-12 mice per group). BL = baseline or pre-stroke tape removal latencies. (C) Sample frames showing a correct and partial (incorrect) forepaw placement on the horizontal ladder. (D) Histograms showing the percentage of correct or partial forepaw placements as a function of total steps taken. *p<0.05, diabetic vehicle vs. diabetic SU5416, †p<0.05, diabetic vehicle vs. non-diabetic vehicle. Note behavior experiments run by and analyzed by Kelly Tennant.

3.4 Discussion

Ischemia is well known to promote VEGF signalling (Hermann and Zechariah

2009). In non-diabetic animals, VEGF mRNA and protein expression peak in the ischemic hemisphere within 6-48 hours after stroke and then decline thereafter (Marti et al. 2000; Zhang et al. 2002). On the other hand, VEGF-R2 follows a slightly different and more protracted course of expression that is maximal 2-14 days following stroke (Marti et al. 2000; Zhang et al. 2002). Our Western blot and VEGF-R2 immunohistochemistry data support these temporally distinct patterns of VEGF signaling after stroke. However, diabetes clearly alters the magnitude and duration of VEGF-R2 expression, and the consequences of this aberrant response are quite striking, with significant BBB permeability, dendritic spine loss and poor functional outcome. Dysregulation of VEGF signalling in the vasculature of the eye, kidney, heart and limbs of diabetics has been

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described (Vinik and Flemmer 2002; Howangyin and Silvestre 2014). In the brain, less is known, although there have been a few recent reports of abnormal VEGF expression in the hippocampus and cortex in rodent models of type 2 diabetes (Zhao et al. 2012;

Prakash et al. 2012). The reasons for this aberrant expression are unknown, but could be a consequence of abnormal blood flow to the peri-infarct cortex (Tennant and Brown

2013), inflammation (Kumari et al. 2007) or hypoxia-inducible transcription factors

(HIFs) which are known to regulate VEGF production and influence cell survival

(Minchenko et al. 1994; Siddiq et al. 2009).

Delayed edema after an ischemic event is one of the most significant complications following a cerebrovascular insult (del Zoppo and Mabuchi 2003; Schlaug et al. 1997; Heo, Han, and Lee 2005). The loss of BBB integrity typically displays a multi-phasic progression (Sandoval and Witt 2008). Initial phases of permeability occur within the ischemic core over the course of minutes to several hours after occlusion, while a second distinct delayed phase occurs in the surviving peri-infarct cortex around

48-72 hours (Kuroiwa et al. 1985; Rosenberg, Estrada, and Dencoff 1998; Knowland et al. 2014). Here we focused on the second phase of BBB permeability and found significantly greater extravasation of Evans blue dye in the peri-infarct cortex of diabetic animals. This finding is in agreement with studies showing that type 1 or 2 diabetes, as well as acute hyperglycemia (induced with glucose injections) increased edema (Li et al.

2013) and the extravasation of HRP or blood borne immunoglobulins following transient ischemia (Dietrich, Alonso, and Busto 1993; Won et al. 2011; Ye et al. 2011). We also demonstrate that controlling blood glucose levels in diabetic mice immediately after stroke does not prevent or even reduce BBB permeability. The impact of insulin

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treatment on stroke recovery is an active area of investigation but in general, there isn’t clear evidence clearly showing that it improves stroke recovery in humans or animal models of diabetes (Baker, Juneja, and Bruno 2011; Ntaios et al. 2014; MacDougall and

Muir 2011).

The cellular underpinnings of BBB disruption after stroke have been hotly debated in recent years. The prevailing wisdom posits that openings of the BBB are due to dynamic changes in endothelial tight junctions (Sandoval and Witt 2008). This hypothesis has been supported in biochemical and fluorescence imaging studies reporting changes to tight junction proteins after stroke (Fischer et al. 2002; Willis, Meske, and

Davis 2010; Fernández-López et al. 2012; Knowland et al. 2014). However, this theory has been questioned in recent years as clear ultrastructural evidence of tight junction disassembly, at least in surviving peri-infarct tissues, has been very sparse. In the present study, we show using orthogonal approaches of albumin, HRP and nanogold tracing followed by light and electron microscopy, that BBB permeability is mediated primarily through increased endothelial transcytosis. In fact, we found little ultrastructural evidence of full tight junction disassembly in peri-infarct cortex which is consistent with other electron microscopy studies (Jackman et al. 2013; Krueger et al. 2013; Lossinsky and

Shivers 2004). However, we cannot rule out a role for tight junctional disassembly based on our results alone. Since tight junction structure can be influenced by multiple experimental factors, especially the degree of reperfusion (Sandoval and Witt 2008), discrepancies between studies may result from the different models used and/or regions of the brain examined. Furthermore, if the disassembly/opening of the tight junction is short lived (eg. on the order of seconds), it is highly unlikely that single snap shot in time

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would catch this ephemeral event. A comprehensive ultrastructural study, likely involving serial sectioning and reconstruction of tight junctions in 3D, will be required to ultimately clarify the role of tight junctions in BBB breakdown after stroke.

The molecular mechanisms underlying the dismal prognosis for recovering function after stroke in diabetic humans and experimental animals, is largely unknown.

The findings of our pre-clinical study shed new light on this issue, suggesting that aberrant VEGF-R2 signalling with resultant BBB dysfunction and spine loss are important factors in dictating poor functional outcome. Indeed, VEGF-R2 expression was significantly increased on peri-infarct blood vessels in the same cortical region and time point (day 3) that we observed the enhanced BBB permeability. Loss of BBB integrity correlated with greater damage to fine synaptic structure in peri-infarct cortex, but not necessarily with larger cerebral infarcts. This finding is in agreement with recent studies showing that micro-hemorrhages can lead to neuronal dysfunction and spine loss without inducing widespread degeneration (Rosidi et al. 2011; Cianchetti et al. 2013; Zhang and

Murphy 2007). Based on the spatial and temporal pattern of VEGF-R2 expression after stroke, we then show that systemic administration of a VEGF-R2 inhibitor (SU5416) in diabetic mice was sufficient to dampen VEGF-R2 signalling and partially restore BBB integrity in peri-infarct cortex. The benefits of SU5416 treatment, even when administered 2.5 days after stroke, were evident in the fact that SU5416 mitigated the loss of dendritic spines commonly observed in peri-infarct regions (Brown et al. 2007; Brown,

Wong, and Murphy 2008; Mostany et al. 2010), and improved recovery of forepaw function on the adhesive tape removal test.

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One unexpected and unique finding was that the beneficial effects of VEGF-R2 inhibition were restricted to hyperglycemic mice. This result highlights the enormous complexity of treating ischemic stroke which preferentially affects people with various constellations of co-morbid conditions such as diabetes, hypertension and hyperlipidemia. Why this same treatment would exacerbate BBB permeability and spine loss in non-diabetic mice is uncertain, but it may reflect the fact that too much or too little

VEGF signalling can disrupt endothelial cell function and integrity (Lee et al. 2007;

Quaegebeur, Lange, and Carmeliet 2011; Hermann and Zechariah 2009). Indeed, there have been reports that VEGF-R2 inhibitors can produce unwanted side effects such as intracranial bleeds in humans (Giles et al. 2003; Eskens and Verweij ; Kamba and

McDonald 2007) and can worsen ischemic injury in neonatal rats (Shimotake et al.

2010). These counterintuitive results likely reflect the significant differences in the intracellular signalling environment between euglycemic and hyperglycemic cells.

VEGF-R2 signals are integrated into multiple downstream pathways such as MAPK,

PKC, RAGE, ERK, JNK, most of which are perturbed in diabetes (Haneda et al. 1997;

Haneda et al. 1995; Igarashi et al. 1999; Tomlinson 1999; Purves et al. 2001). For example hyperglycemia alters NO production and signalling (Pieper and Peltier 1995;

Pieper 1997), and increases ROS production (Spitaler and Graier 2002), which can amplify VEGF-R2 signalling in a VEGF independent manner (Warren et al. 2014). Our study has limitations that should be discussed. For example, most of our experiments focused on hyperglycemic mice that did not have blood sugars controlled with insulin.

This is a potential problem since chronic hyperglycemia can generate (in some instances) a hyperosmotic syndrome that leads to confusion and diminished arousal. While a

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concern, any mice with salient abnormal behaviours (eg. reduced ambulation or laboured breathing) were excluded from the study. One could also comment that our study would not directly model human diabetics that administer blood glucose lowering therapies to control their symptoms. However, it is worth noting that a significant percentage of people are hyperglycemic but unaware of their condition or are aware but have poor glucose control (Go et al. 2014). Insulin treatment could significantly confound data interpretation since it is well known to stimulate vascular and neuronal growth (King et al. 1985; Chiu, Chen, and Cline 2008), independent of its effect on blood glucose levels.

For these reasons, and the fact that insulin did not affect BBB permeability in diabetic mice 3 days after stroke, we focused on a simpler model of diabetes.

No experimental model of stroke is perfect and photothrombosis is no exception.

