The Discovery and Regulation of Modes of Exocytosis Through the Lens of Computer Vision

THE DISCOVERY AND REGULATION OF MODES OF EXOCYTOSIS THROUGH THE LENS OF COMPUTER VISION

Fabio Urbina

A thesis submitted to the faculty at the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the department of Cell Biology and Physiology in the School of Medicine.

Chapel Hill 2020

Approved by:

Stephanie Gupton

Patrick Brennwald

Doug Cyr

Keith Burridge

Shawn Gomez

ABSTRACT

Fabio Urbina: Discovery and Regulation of the Modes of Exocytosis through the lens of computer vision (Under the direction of Stephanie Gupton)

The formation of the nervous system involves establishing complex networks of synaptic connections between proper partners, which requires the rapid expansion of the plasma membrane surface area as neurons grow. Critical to the expansion of the plasma membrane is exocytic vesicle fusion, a regulated mechanism driven by soluble N-ethylmaleimide-sensitive factor attachment proteins receptors (SNAREs). Multiple modes of exocytosis have been proposed, with full-vesicle fusion (FVF) and kiss-and-run (KNR) being the best described. The basis of SNARE-mediated fusion, the opening of a fusion pore, and its contribution to plasma membrane expansion remains enigmatic, as vesicle fusion is spatially small and temporally fast.

We exploited TIRF microscopy to image VAMP-pHluorin mediated exocytosis in murine embryonic cortical neurons and developed computer-vision software and statistical tools to perform unbiased, efficient identification of exocytic events and uncover spatiotemporal aspects of exocytosis during neuron development. We further developed novel classification algorithms to describe. and classify individual exocytic events. Vesicle fusion behavior differed between vesicle types, cell types, developmental stage, and extracellular environment. Spatial statistics uncovered distinct spatiotemporal regulation of exocytosis in the soma and neurites of neurons that was modulated by developmental stage, the guidance cue netrin-1, and the E3 ubiquitin ligase TRIM9.

Four distinguishable fusion modes were uncovered in developing neurons: two FVF-like modes that insert membrane material (FVFi and FVFd) and two KNR-like modes that do not (KNRi

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and KNRd). Experiment-based mathematical calculations and experiments indicated that FVF and FVFd VAMP2-mediated vesicle fusion supplied sufficient material for the plasma membrane expansion that occurred early in neuronal morphogenesis. The mode of exocytosis is dependent on the E3-ubiquitin ligase TRIM67. Neurons lacking Trim67 have increased KNRi and KNRd as well as smaller surface area, presumably due to lowered rates of FVFi and FVFd. Our data suggest this is accomplished in part by limiting incorporation of the Qb/Qc SNARE SNAP47 into

SNARE complexes, and thus, SNAP47 involvement in exocytosis. SNAP47 levels are elevated in neurons lacking TRIM67, and overexpression of SNAP47 in neurons expressing wildtype levels of TRIM67 have increased KNRi and KNRd and decreased FVFi. Thus, we establish a regulatory mechanism of TRIM67, distinct from TRIM9.

To my mother and father, who have supported me and pushed me to always strive to be a better person.

To my love Erin Pedone, who is my bedrock and my anchor. . To all of my brothers, especially Armando, who have been a constant source of familiarity and fun and who remind me to never take life so seriously.

To my best friend Katie Stember, who has put up with my inability to put work down and all of

my flaws throughout, yet still pushes me to be more than I am.

To Bast and Orion, the best and worst roommates I could ever have.

To Billy and all the joy he has brought me.

ACKNOWLEDGMENTS

I would like to first and foremost thank my mentor, Dr. Stephanie Gupton. Dr.Gupton has been one of the greatest mentors I could have asked for, and put in so much effort to train me to become the scientist that I am today. I will always be grateful for her mentorship and guidance. I would like to also thank all of the members of the Gupton lab, past and present, who have been a source of sanity and ideas. I would like to thank the members of my thesis committee, Patrick

Brennwald, Doug Cyr, Shawn Gomez, and Keith Burridge for their input and invaluable advice given to me over the years. I especially want to thank Patrick Brennwald for his thought and ideas and encouragement and all he’s done for not just me, but graduate students at large.

TABLE OF CONTENTS

LIST OF FIGURES ...... xii

LIST OF ABBREVIATIONS ...... xiv

CHAPTER 1: SNARE-MEDIATED EXOCYTOSIS IN NEURONAL DEVELOPMENT1 ...... 1

1 The Remarkable Mammalian Neuron ...... 1

1.1 Development of Neurons ...... 1

2 SNAREs and Exocytosis ...... 5

2.1 Discovery of the Fusion Machinery ...... 5

2.2 SNAREs in Complex ...... 5

3 SNARE Roles in Different Stages of Development ...... 8

3.1 SNARE Expression Changes Throughout Development ...... 8

3.2 The Mature Neuron ...... 12

3.3 Sites of Membrane Growth ...... 14

3.4 Evidence for the Growth Cone ...... 15

3.5 Evidence for the Soma ...... 15

3.4 A Point of Tension ...... 18

4 Modes of Fusion ...... 19

4.1 Beyond Two Modes of Fusion ...... 23

5 The Fusion Pore: Kinetics, Dynamics, and Models of Formation ...... 25

5.1 The Fusion Pore: Expansion and Contraction ...... 25

5.2 Regulation of the Fusion Pore ...... 26

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CONCLUSION ...... 30

CHAPTER 1 FIGURES ...... 32

REFERENCES ...... 37

CHAPTER 2: SPATIOTEMPORAL ORGANIZATION OF EXOCYTOSIS EMERGES DURING NEURONAL SHAPE CHANGE ...... 48

1 Introduction...... 48

2 Results ...... 50

2.1 Automated identification and analysis of exocytosis ...... 50

2.2 Confirmation of exocytic detection ...... 51

2.3 Confirmation of detection of VAMP7-mediated exocytosis...... 53

2.4 Spatiotemporal changes in exocytosis occur during development ...... 54

2.5 A mathematical approximation of membrane expansion ...... 56

2.6 Netrin-1 modulates the spatiotemporal distribution of exocytic events in neurites ...... 58

2.7 TRIM9 is required for netrin-dependent exocytic changes in neurites ...... 59

2.8 Different domains of TRIM9 modulate specific parameters of exocytosis in neurites ...... 60

2.9 Exocytosis in melanoma cells is distinctly organized ...... 61

3 Discussion ...... 62

3.1 Dynamic spatial regulation of exocytosis in developing neurons ...... 63

3.2 Calculating membrane growth in developing neurons ...... 64

3.3 Exocytosis in the soma and neurites are distinctly regulated ...... 64

3.4 Polarized exocytosis in non-neuronal migrating cells ...... 65

4 Materials and Methods ...... 66

4.1 Animal Work ...... 66

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4.2 Media, culture, and transfection of primary neurons and immortalized cell lines ...... 66

4.3 Plasmids and antibodies...... 67

4.4 Imaging and image analysis ...... 67

4.5 Image Analysis ...... 69

4.6 Clathrin-mediated endocytosis detection ...... 70

4.7 NH4Cl and TeNT treatments ...... 70

4.8 Simulation of exocytosis ...... 70

4.9 Surface Area Estimation ...... 71

4.10 Statistical analysis ...... 71

4.11 Bayesian Linear Model ...... 72

4.12 Ripley’s K test of complete spatial randomness or complete temporal randomness ...... 72

4.13 Ubiquitination assays ...... 75

CHAPTER 2 FIGURES ...... 76

REFERENCES ...... 97

CHAPTER 3: TRIM67 REGULATES EXOCYTIC MODE AND NEURONAL MORPHOGENESIS VIA SNAP47 ...... 102

1 Introduction...... 102

2 Results ...... 104

2.1 Heterogeneity in single vesicle fusion event kinetics ...... 104

2.2 Multiple unbiased classifiers converge on four exocytic modes ...... 105

2.3 Distinguishing features of four modes of exocytosis ...... 107

2.4 Expression of a truncated VAMP2 alters exocytic mode ...... 109

2.5 TRIM67 biases exocytic mode towards full-vesicle fusion ...... 110

2.6 The t-SNARE SNAP47 interacts with TRIM67 and localizes to VAMP2-mediated exocytic events ...... 112

2.7 Multiple domains of TRIM67 modulate the interaction with SNAP47 and exocytic mode...... 113

2.8 TRIM67 alters exocytosis via modulated SNAP47 protein levels ...... 114

2.9 SNAP47 forms more SNARE complexes in Trim67-/- neurons...... 115

3 Discussion ...... 116

3.1 Distinguishing exocytic modes with novel classifiers and protein perturbation ...... 116

3.2 Exocytic mode and morphogenesis ...... 118

3.3 Regulation of Exocytic Mode ...... 120

4 Materials and Methods ...... 121

4.1 Plasmids and antibodies...... 121

4.2 Animals...... 122

4.3 Culture, transfection, and treatment of primary neurons and HEK293 cell lines ...... 123

4.4 Imaging...... 124

4.5 Image analysis ...... 125

4.6 Classification of exocytosis ...... 127

4.7 Surface area measurements and modeling predictions ...... 132

4.8 Western blot ...... 134

4.9 Mass Spectrometry ...... 138

4.10 Statistics and data presentation ...... 139

CHAPTER 3 FIGURES ...... 141

REFERENCES ...... 164

CHAPTER 4: REMAINING QUESTIONS AND CONCLUSION ...... 169

1 Where does membrane insertion occur during neuron development? ...... 169

2 Do four modes of exocytosis persist in later stages of development? ...... 170

3 What role do other SNAREs play in neuron development? ...... 171

4 Do the four modes of exocytosis correlated with cargo or other regulatory proteins? ...... 172

5 Do TRIM67 and TRIM9 interact to regulate exocytosis during development? ...... 172

REFERENCES ...... 173

APPENDIX 1: AUTOMATED DETECTION AND ANALYSIS OF EXOCYTOSIS ...... 174

REFERENCES ...... 173

LIST OF FIGURES

Figure1.1 Soluble N-ethylmaleimide-sensitive factor attachment proteins receptors (SNARE) expression and function during different stages of neuron development...... 32

Figure 1.2 (A) Selected list of the classified as Qabc-SNAREs and R-SNAREs and neuronally enriched SNARE expression in vivo in the murine brain...... 33

Figure 1.3 Modes and Methods of exocytosis...... 36

Figure 2.1 Automated identification and analysis of VAMP-pHluorin mediated exocytosis ...... 77

Figure 2.2 Spatiotemporal changes in exocytosis during neuronal development...... 79

Figure 2.3 A mathematical estimation of membrane expansion ...... 81

Figure 2.4 Netrin and TRIM9-dependent changes in the distribution of exocytosis ...... 83

Figure 2.5. Different domains of TRIM9 modulate specific parameters of exocytosis in neurites...... 84

Figure 2.6. Distinct spatiotemporal organization of fusion of different vesicles pools in melanoma cells...... 87

Figure 2.S1 Widefield and TIRF imaging of VAMP2-pHluorin and VAMP7-pHluorin ...... 88

Figure 2.S2 Kalman filter algorithm and parameter selection...... 90

Figure 2.S3 Confirmation of VAMP7-pHluorin exocytosis ...... 92

Figure 2.S4 Estimation of membrane surface area of neurons...... 94

Figure 2.S5 Specific domains of TRIM9 modulate distinct parameters of exocytosis in neurites...... 96

Figure 3.1 Heterogeneity in single vesicle exocytic fusion events ...... 141

Figure 3.2 Multiple unbiased classifiers converge on four exocytic modes...... 144

Figure 3.3 Distinguishing features of four modes of exocytosis...... 146

Figure 3.4 TRIM67 biases exocytic mode towards full-vesicle fusion...... 148

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Figure 3.5 The t-SNARE SNAP47 interacts with TRIM67 and localizes to VAMP2-mediated exocytic events...... 151

Figure 3.6 TRIM67 alters exocytosis by modulating SNAP47 protein levels...... 152

Figure 3.7 TRIM67 reduces SNAP47 incorporation into SNARE complexes...... 155

Figure 3.S1 Distinguishing features of four modes of exocytosis...... 156

Figure 3.S2 Four exocytic modes are also detected with TfR-pHuji and VAMP7∆LD-pHluorin...... 159

Figure 3.S3 Expression of truncated VAMP2 alters exocytic mode...... 160

Figure 3.S4 Multiple domains of TRIM67 and ligase function are required for exocytic mode regulation...... 161

Figure 3.S5 Proteasomal degradation and ubiquitination of SNAP47 are TRIM67 independent...... 163

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LIST OF ABBREVIATIONS

AMPA α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid

BioID biotin Identification

BoNT/A botulinum neurotoxin A

Bort bortezomib

BSA Bovine Serum Albumen

CCP clathrin coated pit

Cdc42 cell division control protein 42 homolog

ChQ chloroquine

ChX cycloheximide

CSR complete spatial randomness

CTR complete temporal randomness

DCC deleted in colorectal cancer

DIC Differential Contrast Interference

DIV Day in vitro

DTT dithiothreitol

DTW dynamic time warping

E13.5- embyronic day 15.5

EM electron microscopy

FVF full-vesicle fusion

FVFd full-vesicle fusion delayed

FVFi full-vesicle fusion instantaneous

GFP Green Fluorescent Protein

GTP nucleotide guanosine triphosphate

HBSS Hank’s Balanced Salt Solution

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HPLC high-performance liquid chromatography

HSV Herpes Simplex Virus

IGF-1 insulin-like growth factor 1

IgG Immunoglobulin G

IP Immunoprecipitation

KD knockdown

KNR kiss-and-run

KNRd kiss-and-run delayed

KNRi kiss-and-run instantaneous

LFQ label-free quantification mRNA messenger Ribonucleic Acid

MSD Mean Squared Displacement

NEM N-ethylmaleimide

NSF N-ethylmaleimide-sensitive factor

PBS Phosphate Buffered Saline

PCA Principal Component Analysis

PDL Poly-D-Lysine

PFA Paraformaldehyde

PIP3 phosphatidylinositol (3,4,5)-triphosphate

PM plasma membrane

RFP red fluorescent protein

RING Really Interesting New Gene

RPKM reads per kilobase of exon model per million mapped reads

ROI region of interest

SDS Sodium Dodecyl Sulfate

SDS-PAGE Sodium Dodecly Sulfate polyacrylamide gel electrophoresis

SEM standard error of the mean

SNAPs soluble NSF-attachment proteins

SNAP-25,47 Synaptosomal-Associated Protein

SNAREs Soluble-NSF-attachment protein receptors

STED stimulated emission depleted

TeNT tetanus neurotoxin

TfR Transferrin receptor

TIRF Total Internal Reflection Fluorescence

TI-VAMP TeNT-insensitive VAMP

TRIM TRIpartite Motif t-SNAREs plasma membrane target SNAREs:

UPLC ultraperformance liquid chromatography

VAMP Vesicle Associated Membrane Protein v-SNARE vesicle SNARE

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CHAPTER 1: SNARE-MEDIATED EXOCYTOSIS IN NEURONAL DEVELOPMENT1

1 The Remarkable Mammalian Neuron1

1.1 Development of Neurons

The human brain is one of the most complex structures in nature, containing an astounding 811 neurons and 811 non-neuronal cells and 1213–1214 synaptic connections in a roughly 1,400 cm3 volume (Drachman, 2005; Azevedo et al., 2009; Micheva et al., 2010). The establishment of precise and complex neural networks in the human brain is a major developmental task; this continues to be an area of rich scientific investigation. Neurons are specialized cells, with both a uniquely polarized structure and polarized function. The extremely polarized morphology of neurons is typified by a single axon and multiple dendrites. During development, the axon extends outward from the neuronal cell body, also known as the soma, toward future synaptic partners. Eventually, synapses form between the axon and these synaptic partners. Dendrites, which receive synaptic signals, are similarly often highly ramified structures that receive input from multiple axons. Such axonal-dendritic synaptic connections constitute the neural networks that orchestrate complex behaviors, such as those of the mammalian brain. Many outstanding questions regarding neuronal development remain incompletely answered, including how do neurons sense their extracellular environment and use this information to expand in both volume and surface area, and consequently create stereotypical network connections. In this review, we explore the role that proteins of the soluble

N-ethylmaleimide-sensitive factor attachment proteins receptor (SNARE) family and SNARE-

1 Urbina, F.L., and Gupton, S.L. (2020). SNARE-Mediated Exocytosis in Neuronal Development. Front. Mol. Neurosci. 13, 133.

mediated exocytosis play in plasma membrane expansion and synaptic function in different stages of neuron development, outgrowth, and function. We highlight recent improvements in technology that have allowed a closer examination of SNARE complex composition, regulation of the fusion pore, and different modes of exocytosis, reinvigorating old questions and proposing new ones in neuronal biology.