We employed this model because it allows us to reliably generate focal infarcts in specific cortical regions in a relatively non-invasive manner. However, since photo- activation of rose bengal dye leads to singlet oxygen production which can induce endothelial damage and severe ischemia with little reperfusion (Watson et al. 1985) it may limit the generalizability of our findings. This may help explain why our study did not find convincing evidence for neovascularization when other studies, using different models of diabetes and stroke have described new vessel growth and remodelling (Ergul et al. 2014). While this is an issue that cannot be resolved without future study, we should note that the spatial and temporal expression of VEGF signalling proteins and BBB permeability described in our study fits well with previous studies using transient and permanent occlusion of the middle cerebral artery (Marti et al. 2000; Zhang et al. 2002;

Krueger et al. 2013).

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3.5 Conclusion

Here we show that chronic hyperglycemia leads to aberrant expression of VEGF-

R2 in vascular networks of the peri-infarct cortex, which previous studies have identified is an important site for recovery of function (Murphy and Corbett 2009; Mostany et al.

2010; Carmichael 2006). Diabetes exacerbated the delayed opening of the BBB (≥3 days post-stroke) in peri-infarct cortex in a VEGF-R2 dependent manner. Excess BBB permeability was mediated through increased endothelial transcytosis and induced greater loss of fine synaptic structure and persistent sensory impairments of the forepaw in diabetic mice. Notably, treating diabetic mice with a clinically tested drug to inhibit

VEGF-R2 signalling (even when initiated 2.5 days after stroke) was sufficient to diminish BBB permeability, spine loss and improve functional recovery. In conclusion, our data provide new mechanistic insights into cerebrovascular disruption and poor functional recovery from stroke in a mouse model of diabetes. Further, our findings emphasize the fact that stroke treatments should take a more individualistic approach where co-morbid conditions are factored into the equation.

3.5 Materials and methods

3.5.1 Animals

Two-to-four-month old male wild-type, GFP-M, or YFP-H line mice on a

C57BL/6 background (Feng et al. 2000) were used. Tie2-Cre-ERT2 mice (referred to as

Tek-Cre-ERT2 in Chapter 2) (EMMA 00715) (Forde et al. 2002) were obtained from the

European Mouse Mutant Archives and bred with either VEGF-R2 flx/flx line (referred to as

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Kdr flx/flx in Chapter 2) (Hooper et al. 2009) or Ai9 tdTomato reporter mice (Jackson labs

007909) to confirm recombinase activity and specificity (Madisen et al. 2010). Cre recombinase activity was induced before stroke by twice daily i.p. injections of 75 mg/kg of Tamoxifen (Sigma) (Monvoisin et al. 2006; Sörensen, Adams, and Gossler 2009).

Mice were housed under 12 hour light/dark cycle and given ad libitum access to water and laboratory diet. All experiments were conducted according to the guidelines set by the Canadian Council of Animal Care and ARRIVE, and approved by the University of

Victoria Animal Care Committee.

3.5.2 Induction of hyperglycemia and stroke

Type 1 diabetes was modeled by injecting food-deprived mice with streptozotocin

(STZ Sigma, 75 mg/kg) dissolved in 50 mM citrate buffer over 2 consecutive days. Non- diabetic controls were administered buffer alone. Mice were given 5% sucrose water for

48 hr after injection to prevent sudden hypoglycemia. Blood glucose levels were measured (Accu-Chek, Aviva, Roche) in fasted mice every 1-2 weeks by withdrawing a drop of blood from the tail. Mice with blood glucose levels above 15 mM/L

(hyperglycemic) were considered diabetic.

Focal ischemic stroke of the right forelimb somatosensory cortex was induced in mice 4-5 weeks following STZ or vehicle injection using the photothrombotic method

(Watson et al. 1985; Brown et al. 2007). Mice were anesthetized with 1.5% isoflurane in medical air (flow rate = 0.7 L/min). Each mouse was kept on a heating pad during surgery to stabilize body temperature at 37°C, which was measured with a rectal thermoprobe and temperature feedback regulator. The scalp was retracted and the skull overlying the forelimb cortex was thinned to 50% with a dental drill. Photothrombosis

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was initiated by exposing the surface vessels over the forelimb cortex to a collimated green laser beam (532 nm beam at 17 mW, Beta Electronics) for 15 min after injecting

1% Rose bengal dye (110 mg/kg, i.p., Sigma) dissolved in HEPES-buffered saline. To normalize blood sugar levels in diabetic mice, 2–3 slow-release insulin pellets (0.1

U/24h/implant, LinBit, Linshin Canada) were implanted subcutaneously between the scapulae in the first hour after stroke. Insulin implanted diabetic mice had blood glucose levels tested daily. Mice were allowed to recover after surgery under a heating lamp. Of the 261 mice subjected to stroke, 19 were excluded from the study due to poor health and

8 were excluded for having no visible infarct.

3.5.3 VEGF-R2 inhibition

Mice were given 50 mg/kg subcutaneous injection of VEGF-R2 inhibitor SU5416

(TOCRIS Bioscience) in solution (0.5% w/v carboxy methly cellulose, 0.9% sodium chloride, 0.4% polysorbate, 0.9% benzyl alcohol in dH2O). Controls were given vehicle alone. For behavioral studies of functional recovery, mice were given an injection of

SU5416 or vehicle 2.5 days after stroke and then every 3 days for up to 42 days post- stroke.

3.5.4 Analysis of vessel permeability and branching

Three, 7 or 28 days following stroke, mice were anesthetized and given a 0.2 ml i.v. injection of 4% Evans blue (Sigma) in saline through the tail vein. Injections were performed slowly over 7-10 min to minimize stress to the vascular system. Evans blue was allowed to circulate for 30 min after which the mouse was quickly decapitated and the brain removed and put into 4% paraformaldehyde (PFA) in phosphate buffered saline

(PBS). Brains were sectioned in the coronal plane at a thickness of 100 µm on a Leica

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VT1000S vibratome. Every sixth vibratome section was stained for 20 min in 50 mM 4',

6-diamidino-2-phenylindole (DAPI) in 0.1 M PBS to visualize the infarct border.

Sections were mounted on charged slides and coverslipped using Fluoromount-G

(Southern Biotech). Diabetic and control mouse brains were processed and imaged in parallel and all sections were imaged immediately after cutting to minimize the possible leaching out of Evans blue dye that can occur over a 24 hr period. Evans blue was excited using a 635 nm laser and imaged using an Olympus confocal microscope with a 10x objective (NA = 0.40) and a Cy5 filter set (exciter: 605–650 nm, emitter: 670–720 nm,

8.0 µs dwell time ) or Zeiss confocal (10x objective, NA = 0.25, 405 nm and 639 nm laser, Cy5 filter set, 3.15 µs dwell time). Confocal image stacks were collected in 2 µm Z steps at a pixel resolution of 1024 x 1024 (1.035 µm/pixel). All imaging parameters including gain and offset were kept consistent, however, in some cases, laser power was adjusted (but not within an imaging cohort) to avoid saturation of Evans blue signal.

For each mouse, image stacks from three regions within the peri-infarct cortex and two from the contralateral hemisphere were analyzed using NIH ImageJ software

(v1.45p). The infarct border was determined from DAPI labelling of nuclei, and the loss of Evans blue-labelled vessels in the infarct core. For each image an average Z-intensity projection was generated from the middle 30 µm. Visualization of extravascular dye fluorescence was performed by subtracting a vessel mask (created by thresholding and binarizing dye-filled vasculature) from the original Z-projection image (see Fig. 3B for example). Extravascular dye fluorescence was quantified at different cortical depths by drawing three lines perpendicular to the infarct border (width 4.14 µm, length 800 µm) at depths of 200, 500 and 800 µm below the pial surface. For the hemisphere contralateral

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to the stroke, the starting point of each line was matched to the position of the ispilateral infarct border. In each brain, the superficial, middle and deep measurements of fluorescence were binned in 100 µm intervals from the infarct border and then averaged over the three sections imaged. Since there was no significant difference in extravascular fluorescence between the contralateral and sham stroke hemisphere for both non-diabetic

(F(3,15) = 1.21, p = 0.33) and diabetic (F(3,13) = 1.39, p = 0.28) mice, dye fluorescence in the peri-infarct cortex was normalized by subtracting the average contralateral dye fluorescence for the corresponding depth in each animal.

We examined the number of vessel branching points within the 400 x 800 µm region adjacent to the infarct where we observed increased BBB permeability (Fig 4A).

Binary vessel masks were skeletonized using Fiji software (Schindelin et al. 2012) and branching points detected and counted using the skeleton analyze plugin by a blind observer. We did not detect any significant differences between the sham and contralateral hemisphere (p>0.05 for all comparisons), therefore contralateral data are presented as the control.

3.5.5 Western blotting

Mice were given an overdose of sodium pentobarbital (175mg/kg, i.p.) and transcardially perfused with PBS to clear the blood. The peri-infarct and homotopic region of the contralateral cortex were dissected and placed immediately in chilled 0.5 mL lysis buffer (10 mM HEPES , 150 mM NaCl, 2 mM EGTA, 10 mM EDTA, 1%

Triton X , 10% glycerol, and 1× Halt™ Proteinase Inhibitor Cocktail). Samples were sonicated and then centrifuged at 2000x g for 15 min at 4°C. The supernatants were then removed and used for gel electrophoresis. The total protein content of the samples was

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measured with a BCA protein assay kit (Pierce, #23225, 540 nm absorbance). Twenty micrograms of protein were loaded per well and separated on a 10% SDS polyacrylamide gel followed by transfer to PVDF membranes (Bio-Rad Cat# 162-0177) at 40 V in transfer buffer (25 mm tris, 192 mm glycine, 20% (v/v) methanol) overnight at 4°C.