Neuronal development is characterized by a progression of morphological events. After birth from neural progenitor cells, immature neurons migrate to specified locations before neuritogenesis (Cooper, 2013). For example, glutamatergic neurons born in the ventricle wall form a bipolar morphology and migrate to specified layers of the cortex, dependent upon their birthdate, with early-born neurons forming deeper layers of the cortex and later-born neurons comprising more superficial cortical layers. Upon reaching their destination, neurons progress through several stereotypical morphological stages during development, which is recapitulated with in vitro neuronal culture (Dotti et al., 1988; Bradke and Dotti, 1997). This progression begins when multiple immature neurites extend from the soma, in a process termed neuritogenesis (Figure 1.1, Stage 1). Subsequently, one neurite is specified as the axon through several molecular events, including the accumulation of axonal components, whereas the remaining neurites accumulate dendritic components (Wisco et al., 2003; Kaibuchi, 2009). Axon specification leads to differences in polarized intracellular transport of materials into these two regions of the neuron (Figure 1.1, Stage 2; Sampo et al., 2003; Wisco et al., 2003).

Microtubules are polarized anterogradely in the axon, with growing plus ends unidirectionally facing away from the cell body. In contrast, dendrites exhibit bipolar microtubule orientation, with approximately equal numbers of microtubule plus-ends facing anterograde and retrograde

(Baas et al., 1988; Stepanova et al., 2003). How this distinct microtubule polarity intrinsically alters the delivery and sites of fusion for exocytic cargo remains an outstanding question.

Following axon specification, the axon matures via several additional stages of growth.

The growing axon extends toward targets of innervation (Figure 1.1, Stage 3). This directional growth is led by the growth cone, a sensory and guidance machine, which navigates in response to extracellular guidance cues, trophic factors, and extracellular matrix components

(Gomez and Zheng, 2006; Tojima et al., 2011; Tojima, 2012; Dudanova and Klein, 2013;

Sutherland et al., 2014; Akiyama and Kamiguchi, 2015). Axon branching, another stage in neuronal morphogenesis, increases axonal territory and size, allowing the axon to ultimately form synapses with multiple partners (Figure 1.1, Stage 4, and 5; Bilimoria and Bonni, 2013).

These connections are subsequently strengthened, weakened, and pruned over the lifespan of an organism, through processes of synaptic plasticity. The proper connectivity, maintenance, and plasticity of synapses are critical for appropriate physiological and behavioral actions, disruption of which can lead to disease (Lüscher and Isaac, 2009; Schulz and Hausmann,

2016).

The developmental extension and branching of axon and dendrites involve a significant expansion of neuronal surface area (Pfenninger and Friedman, 1993). Indeed, the expansive plasma membrane surface area of a neuron is considerably larger for example than a simpler- shaped cell. For example, a mammalian neuron has an average surface area of 250,000 µm2 up to millions of square microns, whereas a fibroblast has an average surface area orders of magnitude smaller, at approximately 3,600 µm2 (Steinman et al., 1983; Pfenninger and

Friedman, 1993). This large neuronal surface area highlights the critical importance of the insertion of membrane material during neuronal development. The primary driver of neuronal surface area expansion, at least early in neuronal development, is thought to be exocytosis, a fundamental cellular mechanism in which a vesicle fuses with the plasma membrane, forming one contiguous surface.

Two distinct vesicle populations exist in mature neurons: small synaptic vesicles, which generally contain low–molecular weight neurotransmitters, and dense-core vesicles, in which neuropeptides and neurotrophins are packaged. Synaptic vesicles are smaller, typically averaging 40 nm in diameter, whereas dense-core vesicles average 100 nm in diameter.

Therefore these vesicles contribute different amounts of surface area material upon fusion (Qu et al., 2009; Merighi, 2018). Additionally, the regulation of their fusion is likely distinct. Dense- core vesicle fusion requires repetitive and prolonged stimulation compared to synaptic vesicle fusion (Lundberg et al., 1986; Hartmann et al., 2001; Balkowiec and Katz, 2002; Frischknecht et al., 2008). The distinct sensitivity of these vesicle populations suggests different fusion machinery or protein regulation between the two. Even though these different vesicle types are known to exist in mature neurons, their relative contributions to membrane expansion in developing neurons are not known. Further, both vesicle types are below the diffraction limit of light, and thus there are unknown differences in fusion parameters between the two. As such, this review frequently does not differentiate between vesicle types.

Exocytosis is mediated by Soluble-NSF-attachment protein receptors (SNAREs) proteins, which form a stable complex thought to provide the energy needed for the fusion of opposing lipid bilayers (Südhof and Rothman, 2009; Jahn and Fasshauer, 2012). Additional mechanisms that add plasma membrane exist, such as lipid transfer at endoplasmic reticulum- plasma membrane contact sites (Yu et al., 2016; Nath et al., 2019). Exogenous membrane addition can also occur later in development via lipoprotein particles donated by glial cells

(Frühbeis et al., 2013; Lewis, 2013). The relative contribution of direct lipid transfer and glial donation of plasma membrane material remains relatively unexplored in literature; exocytosis is thought to provide sufficient membrane for early stages of neuronal development (Urbina et al.,

2018, 2020). SNARE-mediated membrane growth plays an essential role in neuronal

development, mediating neurite outgrowth, neuron polarization, growth cone guidance, synapse formation, and ultimately functional synaptic transmission.

2 SNAREs and Exocytosis

2.1 Discovery of the Fusion Machinery

The search for the proteins involved in transport between membrane-bound compartments in eukaryotic cells identified several components of the fusion machinery. The

Rothman lab discovered and named N-ethylmaleimide-sensitive factor (NSF), a cytosolic

ATPase required for fusion reactions between membranes, from mammalian cells (Block et al.,

1988). Using NSF as bait, they identified soluble NSF-attachment proteins (SNAPs), via their formation of NSF-SNAP complexes (Malhotra, 1988; Clary et al., 1990; Rothman and Orci,

1992). Mutations in genes encoding homologs in yeast for NSF and SNAPs cause secretory defects (Crary and Haldar, 1992; Novick and Schekman, 1979; Shah and Klausner, 1993). A search for SNAP REceptors (SNAREs) using bovine brain identified synaptobrevin-2 (VAMP-2),

SNAP-25 (coincidentally named SNAP, unrelated to the SNAPs of NSF), and syntaxin-

1(Söllner et al., 1993) and revealed that these three SNAREs formed a complex. VAMP-2,

SNAP-25, and syntaxin-1 were sufficient to fuse membranes in liposomal and cell-cell fusion assays, and thus the minimal machinery necessary for fusion, conserved in eukaryotes, had been discovered (Weber et al., 1998; Hu et al., 2003).

2.2 SNAREs in Complex

The crystal structure of the SNARE complex and the identification of a 60–70 amino acid sequence conserved in all SNARE proteins, the SNARE motif, suggested a molecular mechanism for fusion (Sutton et al., 1998). SNAREs attached to separate membranes zipper

into a tight complex, bringing opposing membranes into contact and providing sufficient energy to open a fusion pore (Gao et al., 2012). Four SNARE motifs (two from SNAP-25, one from syntaxin-1, and one from VAMP-2) are oriented into a parallel four α-helical bundle composed of leucine, isoleucine, and valine residues. The SNARE-complex bundle is thus formed of stacked leucine zipper-like layers. Within the leucine-zipper layers, there is a highly conserved and completely buried ‘‘0’’ layer composed of ionic interactions between an arginine (R) from

VAMP-2, glutamine (Q) from syntaxin-1, and two Q from SNAP-25. The flanking leucine-zipper layers act as a water-tight seal to shield the ionic interactions of the R and three Q from the surrounding solvent. This seal further stabilizes the four-helical oligomeric structure by enhancing electrostatic interaction within the ionic layer. Interruption of the 0 ionic layers disrupts SNARE complex formation. This is a stable interaction, and the zippering and SNARE interaction is thought to provide sufficient force to overcome the energy barrier necessary to fuse membranes (Liu et al., 2006; Grafmüller et al., 2009; Gao et al., 2012; Min et al., 2013).

The characterization of the SNARE motif permitted the discovery of homologous SNARE proteins belonging to the VAMP, syntaxin, or SNAP-25 families and their subsequent localization and expression (Weimbs et al., 1997; Steegmaier et al., 1998; Holt et al., 2006;

Arora et al., 2017). Over 40 mammalian SNARE proteins have been annotated to date, and the search for additional SNARE proteins is ongoing (Kloepper et al., 2007; Le and Huynh, 2019; Le and Nguyen, 2019). All SNARE complexes contain the single SNARE motif that contributes an

R and three SNARE motifs that each contribute a Q to the ionic 0 layers. SNARE proteins were thus categorized as R-SNAREs and Q-SNAREs (Fasshauer et al., 1998). The R-SNARE family consists of several VAMP-2 family members, which mediate the fusion of different compartments (Figure 1.2 Ai). Exocytic R-SNAREs expressed in the brain include VAMP-1,

VAMP-2, VAMP-3, VAMP-4, and VAMP-7. Whereas VAMP-2 is brain-enriched, VAMP-4 and

VAMP-7 are broadly expressed and found in multiple tissues (Advani et al., 1998; Wong et al.,

1998; Steegmaier et al., 1999; Adolfsen et al., 2004). The Q-SNARE family is divided into three subfamilies, Qa, Qb, and Qc SNAREs, based on the amino acid sequence homology of the

SNARE domain (Bock et al., 2001; Kloepper et al., 2007). The syntaxins primarily belong to the

Qa subfamily (Figure 1.2Aii). Syntaxin-1, syntaxin-2, syntaxin-3, and syntaxin-4 are known to mediate exocytosis. Syntaxin-1, syntaxin-4, and syntaxin-12 are brain-enriched, whereas syntaxin-6 and syntaxin-7 are found broadly in most mammalian tissues (Bock et al., 1997;

Advani et al., 1998; Hirling et al., 2000; Mullock et al., 2000; Jung et al., 2012). Finally, the

SNAP-25 family members are classified as Qb and Qc SNAREs (Figure 1.2Aiii). The N- terminal SNARE motif (Qb-SNARE) of the SNAP-25 family members are more homologous to each other than the C-terminal SNARE motif (Qc SNARE) of the same protein. The same is also true for C-terminal Qc SNARE motifs. SNAP-23, SNAP-25, and SNAP-47 mediate exocytosis. SNAP-25, SNAP-29, and SNAP-47 are brain-enriched, whereas SNAP-23 is ubiquitously expressed throughout tissue types, including the brain (Arora et al., 2017). Both

SNAP-29 and SNAP-47 are widely distributed in the cell, including on synaptic vesicles as well as other intracellular membranes (Holt et al., 2006).

The distinct tissue expression and cellular localization of a large number of SNARE proteins suggested SNARE interactions may impart selectivity to fusion between distinct cellular compartments. Richard Scheller and collaborators however discovered that recombinant or reconstituted R-SNAREs and Q-SNAREs were surprisingly promiscuous, and any combination of Qa, Qb, Qc, and R SNARE formed SNARE complexes in vitro (Prekeris et al., 1999). R-

SNARE and Q-SNARE combination experiments with VAMP-2, VAMP-4, VAMP-7, VAMP-8, rSec22b; syntaxin-1A, syntaxin-4, syntaxin-12; and SNAP-23, SNAP-25, or SNAP-29 revealed that all 21 combinations of an R-SNAREs and either of the syntaxins and SNAP-25 family members led to thermally-stable complexes. Despite the promiscuity of SNAREs in complex formation in vitro, not all SNARE complexes are fusogenic (Parlati et al., 2000, 2002),

suggesting additional mechanisms can regulate fusion after SNARE complex formation. If

SNAREs provided specificity to which compartments fuse, yet were not selective in vitro, this suggests that additional mechanisms must regulate their interactions, such as regulatory proteins or even subcellular localization, and thus likelihood to interact. Although the majority of

SNAREs interact in vitro, the total number of relevant combinations of SNAREs formed in vivo has not been studied in- depth, and whether these combinations of SNAREs change over time is unknown.

3 SNARE Roles in Different Stages of Development

3.1 SNARE Expression Changes Throughout Development

The stereotypical progression of neuronal morphogenesis involves dramatic plasma membrane expansion. Experiments exploiting pharmacological disruption of vesicle trafficking from the Golgi halted the growth of neurons (Craig et al., 1995). Electron microscopy-based studies demonstrated that developing axons and growth cones contained membrane-bound vesicles in the growth cone and cell soma (Igarashi et al., 1997). Subcellular fractionation and subsequent immunoblot analyses demonstrated that these vesicles contained SNAREs

(Igarashi et al., 1997). Together, these classic findings suggested that these vesicles undergo

SNARE-mediated fusion with the plasma membrane throughout the developing neuron, and this was required for neuronal growth. These and other studies suggested that SNARE-mediated exocytosis delivered the bulk of membrane material to increase the surface area of a growing neuron (Craig et al., 1995; Pfenninger and Friedman, 1993; Horton et al., 2005). Years of research revealed this exocytosis- driven expansion is concomitant with temporal coordination and switches in SNARE protein expression, localization, and function. Interruption of exocytosis

or SNARE expression leads to incomplete development or non-viability (Schoch et al., 2001;

Washbourne et al., 2002; Winkle et al., 2014; Arora et al., 2017).

To characterize the developmental expression and relative enrichment of different

SNARE proteins, we explored two RNA sequencing databases covering time points throughout murine brain development (Cardoso-Moreira et al., 2019; La Manno et al., 2016; Figure 1.2B).

VAMP-2 was the highest expressed R-SNARE, increasing in expression late into development, whereas SNAP-25 and SNAP-47 were the most enriched Qbc- SNAREs, and syntaxin-1 and syntaxin-12 the highest expressed Qa-SNAREs. After birth, SNAP-25 and VAMP-2 expression increased two-fold over other SNAREs, suggesting these SNAREs had a significant role in mature neuronal function (Figure 1.2B, P0-P63). At E18.5 in the single-cell sequencing database, the majority of genes in the midbrain have lower expression compared to E15.5, differing from the trend in whole-brain sequencing. This observation remains to be explored.

The heterogeneity in developmental time points in which different SNARE expression peaked suggests that each SNARE may play a distinct role in neuronal development.

Several SNARE proteins are implicated in developmental plasma membrane expansion, with different SNARE complexes involved depending on the extracellular environment, morphogens, and state of morphogenesis. For example, neuritogenesis in vitro is context- specific. Poly-D-Lysine (PDL) is an inert, synthetic adhesive polymer that promotes cell adhesion to tissue culture-treated plastic and glass surfaces without activating known adhesion receptors. In cortical neurons grown on PDL-coated surfaces, the outgrowth of neurites was tetanus neurotoxin (TeNT) sensitive at early stages. TeNT cleaves VAMP-1 and VAMP-2 and prevents their assembly into SNARE complexes. The TeNT sensitivity of neuritogenesis, therefore, suggested neurite outgrowth required VAMP-2 (and possibly VAMP-1)-mediated exocytosis. This is consistent with the high expression of VAMP-2 relative to other R-SNAREs at this developmental time point (Figure 1.2B). Laminins are extracellular matrix ligands that

bind specific integrin heterodimers, syndecan, and α-dystroglycan, and are the major component of the basal lamina and are found at specific areas in the brain. When neurons were grown on laminin, neurite outgrowth was no longer TeNT-sensitive and was instead mediated by TeNT-insensitive VAMP(TI-VAMP), also known as VAMP-7 (Osen-Sand et al., 1996; Sosa et al., 2006; Gupton and Gertler, 2010). Laminin engagement of integrin receptors was suggested to activate distinct signaling pathways and SNARE proteins, such as VAMP7(Gupton and

Gertler, 2010). During neuritogenesis of cortical neurons on PDL, we recently found that VAMP-

2-mediated exocytic events localized in a spatially random fashion and occurred at a higher frequency than at later points of neuronal morphogenesis, after a presumed axon had formed

(Urbina et al., 2018). The rapid and random occurrence presumably inserted maximal membrane material before the axon was specified. The context-dependent usage of VAMP-2 or

VAMP-7 suggested that neurons utilized specific VAMPs based on the extracellular environment. Whether other SNARE proteins function in a context-dependent fashion has not been explored. For example, if a neuron encounters a gradient of laminin or other morphogens, does R or Q-SNARE utilization change based on a threshold?

After neuritogenesis, a subsequent outgrowth of neurites from the polarized hippocampal neuron is mediated by VAMP- 7, but not VAMP-2 (Martinez-Arca et al., 2001;

Osen-Sand et al., 1996). SNAP-25, syntaxin-1, and syntaxin-12 were also reported necessary for axon outgrowth (Igarashi et al., 1996; Hirling et al., 2000; Zhou et al., 2000;

Kabayama et al., 2008). In vivo, VAMP-7 and syntaxin-12 expression peaked early-to-mid brain development ( E13.5-E18.5), whereas syntaxin-1 and SNAP-25 expression increased substantially during these time points (Figure 1.2B). Using acute treatment of botulinum neurotoxin A(BoNT/A) to cleave SNAP-25 resulted in the loss of neurite outgrowth, whether neurons were grown on laminin or PDL(Osen-Sand et al., 1996). Curiously, in contrast to cleavage with TeNT or BoNT, genetic deletion of either murine Vamp2 or Snap25 did not inhibit

neuritogenesis in vitro, and neurite outgrowth proceeded (Schoch et al., 2001; Washbourne et al., 2002). Snap25−/− cultured cortical neurons have reduced dendritic length, and soma area, but similar axonal lengths compared to wildtype neurons at day 4 in vitro (Arora et al., 2017).

This apparent disagreement suggested that the SNAREs compensated following the genetic deletion of either Vamp2 or Snap25, whereas this did not occur sufficiently following acute cleavage and disruption of SNARE protein function. For example, experimental evidence suggested VAMP-7 can substitute for VAMP-2, and overexpression of SNAP-23 or SNAP-29 rescued SNAP-25 deletion phenotypes in vitro (Winkle et al., 2014; Arora et al., 2017).

The signals that initiate axon specification and outgrowth are not fully understood; however, the localization of insulin-like growth factor 1 (IGF-1) receptor in the plasma membrane of hippocampal neurons to a single neurite, followed by phosphatidylinositol (3,4,5)- triphosphate (PIP3) accumulation at the growth cone of that neurite were found to be critical events (Sosa et al., 2006). The localization and insertion of the IGF-1 receptor were mediated by another set of SNAREs: VAMP-4, syntaxin-6, and SNAP-23 (Grassi et al., 2015). IGF-1 receptor insertion was suggested to promote a positive feedback loop, eventually specifying the single neurite as the axon. Following axon specification, the growth cone navigates toward targets of innervation by sensing extracellular cues. Several studies have indicated SNARE- mediated fusion was critical for axon guidance (Tojima et al., 2007; Zylbersztejn et al., 2012;

Tojima and Kamiguchi, 2015). For example, VAMP-2 was required for attractive responses to nerve growth factor (Tojima et al., 2007; Akiyama and Kamiguchi, 2010). TeNT treatment or genetic deletion of VAMP-2 nullified the turning response of a growth cone in a repulsive dose of sema3A. Similarly, SNAP-25 and syntaxin-1 were involved in the axon turning response to the guidance cue netrin-1. Netrin-1 induced coupling of its receptor,

Deleted in Colorectal Cancer (DCC) to syntaxin-1 (Cotrufo et al., 2012). Abolishing syntaxin-1 function through syntaxin-1- shRNA knockdown or BoNT/C1 treatment (which cleaves both

syntaxin-1 and SNAP-25), disrupted axon turning, whereas BoNT/A cleavage of SNAP-25, or

TeNT cleavage of VAMP- 2, did not disrupt turning. The lack of phenotype with acute SNAP-25 or VAMP-2 disruption suggests that syntaxin-1 may form SNARE complexes with additional

SNAREs. Indeed, netrin-1 treatment of murine cortical neurons increased the frequency of both

VAMP2 and VAMP7-mediated exocytosis (Winkle et al., 2014). Further experiments determined the spatial distribution of exocytosis on the growth cone was important in the turning response.

Most guidance cues instruct growth cone turning via asymmetric Ca2+ signals, with a higher

Ca2+ concentration on the side of the growth cone facing the source of the cues, regardless of whether the cues are attractive or repulsive (Gomez and Zheng, 2006). The repulsive cue sema3A suppressed VAMP-2 mediated exocytosis. Tojima et al. (2011) discovered that using local photo-uncaging of Ca2+ on one side of a growth cone increased local VAMP- 2-mediated exocytosis and subsequent growth cone turning. Asymmetrical VAMP-2-mediated exocytosis was suggested to turn the growth cone toward the side of the highest exocytic release.

3.2 The Mature Neuron

After the growth cone reaches its target destination, synaptogenesis begins. SNAP-25 is recruited to presynaptic sites, where it participates with VAMP-2 and syntaxin-1 in synaptic vesicle release (Südhof, 2004). SNARE proteins are best characterized for their role in synaptic transmission. Evoked synaptic response is non-existent in neurons cultured from mice lacking

VAMP-2 or SNAP-25 (Sørensen et al., 2003). Snap-25−/− cultured neurons eventually die before synaptogenesis (Arora et al., 2017). This may depend on survival factors, as lower densities of cultured Snap-25−/− hippocampal neurons die within 2–3 days in vitro, whereas higher densities died only after 7 days in vitro, after forming immature synapses (Washbourne et al., 2002; Arora et al., 2017). Of the neurons that survived to day 8 in vitro (2%), they have fewer synapses and dendrites compared to Snap-25+/+ neurons. Expression of SNAP-23 or SNAP-29 in SNAP-25

deficient neurons rescued dense-core vesicle release; however, expression of SNAP-47 did not rescue this defect, suggesting that although SNAREs can substitute for each other in

SNARE complexes, they were not always capable of restoring the functional role of another

SNARE family member (Washbourne et al., 2002; Arora et al., 2017). These results and their contrast to acute inhibition of SNAREs with BoNT or TeNT suggest that compensatory actions of other SNARE proteins occurred following genetic deletion, or that acute inhibition vs. genetic deletion triggered a difference in growth and survival cues. At the post synapse, SNAP-25, and likely VAMP-2 are involved in the exocytosis-dependent insertion of NMDA receptors (Lau et al., 2010). In addition to the well-studied SNAP-25, other Qbc-SNAREs are also important in synaptic function. SNAP-23 mediates NMDA receptor insertion in postsynaptic dendritic spines, a marker of synaptic plasticity, whereas overexpression of SNAP-29 inhibited synaptic vesicle fusion (Hunt and Castillo, 2012; Wesolowski et al., 2012; Cheng et al., 2013). SNAP-47 regulated the release of BDNF, which modulated long-term potentiation and spine regulation in hippocampal neurons (Shimojo et al., 2015).

Although SNAREs are necessary for evoked synaptic release and proper neuronal development in vitro, one striking result was the lack of discernible morphological phenotypes when critical SNARE genes were deleted globally during development in vivo. In both VAMP-2 and SNAP-25 deficient mice, fetuses were born with a rounded body shape distinct from wildtype, but no gross brain malformations. The mice lacked synaptic transmission, however, and died immediately following birth, presumably due to paralysis of the lungs (Washbourne et al., 2002; Arora et al., 2017). Histological examination of stained paraffin brain sections from

E17.5 and E18.5 Snap-25−/− mice showed no evidence of cellular defects (Washbourne et al.,

2002). Although there has not been a careful examination of neuronal morphology in these knockout mice, axon and dendrites were present. Based on the phenotype associated with acute inhibition of SNAP-25 with BoNT-A in vitro, this suggested compensation for fundamental

SNARE exocytosis occurs in this developmental time frame, whereas specialized roles for synaptic transmission cannot be similarly compensated for. Immunocytochemical stain for tyrosine hydroxylase, a marker for mature catecholaminergic neurons, demonstrated a normal pattern of immunoreactivity in the Snap-25−/− brainstem. Thalamocortical axon projections, traced using carbocyanine dye from E17.5-E19, were unaffected by the deletion of Snap-25

(Molnár et al., 2002). In Vamp-2 null mice, analysis of brain sections revealed no neurodegeneration or other gross changes to brain structure (Schoch et al., 2001). Although a careful examination was not performed, neurons lacking VAMP-2 had normal-appearing neurons with dendrites and axons, again suggesting that compensation via other SNARE proteins occurred during the developmental time window (Schoch et al., 2001). The context- dependence of SNAREs and their varying expression levels during development suggest that

SNAREs may perform multiple roles throughout development. One question in the field is whether SNARE-composition changes during different stages of development.

3.3 Sites of Membrane Growth

Where the material is added to the plasma membrane during neurite outgrowth continues to be an outstanding question. Early attempts to address this conundrum ended with mixed results; both the cell body and distal growth sites, such as the growth cone, were proposed as sites of membrane addition, and both locations have garnered support and contradictory evidence. Despite this ambiguity, membrane addition at the sites of growth has become the favored hypothesis for where surface area expansion is occurring (Pfenninger,

2009; Tojima and Kamiguchi, 2015).

3.4 Evidence for the Growth Cone

The debate surrounding where membrane insertion occurs in growing neurons was introduced by Dennis Bray. In classic experiments, Bray observed that glass particles resting on rat sympathetic neurons remained stationary relative to the soma as the neuron elongated

(Bray, 1970). He proposed that membrane insertion occurred primarily at the distal ends of growth. Following the fate of a radio-labeled phospholipid precursor, [3H]glycerol, in pulse- chase experiments in conjunction with electron microscopy-based visualization of silver grains marking incorporation sites of radioactive material demonstrated that pulsed lipids initially accumulated in ER and vesicles, and subsequently at distal ends of growing axons (Pfenninger and Friedman, 1993). An alternative approach used to suggest sites of membrane insertion involved following the fate of transmembrane proteins, which were presumably trafficked to and inserted at sites of membrane growth. The expression of an exogenously tagged transmembrane protein CD8a, followed by fixation and immunolabeling of CD8a, showed that

CD8a localized in the growth cone 8-h post-transfection, and a smaller amount localized in the cell body (Craig et al., 1995). If protein transport was blocked using Brefeldin A treatment, CD8a was not detected in the growth cone and rather accumulated in the soma. Whereas authors interpreted these results to suggest the addition of new membrane occurs at sites of growth, each of these early experiments suffers from being indirect methods of determining where a new surface area is added.

3.5 Evidence for the Soma

Another line of experiments, however, suggested that the soma was the primary site of plasma membrane expansion. Popov et al. (1993) directly labeled the lipid bilayer of Xenopus spinal neurons by application of fluorescent lipids. After local infusion of fluorescent lipids near

the soma, the authors detected anterograde transport of the exogenous fluorescent lipids using time-lapse microscopy. This anterograde transport persisted regardless of where along the axon the lipid dyes were infused, suggesting that membrane inserted at the soma and flowed toward the growth cone. Although Pfenninger and Friedman’s conclusions were at odds with Popov et al, without the capability of following where vesicle fusion with plasma membrane inserts membrane material, all these methods only provided snapshots that could lead to different interpretations depending on the context. SNARE localization and early tracking of fusion events added support for SNARE-mediated fusion as a driver of membrane insertion. VAMP7- containing vesicles translocated from the soma to become enriched in the growth cone, where they then fused (Burgo et al., 2012). Evidence indicated that VAMP2 was found throughout the cell and VAMP-2-mediated exocytosis also occurred at the growth cone periphery, including at filopodia (Sabo and McAllister, 2003; Tojima et al., 2007; Ros et al., 2015). Recent improvements in imaging, such as the use of pH-sensitive probes, provided new ways to visualize where membrane insertion occurs more directly. Using vSNAREs lumenally labeled with pHluorin, a pH-sensitive GFP allowed direct visualization of exocytic events. The fluorescence of pHluorin is quenched in the acidic environment of the vesicle lumen. However, upon opening a fusion pore between the vesicle and plasma membrane, protons rapidly escape the vesicle lumen, and the neutralized pH promotes rapid unquenching and fluorescence of pHluorin, as a readout of fusion pore opening (Miesenböck et al., 1998). In conjunction with

Total Internal Reflection Fluorescence (TIRF) microscopy, which specifically illuminates the basal plasma membrane of the neuron, and an unbiased, automated image analysis method, we recently reported that the majority of constitutive VAMP2-mediated exocytosis occurred in the soma in stage one and stage two neurons, with a much smaller proportion occurring in distal neurites and growth cones (Urbina et al., 2018). By the time neurons reached stage three,

VAMP-2- mediated exocytic events were evenly distributed between soma and neurites but were still rarely observed in growth cones. In contrast to VAMP-2, VAMP-7-pHluorin mediated

exocytic events were less frequent and sometimes undetected, suggesting that VAMP-2- mediated exocytosis primarily accounted for exocytic membrane addition during these early developmental stages. This result was consistent with cortical neuritogenesis being VAMP-2 but not VAMP-7 dependent (Gupton and Gertler, 2010). During this developmental stage, VAMP-2 mRNA expression increased (Figure 1.2B), also consistent with an important role for VAMP-2 during neuritogenesis. We detected an average of two VAMP2- pHluorin events per growth cone per minute, in agreement with other reports (Ros et al., 2015; Urbina et al., 2018). In contrast, we detected 8–10 the number of exocytic events in the soma in a similar period. In addition to quantifying rates and spatial location of individual exocytic events, we measured the size of non-coated vesicles (presumably exocytic) and clathrin-coated vesicles by EM, the size of neurons at multiple developmental time points, as well as the rate of endocytosis, to establish a model of cell growth. From these data and modeling, we concluded that the rate of exocytosis in growth cones was orders of magnitude less than was required for the growth of the neuron. In contrast, exocytosis in the soma, not the growth cone, provided more than sufficient membrane to account for membrane expansion concomitant with the growth of developing neurons.

Although this system used overexpression of VAMP-2, VAMP-2-pHluorin marked the vast majority of VAMP2 containing vesicles, suggesting it accurately represented the population of fusing events (Urbina et al., 2020). Our results called into question the traditional view that membrane was preferentially inserted at sites of neurite extension. Prior results demonstrating the accumulation of transmembrane proteins at the growth cone, which appeared to contradict our findings, may suggest that specialized vesicles are primarily trafficked to the growth cone for signaling and chemotrophic-induced asymmetric exocytosis required for turning, whereas the bulk of membrane material was inserted at the soma. Alternatively, the soma and growth cone may both be sites of growth and insertion, with membrane preferentially removed from the soma, but not the growth cone.