Membranes were blocked for 1 hr at RT with 5% (w/v) Difco™ skim milk (Beckon,

Dickson and Company) and incubated with primary antibody diluted 1:1000-5000 in PBS

Tris (PBST) overnight at 4°C. The following primary antibodies were used: Rabbit anti-

VEGF (Abcam ab46154); anti-VEGF Receptor 1 (Abcam ab32152); anti-VEGF

Receptor 2 (Cell Signalling, CS2479s); anti-phosphorylated VEGF Receptor 2 (Abcam ab5473) and anti-beta actin (Sigma A-5441). Anti-mouse IgG-horseradish peroxidase

(HRP) and anti-rabbit IgG-HRP (CS7076 and CS 7054, Cell signalling) were used as secondary antibodies. Blots were washed in PBST and incubated with the HRP- conjugated antibody (1:2000) in PBST for 1 hr at RT (TBST for p-VEGFR2). Blots were developed by enhanced chemiluminescence (Amersham ECL Plus Western Blotting

Detection Reagents, RPN2132) and imaged with a Gel Doc XR camera (Bio-Rad).

Densitometric scanning of the films was performed under linear exposure conditions with

Quantity One software (Bio-Rad) or ImageJ. Levels of VEGF and its receptors in each hemisphere was first normalized to the levels of the loading control (beta actin) and then calculated as fold change of peri-infarct / contralateral.

3.5.6 Immunohistochemistry

One, 3, 7 or 28 days following stroke mice were given an overdose of sodium pentobarbital and transcardially perfused with PBS. Brains were removed and frozen at -

80 °C overnight and sectioned at 25 µm on a Leica CM1850 cryostat. Tissue mounted

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onto slides was briefly fixed in 4% paraformaldehyde in PBS (5 min), washed with PBS, and then incubated overnight with anti-VEGF receptor 2 (Cell Signalling CS2479s) diluted 1:250 in 0.1 M PBS containing 0.2% Triton-X 100. Slides were washed in PBS and then incubated in secondary antibody (Cy5 conjugated anti-rabbit IgG, 1:400,

Invitrogen) for 4 hr followed by double labeling with Alexa 488-Isolectin B4 (1:400, Life

Technologies) overnight. Sections were imaged using an Olympus confocal microscope with a 10x objective (NA= 0.40). Fluorophores (Cy5 and Alexa Fluor 488) were excited with 615 and 488 nm lasers respectively, and a pixel dwell time of 8.0 µs/pixel. Image stacks were collected in 2 µm Z steps at a resolution of 0.828 µm/pixel. For quantification, 10 planar images were Z projected (maximum intensity) and Gaussian filtered. VEGF-R2 labelled vessels in the peri-infarct region (<250 µm from the infarct border) were thresholded using the Isolectin B4 signal as a guide to identify vascular

VEGF-R2 expression and reported as percentage of area.

For confirmation of vascular specificity of Cre expression, Tie2-Cre-ERT2 mice were bred with Ai9 tdTomato reporter mice and offspring were administered 75 mg/kg

Tamoxifen injections twice daily for 3 days. Paraformaldehyde fixed brains were sectioned at 50 µm and incubated overnight in anti-Cre primary antibody (Covance

MMS-106) diluted 1:1000 in 0.1 M PBS containing 0.2% Triton X-100. Sections were washed in PBS and then incubated in secondary antibody (Alex Flour 488 conjugated anti-mouse IgG, 1:400, Life Technologies) for 4 hr. Fluorophores (tdTomato and Alexa

Fluor 488) were imaged using an Olympus confocal microscope with a 20x objective

(NA= 0.75).

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3.5.7 Dendritic spine density

After 1 week recovery from stroke, diabetic or non-diabetic YFP-H or Tie2-Cre

ERT2 / VEGF-R2 flx/+ mice were overdosed, perfused with PBS and 4% PFA and brains were immersion fixed for 24 hr at 4°C. Brain sections were cut at 50 or 70 µm on a vibratome in the coronal plane. Two - three weeks prior to induction of stroke, and administration of Tamoxifen, Tie2-Cre ERT2 / VEGF-R2 flx/+ (n=3 per group) mice were given three injections (2 ipsilateral, 1 contralateral) of 0.8 µL of 1/40 AAV-CAG-GFP

(Vector Labs, 7072) in filtered PBS at a cortical depth of -700 µm using a stereotaxic surgical stage and a 33 gauge Hamilton syringe. Using an Olympus confocal microscope, low (10x, NA = 0.40) magnification images were first taken to identify the infarct border.

The apical dendrites of layer 5 YFP or GFP-labelled neurons were identified and targeted for higher magnification imaging (see Fig. 8B). Only the primary apical dendrites located within layers 2/3 were chosen for imaging and analysis, since previous studies have shown that these circuits undergo extensive structural and functional remodelling after stroke (Brown et al. 2009; Brown et al. 2007). High-resolution 1024 × 1024 image stacks

(0.103 µm/pixel; 0.5 µm Z-steps) were collected with a 60x oil objective (NA = 1.35) using a 488 nm laser to excite YFP or GFP. Laser power was manually adjusted to prevent saturation of pixel intensity values in the dendritic shaft. Spine counting was performed manually with ImageJ, and blind to experimental condition. In order to normalize variable fluorescence levels between different neurons, the grayscale of each image was adjusted to 40% of the maximum pixel intensity of the dendritic shaft. For a protrusion to be counted as a spine, it must have been clearly visible and project at least

0.4 µm (4 pixels) from the dendritic shaft. Since morphological features of spines (eg.

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neck width) could not be spatially resolved with our imaging technique, all dendritic protrusions were counted as spines.

3.5.8 Measurement of infarct volume

Fixed brain sections (100 µm thick) were mounted onto charged slides and stained with 0.0001% Fluoro-Jade C (FJC, Millipore) or cresyl violet, as previously described (Schmued et al. 2005; Türeyen et al. 2004) to detect ischemic cell death.

Widefield fluorescence images of sections were collected using a 4x objective (NA =

0.13) and a GFP excitation/emission filter set or with brightfield illumination on a BX-51

Olympus microscope. Using ImageJ software, the infarct region was identified by the absence of cresyl violet staining or dense FJC labelling and outlined in each section by an observer blind to condition. Infarct volume was calculated by multiplying the infarct area in each section by the distance between each section. Since there was no difference in infarct volumes measured by FJC and cresyl violet (F(9,45) = 0.3412, p = 0.9560), only values obtained with cresyl violet are presented. An a priori power analysis based on a previous studies indicated that a minimum sample size of five mice per group was necessary to detect significant differences in infarct size at 80% power with an α level of

0.05 (Clarkson et al. 2011; Seto et al. 2014).

3.5.9 Electron microscopy

Animals were lightly anesthetized with isoflurane and given an i.v. injection of either 0.2 mL of 5 nm gold particle solution (5%) in dH2O (AC Diagnostics) and/or 2 mg/g horseradish peroxidase (HRP, Type 2, Sigma). After 60 min of circulation, mice were deeply anesthetized and perfused transcardially with heparinized PBS (Sigma

Aldrich) and then 3.2% paraformaldehyde in PBS. Brains were extracted and fixed for 5

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hr on ice and then vibratome sectioned at 150 µm. Regions of peri-infarct HRP extravasation were visualized by reacting sections with a chromagen solution (0.2 mg/ml

3, 3' diaminobenzidine, 0.01% H2O2) for 5 min. Peri-infarct regions were microdissected into 1 x 1 mm sections. For morphological evaluation of tight junctions, samples were post-fixed in 1% osmium tetroxide and 1% potassium ferrocyanide in 0.15 M Na- cacodylate buffer for 4 hr, rinsed in dH2O, and then en bloc stained in 2% uranyl acetate

(aq) for 4 hr. Sections were washed with dH2O and dehydrated with ascending ethanols to 100%. For HRP extravasation experiments, sections were washed with dH2O and dehydrated with ascending ethanols without post-fixation with osmium. Tissue blocks were infiltrated with Spurr’s low viscosity embedding resin (Electron Microscopy

Sciences) overnight, embedded in capsules, and polymerized overnight at 65°C. Tissue blocks were sectioned at 70 nm thickness with a diamond knife (Diatome) on an Ultracut

E ultramicrotome and collected on 200 hex mesh copper grids. For morphological analysis of tissue, sections were post-stained with 0.5% lead citrate (aq) for 5 min at RT to increase contrast. The HRP-developed sections were post-stained for 10 min in 2% uranyl acetate (aq). Only blocks with the infarct border clearly visible and an additional border to determine cortical depth (pia mater or corpus callosum) with several suitable capillaries were analyzed. The criteria for including capillaries in the analysis were i) they were not collapsed or contained a red blood cell, ii) were cut in cross section, iii) were <10 µm in diameter, iv) were 50 - 500 µm from infarct border and 200 - 800 µm below pial surface, and v) did not contain an endothelial nucleus in the vessel cross- section (Fig 5A). Sections were observed on a JEOL 1400 transmission electron microscope equipped with a Gatan SC-1000 digital camera. In total 24 peri-infarct

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capillaries were examined for TJ integrity (plus 18 contralateral) and 20 peri-infarct capillaries for HRP extravasation (plus 11 contralateral). Assessments of tight junctional integrity were performed by an observer blinded to condition and using high magnification images of the tight junctions (80 - 100k magnification, 0.6 nm/pixel - 0.5 nm/pixel).