3.4 A Point of Tension

Membrane tension, defined as the force per unit length acting on a cross-section of the plasma membrane, regulates the frequency and the localization of exocytic events (Gauthier et al., 2011; Diz-Muñoz et al., 2013; Bretou et al., 2014; Wen et al., 2016). For example, mechanical tension can alter the organization of the submembranous actin network, which may regulate vesicle access and fusion (Wen et al., 2016). Mechanical forces on the growth cone, originating from adhesions to the extracellular environment, as well as cytoskeleton dynamics, induce changes in membrane tension, and influence growth rates. For example, the location of sites of growth was altered by mechanical forces encountered by the neuron (Zheng et al.,

1991). An experimentally-induced towing force transduced by pulling a glass needle attached to the growth cone increased axon elongation rate by 1.5 µm/h/µdyne, with some neurons exhibiting a 350 µm/h increase in growth rate (Zheng et al., 1991). Neurons were labeled with polyethyleneimine-treated microspheres, and the position of microspheres along the axon was recorded. Without towing, the microspheres remained stationary relative to the soma during neurite outgrowth. However, in pulled neurites, all the microspheres along the axon increased in distance from the soma as well as from each other, suggesting that membrane insertion occurred along the entire length of the neurite. This suggested that the spatial location of exocytic growth was context-dependent, and regulated by membrane tension. The ability of local changes in membrane tension to regulate membrane insertion distally remains unclear, as the capability for the plasma membrane to propagate tension is not fully understood. The fluid-mosaic model, which posits the plasma membrane as liquid-like, suggests that membrane flow transmits local changes in membrane tension across the cell in milliseconds, providing a long-range signaling pathway. The fluid-mosaic model suggests tension propagates across the entire surface of artificial membranes, based on the capacity for fluorescently-tagged proteins to freely diffuse in both artificial bilayers and intact cells (Mouritsen and Bloom, 1984; Sperotto et

al., 1989; Kusumi et al., 2005). In contrast, the Cohen lab proposed that the density of transmembrane proteins influences gel-like properties of the plasma membrane and that tension does not propagate across the plasma membrane, but only locally activates tension-related signaling (Shi et al., 2018). The gel-model of the plasma membrane has wide implications for neurons. If membrane addition is primarily added to the soma, the gel-model of the plasma membrane suggests the added material generates a pushing force to extend axons during growth. The potential pulling force of the growth cone or pushing force of the membrane at the soma could, in effect, change exocytic fusion dynamics. The potential influence of membrane tension or fluidity on the spatial location and rates of exocytosis suggest that a different cell type, a different substrate, or indeed the variety of stiffnesses of substrates found in the brain may orchestrate entirely different spatial organization to exocytosis. An outstanding question in the field is, then, whether in vitro studies accurately reproduce conditions to define where membrane addition is occurring in vivo, and whether different neuronal types add membrane at different spatial locations based on differences in tension in the extracellular environment.

4 Modes of Fusion

Our model of membrane growth concerning exocytosis and endocytosis in developing neurons suggested exocytosis added superfluous membrane material for neuronal morphogenesis. One caveat of the model however was that it assumed that all exocytic events inserted the entirety of the vesicular membrane into the plasma membrane (Urbina et al., 2018).

However, two modes of exocytosis are described prominently in literature, each of which has different consequences for plasma membrane expansion and cargo secretion. In this section, we review the history of findings regarding modes of fusion and their repercussions on neuronal growth. Visualization of the fusion pore by electron microscopy initiated a controversy regarding modes of fusion, as the initial results were interpreted in different ways. In one camp, Ceccarelli

et al. (1972) proposed that vesicle fusion involved the opening of a small fusion pore between the vesicle and plasma membrane. This was followed by rapid pore closure at the site of fusion, without full dilation and collapse. This model referred to primarily as kiss-and-run (KNR), originated from the electron microscopic observation of the uncoated omega-shaped membrane profile with a narrow neck connected to the plasma membrane at the active zone of the frog neuromuscular junction (Figure 1.3Ai). Meanwhile, other electron microscopy-based studies of the frog neuromuscular junction by Heuser and Reese (1973) demonstrated omega-shaped profiles with narrow or wide necks, as well as areas interpreted as a fully-dilated fusion pore

(Heuser and Reese, 1973; Heuser, 1989). They suggested a model in which fusion pore opening was followed by fusion pore dilation and the full collapse of the vesicle membrane into the plasma membrane. This was followed by membrane retrieval at sites distal to fusion

(Figure 1.3Ai). This mode of fusion has different names throughout literature, including full- collapse fusion and full fusion, but we shall refer to it as full-vesicle fusion (FVF). FFV events add material to the plasma membrane, expanding the neuronal surface area, whereas KNR events do not. FVF events allow the quantal release of all cargo, whereas KNR events allow partial cargo release. These fundamental distinctions suggest that KNR and FVF are poised to play diverse roles in development and at the synapse. During neuronal development, FVF can add plasma membrane material and insert transmembrane receptors. FVF can also add membrane to the synapse, expanding surface area. Therefore, to maintain a consistent synapse size, compensatory endocytosis must retrieve excess plasma membrane (Heuser and Reese,

1973). KNR does not provide for developmental plasma membrane expansion but may enable more rapid and economical vesicle recycling at the synapse. Second, the narrow fusion pore during KNR may limit the rate of transmitter discharge, resulting in a more tuneable synaptic response (Stevens and Williams, 2000). Switching between KNR and FVF provides a mechanism to regulate membrane addition and cargo release, as well as regulate synaptic transmission. The controversy of modes of fusion both at synapses and in non-neuronal cell

types continued for decades, in which evidence accumulated to support multiple fusion modalities (Stevens and Williams, 2000; Rizzoli and Jahn, 2007). The existence of multiple exocytic modes at the synapse remained controversial as multiple vesicles fuse near- simultaneously in a diffraction- limited area, and thus single-fusion event conclusions were often extrapolated from the aggregate of a population of fusing vesicles. Evidence using imaging and electrophysiological approaches to track exocytosis at high spatial and temporal resolution suggested that low frequency or low-release probability fusion events were primarily KNR

(Gandhi and Stevens, 2003; Harata et al., 2006). Using FM dyes, Aravanis et al. (2003) demonstrated that vesicles loaded with FM1–43 dye exhibited partial or complete dye loss during stimulation. Using the hydrophilic FM1–43 quencher bromophenol blue revealed vesicles retained part of the dye after fusion, consistent with KNR. Using VAMP2-pHluorin, Harata et al.

(2006) investigated the time course of exocytosis fusion and fluorescence decay. They found that there was a population of fast-fluorescing events that exhibited a rapid decrease in fluorescence after fusion pore opening distinct from that of average exocytic fusion event decay.

This rapid decrease in fluorescence was slowed through the addition of the pH buffer TRIS, which slowed vesicle re-acidification, suggesting these events were KNR (Harata et al., 2006).

Although the challenges of imaging individual fusion events at the synapse remain a hurdle, the evidence for KNR has continued to grow.

Electrophysiological evidence supporting distinct FVF and KNR fusion have also been obtained in neuroendocrine cells. For example, membrane capacitance, measured by electrophysiology techniques, is proportional to membrane area, and thus offers a readout of membrane expansion. As such, membrane capacitance increases when a vesicle fuses with the plasma membrane and increases the plasma membrane surface area. A sustained increase in capacitance following fusion indicates FVF; capacitance ‘‘flickers,’’ in which transient capacitance increases occurred, suggests KNR. Both types of events were detected in

neuroendocrine cells (Fernandez et al., 1984). Subsequent capacitance measurements suggested the fusion pore diameter remained under 3 nm for a variable period before expanding in the majority of events, presumably FVF, whereas a smaller proportion of events never fully dilated but instead closed, presumably performing KNR (Albillos et al., 1997).

Imaging data of single vesicle fusion events supported the existence of KNR and FVF in astrocytes and in neurons outside of synapses. Using VAMP2-pHluorin and TIRF microscopy,

Bowser and Khakh discerned the kinetic profiles of individual fusion events in astrocytes. They differentiated FVF events by the diffusion of VAMP2-pHluorin away from the site of fusion, whereas KNR events were differentiated by the disappearance of fluorescence without diffusion

(Bowser and Khakh, 2007). Exocytic events that did not exhibit VAMP2- pHluorin diffusion had half-lives that were sensitive to changes in extracellular pH and buffering capacity, suggesting that the decay of fluorescence was attributable to vesicle re-acidification. Our results corroborated VAMP2-pHluorin diffusion in FVF and reacidification in KNR in developing neurons before synaptogenesis (Urbina et al., 2020). By simultaneously using VAMP2-pHluorin and

VAMP2-tagRFP, we followed the fate of VAMP2-containing vesicles before, during, and after fusion.

This revealed distinct modes of fusion exhibiting diffusion of both fluorophores (FVF) or retaining a visible VAMP2-tagRFP containing vesicle after the loss of fluorescence of VAMP2- pHluorin fluorescence (KNR). With knowledge of these distinct fusion modes, we updated our model of plasma membrane expansion to reflect the proportion of exocytic events that would not add membrane (Urbina et al., 2020). Although our original model overestimated the amount of surface area added by exocytosis, our new model, which incorporated the mode of fusion, reflected measured surface area increases of neurons in vitro more accurately. Single vesicle fusion events also occur in mature neurons outside the synaptic transmission. Richards combined imaging and electrophysiology to reveal that vesicles containing AMPA receptors at

the postsynapse fused primarily through FVF, whereas vesicles containing NMDA receptors fused primarily using KNR (Richards, 2009). Therefore, fusion mode can be specific to distinct vesicle populations with different functional roles and cargo. With sufficient evidence that KNR and FVF are major modes of fusion, the relative contribution of each mode to neuronal development and synapse function remains an important question in cell biology.

4.1 Beyond Two Modes of Fusion

The heterogeneity of fusion pore behavior and the myriad regulatory proteins that govern fusion pore kinetics raised the question of whether exocytic events can be described by only two fusion modes. For example, additional modes of fusion have been argued to exist in developing neurons, neuroendocrine cells, and at dopaminergic synapses (Figures 1.3Aii,iii; Stevens and

Williams, 2000; Staal et al., 2004; Shin et al., 2018; Urbina et al., 2020). These modes include flickering fusion, shrink- fusion, and subclasses of FVF and KRN. In flickering fusion, the fusion pore is suggested to open and close transiently before the vesicle eventually retreats from the plasma membrane. This mode has been observed primarily in neuroendocrine cells, although evidence suggests it may also occur at synapses (Staal et al., 2004; Wightman and Haynes,

2004; Stratton et al., 2016; Bao et al., 2018). Utilizing pH-sensitive fluorescent proteins within the vesicle lumen, coupled with rapid switching of the extracellular pH via microfluidics allowed the detection of opening and closing of fusion pores. In this experiment, fluorescence coinciding with a neutral extracellular pH indicated the opening of a fusion pore. A vesicle with an open fusion pore flickered with the pH change, whereas a sealed vesicle did not change fluorescence. Using this system, a third fluorescence behavior was observed: irregular switching between flickering and stable fluorescence. This observation suggested a population of vesicles with fusion pores that repeatedly opened and closed, hence the name flickering fusion. In shrink-fusion, the omega- shape of vesicles shrinks due to the osmotic pressure of the cell

without fusion pore dilation, and enlarge-fusion, in which the omega-profile of the fused vesicle increased in size after the opening of the fusion pore (Shin et al., 2018, 2020). More recently, Wu et al imaged over 300 fusion events in neuroendocrine cells using stimulated emission depleted (STED) microscopy and remarkably found no events that fully dilated until vesicle collapse (Shin et al., 2018). The authors found instead that fusion pore size positively correlated with vesicle size, which ranged from 180 to 720 nm in diameter. Although fusion pore size was variable, full dilation was not observed, and rather fusion pore opening was followed by a shrinking omega-shaped profile. Of note, this variation on fusion mode adds material to the plasma membrane. Whether this omega shrink fusion happens in other cell types or at the synapse is an open question. Diverse fusion behavior was also observed in developing neurons before synaptogenesis. As described above, FVF and KNR can be differentiated based on

VAMP2-pHluorin diffusion, pH sensitivity, or the behavior of a pH-sensitive VAMP2- RFP.

Recently we identified distinguishable subclasses of FVF and KNR in developing cortical neurons using an unbiased classification of the fluorescence profiles of VAMP2-pHluorin fluorescence after fusion pore opening (Urbina et al., 2020). A subset of exocytic events exhibited an immediate (i) decay of VAMP2-pHluorin fluorescence after fusion pore opening.

Both FVF and KNR events were observed with this behavior and subsequently termed FVFi and

KNRi. FVFi and KNRi are analogous to ‘‘classical’’ FVF and KNR fusion as described in the literature. A previously unappreciated subset of events exhibited a plateau in VAMP2-pHluorin fluorescence after fusion pore opening, indicating a delay (d) in fluorescence decay after fusion pore opening, and thus a delay in the vesicle fusion process. Some of these delayed events eventually proceeded to FVF (FVFd), and some proceeded to KNR (KNRd). The existence of these delayed events suggested mechanisms were capturing an open fusion pore before the vesicle proceeded to a final vesicle fate of FVF or KNR. The distribution of these four modes of exocytosis between developing neurons was remarkably consistent. The heterogeneity of cell type-specific fusion behaviors may indicate that classic FVF and KNR are the predominant

modes of fusion only in a subset of cell types. The number of different modes, how fusion modes differ between cell types, and the mechanisms that govern modes of fusion are open questions in the field.

5 The Fusion Pore: Kinetics, Dynamics, and Models of Formation

5.1 The Fusion Pore: Expansion and Contraction

The mode of fusion is necessarily dictated by the fate of the fusion pore. Analyzing the fate of the fusion pore has remained controversial due to the enigmatic nature of fusion events, which are spatially small and temporally rapid. Reconstituted systems allow examination of how fusion proceeds using the minimal machinery required. in vitro experiments of liposomal fusion with varying densities of reconstituted SNARE proteins suggested a critical number of SNARE complexes were required to open and dilate the fusion pore and accomplish content mixing (Lu et al., 2005). Similarly, cell fusion assays using nanodiscs, synthetic lipoprotein particles that contain a small ( 17 nm diameter) circular lipid bilayer with differing numbers of SNAREs, suggested two SNARE complexes were required to open a fusion pore, but seven SNARE complexes were required to fully dilate the pore (Shi et al., 2012). This number of SNARE complexes required for fusion was consistent with predictions from the Karatekin and

O’Shaughnessy lab supporting their biophysical model of SNAREpin repulsion as an explanation for fusion pore expansion (McDargh et al., 2018).

With more complex reconstitution assays, the heterogeneity of fusion modes emerged.

Using nano-discs and HeLa cells, Wu et al. (2016) found two fates for the fusion pore after opening: fully dilated or closed. In this assay, a pipette was sealed onto a HeLa cell expressing flipped Q-SNAREs, with the active SNARE domains facing the extracellular space. Nanodiscs with R-SNAREs were then flooded into the pipette, and fusion events were probed by direct-

voltage electrophysiology. The majority of events measured in this assay were FVF. The prevalence of FVF over KNR is consistent with biophysical models, which suggested FVF be energetically favorable over KNR (Mostafavi et al., 2017; Risselada et al., 2014; Wu et al.,

2017). Bao et al. (2018) detected rapid opening and closing of fusion pores in a reconstituted fusion assay through electrophysiological spikes using nanodiscs. As the number of SNAREs per nanodisc increased, the probability of the fusion pore remaining open increased, and fusion pore dilation increased. The recapitulation of KNR-like, FVF-like, and flickering fusion suggested the number of SNAREs available for fusion regulated the mode of fusion in SNARE reconstitution assays.