3.5.10 Behavioral assessment of forepaw sensory-motor function

The adhesive tape removal and horizontal ladder tests are highly sensitive for detecting damage related changes in sensory and/or motor function of the forepaw

(Shanina et al. 2006; Tennant and Jones 2009; Sweetnam et al. 2012). Behavioural tests were administered at weekly intervals for 2 weeks before stroke and 10 weeks afterward.

For the tape removal test, a circular piece of tape (5 mm diameter) was adhered to the palmar surface of each forepaw. Mice were then placed in a glass cylinder and filmed for

120 seconds. This was repeated three times per testing session, and the time taken to remove tape from each paw was scored. Sensorimotor function of the forepaw during locomotion was assessed by videotaping mice as they walked across an elevated 70 cm long horizontal ladder that had rungs (1 mm diameter) randomly spaced 1 or 2 cm apart.

A blind observer scored forepaw grasping of the rungs on a frame by frame basis using criteria similar to that of previous work (Farr et al. 2006; Sweetnam et al. 2012). Briefly, forepaw placements were scored as: (1) “correct” (forepaw placement centered on the rung), (2) “partial” (forepaw partially grasping rung or required a correction of the placement), or (3) “slip/miss.” Due to inherent variability in behavioral measurements, data for each mouse was averaged in 2 week bins.

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3.5.11 Statistics

Statistical analysis of the data was conducted in GraphPad Prism 5. Two-way analysis of variance (ANOVA) was conducted to analyze treatment effects on Evans blue dye extravasation at each cortical depth. A one-way ANOVA was used to examine group differences in blood glucose levels and spine density. Corrected independent sample t- tests were used to follow up significant ANOVAs. Planned independent samples t-tests were used to assess group differences in protein expression or VEGF-R2 immunohistochemical labelling of vessels. P values ≤ 0.05 were considered statistically significant for all ANOVAs. For adhesive tape test and horizontal ladder test, repeated- measures ANOVA were used, taking each paw into account separately, as well as individual analyses for correct and partial placements. Data are presented as mean ± standard error of the mean (SEM).

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210

Discussion

4.1 Summary

The vasculature is a fundamental support network of the central nervous system, without the extensive tree of vessels providing continuous blood flow, neural circuits quickly fall into dysfunction and eventually die (Brown, Wong, and Murphy 2008; Zhang et al. 2005; Zhang and Murphy 2007; Lam et al. 2010; Shih et al. 2013). The structural analogy of a tree is particularly appropriate for the cerebral vasculature, larger branches provide stability and the scaffolding, but like a tree, it is the many small branches that provide the large surface area for most of the gaseous, fluid, and metabolic exchange between the circulation and the brain. The cortical capillary bed is prone to spontaneous obstructions and stalls of flow (Erdener et al. 2017; Lam et al. 2010; Kleinfeld et al.

1998; Santisakultarm et al. 2014; Villringer et al. 1994). In the first aim of this dissertation I used 2-photon in vivo imaging of the living mouse cortical circulation to show that a subset of mostly superficial cortical capillaries near penetrating arterioles

(low arteriole branching order) are uniquely susceptible to persistent obstructions lasting longer than 20 minutes (Figure 7). I found that 0.118% of cortical capillaries were prone to persistent (>20 min) obstructions per day. These obstructions were not an artifact of anesthetics, as I along with others, have shown spontaneous obstructions occur in awake mice (Erdener et al. 2017). Following these cortical capillary obstructions over weeks I discovered that by 21 days 70% of obstructed capillaries recanalized, and approximately

15% failed to restore blood flow, of which all were eventually pruned (Figure 12). A further 15% of initially obstructed capillaries managed to recanalize (regain flow), and yet were still subsequently pruned (Figure 12). Surprisingly, capillary pruning was never

211 compensated for by any angiogenesis within the imaged volume. Using parallel approaches of genetic knockdown and pharmacological inhibition of endothelial VEGF-

R2 signaling I also showed that lowering VEF-R2 signaling in obstructed capillaries enhances recanalization, leading to less capillary pruning (Figure 21).

Figure 33 Summary of obstructed cortical capillary fates and the effects of VEGR-R2 inhibition.

212 In the healthy cortex approximately 0.118% of cortical capillaries, primarily low arteriole branching order superficial capillaries, are obstructed per day. Of these obstructed capillaries we found 68% recanalized by extruding the emboli back into the circulation and 2 % recanalized through angiophagy. However, this ratio is likely influenced by the emboli size and composition. None the less imaging spontaneous obstructions (natural and variable emboli) confirmed that approximately 70% of all obstructed capillaries recanalize. The remaining 30% of obstructed capillaries were ultimately pruned from the vascular tree, despite about half of pruned vessels (~15%) regained flow for several days before being pruned. None of the lost capillaries were replaced by any detectable angiogenesis anywhere in any imaging region. Inhibiting VEGF-R2 signaling, either by genetic knockdown or pharmacological inhibition with SU5416, dramatically shifted the fates of obstructed capillaries towards recanalization.

After VEGF-R2 inhibition roughly 95% of all obstructed capillaries recanalized and only ~ 5% were eventually pruned.

In the second aim I investigated how the cerebrovascular system in an animal model of type 1 diabetes (a known risk factor for stroke) (Figure 24) responds to a large vascular insult, and how this may undermine functional recovery and remapping, which is diminished in diabetic mice (Sweetnam et al. 2012). Following stroke, I discovered there was a significant increase in vascular expression of VEGF-R2 in the diabetic brain three days after stroke, compared to non-diabetic controls (Figure 25). Using Evans blue

213 dye to asses BBB permeability, I found coincident with this increase in VEGF-R2 expression, significantly elevated BBB disruption in diabetic peri-infarct cortex (Figure

26). Electron microscopy in diabetic and non-diabetic animals revealed the cellular mechanism for this permeability; specifically, that endothelial transcytosis mediated the passage of blood borne tracers (such as HRP or gold particles) into the brain rather than opening of tight junction (Figure 28). This strongly argued the main driver of BBB permeability after stroke was an increase in transcytosis, not TJC disassembly, consistent with other studies (Nahirney, Reeson, and Brown 2015; Krueger et al. 2013; Cipolla et al.

2004). Applying the same genetic and pharmacological approaches as in Aim 1, I showed that VEGF-R2 inhibition in diabetic mice was sufficient to significantly reduce BBB permeability after stroke (Figure 29). Of note, this effect was confined to diabetic mice, since their treated non-diabetic counterparts had worsened permeability 3 days after stroke (Figure 29). Furthermore, differences in peri-infarct BBB permeability did not correlate with infarct size (Figure 30), however there was a strong relationship with BBB breakdown and synaptic loss in the peri-infarct (measured by layer 5 pyramidal neuron apical dendrite spine density) (Figure 31). Greater permeability in diabetics was associated with greater spine loss, and rescuing permeability with VEGF-R2 inhibition also prevented excessive spine loss in diabetics (Figure 31). Functionally the impairment in recovering forelimb function in diabetics was also rescued by VEGF-R2 inhibition

(Figure 32), providing a link between excessive BBB permeability, synaptic loss, and functional recovery.

Together this dissertation has shown the cerebrovascular system is capable of dynamic structural plasticity following small sub-ischemic or large ischemic vascular

214 insults. In the case of cortical capillary obstructions, microvessels were often able to recanalize either through angiophagy or by extruding emboli back into circulation. When recanalization failed capillaries were pruned, but in an orderly fashion with no evidence for cell death, but rather by endothelial migration and integration into adjacent branches

(Figure 33). In the case of ischemic stroke, surviving microvessels ravaged by diabetes showed greater BBB permeability, which was associated with greater synaptic damage and impaired functional recovery (Figure 34). Interestingly one commonality between these vascular responses to small or large obstructions was they were both regulated by endothelial VEGF-R2 signaling. In both cases reducing VEGF-R2 signaling improved the outcomes of the microvascular response (Figure 33-34). Specifically, reduced levels of VEGF-R2 signaling increased capillary recanalization and mitigated pruning in an otherwise normal healthy brain, whereas inhibition of VEG-R2 in diabetic animals reduced BBB permeability, limiting spine loss, and rescuing functional recovery.

Therefore, I have shown that cortical microvessels undergo structural plasticity in response to vascular events, that the extent of this plasticity has significant consequences for brain health, and that under certain conditions diminishing VEGF-R2 signaling improves vascular responses, either by increasing capillary recanalization following obstruction or by reducing permeability in diabetics after.