5.2 Regulation of the Fusion Pore

Whereas these assays have reconstituted a variety of fusion pore behavior, such as dilation consistent with FVF, closure consistent with KNR, and rapid opening and closing consistent with flickering, other variations of fusion such as omega shrink have yet to be reconstituted. Not only is the outcome of the fusion pore distinct in different systems, the kinetics of fusion pore opening, dilation, and closing between a liposomal fusion assay event and a fusion event in a cell can be orders of magnitude apart. The difference in fusion pore behaviors in an artificial system compared to cells may be explained by the lack of regulatory proteins as well as differences in the cellular and extracellular environments that fusion takes place compared to in vitro.

Sufficient numbers of SNAREs, the minimal machinery for fusion in liposomes, promoted full dilation of the fusion pore, suggesting that additional regulatory mechanisms constrain the fusion pore in the cellular context (Wu et al., 2017; Bao et al., 2018). In nano-disc reconstitution assays, the probability of pore dilation increased with the number of SNARE complexes, suggesting that SNARE density and availability for fusion regulated exocytic fusion (Figure

1.3Bi; Bello et al., 2016; Wu et al., 2017). Using super-resolution microscopy, Yang et al. (2012) showed that SNAP-25 was clustered in microdomains on the plasma membrane of PC-12 cells and that R-SNARE containing vesicles were trafficked to locations with a lower density of

SNAP-25 than the surrounding area, suggesting spatial distancing as a method of regulating fusion. Evidence from Bowser and Khakh (2007) and Urbina et al. (2020), however, suggested the concentration of VAMP-2 was not different between vesicles that fuse by FVF or KNR in astrocytes and developing neurons. These assays relied on measuring the fluorescence of

VAMP2-pHluorin, however, and may not be a suitable proxy to capture differences in SNARE complex numbers. In addition to SNARE complex numbers, SNARE complex composition may play an important role in heterogeneity in fusion in cells. SNARE proteins can substitute for each other in complex and influence fusion kinetics. However, the role that SNARE complex composition plays in the regulation of exocytosis has not been explored until recently. Most artificial assays have utilized SNAP-25, VAMP2, and syntaxin-1 to reconstitute fusion and explore the dynamics of the fusion pore. In such assays, SNAP-47 competed with SNAP-25 to form stable SNARE complexes with VAMP2 and syntaxin-1(Holt et al., 2006). Although capable of fusing proteoliposomes, SNAP-47, VAMP2, syntaxin-1 complexes exhibited slower fusion than SNAP-25, VAMP2, syntaxin-1 complexes. Our recent work in developing neurons suggested SNAP-47 was involved in VAMP2-mediated exocytic fusion at the plasma membrane of developing neurons, and that SNAP-47 containing SNARE complexes may regulate fusion pore kinetics and bias the mode of exocytosis toward KNR events (Figure 1.3Bii; Urbina et al.,

2020). If heterogeneous SNARE complexes existed in the same location spatially, then a combinatorial effect on fusion behavior may emerge, where the kinetics and mode of the fusion are influenced not only by the number of SNARE complexes but also the composition of each of those SNARE complexes. This effect is unexplored in current literature, and further studies are needed to determine the composition of the SNARE complex surrounding fusion pores in cells, and whether heterogeneity in SNARE composition may regulate kinetics and mode of fusion.

An important consideration for comparisons between in vitro and artificial systems is that the lipid and protein composition of the plasma membrane, as well as tension propagation, is substantially different from simplistic liposomes, and the proteins that may regulate the opening and kinetics of the fusion pore are not included in most in vitro fusion assays. For example, Wu et al suggested that actin dynamics and osmotic pressure are responsible for the omega shape shrinking, whereas actin and calcium-dependent dynamics regulated the size of the fusion pore, both regulatory elements not found in most artificial systems reconstituting fusion (Shin et al.,

2020). Indeed, the proximity of the cortical actin network to the plasma membrane suggested that actin may also help organize the clustering of the fusion machinery by linking vesicles to the plasma membrane. For example, the Arp2/3 actin nucleator interaction with lipids and signaling proteins, such as PtdIns(4,5)P2 and Cdc42/N-WASP, may link vesicles to the plasma membrane (Figure 1.3Biii; Gasman et al., 2004). Cortical F-actin was found to control the localization and dynamics of SNAP-25 membrane clusters in neuroendocrine cells (Torregrosa-

Hetland et al., 2013). Quantitative imaging of SNAP-25 and the actin probe lifeact-GFP revealed colocalization at the plasma membrane, indicating the association of secretory machinery to the

F-actin cortex. In agreement with these data, Yuan et al. (2015) found evidence that the organization of fusion hotspots in insulin- secreting INS-1 cells relied on the cytoskeleton. Using

TIRF microscopy they found that individual fusion events were clustered and that this clustering disappeared upon inhibition of either the actin cytoskeleton using cytochalasin D or microtubule cytoskeleton with nocodazole. Defining how the cytoskeleton regulates fusion and the plasma membrane remains a complicated area of study, due to the pleiotropic effects of manipulating any single cytoskeletal element.

In addition to the actin cytoskeleton, several other proteins such as dynamin, alpha- synuclein, complexin, synaptotagmin, and TRIM67 regulate fusion pore kinetics and fusion mode (Figure 1.3Biv). Both dynamin and alpha-synuclein alter the kinetics of fusion pore dilation

and the mode of exocytosis. Dynamin I is enriched in neurons, while dynamin I and II are found in neuroendocrine cells, where they regulate the amount of exocytic cargo release (González-

Jamett et al., 2013; Jackson et al., 2015). Anantharam et al. (2011) found that when dynamin I

GTPase activity was reduced, a slower onset of pore dilation occurred along with increased FVF

(Anantharam et al., 2011; Jackson et al., 2015). Neuroendocrine cells transfected with a dynamin I mutant with reduced GTPase activity compared to wildtype dynamin I showed an increase in the length of the pre-spike foot of amperometry measurements of catecholamine, suggesting a slower onset of pore dilation. Treatment of neuroendocrine cells with the dynamin activator Ryngo, however, slowed the kinetics of FVF events and decreased the number of KNR events (Jackson et al., 2015). This suggested that dynamin I was a tuneable regulator of both kinetics and mode of exocytosis. Overexpression of alpha-synuclein, a protein that localizes to the presynapse, altered fusion pore kinetics, reducing the time until the full release of BDNF- pHluorin from vesicles (Logan et al., 2017). Unlike dynamin I overexpression, however, the faster kinetics of fusion pore dilation was not accompanied by decreased FVF, but an increased number of presumed FVF events. This suggested that modes of fusion may be regulated distinctly from fusion pore kinetics. Recently we described the brain enriched E3 ubiquitin ligase

TRIM67 as a regulator of fusion mode in developing cortical neurons (Urbina et al., 2020).

Deletion of Trim67 led to a decrease in FVFi and FVFd classified VAMP2-pHluorin events, and an increase in KNRi and KNRd events, with no change in the total frequency of exocytic events.

Further investigation suggested that TRIM67 acted to decrease levels of SNAP-47 and reduced the level of SNAP-47 in complex with other SNAREs. Deletion of Trim67, therefore, caused an increase in SNAP-47 levels, altered SNARE complex composition, and altered fusion behavior that suggested SNAP-47 containing SNARE complexes may have reduced ability to dilate the fusion pore after opening. Knocking down SNAP-47 with siRNA reduced KNRi and KNRd in

Trim67−/− neurons. These results suggested that altering SNARE complex composition as a way to regulate the mode of fusion. While dynamin and alpha-synuclein altered fusion pore

dilation rate and mode of exocytosis, other proteins, such as complexin II, are suggested to bind

SNARE complexes and stabilize the fusion pore (Archer et al., 2002). Overexpression of complexin II in neuroendocrine cells reduced the number of exocytic events and decreased amperometrically measured fusion rate, rise time, and fall time of fusion events, consistent with induced earlier fusion pore closure and kiss-and-run exocytosis. Unlike dynamin or alpha- synuclein, however, complexin II did not modify the rate of fusion pore expansion but instead limited the time over which cargo release occurs. Another mechanism for fusion pore regulation briefly touched upon here suggests that distinct populations of vesicles with different cargo exist, which may also have different fusion machinery. Synaptotagmins are a family of membrane trafficking proteins, several of which act as calcium sensors in the regulation of neurotransmitter release including synaptotagmin-1 and synaptotagmin-7 (Chapman, 2008).

Synaptotagmin-1 regulated fusion exhibited slower fusion kinetics and was less spatially- clustered than synaptotagmin-7 fusion, suggesting diversity in vesicle population fusion (Rao et al., 2017). One question that remains is whether different vesicle populations exist at different time points in development, and whether the mode of fusion changes based on the developmental need of the neuron.

CONCLUSION

Although SNARE-mediated fusion is an essential and well-known mechanism that drives the synaptic transmission and neuron development and growth, several questions remain as to the regulation of the fusion pore. A large and rich body of evidence has been gathered since

SNAREs were first discovered, suggesting a highly-regulatable and tuneable system of cargo release and membrane insertion. Emerging evidence concerning the existence of new modes of exocytosis, differences in SNARE activity at different developmental stages, and the

number of elements that SNARE-mediated fusion regulate continues to expand and drive the field towards the new question and ultimately new insights into SNARE-mediated exocytosis.

CHAPTER 1 FIGURES

Figure1.1Soluble N-ethylmaleimide-sensitive factor attachment proteins receptors

(SNARE) expression and function during different stages of neuron development.Stage 1,

R-SNARE usage is context-dependent, as cortical neurons grown on laminin require VAMP7 for neurite outgrowth, whereas neurons grown on PDL require VAMP2. Stage 2, a multitude of

SNAREs are required for neurite outgrowth and axon specification. Neurite outgrowth becomes

VAMP7-dependent, and soluble NSF-attachment proteins (SNAP-25) expression increases while SNAP-23 expression decreases. Stage 3, VAMP2, SNAP-25, and syntaxin-1 are required for axon attraction and repulsion in response to chemotrophic factors. Stage 4 and 5, VAMP2, syntaxin-1, and SNAP-25 are required at the pre-synapse for synaptic transmission. SNAP-25 is required at the post-synapse for SNARE-mediated NMDA receptor insertion.

Figure 1.2 (A) Selected list of the classified as Qabc-SNAREs and R-SNAREs and neuronally enriched SNARE expression in vivo in the murine brain. SNAREs are classified into: (Ai) R-SNAREs; (Aii) Qa-SNAREs; and (Aiii) Qbc-SNAREs. These SNAREs were further subset into brain-enriched or not brain-enriched. White bars in the illustration represent SNARE

domains. (B) Expression from two different databases of select SNARE mRNA expression, one from whole-brain and forebrain sequencing, and another database from single-cell sequencing of the mid-brain over multiple developmental time points. SNARE expression was normalized to all SNAREs (blue) or normalized within-row (red) for identification of temporal peak expression for each individual. To compare across all SNAREs (blue), datasets of reads per kilobase of exon model per million mapped reads (RPKM) were filtered for SNARE expression. The expression data were normalized to the highest RPKM among all SNAREs was set to 1, and the lowest RPKM among all SNARES set to 0, and all SNAREs RPKMs were normalized between these two values. To compare SNARE expression within-row (red), the highest RPKM for each

SNARE was set to 1, and the lowest RPKM was set to 0 separately for each SNARE.

Figure 1.3 Modes and Methods of exocytosis. (A) Modes of exocytosis described in the literature for neuronal and non-neuronal cells. Full-vesicle fusion (FVF) and KNR were the first proposed modes of exocytosis. Recently we proposed the subclasses of FVFi, FVFd, KNRi, and

KNRd to best describe the heterogeneity observed in developing cortical neurons. Flicker fusion has been observed at hippocampal synapses, resulting in the vesicle retreating from the membrane. In neuroendocrine cells, omega-shrink and omega-enlarge have been described as the primary modes of exocytosis observed. (B) Known regulations of SNARE-mediated fusion are described, including regulation of the modes of exocytosis. (i) The number of SNARE complexes at the fusion pore increases the probability of fusion pore opening and accelerates fusion pore expansion. (ii) SNARE-substitution can alter fusion dynamics, such as SNAP-47 substituting for SNAP-25. SNARE complexes made up of different SNAREs have distinct kinetic profiles. (iii) Spatial segregation or aggregation, such as linking of vesicles to the plasma membrane by N-WASP, can regulate fusion. Actin dynamics also regulate fusion dynamics, and actin as well osmotic pressure is suggested to drive FVF as well as omega-shrink fusion in neuroendocrine cells. (iv) A limited list of proteins suggested altering modes of fusion and the kinetics of SNARE-mediated fusion in neurons.

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CHAPTER 2: SPATIOTEMPORAL ORGANIZATION OF EXOCYTOSIS EMERGES DURING NEURONAL SHAPE CHANGE2 1 Introduction

Exocytosis is a fundamental behavior, ubiquitous across eukaryotes and cell types.

Vesicle fusion promotes secretion of biomolecules and insertion of transmembrane proteins and lipids into the plasma membrane, which can affect physiological processes including polarized growth and motility (Mostov et al., 2000; Winkle et al., 2014). Where and when vesicle fusion occurs may be a critical regulatory point in cellular physiology. The minimal machinery required for fusion is the SNARE complex (Söllner et al., 1993), comprising a tightly-associated bundle of four α-helical coiled-coils. For exocytosis, one α-helix is provided by a vesicle (v)-SNARE, such as vesicle-associated membrane protein 2 (VAMP2, synaptobrevin), VAMP3, or VAMP7

(tetanus-insensitive VAMP) in mammals or snc1/2 in yeast (Galli et al., 1998; McMahon et al.,

1993; Protopopov et al., 1993). Other α-helixes are provided by plasma membrane target (t)-

SNAREs: syntaxin-1 and synaptosomal-associated protein 25 (SNAP25) in mammals or

Sso1p/Sso2p and sec9 in yeast (Aalto et al., 1993; Brennwald et al., 1994; Söllner et al., 1993).

VAMP2, SNAP25, and syntaxin-1 were identified in brain, where they mediate synaptic vesicle fusion and neurotransmitter release. VAMP7 functions in SNARE-mediated exocytosis in both neurons and non-neuronal cells (Galli et al., 1998; Voets et al., 2016). Subsequent to synaptic vesicle release, clathrin-dependent endocytic retrieval of membrane material maintains membrane homeostasis (Heuser and Reese 1973; Pearse, 1976). Perhaps less appreciated

2 Urbina, F.L., Gomez, S.M., and Gupton, S.L. (2018). Spatiotemporal organization of exocytosis emerges during neuronal shape change. J. Cell Biol. 217, 1113–1128.

than synaptic exocytosis is the developmental exocytosis that occurs prior to synaptogenesis.

The acquisition of an elongated, complex neuronal morphology entails significant plasma membrane expansion, estimated at 20% per day (Pfenninger, 2009). This is remarkable when compared to concomitant neuronal volume increases estimated at less than 1%. We previously demonstrated that constitutive SNARE-mediated exocytosis is required during neuritogenesis and axon branching (Gupton and Gertler, 2010; Winkle et al., 2014). We hypothesize exocytosis provides membrane material to the expanding plasma membrane, which can only stretch 2-3% before rupturing (Bloom et al., 1991), however whether SNARE-mediated exocytosis supplies sufficient material for membrane expansion has not been addressed. Asymmetric exocytosis is linked to attractive axonal turning (Ros et al., 2015; Tojima et al., 2014, 2007). As a number of neurological disorders are accompanied by disrupted neuronal morphology (Engle, 2010; Grant et al., 2012; Paul et al., 2007), regulated exocytosis involved in appropriate neuronal morphogenesis is likely central to the formation and maintenance of a functional nervous system. However how exocytosis is spatially and temporally organized in developing neurons is not known.

To visualize exocytic vesicle fusion, here we exploited the pH-sensitive variant of GFP

(pHluorin) attached to the lumenal side of a v-SNARE, which illuminates the occurrence of fusion pore opening between the acidic vesicular lumen and the neutral extracellular environment (Miesenböck et al., 1998). Analysis of such images has remained a manual and potentially biased, time-intensive process. Here we developed computer-vision software and statistical methods for unbiased automated detection and analysis of VAMP-pHluorin mediated exocytosis. This uncovered spatial and temporal organization and regulation of exocytosis in developing neurons that was distinct in soma and neurites, modulated by the developmental stage of the neuron, and sensitive to the axon guidance cue netrin-1. Mathematical estimates based on empirical findings suggested that VAMP2-mediated exocytosis and clathrin-mediated

endocytosis approximately describe membrane expansion in developing neurons. Compared to neurons, melanoma cells exhibited slower frequencies and a distinct organization of exocytosis.

2 Results

2.1 Automated identification and analysis of exocytosis

Whether exocytosis is sufficient for neuronal plasmalemmal expansion, how fusion is organized spatially and temporally, and the mechanisms that regulate developmental exocytosis are not established. We imaged VAMP2-pHluorin or VAMP7-pHluorin in murine embryonic cortical neurons using widefield epifluorescence or total internal reflection fluorescence (TIRF) microscopy to visualize exocytosis (Fig 2S1A). The area-corrected frequency of VAMP2- mediated exocytosis at the basal plasma membrane measured from the TIRF images was ~1.4 fold higher than measured by widefield microscopy, which captures events at both basal and apical membranes (Fig 2S1B). In contrast, the frequency of VAMP7-mediated exocytosis was comparable between the two imaging modes (Fig 2S1B). VAMP2-pHluorin exhibited a higher plasma membrane fluorescence than VAMP7-pHluorin (Fig 2S1C), which may occlude identification of apical VAMP2-mediated events. Indeed, events at the apex of the soma were visible by widefield microscopy only when the focal plane was shifted (Fig 2S1D, black arrows).

This suggests that VAMP2-mediated events were not captured simultaneously on both membranes, and that the frequency of fusion observed by widefield microscopy was an underestimation. We conclude that the frequency of exocytosis is comparable between apical and basal plasma membranes for both VAMP2 and VAMP7-mediated exocytosis (Fig 2S1B, D), and was best captured by TIRF microscopy. For subsequent analysis, we relied on TIRF microscopy, which also offered increased signal-to-noise and spatial resolution, reduced photobleaching and phototoxicity, and faster image acquisition.

To perform unbiased, efficient identification and analysis of VAMP2-pHluorin mediated exocytosis from TIRF images, we developed a computer vision-based approach implemented in

MATLAB and R (Fig 2.1A, Movie 1). In the analysis pipeline, the cell was identified and segmented from non-cellular background and a single cell mask was created, as cells did not significantly change shape or size over imaging (~2 min). We developed an algorithm that detected both diffraction-limited and larger fluorescent objects (Fig 2.1A, see Methods). To assemble a coherent picture of exocytic events over time, fluorescent objects representing individual vesicles that may be undergoing exocytosis are linked across frames using a Kalman filter (Kalman, 1960, Fig 2.1B, 2S2A). The Kalman filter used a cost matrix to determine whether an object detected in one frame was a new object, i.e. a fusion pore opening, or an existing object, i.e. the continuation of an exocytic event. Critically, the Kalman filter can handle the linkage of both motile objects, such as a motile fluorescent vesicle, and non-motile objects, such as a fusing vesicle. Bona fide exocytic events were defined as transient, non-motile objects that reached a significant intensity above the average local background intensity calculated from previous frames, followed by an exponential fluorescence decay, with no limit on how long an event could last (Fig 2.1C, see Methods).

2.2 Confirmation of exocytic detection

To test the algorithm accuracy at identifying transient events, neurons were treated with

NH4Cl (Fig 2.1D, Movie 2), which alkalinizes intracellular vesicular compartments, reversing quenching of pHluorin fluorescence (Miesenböck et al., 1998). NH4Cl treatment increased vesicular fluorescence (Fig 2.1D) and events detected were near or at zero, indicating the algorithm only identified transient fluorescent exocytic events. To confirm identified events were

VAMP2 dependent, neurons were treated with Tetanus NeuroToxin (TeNT), which cleaves the cytoplasmic face of VAMP2, preventing SNARE complex formation, and fusion (Link et al.,

1992). VAMP2-pHluorin-mediated fusion events were rarely detected, if at all, indicating that the algorithm identified TeNT-sensitive events (Fig 2,1D, Movie 2). To ensure images were acquired at a sufficient framerate to capture all events, we artificially decreased sampling. At an acquisition rate of 1 Hz, the frequency of detected events was underestimated as compared to 5

Hz; no further improvement occurred at 10 Hz (Fig 2.1E). We subsequently used an acquisition rate of 10 Hz to satisfy the Nyquist criterion and ensure ample resolution for analyses of individual exocytic events. To assess the robustness of the algorithm to local background variation due to plasma membrane localized VAMP2-pHluorin and system noise, and to calculate true error rates, computer simulations of exocytic events based on empirical measurements were created (Fig 2.1F, see Methods). The detection software was robust to the typical signal-to-noise ratio observed experimentally, with an error rate between 2-7% (Fig

2.1G), comparable or better than other algorithms (Sebastian et al., 2006; Yuan et al., 2015).

A series of measurements were made to describe the population of fusing vesicles (Fig

2.1H-L), including the frequency of exocytosis per neuron (Figure 2.1I), the peak change in fluorescence per event (ΔF/F, Fig 2.1H,J), which estimates the amount of VAMP2-pHluorin per vesicle, the event half-life (t1/2, Fig 2.1H,K), which represents how quickly the VAMP2-pHluorin fluorescence decayed, the shape of the initial exocytic boundary (major/minor axis, Fig 2.1L), and the time (t) and location (x,y) of fusion. The shape of the half-life distribution exhibited no artificial truncation, indicating events with short or long half-lives were detected. The frequency of VAMP2-pHluorin exocytic events detected from TIRF images by the software was not different from that counted by several independent, trained users (Fig 2.1I, p=0.56).

2.3 Confirmation of detection of VAMP7-mediated exocytosis.

We next tested whether the algorithm also accurately detected VAMP7-mediated events.

Surprisingly, the frequency of VAMP7-mediated exocytosis detected was considerably lower than we previously reported using manual analysis (~15 fold less, Fig 2S1B, Winkle et al.,

2014). Alkalization with NH4Cl revealed numerous VAMP7-containing vesicles and confirmed

VAMP7-pHluorin fluorescence was quenched by low pH (Fig 2S3A). To ensure the algorithm was capable of detecting VAMP7-mediated fusion, we co-expressed VAMP7-pHluorin along with an activating VAMP7 mutant lacking the autoinhibitory longin domain (VAMP7 LD,

Martinez-Arca et al., 2000). VAMP7 LD expression increased the frequency of VAMP7- mediated exocytosis (Fig 2S3B), as previously shown (Burgo et al., 2013), demonstrating the algorithm was capable of detecting VAMP7-mediated events.

Several potential factors could account for the disparity between the algorithm and hand- counted VAMP7-mediated exocytosis, including different image acquisition rates, unique attributes of VAMP7-containing vesicles, and the more rigorous definition of a fusion event employed by the algorithm. Artificially decreasing the temporal resolution of images to match the 2 Hz sampling rate used previously (Winkle et al., 2015) increased apparent exocytic frequency (Fig 2S3C), but did not account for the differences between automated and manually- defined values. Since these newly identified events only met algorithm requirements at reduced temporal resolution, we conclude they were unlikely true events. The peak F/F and half-life were not significantly different between VAMP2-pHluorin and VAMP7-pHluorin (Fig 2S3D), indicating these were not the sources of incongruence. Along with artificially lowering the sampling rate, relaxing the algorithm requirement that events be non-motile, a parameter that was difficult to assess manually, permitted the software to capture an exocytic frequency indistinguishable from previous reports (Fig 2S3C, Winkle et al., 2015). The mean squared displacement of events detected under this relaxed requirement was higher for VAMP7 than

VAMP2 (Fig 2S3E), explaining why VAMP7-mediated events were previously overestimated, and VAMP2-mediated events were not. To examine whether motile fluorescent puncta represented actual exocytic events, we co-expressed the autoinhibitory longin domain (VAMP7-

LD, Martinez-Arca et al., 2000) with VAMP7-pHluorin. As reported, VAMP7-LD expression decreased VAMP7-pHluorin mediated exocytic events, when the non-motile requirement was included in the detection algorithm (Fig 2S3B). Upon relaxation of this parameter, an increase in motile, VAMP7-mediated events were detected (Fig 2S3B,E). Together these results suggest that motile fluorescent events are not true exocytic events, and that the algorithm accurately detects exocytosis.

2.4 Spatiotemporal changes in exocytosis occur during development

With the algorithm validated, we set out to investigate developmental exocytosis.

Dissociated neurons initially exhibit a “pancake-like” shape with peripheral protrusions. By 24 hours in vitro, protrusions coalesce into immature neurites. By 48 hours, neurons typically have several defined neurites, one of which is significantly longer and considered the axon. By 72 hours, neurons are characterized by longer neurites and a longer, potentially branched axon.

We hypothesized the temporal and spatial distribution of exocytosis in neurons would change over developmental time, and potentially involve fusion of both VAMP2 and VAMP7-containing vesicles. We measured VAMP2-pHluorin and VAMP7-pHluorin exocytic parameters in mouse embryonic (E) day 15.5 cortical neurons after 24, 48, and 72 hours in vitro (Fig 2.2A-I, Movie 3-

4). The rate of VAMP7-mediated exocytosis was negligible under these conditions (Fig 2.2E), so we focused on VAMP2-mediated exocytosis. We mapped the spatial occurrence and density of

VAMP2-pHluorin mediated events (Fig 2.2A), which suggested that their spatial distribution was not uniform. Neurons were segmented into soma and neurite regions of interest (ROI), and the frequencies of VAMP2-mediated exocytosis were compared, controlling for basal cell surface

area (Fig 2.2B-D). At 24 hours, exocytic frequency was faster compared to 48 and 72 hours (Fig

2.2B), and faster in the soma compared to neurites (Fig 2.2C-D). This relationship between exocytic frequency in soma and neurites remained between 24 and 48 hours (Fig 2.2C-D).

However, at 72 hours a redistribution occurred, with an increased frequency of events in neurites and decreased frequency in the soma (Fig 2.2C-D).

Exocytic heatmaps revealed that events were potentially clustered into spatial hotspots

(Fig 2.2A). To determine if exocytosis was organized non-randomly and to distinguish between clustering, randomness, or regular spacing (dispersal) of exocytic events, we adapted Ripley’s K function (Ripley et al, 1976, see Methods, Fig 2.2F-G). There has been limited use of the K function for analysis of exocytosis, restricted to individual cells of simple morphology (Díaz et al., 2010, Yuan et al., 2015). Group-comparisons have not been performed, which limits statistical power, comparisons, and conclusions. In our case, an aggregated Ripley’s K function

(L(r)), compares the experimental distances in space or time between detected events in multiple cells (Fig 2.2F) against the theoretical distances between the same number of events if they occurred randomly in the same space or time. Simulated random events were performed for each test group, based on the experimental events and experimental space or time. When the experimental Ripley’s K function +/- SEM (LObs(r)) and the theoretical random Ripley’s K function (Ltheo(r), red line) overlap, events were randomly organized (Fig 2.2G). At distances where LObs(r) rose above Ltheo(r), exocytosis was clustered (p<.05), and at distances where the

LObs(r) function fell below Ltheo(r), events were dispersed (p<.05). At 24 hours, exocytosis exhibited spatial randomness in soma and neurites as well as temporal randomness (Fig

2.2H,I). In contrast, at 48 and 72 hours, exocytosis was clustered into spatial hotspots in both neurites and soma (Fig 2.2H), with higher clustering in the soma (p<.05). Spatial clustering in each region did not change between 48 and 72 hours (p>.05). However exocytosis became clustered into temporally bursts at these timepoints. Thus, spatial and temporal organization of

exocytosis evolved from random to clustered during neuronal morphogenesis and was distinct in the soma and neurites.

2.5 A mathematical approximation of membrane expansion

To determine the significance of VAMP2-mediated exocytosis in the expansion of neuronal surface area, we sought to calculate plasma membrane expansion and exocytic addition of membrane. A similar basal neuronal surface area was estimated from TIRF images of VAMP2-pHluorin and CAAX-GFP (Fig 2S4A). Folding of the plasma membrane was not observed in neurons expressing CAAX-GFP or VAMP2-pHluorin, whereas plasma membrane folding was apparent in non-neuronal cells in the culture expressing CAAX-GFP (Fig 2S4B).

Scanning electron microscopy revealed that the apical membrane of neurites were flat, and only minimal apical membrane ruffling occurred on the growth cone and soma (Fig 2S4C).The neurite surface area was thus approximated by doubling the basal area. To account for the three dimensional shape of the soma with a flat basal surface attached to the coverslip, the soma was fit to a truncated ellipsoid. The height of the ellipsoid was approximated via confocal z stacks (Fig 2S4D), and the length and width via the longest and shorted axes of the soma (Fig

2S4E). Neurite and soma surface areas were summated to estimate neuronal surface area. We derived the increase in cell surface area between 24 and 48 hours using a Bayesian linear model, based upon empirical neuronal surfaces area measurements, using time in vitro as a categorical predictor (Fig 2.3A, see Methods). Posterior predictive checking indicated that the model was a good fit (Fig 2.3B), with predicted credible regression lines showing a positive increase in surface area at 24 and 48 hours (Fig 2.3C). The 90% confidence interval showed that the surface area increased by 979-1018 μm2 with a mean of 999 μm2 (Fig 2.3D, blue line).

This amounted to a ~57% increase over a 24-hour time period, more than previous estimates

(Pfenninger, 2009).

We estimated the surface area added by VAMP2-mediated exocytosis using the frequency of exocytosis (Fig 2.2B) and surface area of vesicles. A potential range of vesicular surface areas was approximated from vesicle diameters measured from transmission electron micrographs of cortical neurons at 48 hours in vitro, with the assumption that vesicles were spherical (Fig 2.3E). Assuming a single population of vesicles with unimodal size distribution, and correcting for capturing random slices smaller than the actual vesicle diameter, we took the conservative estimate that the 75th percentile of the interquartile-range represented the vesicle diameter (130 nm, Plooster and Menon et al., 2017). We also considered a smaller vesicle population, using the 25th percentile of the interquartile range (80 nm). We estimated membrane addition between 24 and 48 hours in vitro, using the formula

where y represents predicted neuronal surface area at 48 hours, S is neuronal surface area at

24 hours, f24 and f48 are exocytic frequency at 24 and 48 hours, respectively, and z is surface area of vesicles. Since the frequency of exocytosis was not measurably different on basal and apical membranes (Fig 2S1), we used the mean frequency of basal exocytosis at 24 and 48 hours (Fig 2.2B) to define upper and lower bounds of VAMP2-mediated addition. The predicted average membrane addition was ~868-1737 μm2 using the smaller vesicle size, and ~2200-

4587 μm2 using the larger vesicle size. These results suggested that VAMP2-mediated exocytosis provided excess material for developmental plasma membrane expansion (Fig 2.3D, green lines).