215

216 Figure 34 Summary of changes to peri-infarct blood vessels and dendritic structure after stroke.

At baseline (before stroke), no significant differences were seen between diabetic and nondiabetic mice in blood flow velocity, vessel width, VEGF-R2 expression, or neuronal dendritic spine densities. Three days after stroke, microvessels in the nondiabetic peri-infarct cortex were dilated, with blood flow and VEGF-R2 expression increased. The endothelium was swollen and packed with caveola- like vesicles, which coincided with extravagation of blood-borne dyes through the

BBB. In the diabetic peri-infarct cortex, vessels were also dilated, but blood flow was significantly increased and VEGF-R2 overexpressed compared with nondiabetic mice. Diabetic mice exhibited much greater disruption of the BBB

(note extravagation of plasma) and loss of dendritic spines

4.2 Spontaneous cortical capillary obstructions and models

While the propensity for cortical capillaries to spontaneously stall or become obstructed is well known, clarifying the rate of obstructions is of critical importance to understanding their long-term impact on the brain. Earliest reports of spontaneous obstructions were qualitative and did not attempt to estimate a frequency (Kleinfeld et al.

1998; Villringer et al. 1994). A study comparing spontaneous stalls in control mice to models of chronic myeloproliferative disorders found a baseline rate of spontaneous obstructions in wildtype mice of 3±1% (Santisakultarm et al. 2014). However, this included transient stalls and obstructions (ranging from > 1 sec to 2 hours)

(Santisakultarm et al. 2014). More recently the Boas lab used Optical Tomography

217 Coherence to follow blood flow in ~200 capillaries at high temporal resolution (~0.1Hz)

(Erdener et al. 2017). They found that over 9 min of imaging 7.5% of capillaries experienced a stall (Erdener et al. 2017). While both studies group transient stalls with longer lasting intransigent obstructions, they provide an important estimate of the propensity for spontaneous obstructions in the capillary bed. My focus on persistent obstructions (>20min) found a substantially lower rate, of 0.118% obstructions per day.

This suggest that only a fraction of the 3-7% of spontaneous stalls become persistently obstructive. One important and unresolved question is what factor(s) distinguish transient verses long-lasting obstructions. One possible explanation is that emboli composition is the primary determinate of obstruction duration. A study examining different types of chronic myeloproliferative disorders in mice found disease specific differences in the most common emboli composition (usually RBC and platelets), and variability in the average duration of obstruction (Santisakultarm et al. 2014). By contrast, a study from the

Grutzendler lab found that while emboli composition affected the route of recanalization

(washout vs angiophagy), equal numbers of fibrin or cholesterol-based clots remained stuck in vessels. This finding argues against a strong effect of emboli composition (Lam et al. 2010) in dictating recanalization rate. If not in the emboli, the variable affecting obstruction duration may resides in the vessels themselves. Since I have shown that levels of activated VEGF-R2 influence capillary recanalization, it is also possible that a capillary’s resting level of p-VEGF-R2 affects the conversion of a transient stall to a prolonged obstruction. While I did not find a wide range of capillary p-VEGF-R2 in sham mice (Figure 21), a detailed analysis of capillary p-VEGF-R2 levels and risk factors for persistent obstructions (>20min), such as cortical depth and arteriole branch

218 order, is required to definitively answer this question. Lastly, stall duration may be a product of the local vascular structure. Persistent obstructions occurred preferentially in superficial and low branch order capillaries, which experience the largest pressure differential, even compared to deeper cortical capillaries (Schmid, Barrett, et al. 2017).

Likewise, the only predictive measure of recanalization was distance to nearest flowing vessel (Figure 15), and thus proximity to the higher-pressure end of the capillary.

Consistent with our findings, a recent study from the Boas lab looking at transient stalls

(seconds to minutes) found a similar distribution of arteriole branch orders for briefly stalled capillaries (Erdener et al. 2017). They were also able to compare RBC and plasma velocities in stalled vessels prior to stalling and found stalled vessels had significantly lower baseline (pre obstruction) velocities (Erdener et al. 2017). An important question for future studies will be to correlate the obstruction material and local endothelial or vascular conditions with obstruction longevity to understand what factor(s) affect the transformation of transient to prolonged obstructions.

To work around the sparse and spontaneous nature of persistent obstructions, I utilized a microsphere model of obstruction (Figure 9-11). This model is however, as discussed, not without caveats. It is impossible to verify the number of microspheres that reach and flow through the cerebral vasculature (number of possible obstructions) between mice, even if we assume identical injections into the tail vein. The range of initially obstructed capillaries 30 mins following microsphere injection was on average

3.67%, with a 95% CI of 2.5-4.8% and a coefficient of variation of 60.7%. This variability between animals in the number of obstructions limits what conclusions can be drawn solely from histological analysis, such as microsphere density. While normalizing

219 within cohorts (e.g. Saline and SU5416 treated mice injected with the same microsphere stock solution) reduces variability, the present work highlights the advantage of combining post-mortem estimates of microsphere density with longitudinal in vivo imaging experiments. Since the latter approach permits imaging the same obstructions over time, one can directly assess vessel fates without having to overcome variability in the initial load of obstructions.

Another caveat of the microsphere model is the unnatural composition. While I have shown the fates of capillaries were nearly identical regardless of whether the obstruction was in spontaneous (natural) emboli or artificial (microspheres), emboli size and composition certainly influence the route of recanalization. The challenge becomes trying to merge the benefits of natural emboli (and potential emboli-endothelial interactions) with the ability to standardize in the microsphere model. Injecting solutions loaded with natural emboli (such as fibrin clots or cholesterol crystals) have much greater variability in emboli concentration, fluorescent labelling of emboli (added experimentally), and size. With so many sources of variability (compared to microspheres), the only viable method using natural emboli is in vivo imaging, which is significantly more time and labor intensive than histological approaches. This precludes faster high throughput screening of interventions, such as SU5416, which can then be followed up with in vivo 2-photon imaging. An important future direction will be to combine the benefits of both models. I have already demonstrated the utility of coating microspheres with DiI (Figure 21). It is not a large step forward to coat microspheres in natural components such as cholesterol or adhesive proteins. This would not only improve the ability of microspheres to model more natural emboli but potentially allow

220 direct testing of single molecule interactions effects on recanalization. For example, while I have found that cholesterol emboli also lead to vessel pruning, it remains technically challenging to generate sufficient numbers of ~4 μm labelled cholesterol crystals for in vivo imaging studies to compare recanalization rates to other emboli.

Similarly, protein-endothelial cell interactions could be directly tested with microspheres coated in specific isolated proteins, such as the vast and under appreciated spectrum of

RBC surface proteins (Hegedűs et al. 2015).

By following natural and microsphere induced obstructions over 21 days, I found that 70% of vessels remain patent whereas 30% are pruned. Using these percentages, we predicted capillary loss with age that closely matched our experimental estimates in aged mice. However, it is important to note that our fate mapping experiments were all conducted in young mice. This leaves open the possibility that rates of recanalization and pruning might change with age. For example 22 month old mice have been shown to have significantly reduced levels of angiophagy, and show greater tissue damage following larger, hypoxia inducing obstructions (affecting arterioles ~20 μm) n (Lam et al. 2010). Aging has several well documented effects on the vasculature that could impair recanalization in the brain. During aging, larger blood vessels lose myogenic tone which increases pressure and downstream microcirculatory pathologies (Xu et al. 2017; Springo et al. 2015). Microvessels themselves stiffen with age (lose elasticity) further increasing vascular resistance (Anversa et al. 1994; Hajdu et al. 1990). Aged capillaries also have decreased luminal volumes (Desjardins et al. 2014), increased tortuosity, reduced hematocrit, thickened basement membrane (Anversa et al. 1994; Nahirney, Reeson, and

Brown 2015) and reduced blood flow (Xu et al. 2017; Desjardins et al. 2014; Hajdu et al.

221 1990). Likewise, endothelial cells show significant structural and functional changes in aging. Endothelial cells in aged animals become elongated, with increases in cellular stress fibers, decreased focal adhesion complexes and a general diminishment of endothelial mediated vasodilation (Popescu et al. 2009; Shah and Mooradian 1997;

Stewart et al. 1987; Xu et al. 2017). Any of these age-related changes in the vasculature could shift the balance away from recanalization and towards pruning. Several studies

(examining either mRNA or protein levels) have also found significant increases in vascular expression of VEGF-R2 in the aged brain (Rossiter et al. 2005; Ahmed-Jushuf et al. 2016; Muche et al. 2015), which could lower recanalization rates. Surprisingly however, preliminary experiments inducing microsphere obstructions in 16-18-month-old wildtype mice suggest however the 70% recanalized / 30% pruned ratio holds even in aged animals.

Another important future direction for this work is to understand how disease states could influence capillary obstructions and recanalization, and thus provide a causal link between vascular risk factors for dementia and cognitive decline. It has already been shown that diseases influencing hematocrit can dramatically increase obstruction prevalence (Santisakultarm et al. 2014), however to date no study has examined how vascular diseases such as diabetes, affect the ability of capillaries to recanalize. This is surprising given that diabetes is a risk factor for stroke, cognitive impairment and

Alzheimer’s disease (Iadecola 2013; Go et al. 2014; Huber 2008; Brands et al. 2005;

Luchsinger et al. 2007; Taguchi 2009). Diabetes is associated with significant inflammation and associated changes to endothelial cell surface protein expression

(Deshpande, Harris-Hayes, and Schootman 2008; Assar, Angulo, and Rodríguez‐Mañas

222 2016). Any of the multitude of hyperglycemic effects on endothelial cells (see introduction) could impair recanalization. In fact, my preliminary experiments injecting microspheres into diabetic mice suggest that diabetes significantly impaired capillary recanalization. Understanding how various disease states undermine microvascular recanalization will be an important future step in linking these dementia risk factors mechanistically to vascular or cognitive impairment.