This excess delivery may be balanced by compensatory endocytosis. Both clathrin- mediated and clathrin-independent endocytosis occur at the synapse (Delvendahl et al., 2016;

Watanabe et al., 2013), but the relative contributions of different forms of endocytosis at the synapse and in developing neurons is not known. In a number of cell types investigated, 95% of

membrane internalization occurs through clathrin-mediated endocytosis (Bitsikas et al., 2014;

Pearse, 1976). Therefore, we used tagRFP-clathrin light chain (Shaner et al., 2008) in conjunction with TIRF microscopy to measure the frequency of clathrin-mediated endocytosis

(Fig 2.3F). The rate of endocytosis did not change between 24 and 48 hours (Fig 2.3F). To exclude the clathrin coat, we measured the inner diameter of clathrin-coated vesicles from electron micrographs (34 nm, Fig 2.3G). With an additional parameter of clathrin-mediated membrane internalization, we updated our formula

where e24 and e48 are the endocytic frequency at 24 and 48 hours and x is the surface area of clathrin-coated vesicles.

The new predicted average membrane addition between 24 and 48 hours was ~1-868

μm2 using the smaller exocytic vesicle estimation, and ~547-2416 μm2 using the larger vesicle estimation (Fig 2.3D, orange lines). These results suggest that, by first approximation, plasma membrane expansion in cortical neurons can be explained by VAMP2-mediated exocytosis and clathrin-mediated endocytosis.

2.6 Netrin-1 modulates the spatiotemporal distribution of exocytic events in neurites

To further determine how exocytosis is organized during neuronal morphogenesis, we investigated the consequences of bath application of netrin-1 (Fig 2.4A-I), a guidance cue that accelerates neuronal morphogenesis (Winkle et al., 2016). Further validating the accuracy of the automated software, we confirmed previous findings (Winkle et al., 2014) that netrin stimulation increased the frequency of exocytosis (Fig 2.4E). Analysis of individual events identified parameters that were insensitive to netrin, including the major/minor axis of the vesicle

fusion event, the peak ΔF/F, and the t1/2 (Fig 2.4A-C). This suggests that netrin altered the probability of fusion, but the same vesicles fuse with the plasma membrane using similar mechanisms, as they contained indistinguishable amounts of VAMP2-pHluorin, and fluorescence dissipated with identical patterns.

Mapping the location and density of events (Fig 2.4D) revealed that netrin altered their non-uniform spatial distribution. Interestingly, netrin increased the frequency of exocytosis in the soma by ~ 31% (Fig 2.4F), yet increased the frequency in neurites by 92% (Fig 2.4G), indicating neurites have increased responsiveness to netrin. Ripley’s K function revealed that exocytic events were non-randomly clustered at all distances in soma and neurites. In neurites, netrin enhanced clustering (Fig 2.4H). In contrast, spatial clustering in the soma appeared to relax following netrin addition, although these changes were not significant. Ripley’s L(r) function showed temporal exocytic clustering that was lost upon netrin addition (Fig 2.4I). These results suggest that netrin modulates the distribution of exocytosis specifically in neurites and dissipates temporal clustering.

2.7 TRIM9 is required for netrin-dependent exocytic changes in neurites

To determine how netrin differentially affected exocytosis in the soma and neurites, we investigated the brain-enriched E3 ubiquitin ligase TRIM9, implicated in constraint of exocytosis and netrin responses (Winkle et al., 2014). Genetic loss of Trim9 did not change the major/minor axis of the initial vesicle fusion event, the peak ΔF/F, or the event t1/2 (Fig 2.4A-C).

Although Trim9 deletion increased exocytic frequency and blocked netrin-dependent increases in exocytic frequency (Fig 2.4E), responses in the soma were similar to Trim9+/+ neurons (Fig

2.4F), indicating somatic responses were TRIM9-independent. Neurites of Trim9-/- neurons however exhibited a ~three-fold increased frequency of basal exocytosis that was not further

increased by netrin (Fig 2.4G). This suggests that TRIM9 regulates VAMP2-mediated exocytosis in neurites.

Ripley’s K function indicated that exocytic events were non-randomly clustered at all distances measured in soma and neurites of Trim9-/- neurons (Fig 2.4H). Similar to Trim9+/+ neurons, spatial clustering in the soma of Trim9-/- neurons appeared to relax following netrin addition. In contrast, spatial clustering in Trim9-/- neurites failed to increase in response to netrin

(Fig 2.4H). Further, temporal clustering of exocytic events in Trim9-/- neurons persisted following netrin-1 addition (Fig 2.4I). This suggests that netrin dependent modulation of the spatial distribution of exocytosis in neurites and the temporal clustering of exocytosis are TRIM9- dependent.

2.8 Different domains of TRIM9 modulate specific parameters of exocytosis in neurites

TRIM9 is a TRIpartite Motif (TRIM) E3 ubiquitin ligase, characterized by a ubiquitin ligase RING domain, two BBox domains that modulate ligase activity, and a coiled-coil (CC) motif that mediates multimerization (Fig 2.5A). The TRIM9 CC motif also binds the exocytic t-

SNARE SNAP25 (Li et al., 2001) and blocks SNARE complex formation and vesicle fusion

(Winkle et al., 2014). The TRIM9 COOH-terminal SPRY domain binds the netrin receptor DCC

(Winkle et al., 2014). To elucidate how these domains modified parameters of exocytosis and netrin response, we performed structure-function analysis. Expression of full length and domain mutants of TRIM9 (Fig 2.5A) lacking the RING domain (TRIM9ΔRING), SPRY domain

(TRIM9ΔSPRY), or CC motif (TRIM9ΔCC) in Trim9-/- neurons further indicated that TRIM9- dependent regulation of exocytosis occurred only in neurites (Fig 2.5B-D). Reintroduction of full length TRIM9 reduced the elevated basal frequency of exocytosis and returned netrin sensitivity

to exocytic frequency in neurites (Fig 2.5C), exocytic clustering in neurites (Fig 2S5A), and relaxation of temporal clustering (Fig 2S5B).

TRIM9ΔRING expression reduced elevated basal frequency of exocytosis in neurites, but failed to rescue netrin-dependent changes in exocytic frequency (Fig 2.5C), spatial clustering (Fig 2S5A), or temporal clustering (Fig 2S5B). This indicated the ligase domain was unnecessary to constrain exocytosis, but required for response to netrin. Although previous studies demonstrated that TRIM9-dependent ubiquitination altered substrate function (Menon et al., 2015; Plooster et al., 2017), we did not observe netrin-1 or TRIM9-dependent changes in the ubiquitination of SNAP25 (Fig 2S5C), suggesting that ubiquitin-dependent modulation of

SNAP25 did not explain TRIM9-dependent changes in exocytosis. Expression of TRIM9ΔSPRY or TRIM9ΔCC failed to reduce the elevated frequency of exocytosis in the neurites (P = 0.23 and P = 0.33, respectively, Fig 2.5C) or the netrin-dependent reduction in temporal clustering

(Fig 2S5B). Expression of TRIM9ΔSPRY, but not TRIM9ΔCC, rescued the spatial exocytic clustering in response to netrin-1 (Fig 2S5A), suggesting that TRIM9ΔSPRY retains partial netrin sensitivity. Together these results suggest that distinct domains of TRIM9 specifically constrain exocytosis and regulate spatial and temporal clustering in response to netrin.

2.9 Exocytosis in melanoma cells is distinctly organized

In contrast to developing neurons, non-neuronal interphase cells are typically at a steady-state size. To determine if spatial and temporal control of exocytosis varied in such a case, we investigated human VMM39 melanoma cells. We monitored exocytosis mediated by ubiquitous v-SNAREs, by imaging VAMP3-pHluorin or VAMP7-pHluorin (Fig 2.6, Movie 5).

VAMP3-pHluorin and VAMP7-pHluorin exhibited no difference in the frequency of exocytosis

(Fig 2.6A), however this frequency was slower than mediated by VAMP2 and faster than

mediated by VAMP7-mediated in neurons (P = 0.002 and 0.017, respectively). Mapping the location and density of VAMP3- and VAMP7-mediated events revealed their spatial distributions were distinct (Fig 2.6B). VAMP7-mediated events appeared polarized, whereas VAMP3- mediated events were distributed throughout the cell. To quantify this distribution, we segmented the cell with lines 10 μm from the edges of the cell, perpendicular to the longest axis

(Fig 2.6B). VAMP7-mediated exocytosis occurred more in the larger end of the cell than in the middle and smaller end, whereas the frequency of VAMP3-mediated exocytosis was similar in each region (Fig 2.6C).

2D Ripley’s L(r) analysis demonstrated that VAMP7-mediated events clustered at all distances measured (Fig 2.6D). VAMP3-mediated exocytosis was organized distinctly; exocytic clustering was observed at 1.5-2.5 μm, (Fig 2.6D, inset) however at larger distances (4.5 μm), the Ripley’s L(r) value fell below spatial randomness, indicating dispersal of exocytic hotspots.

1D Ripley’s L(r) analysis of the timing of VAMP3- and VAMP7-mediated exocytic events revealed that both were temporally random (Fig 2.6E). Together these investigations of the spatial and temporal organization of exocytosis in interphase cells revealed an organization and likely regulation of exocytosis distinct from developing neurons.

3 Discussion

Here we built a computer-vision image analysis tool and applied extended clustering statistics to demonstrate that the spatiotemporal distribution of constitutive VAMP2-mediated exocytosis is dynamic in developing neurons, modified by both developmental time and the guidance cue netrin-1, regulated differentially in the soma and neurites, and distinct from exocytosis in non-neuronal cells. To our knowledge this represents the first systematic, automated investigation of exocytosis in developing neurons. Our algorithm has several

advantages over previously reported detection algorithms (Díaz et al., 2010; Sebastian et al.,

2006), including a more rigorous definition of an exocytic event, use of a Kalman filter for better tracking of events, and both simulated and experimental validation (Figure 2.1A; Movie 1).

Although statistics for spatial clustering exist, they have not been applied in cells with extreme polarized morphology such as primary neurons, nor have they been aggregated to compare spatial and temporal parameters of fusion events across conditions. Our data and mathematical calculations suggest that in developing neurons, addition of membrane material by VAMP2- mediated exocytosis and endocytic retrieval of membrane by clathrin is sufficient to explain developmental plasma membrane expansion by first approximation.

3.1 Dynamic spatial regulation of exocytosis in developing neurons

Classic studies hypothesized that vesicles fuse at sites of cell growth (Craig et al., 1995;

Feldman et al., 1981; Pfenninger and Friedman, 1993). Others suggested that membrane translocates anterogradely to the extending neurite tips (Popov et al., 1993), yet a systematic investigation of where vesicle fusion occurs has been lacking. We suggest that at earlier developmental stages, rapid membrane expansion is needed, but the localization of insertion is inconsequential. Perhaps with a less complex neuronal shape, cytoskeletal forces are sufficient to shape the plasma membrane. The developmental reduction in exocytic frequency but emergence of exocytic hotspots and shift of exocytosis toward neurites as neurons acquire a more complex morphology suggests that growth of individual neurites or individual branches requires local membrane insertion. Differences in the spatial distribution of exocytosis between developmental time points and cellular shapes suggest that the location and clustering of membrane insertion via exocytosis is regulated and developmentally critical to reach appropriate neuronal morphology. Our findings indicate that VAMP7-mediated vesicle fusion events were rare at the conditions and developmental time points investigated, and that

VAMP2-mediated vesicle fusion occurred proximal to sites of growth, such as at the base of neurites and branches, supporting the anterograde membrane flow hypothesis at early developmental time points.

3.2 Calculating membrane growth in developing neurons

Several sources for membrane expansion have been identified, including exocytosis of secretory vesicles and/or lysosomes and membrane transfer at ER-plasma membrane contact sites (Arantes et al., 2006; Pfenninger, 2009; Stefan et al., 2013). Additionally, astrocytes donate membrane material to neurons through lipoprotein particles (de Chaves et al., 1997;

Mauch et al., 2001). The relative contributions of these sources have not been explored. Our results and calculations showed that the predicted surface area supplied by VAMP2-mediated exocytosis was roughly 2-10 times greater than the measured expansion of neuronal surface area, and further that clathrin-mediated endocytosis compensates by removing excess material.

The measured increase in surface area most closely overlaps with vesicle membrane addition estimated by the addition of larger vesicles. This vesicle size is supported by the likely underestimation of vesicle diameter from electron micrographs and the unimodal distribution of vesicular VAMP2 content (Fig 2.1J). While other modes of addition and retrieval may also be involved, VAMP2-mediated exocytosis and clathrin-mediated endocytosis together predict membrane addition that agrees with the membrane growth observed, suggesting that they may be the predominate mechanisms of membrane flux in developing neurons.

3.3 Exocytosis in the soma and neurites are distinctly regulated

Netrin-1 affects exocytosis distinctly in neurites and soma, suggesting there is regional regulation of exocytosis. Different proteins and vesicles have been shown to selectively enter

certain regions of the developing neuron (Dean et al., 2012; Petersen et al., 2014; Winckler et al., 1999). We found that TRIM9 specifically regulated exocytosis in neurites, however TRIM9 is also present in the soma. Whether specific TRIM9 isoforms localize and function distinctly, or there is differential regulation of TRIM9 in the soma and neurites remain to be seen. In vitro, we found that cortical axons turn up attractive gradients of netrin-1 (Taylor et al., 2015) and that this is absent in Trim9-/- neurons (Menon et al., 2015). Whether a gradient of netrin-1 induces

TRIM9-dependent asymmetrical changes in the spatiotemporal pattern of exocytosis in the growth cone that are required for axon turning is not known.

Unlike TRIM9, F-actin is distributed differently in the neurite and soma, as well as within the turning growth cone (He et al., 2010). Actin polymerization exerts a mechanical force on the plasma membrane, which increases tension and alters vesicle fusion (Kliesch et al., 2017).

Since increased membrane tension has been coupled to increased exocytic activity (Wen et al.,

2016), this may provide distinct regulation of exocytosis in the soma and neurites, and across the growth cone. While a role for the actin cytoskeleton in exocytosis has been demonstrated in multiple cell types (Eitzen, 2003), a systematic investigation of this in developing neurons has not been performed.

3.4 Polarized exocytosis in non-neuronal migrating cells

The slower frequency of exocytosis in melanoma cells is consistent with melanoma cells not expanding their membrane. Polarized exocytosis has been suggested as a driving force in directional cell migration (Bretscher, 1996). Indeed, the rate of polarized exocytosis at the leading edge of fibroblasts correlates with the speed of migration, whereas disruption of cell polarization and polarized exocytosis is associated with loss of migration (Bergmann et al.,

1983; Bretscher, 1996; Schmoranzer et al., 2003). The role of different VAMPs in migration has not been systematically explored, even though evidence exists of their heterogeneous

distribution, transport, and cargo (Advani et al., 1998; Alberts et al., 2006; Bentley and Banker,

2015; Burgo et al., 2012; Oishi et al., 2006). The distinct spatial distributions of VAMP3- and

VAMP7-mediated exocytosis observed here suggests unique roles for these two vesicle populations in melanoma cells, with polarized VAMP7-mediated exocytosis potentially associated with expansion of the leading edge during motility.

4 Materials and Methods

4.1 Animal Work

All mouse lines were on a C57BL/6J background and bred at UNC with approval from the Institutional Animal Care and Use Committee. Mouse colonies were maintained in specific pathogen-free environment with 12-12 hour light and dark cycles. Timed pregnant females were obtained by placing male and female mice together in a cage overnight; the following day was designated as embryonic day 0.5 (E0.5) if the female had a vaginal plug. The generation of

Trim9-/- mice is described in Winkle et al., 2014. Mice were genotyped using standard genotyping procedures.