One unanswered question is to what extent do different brain regions show unique susceptibilities to obstruction, ability to recanalize, or circuit vulnerability to capillary loss. This question has profound implications for the progression and susceptibility to age related cognitive decline and dementia which impacts certain brain regions more than others. Perhaps brain regions with higher susceptibility to obstructions or lower rates of recanalization are more prone to age related synapse loss or deposition of beta-amyloid plaques. In validating our microsphere model, I found no difference in obstruction density along an anterior to posterior cortical regions. However, these measurements did not take into account medial-lateral differences. For example, I did not directly test whether functional brain regions, such as the entorhinal cortex or hippocampus show region specific differences in obstructions or recanalization. While it has been shown that cortical vasculature is generally homogeneous and does not vary with local neuronal functional / structural boundaries, such as whisker barrel cortical columns (Blinder et al.

2013), larger regional differences in vascular densities have been suggested (Hirsch et al.

2012) but not rigorously tested. Even if the cortical vasculature shows little structural variability across regions, it is plausible that considerable functional variability exist across and even within cortical lamina based on differences in synaptic density / wiring

223 or mean firing and neurovascular coupling (Tsai et al. 2009; Schmid, Barrett, et al. 2017).

Even specific neural populations have a unique impact on blood flow. For example, it has been shown that VIP interneurons, that preferentially reside in superficial cortical regions, are capable of dilating microvessels (Cauli et al. 2004). Conversely, a recent imaging study implicated neuropeptide Y expressing cortical interneurons in mediating vasoconstriction following sensory stimulation (Uhlirova et al. 2016). Taken together, considerable work remains to characterize potential regional differences in vulnerability to microsphere occlusion or recanalization ability.

4.3 Pruning and angiogenesis

The mechanism by which capillaries are pruned suggest a previously unappreciated capacity of mature microvessels to remodel, and the need for a fundamental rethink on vascular plasticity in the brain. While further study is required to confirm this finding, the absence of any detectable cell death (Figure 10) and the increase in endothelial cell nuclei surrounding sites of pruning (Figure 12) strongly suggest that capillary endothelial cells retracted into adjacent vessels in a controlled manner, reminiscent of similar pruning seen in development (Chen et al. 2012; Franco et al. 2015; Kochhan et al. 2013; Lenard et al. 2015). Not only are both pruning events

(those observed in development and mature brain) dependent on reduced blood flow, they both bare striking morphological similarity in the pinching and retraction of endothelium

(Figure 12-14) (Chen et al. 2012). On a mechanistic level, the question is whether the regulatory signals and pathways that help refine and prune nascent developing vascular networks are the same as in the mature brain. Colloquially vessel pruning has been described as “reverse angiogenesis” (Chen et al. 2012) to suggest similar molecular

224 pathways maybe involved in regulating vascular regression. However, this has yet to be tested in either developing or mature vasculature. Given that pruning likely requires similar remodelling of the actin cytoskeleton, cell-cell adhesion complexes, and cell- basement membrane connections (focal adhesion complexes) as angiogenesis (for endothelial cell motility), an obvious candidate would be the Rho GTPase Rac1. Rac1 is expressed in cerebrovascular endothelial cells and directly regulates actin remodeling and cell-cell adhesion through VE-Cadherin and Tiam1 (Galan Moya, Le Guelte, and Gavard

2009; Walsh Tony et al. 2011). Rac1 is also activated by VEGF-R2 through AKT, and

VEGF-R2 activated Rac1 affects endothelial contractility, as well as through Src and

Vav2, supports membrane protrusions and cell migration (Galan Moya, Le Guelte, and

Gavard 2009; Abraham et al. 2009). A critical downstream effector of Rac1 is p21- activated serine/threonine kinase (PAK) which can directly phosphorylate actin fibers, increase Focal Adhesion Kinase (FAK) activity and regulate integrin and proliferation associated genes like FOXO1 (Orr et al. 2007; Galan Moya, Le Guelte, and Gavard

2009). PAK is also directly activated by VEGF-R2 and can act independently of Rac1.

Thus, the mechanism behind active capillary pruning and endothelial cell migration probably require Rac/PAK mediated cytoskeletal/adhesion remodeling, of which VEGF-

R2 signaling is an obvious common denominator.

The contrast between frequently observed capillary pruning and completely absent (or at best undetectable) angiogenesis is consistent with a growing body of evidence that suggest a need for a fundamental shift on our views of vascular plasticity in the mature brain. While the prevailing dogma suggest the capacity for angiogenesis is commonplace, and easily inducible by experimental interventions such as hypoxia,

225 VEGF injections, and exercise (Croll et al. 2004; Harik, Hritz, and LaManna 1995;

Louissaint et al. 2002; Rosenstein et al. 1998). In pathological states of the brain, its even more dogmatic to see angiogenesis as coming to the rescue, in diabetes (Prakash et al.

2012), following stroke (Ergul et al. 2014; Zhang et al. 2000; Greenberg and Jin 2013), and in dementia (Vagnucci and Li 2003; Paris et al. 2004). Historically the picture of the adult brain is one of a fertile ground for the growth of new blood vessels under any number of circumstances. However, detecting angiogenesis is often done through indirect and correlative measures, such as differences in vascular length, volume, or branching points. Furthermore, this is commonly done post mortem, comparing between groups rather than within animals (before and after). While this is often unavoidable, for example I used manual post mortem counts to compare cortical capillary densities in young versus aged mice, most studies use methodologies prone to error. For example, measuring increased vascular area could report changes in average vessel width rather than the growth of new vessels (Tennant and Brown 2013). The most compelling evidence for angiogenesis is longitudinal in vivo imaging, where on can directly observe the sprouting, growth, and stabilization of a de novo vascular segment. And yet in nearly every case of this type of study in the adult vasculature, the story is the same, including the present one. Angiogenesis is present during a brief postnatal period, where the vasculature is also highly receptive to pro-angiogenic conditions (such as hypoxia), and then quickly subsides (Harb et al. 2013). The adult vasculature seems generally incapable of significant cortical angiogenesis, even in response to hypoxia (Harb et al. 2013) or ischemic stroke (Tennant and Brown 2013; Mostany et al. 2010; Guo et al. 2011). One important caveat to note is that these longitudinal imaging studies focused on vasculature

226 below the pial surface, therefore angiogenesis on the cortical surface is certainly plausible. While the evidence for adult cerebral angiogenesis been underwhelming, the importance of vessel pruning has slowly gained prominence in vascular research

(Dimmeler and Zeiher 2000; Im and Kazlauskas 2006; Korn and Augustin 2012; Korn and Augustin 2015; Simonavicius et al. 2012; Wietecha, Cerny, and DiPietro 2013).

Overall the most direct evidence generated by others, and myself argue that vessel pruning is the dominant form of vascular plasticity in the mature brain, with angiogenesis at best being exceptionally rare and/or happening only in extreme cases. Unfortunately, there is a dearth of information on the molecular mechanisms that regulate vessel pruning. This knowledge gap should be rectified and will hopefully shift research and clinical focus to preserving capillaries rather than growing new ones.

4.4 Mechanisms of recanalization

An open question is the mechanism by which most microspheres are extruded back into the circulation (Figure 14). If this is mediated by vascular constriction or dilation, one approach is to conduct continuous time lapse imaging of microsphere recanalization. However, such an effect maybe be subtle, requiring only micrometer changes to sufficiently reduce resistance on the emboli for wash out. Likewise, an alternative explanation is that changes in vascular tone (vessel wall elasticity or resistance to stretch), rather than radius, could be sufficient to lower the resistance of endothelium against the microsphere, allowing it to be washed out. These possibilities may explain why I was unable to find any relationship between local blood flow on the probability of recanalization. If most levels of flow / pressure are sufficient for washout, then the operative variable is endothelial resistance. Endothelial-microsphere interactions are also

227 possible, with changes to luminal surface protein expression possibly being equally relevant to washout. While the similarity in recanalization rates between microspheres and natural spontaneous emboli suggest that emboli composition may not be a critical factor in recanalization, this is not something that can be concluded from this work.

If endothelial emboli interactions are important, then several possibilities exist for how dampening VEGF-R2 signaling could improve recanalization and vice versa.

VEGF-R2 signaling can directly up regulate cell adhesion proteins such as α1β1 and α2β1 integrins, and through Src/FAK increase VE-Cadherin (Ramjaun and Hodivala-Dilke

2009). VEGF-R2 activation under inflammatory conditions can also increase P/E- selectin, VCAM1 and ICAM1 Platelet Activating Factor (PAF) expression (Ramjaun and

Hodivala-Dilke 2009; M. Clauss et al. 1990; Hong et al. 2004). Even acutely VEGF-R2 signaling associated Ca2+ release can cause rapid translocation of selectins to the membrane surface (Pober and Sessa 2007). Which of these pathways are relevant, if any, is an interesting question and likely depends on the emboli. For example, inert emboli such as cholesterol would be predicted to have far less specific endothelial interactions compared to fibrin clots and RBCs.

The robust effects of dampening VEGF-R2 signalling provides the first clue in unraveling the cellular mechanisms of recanalization. While dissecting the many possible downstream effectors of VEGF-R2 (Figure 5) on recanalization is likely the work of an entire dissertation itself, the effects of VEGF-R2 on calcium influx, and cytoskeletal rearrangements is a logical starting point (Olsson et al. 2006; Andrews et al. 2014; Bates and Harper 2002; McCue, Noria, and Langille 2004; Tzima 2006). VEGF-R2 signaling activates TRPC1 cation channels, allowing Ca2+ influx into endothelial cells (Cheng et al.

228 2006; Kusaba et al. 2010; Jufri et al. 2015). Intracellular calcium influences endothelial contractility, and thus vascular tone (resistance to stretch) through Ca2+ dependent

Calmodulin/MLCK activation of myosin-actin interactions (Jufri et al. 2015; Cheng et al.

2006). Thus, a loss of VEGF-R2 calcium influx would be predicted to have a vasodilatory effect in the endothelium. However, VEGF-R2 mediated calcium influx has also been suggested to affect coagulation factors (Nilius, Droogmans, and Wondergem

2003), and luminal surface expression of adhesion proteins (Pober and Sessa 2007).

Independent of calcium, VEGF-R2 signaling can directly affect cytoskeleton remodelling through Rac1 and FAK (Rousseau, Houle, and Huot) as described above.

The need for a more detailed understanding of mechanisms underlying recanalization becomes apparent when considering possible clinical applications.

Vascular surgical patients are at high risk for sudden onset of multiple cerebral microvascular obstructions, which likely play a role in post operative dementia and increased risk for dementia later in life (Brown, Moody, and Challa 1999; Brown et al.

2000; Heyer et al. 2002; Newman et al. 2001; Rodriguez et al. 2005; Selnes et al. 2001).

In these cases, an acute treatment of a VEGF-R2 inhibitor to improve recanalization could be feasible, although it may complicate post operative healing processes. However, for long term reductions in capillary loss, to treat or reduce risk of dementia, chronic use of VEGF-R2 inhibitors is impractical. As has been described with cancer treatments, chronic use of VEGF-R2 inhibitors can be associated with serious complications (Giles et al. 2003; Kamba and McDonald 2007; Eskens and Verweij). Given that VEGF-R2 is likely only the entry point into pro-recanalization pathways, future work should focus on

229 more specific downstream effectors to identify clinical targets that could be used with greater efficacy and for longer durations with less side effects.

Although my experiments clearly show that we can prevent capillary loss with

VEGF-R2 inhibitors over a 3-week period, I have not shown that this same strategy enacted over a much longer period could be used to prevent age related capillary loss.

One approach to test this experimentally is with our genetic knockdown model. By allowing either Tamoxifen or vehicle injected Tek-Cre-ERT x Kdr+/fl to age to ~16-18 months (~ 12 months post VEGF-R2 knockdown or vehicle injection) one could determine if persistent dampening of VEGF-R2 signaling improved recanalization of spontaneous obstructions (and thus reducing capillary pruning) over a mouse’s lifetime.

This experiment is ongoing and when finished will be an important first proof of concept in trying to prevent age related microvascular loss through reducing obstruction related capillary pruning.

Even with better targeted interventions to improve recanalization, the prospect of years or decades taking preventative medicine might excite pharmaceutical executives but is of little appeal the general population. Especially for those at higher risk due to comorbidities, who likely already receive chronic medical care. The ideal intervention would be a preventative approach that emphasised improving general cognitive and cardiovascular health to reduce the risk of dementia. This is already a major focus of public health efforts (Larson et al. 2006; Rovio et al. 2005; Verghese et al. 2003;

Fratiglioni, Paillard-Borg, and Winblad 2004). While the benefits of improved cardiovascular health, especially in the elderly, is obvious, it is interesting that just increased social interaction and engagement with the environment alone may reduce

230 cognitive decline and dementia risk (Fratiglioni, Paillard-Borg, and Winblad 2004;

Verghese et al. 2003). This, as has been suggested, points to increased brain activity alone as an important preventative measure. The interesting question then is if a direct link between neural activity and capillary recanalization exist. Providing a mechanistic link between increased brain activity and improved capillary recanalization would help explain the benefits of social, physical engagement in aging populations. Along this line I have generated preliminary data showing that silencing cortical activity with focal muscimol injections (GABAA agonist) significantly reduces the clearance of injected microspheres, specifically within the muscimol silenced cortex. Conversely providing environmental enrichment such as novel objects, expanded cage, running wheels and social interactions led to a 25% reduction in post mortem cortical microsphere density

(12 hours after injection) compared to mice traditionally housed. This is consistent with other work showing on shorter timescales that sensory stimulation reduces the number of capillary stalls (Erdener et al. 2017), and that enrichment in rodents can impact arteriole tone (Mehina, Murphy-Royal, and Gordon 2017). The mechanistic link between neural activity, neuro-vascular coupling, endothelial VEGF-R2 signaling, and recanalization is the most promising direction towards a clinical application of this research.

4.6 Diabetics and stroke

Several important caveats regarding animal models of stroke and diabetes have been raised by ourselves and others (Reeson, Jeffery, and Brown 2016). Particularly as mentioned, both the model of uncontrolled type 1 diabetes and the smaller focal photothrombotic stroke each come with their own limitations (Carmichael 2005).

However, a recently published study subjecting both type 1 and type 2 diabetic mice to

231 transient MCAO strokes has replicated my major findings (Kim et al. 2017). While this paper reported larger cerebral infarcts in diabetic mice (unlike my study where no infarct volume differences were found) they confirmed greater edema in diabetic mice which correlated with increased VEGF-R2 mRNA levels 3 days after stroke (Kim et al. 2017).

The study also used SU5416 to treat diabetic mice and replicated my finding that dampening VEGF-R2 signaling in diabetic mice after stroke significantly reduced BBB permeability. This confirmation of my findings, in both a different model of diabetes and stroke further suggests that VEGF-R2 mediated increases in peri-infarct BBB permeability is a complication across different forms of diabetes, and sizes of ischemic stroke. An important next step will be to attempt to confirm the increase in VEGF-R2 expression in human diabetic tissue. Since our study was published in 2015, a new MRI based imaging study in humans found that diabetic patients exhibited significantly greater

BBB permeability (based on effusion of a contrast agent) 3-7 days after stroke than non- diabetic controls (Yu et al. 2016). Furthermore, the extent of BBB disruption in diabetic patents was a strong predictor of unfavorable stroke outcome (judged 3 months post- stroke). Collectively, my work and others have provided a mechanistic understanding of how aberrant VEGF signalling in the diabetic brain exacerbates BBB disruption and by extension, impairs recovery of function.

The recovery of lost brain function after stroke requires large scale synaptic reorganization of adjacent cortical circuits (Murphy and Corbett 2009; Brown et al.

2009). How diabetes mediated BBB disruption interferes with this process is still not fully understood. My work suggest excessive dendritic spine loss in the peri-infarct region plays an important role (Figure 31). To what extent diabetes may also affect

232 axonal projections in surviving brain regions after stroke is less known. However, one study found type 1 diabetic rats had reduced intracortical axons (labeled with biotinylated dextran or axonal markers) compared to non-diabetic controls (Yan et al. 2012). This also matches reports of reduced synaptophysin protein levels in diabetics after stroke (Zhang et al. 2010), all pointing towards a general loss of synapses in the peri-infarct region.

Synaptic rewiring is only part of many possible forms of adaptive plasticity following stroke. How or if diabetes affects activity dependent synaptic plasticity in the cortex remains to be studied. One study reported reductions in long term potentiation in the hippocampus of diabetic rats following stroke, but this study lacked non-diabetic controls

(Jing et al. 2008). Furthermore, stroke can affect distant but connected neurons

(Carmichael et al. 2004; Silasi and Murphy 2014), so how diabetes alters cortical networks in local and distant regions after stroke, thalamocortical projections (and encoding of sensory information) and inhibitory neuron control of cortical excitability and sensory gain circuits (Olsen et al. 2012; Pfeffer et al. 2013) remains to be explored.

4.7 Conclusion

The vasculature is a fundamental pillar of the nervous system, providing nutrients and helping to establish the privileged micro-environment necessary for neural signaling.

While much of neuroscience research is rightfully focused on neural plasticity, this dissertation has shown that vascular integrity and structural plasticity is also of paramount importance to brain health. Specifically, I have shown in Aim 1, that cortical superficial capillaries are prone to spontaneous obstructions and using a microsphere model of induced obstructions, that around 70% of obstructed capillaries will recanalize.

The remaining 30% of obstructed capillaries however were pruned from the vascular tree

233 and not replaced by any detectable angiogenesis. The net effect of this risk of obstruction and rate of pruning was modelled to predict an age-related loss in capillary density that closely matched experimental measurements. Mechanistically I have shown that recanalization correlated with pronounced reductions in endothelial VEGF-R2 phosphorylation and using parallel approaches of genetic knock down and pharmacological inhibition, that reducing endothelial VEGF-R2 signaling was sufficient to significantly increase recanalization and reduce capillary loss. In the second aim I have shown that diabetes leads to an abnormal increase in VEGF-R2 expression in the peri-infarct region which correlated spatially and temporally with abnormally high BBB permeability 3 days after stroke. Ultrastructural imaging of the peri-infarct did not show any evidence of TJC disassembly, but rather a loss of BBB integrity due to increased transcytosis. Increased peri-infarct BBB disruption was also associated with great synaptic loss compared to non-diabetic controls. Inhibiting VEGF-R2 signaling in diabetics after stroke was sufficient to reduced excessive BBB permeability, mitigate peri-infarct spine loss, and normalize functional recovery to levels commensurate with non-diabetic controls.

Collectively this dissertation has shown how vascular plasticity, regulated largely by endothelial VEGF-R2 signaling, has profound consequences for the brain, whether it be through slow rarefaction of capillary networks over time, or acutely after an ischemic event.

234

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276

Appendix Code available at https://preeson.github.io/eLIFE-2018-Software/

Appendix 1 Supplementary code 1 and 2 / VesselNumEst // Written by Patrick Reeson // Brown Lab, University of Victoria, BC, Canada // Written Aug 25 2016

// Only intended to work on 2-P imaging stacks taken of cortical vasculature in the horizontal plane

// elimiante scale - all numbers in pixels run("Set Scale...", "distance=0 known=0 pixel=1 unit=pixel global");

//Loop to call function processImage() for number of substacks to run

for (i=1; i

print("LOOP 2"); print(i); print("nImages is = " + nImages); }

} // Run 2 loops to close all open stacks after running processImage() i=1; for (i; i 1) { i=i-1; close() ; print("LOOP 4"); print(i); print(slicenumber); print("nImages is = " + nImages); } }

// Run loop to save each image by function saveImage()

for (i=1; i<=nImages; i++) { selectImage(i); saveImage(); }

function saveImage() { print("Save Image loop") title = getTitle(); print("Save Image title: " + title); // CD to prefered save location // renames file saveAs("Tiff", "C:\\fiji_output_folder//" + title); }

// Run loop for thresholding each zprojected image by function thresholdImage()

for (i=1; i<=nImages; i++) { selectImage(i); thresholdImage();

} //Function autothresholds images function thresholdImage() { setAutoThreshold("Triangle dark");

setOption("BlackBackground", false); run("Convert to Mask"); run("Median...", "radius=1");

}

278

// runs loop to record pixel area, full list of all substacks is always found in saved file for the last substack

for (i=1; i<=nImages; i++) { selectImage(i); pixelAreaImage(); }

// sets measument to area (pix) and % area of black pixels function pixelAreaImage() { run("Set Measurements...", "area area_fraction redirect=None decimal=3"); run("Measure");

title = getTitle(); print("Pixel Area title: " + title); saveAs("Results", "C:\\fiji_output_folder//" + title + "Area Results");

}

// Saves thresholded images

for (i=1; i<=nImages; i++) { selectImage(i); saveImage(); }

function saveImage() { title = getTitle(); print("THRESHOLD title: " + title); rename ("THRESHOLDED_" + i + title ); title = getTitle(); saveAs("Tiff", "C:\\fiji_output_folder//" + title);

}

// Another loop to close any stacks open for (i=1; i<=nImages; i++) { selectImage(i); processImage();

function processImage() { slicenumber = nSlices; if (slicenumber != 1) {

}

}

i = 0

279 for (i=1; i<=nImages; i++) { selectImage(i); closeImage();

function closeImage() { slicenumber = nSlices; if (slicenumber > 1) { close() ; } }

// Loop to skeletonize thresholded images for (i=1; i<=nImages; i++) { selectImage(i); skeletonImage(); }

function skeletonImage() { run("Median...", "radius=10"); setOption("BlackBackground", false); run("Skeletonize", "stack");

// Measure the number of 255 pixels, which is length run("Set Measurements...", "area area_fraction redirect=None decimal=3"); run("Measure");

title = getTitle(); print("Skeleton title: " + title); saveAs("Results", "C:\\fiji_output_folder//");

}

}

Supplementary code 2

%% Capillairy Loss Modeling based on experiemntal data % Written by Patrick Reeson % Brown Lab, Division of Medical Sciences % University of Victoria, Victoria BC Canada

% capillary branch order distrubution and obstruction risk loaded from % excel file % risk of pruning is 0.3

280 %% Import data from spreadsheet % Script for importing data from the following spreadsheet: % % Workbook: C:\Users\P.Reeson\Documents\MATLAB\mod_data.xlsx % Worksheet: Sheet1 % % To extend the code for use with different selected data or a different % spreadsheet, generate a function instead of a script.

% Auto-generated by MATLAB on 2017/07/06 17:30:37 % clear variables clear %% Import the data %% CD to open excel files with branch order and risk distrubutions %% File is named mod_data % mod_data (risk distrubuted across all branch orders based on obstruction distrubution) moddata = xlsread('C:\Users\P.Reeson\Documents\MATLAB\mod_data.xlsx','Sheet1');

%% Clear temporary variables clearvars raw; %% moddata is excel sheet with obstruction risk for each branch order (min 1, max 16 depending on how risk is split) in order moddata;

% List of all Branch Orders considered brancho = moddata(:,1); % get number of Branch Order Bins nbin = brancho(end); % Frequency of each Branching Order bin (based on real in vivo data, either % for each bin or pooled if risk was also pooled (based on moddata file) binfrq = moddata(:,2); % Risk of Obstruction for each bin based on pooling stratagey, sum always % adds up to the experiemntally observed risk for all vessels obstrisk = moddata(:,3); % Starting number of vessels for theoretical 100k cappilairies based on % experimentally observed distrubution of branch orders and stratagey for % pooling risk startves = moddata(:,4); % Prune risk is 30% of all obstructed prunerisk = obstrisk .*0.30; % Start matrix that will be the model, each row is a branch order's # % vessels, eacg column is next iteration of 2 hour cycle of obstruction and % pruning vmodel = [brancho,startves]; % number of cycles to run run = 50000;

% Run loop for each 2 hour window pruning iteration i = 3; k = 3; % start with starting distrubution of vessels across branching orders vmodel(:,1) = startves;

281 % newves is new # of vessels for each branch order after 1 2 hour window newves = startves; for i = 3:run-2 iprune = newves .* prunerisk; newves = newves - iprune; vmodel(:,k) = newves; i = i+1; k = k+1; end

% tworun is the total number of hours passed, ie number of 2 hours cycles tworun = run*2; xaxis = linspace(2,tworun,run-2); vtime = transpose(xaxis); % vsum is total sum of vessels at any time vsum = sum(vmodel,1); % vnorm is the normalized number of vessels for each branch order, % normalized to vsum vnorm = vsum./100000; vnormt = vnorm.'; %tvsum is vsum transposed for graph tvsum = vsum.'; vtime_2 = horzcat(vtime,tvsum);

% Generate each bin as fraction of total vessels for each time q = 1; z = 1; for q = 1:run-2 for z = 1:nbin binf(z,q) = vmodel(z,q)/vsum(q); z=z+1; end q=q+1; end figure % Create xlabel xlabel('Time (hours)');

set(0,'defaultlinelinewidth',2); hold on

ax1 = subplot(1,3,1); plot(ax1,vtime,vnormt) ax2 = subplot(1,3,2); p = 1; ax3 = subplot(1,3,3); r = 1;

282 for r = 1:nbin plot(ax3,vtime, binf(r,:)) hold on end hold on

% select risk is from BO distrubutions based on gettin x number of % obstructions OVERALL if only at select risk vessels obstructed u = 1; a = 1; vsummat = vsum; % Concat vert vsum to get a nbinXrun-2 matrix for a = 1:nbin-1 vsummat = vertcat(vsummat,vsum); a=a+1; end

for u = 1:run-2 % number of at risk vessels riskfinal = obstrisk.*vmodel(:,u); % normalized to number of vessels in the branch order risknorm = riskfinal/vsummat(:,u); % sum of all risk across branch orders sumriskfinal(:,u) = sum(risknorm,1);

u = u+1; end % starting numbers of at risk population, ie zero is time zero zerorisk = sumriskfinal(1,1); % Normalized sumrisk final nsrf = sumriskfinal./ zerorisk; ax4 = subplot(1,3,2); plot(ax4, vtime, nsrf(1,:)); set(ax1,'FontName','Calibri','FontSize',14,'FontWeight','bold',... 'LineWidth',2,'XColor',[0 0 0],'YColor',[0 0 0],'ZColor',[0 0 0]); % Set the remaining axes properties set(ax3,'FontName','Calibri','FontSize',14,'FontWeight','bold',... 'LineWidth',2,'XColor',[0 0 0],'YColor',[0 0 0],'ZColor',[0 0 0]);

set(ax4,'FontName','Calibri','FontSize',14,'FontWeight','bold',... 'LineWidth',2,'XColor',[0 0 0],'YColor',[0 0 0],'ZColor',[0 0 0]); % Create legend legend1 = legend(ax4,'show'); set(legend1,... 'Position',[0.906051734112396 0.140358770176528 0.0739385045595783 0.795792056634875]); title(legend1,'Branch order');