4.2 Media, culture, and transfection of primary neurons and immortalized cell lines

Both male and female embryos were used to generate cultures and were randomly allocated to experimental groups. Cortical neuron cultures were prepared from day 15.5 embryos as previously described (Viesselmann et al., 2011). Briefly, cortices were microdissected and neurons were dissociated with trypsin and plated on poly-D-lysine coated glass-bottom culture dishes in neurobasal media supplemented with B27 (Invitrogen). This same media was used for all time-lapse experiments. For transfection, neurons were resuspended after dissociation in

solution (Amaxa Nucleofector; Lonza) and electroporated with a nucleofector according to manufacturer protocol. Recombinant netrin was concentrated from the supernatant of HEK293 cells (Serafini et al., 1994). Neurons were stimulated with 500 ng/ml of netrin-1 added 1 hour before imaging. CRISPR generated TRIM9-/- HEK293 cells were described previously (Menon et al., 2015). HEK293 and VMM39 cells were maintained in DMEM + glutamine supplemented with

10% FBS. For HEK293 and VMM39 cell transfections, HEK293 cells were transfected using

Lipofectamine 2000 per manufacturer protocol.

4.3 Plasmids and antibodies

pEGFP-SNAP25B was acquired from L. Chamberlain (University of Edinburgh,

Edinburgh, Scotland, UK). pcDNA3.1-HA rat DCC was acquired from M. Tessier-Lavigne

(Stanford University). VAMP2-pHluorin, VAMP3-pHluorin, and pCax-human VAMP7-pHluorin were previously described (Gupton and Gertler, 2010). pEGFP-human VAMP7 N terminus

(longin; aa 1–120) and VAMP7 C terminus was cloned into an mcherry vector. mTagRFP-T-

Clathrin-15 was obtained from through Addgene from Michael Davidson. Flag-tagged Ubiquitin was obtained from Ben Philpot (University of North Carolina at Chapel Hill, Chapel Hill, NC).

GFP-CAAX was obtained from Richard Cheney (University of North Carolina at Chapel Hill,

Chapel Hill, NC ). Antibodies include a rabbit polyclonal against GFP (A11122; Invitrogen) and ubiquitin (sc-8017, SCBT).

4.4 Imaging and image analysis

All TIRF and widefield time-lapse imaging was performed using an inverted microscope

(IX81-ZDC2) with MetaMorph acquisition software, an electron-multiplying charge-coupled

device (iXon), and a live cell imaging chamber (Tokai Hit Stage Top Incubator INUG2-FSX). The live cell imaging chamber maintained humidity, temperature at 37°C, and CO2 at 5%.

Z-stack images of e.15.5 cortical neurons at 2DIV were acquired using a Zeiss 880 confocal laser scanning microscope with a 32 channel GaAsP spectral detector with 45% quantum efficiency using a Plan Apo 63x/1.4 NA oil objective, equipped with an incubator ensuring temperature at 37°C, and CO2 at 5%. Scanning electron microscopy images were acquired with a Zeiss Supra 25 Emission Scanning Electron Microscope with a Standard T-E secondary electron detector at 3500x magnification, 5 Kv.

For all neuronal exocytosis assays, neurons expressing VAMP2-pHluorin or VAMP7- pHluorin were imaged at the indicated timepoint in vitro with a 100x 1.49 NA TIRF objective and a solid-state 491-nm laser illumination at 35% power at 110-nm penetration depth or in widefield epifluorescence using Xenon lamp. Images were acquired using stream acquisition, imaging once every 100ms for 2 minutes (10 Hz) or indicated acquisition rate. For measuring exocytosis in melanoma (VMM39) cell lines , images of interphase cells were acquired at 24 hours after transfection with the indicated VAMP-pHluorin construct with a 100x 1.49 NA TIRF objective and a solid-state 491-nm laser illumination at 35% power at 110-nm penetration depth. Images were acquired every 500 ms for 3 minutes. For all endocytosis assays, neurons expressing clathrin light chain-tagRFP were imaged at 1 Hz with a 100x 1.49 NA TIRF objective and a solid-state 561-nm laser illumination at 40% power at 110-nm penetration depth.

4.5 Image Analysis

ImageJ software was used for general viewing of images and movies.

Exocytic detection algorithm

The automated exocytic detection and image processing was written as a suite of

MATLAB files with an execution script and performed in MATLAB, with analysis of Ripley’s K and other statistics performed in the statistical language R with RStudio. To detect exocytic events, frames were first convolved with a Gaussian filter followed by local application of an h-dome transform (Vincent, 1993), to detect bright, round-shaped objects inside the cell mask against local variations in fluorescence. Gaussian mixed models were fit to local fluorescent maxima in each frame (Bishop, 2006), allowing the detection of both diffraction-limited and larger fluorescent objects. We defined a small radius expected to encompass the expansion and/or movement centered on potential fusion events identified in the initial frame (t0). Using a cost matrix (Jaqaman et al., 2008), detected events that fell into the prediction radius were identified as the same object; if not, they were identified as a new object. The parameters of the cost matrix, including distance between predicted and detected locations, were determined experimentally based on the number of false positives and false negatives detected (Figure 2S2B,C; Matov et al., 2010). The prediction radius was refined in subsequent frames (ti+1) based on prior behavior of detected objects. This iterative process continued through the sequence of images to build connected object tracks, which represented potential exocytic events. Bona fide exocytic events were defined as transient, non-motile (mean squared displacement <0.2 m2) objects that reached peak fluorescence intensity four deviations above the average local background intensity at t0 (the first frame the object was detected in), followed by a detectable exponential fluorescent decay (≥3 frames) with

no limit on how long the events could last. Objects that did not meet these requirements were discarded.

4.6 Clathrin-mediated endocytosis detection

An h-dome transform was performed on neurons expressing clathrin-light chain-TagRFP to detect fluorescent maxima in each frame. Fluorescent puncta that lasted at least 20 seconds prior to disappearing were considered endocytic events (Loerke et al. 2009).

4.7 NH4Cl and TeNT treatments

Neurons transfected with VAMP2-pHluorin or VAMP7-pHluorin were imaged at 48 hours as above. Either 25 mM of NH4Cl or 50 nM of TeNT (Sigma) were added to the neurons followed by either immediate imaging for NH4Cl treatment or imaging 30 minutes after the addition of TeNT. Analysis of exocytic events were performed as described above.

4.8 Simulation of exocytosis

VAMP2-pHluorin signal on the cell surface was simulated with a normal distribution using a mean and standard deviation equivalent to the measured median, to account for outlier fluorescent pixels, and standard deviations of experimental VAMP2-pHluorin signal. Gaussian fluorescent peaks with varying means and standard deviations, approximated from user-identified exocytic events, were added to simulate exocytic events. To represent random noise, a range of intensities based on typical images that followed a Poisson or Gaussian distribution were added to simulated images (Pawley, 1990; Waters, 2009).

4.9 Surface Area Estimation

To estimate the surface area, we used TIRF images of VAMP2-pHluorin expressing cells.

The soma was identified and segmented from the neurites, and the basal surface area of each were calculated separately. Total neurite area was estimated as doubling the measured basal membrane area. The average height of the soma (~11 μm) was obtained from confocal image z stacks through cortical neurons at 48 hours in vitro. The major and minor axis of the soma were measured, and used with the average height of the soma to calculate the surface area using the following calculation for a truncated ellipsoid:

Where a,b, and c represent the 3 axes of the soma (length, width, and height, respectively). The surface area of soma and neurites were summated to obtain the final surface area estimate of the neuron. For calculating local standard deviation of fluorescence intensity of GFP-CAAX, a 9x9 square filter was convolved on each pixel, replacing the value with the standard deviation of the

9x9 block of pixels.

4.10 Statistical analysis

The software package R was primarily used for statistical analysis of data. Both R and

Adobe Illustrator were used for the generation of figures. At least three independent experiments were performed for each assay. Normality of data was determined by Shapiro-Wilkes test. For two-sample comparisons of normally distributed data, an unpaired t-test was used for two independent samples, or paired t-test for paired samples. For multiple comparisons, ANOVA was used to determine significance, followed by unpaired or paired t-tests corrected using bonferroni- holms method. For analysis of non-normally distributed data, the Mann-Whitney test was used for

two samples. For more than two samples, Kruskal-Wallis non-parametric ANOVA was used to determine significance followed by the method described above. For all Ripley’s L(r) functions, comparisons between individual L(r) functions were made using the studentized permutation test

(Hahn, 2012). To test for differences between groups of L(r) functions, we first aggregated K functions and performed Diggle’s test, described in the text (Diggle et al., 1991). The package R package “brms” was used to run the Bayesian linear model and generate the graphs used in

Figure 2.3.

4.11 Bayesian Linear Model

A Bayesian linear model was used to calculate the increase in neuronal surface area between 24 and 48 hours in vitro. We made the assumption that neuronal surface area can only increase over time, and thus used a Gamma link function for the response variable. To give more weight to the data, we chose a weakly informative prior distribution for the predictor (normal distribution with mean of 500, sigma of 100), with the assumption that neurons increased surface area over time with no chance of decreasing surface area, and a uniform distribution between 0-

10 for the variance.

4.12 Ripley’s K test of complete spatial randomness or complete temporal randomness

Ripley’s K function is defined in two dimensions as

Where λ is the intensity, described as λ = n/A, A is the area encompassing the points, n is the number of points, I is an indicator function (1 if true, 0 if false), dij is the Euclidian distance between

the ith and jth point, and r is the search radius. To correct for edge effects, the function is weighted by w(li,lj), which represents the fraction of search radius that contains A.

The soma of neurons were analyzed using Ripley’s K function. However long, narrow, and branched neurites posed a challenge to the spatial analysis, even with an edge corrector. Thus, we skeletonized neurites, and considered them a 1D network (Fig 2.2F). Exocytic events in neurites were mapped to the nearest point on the network. We then performed an extension of

Ripley’s K function, the network K function, which takes the form

, where Lt is the union of all links in the network, and dij is the network distance between points

(Okabe and Yamada, 2001).

Finally, to test the temporal clustering of exocytosis, we performed Ripley’s K function in one dimension,

Where uij is the distance between points on a one-dimensional line (uij = |ui-uj|), and w(li,lj) is the fraction of the search length, r, that overlaps the number line (0,T), and T is the length of the one dimensional search space (Fig 2.2F, total time imaged for temporal analysis).

With the assumption that all cells measured in a group exhibit the same clustering pattern, the group-specific mean function is defined as

For each ith group, and the average K-function mean of the total population is

th Where wij = nij/ni, ni = the number of points in the i group, and n = the total number of points of all groups. A test statistic

Is used to test differences between groups. We use the average sampling variance of the functions weighted by wij to construct confidence intervals +/- 2 SE from the mean. To attach P- values to observed test statistics, we use a bootstrapping procedure to determine the distribution of Dg. To do so, we define

as the residual K functions. We obtain the approximate distribution of Dg by bootstrapping the residual K functions

, drawing at random with replacement from Rij(t), keeping the group sizes fixed. This bootstrapping procedure is performed 999 times, and the observed Dg values are ranked among the bootstrapped Dg values to obtain p-values of the null hypothesis: that the two groups being compared came from the same distribution (Diggle, 1991).

For ease of interpretation, we use the variance-stabilized Ripley’s K function, L(r), which gives an L(r) equivalent to the radius (r) under spatial or temporal randomnest (Ltheo(r)). To test whether exocytic events are clustered, the L(r) function of aggregated observed data is compared

to Monte Carlo simulations of Compete Spatial Randomness (LCSR(r)) or Complete Temporal

Randomness (LCTR(r), Diggle, 2013). Under traditional Ripley’s K analysis, LCSR(r) is equivalent to

2 πr ; in the case of a network function, LCSR(r) is equivalent to the uniform distribution of points on the network. LCTR(r) is equivalent to a uniform distribution.

4.13 Ubiquitination assays

To measure ubiquitination of SNAP25, HEK293 cells were transfected with HA-DCC,

FLAG-ubiquitin, and SNAP25-GFP and cultured for 24 hours. 4 hours prior to lysing, the cells were treated with 10 M MG-132 or 10 M MG-132 and 600 ng/ml of netrin-1. The treated HEK cells were lysed in 20 mM Tris-Cl, 250 mM NaCl, 3 mM EDTA, 3 mM EGTA, 0.5% NP-40, 1%

SDS, 2mM DTT, 5 mM NEM (N-ethylmaleimide), 3mM iodoacetamide, protease and phosphatase inhibitors (15mM Sodium Pyrophosphate, 50mM Sodium Fluoride, 40mM β- glycophosphate, 1 μM Sodium Vanadate, 150 μg/mL phenylmethylsulfonyl fluoride, 1 μg/mL

Leupeptin, 5 μg/mL Aprotinin), pH = 7.4. For 6 million cells, 270 l of lysis buffer was added and incubated on ice for 10 minutes. Cells were removed from the dish and transferred into tubes.

30 l of 1x PBS was added and samples were gently vortexed. Samples were boiled immediately for 20 minutes until they turned clear, then were centrifuged at 14,000 rcf for 10 minutes. The boiled samples were diluted using lysis buffer without SDS to reduce the SDS concentration to 0.1%. For immunoprecipitation of GFP-SNAP25, IgG-conjugated A/G beads

(sc-2343, SCBT) were utilized to preclear lysates for 1.5 hours at 4°C with agitation. A/G beads coupled to anti-GFP Ab (Neuromab) were agitated within pre-cleared lysates overnight at 4°C to precipitate GFP-SNAP25. Beads were washed three times with lysis buffer and bound proteins were resolved by SDS-PAGE and analyzed by immunoblotting for GFP, FLAG, and HA.

CHAPTER 2 FIGURES

Figure 2.1 Automated identification and analysis of VAMP-pHluorin mediated exocytosis

A) Overview of automated software. A cell mask is generated, followed by fluorescent maxima detection. A tracking matrix connects candidate events over time (Frame-to-Frame Tracking). B)

The tracking matrix was constructed using a Kalman filter to link a single exocytic event into a track over multiple frames. C) Schematic and TIRF image montage show typical exocytic event.

Pre-fusion, VAMP2-pHluorin is not fluorescent (1, blue). Upon fusion pore opening (2, green), a bright fluorescent diffraction-limited spot appears. The VAMP2-pHluorin diffuses in the membrane over time (3, orange). D) Example minimum projections of inverted TIRF time-lapse images before or after treatment with NH4Cl or Tetanus neurotoxin (TeNT). NH4Cl or TeNT treatment reduced the number of events detected (boxplot, median ± S.D., n = 6 neurons per condition). See Movie 2. E) Effect of acquisition rate on exocytic detection. F) Examples of simulated images with noise of increasing Gaussian intensity. G) Six simulations of exocytic events with increasing Gaussian or Poisson intensity demonstrate the robustness of the algorithm to image background and system noise. Typical experimental signal-to-background is highlighted in cyan. H) Change in fluorescence intensity relative to initial fluorescence (ΔF/F) for an individual exocytic event. Fusion pore opening occurs at t = 0 sec. The normalized peak change in fluorescence intensity (peak ΔF/F) and event half-life (t1/2, sec) are indicated. Initial fluorescence is estimated from 10 frames (1 sec) prior to peak of fluorescence (initiation of exocytosis). Half-life is estimated using a negative exponential decay. I) User-based and automated detection of the frequency of VAMP2-pHluorin-mediated exocytosis were not different (paired t-test, p = 0.56). Data points represent frequency per cell. Histograms of measured peak ΔF/F (J), event t1/2 (K), and major/minor axis of the first frame of each detected exocytic event (L). n = 462 exocytic events for each.

Figure 2.2 Spatiotemporal changes in exocytosis during neuronal development. A) Heatmaps of the density of VAMP2-mediated exocytosis in cortical neurons cultured in vitro for 24, 48 or

72 hours prior to imaging. The blue and orange boxes demarcate soma and neurite examples used in 2F, respectively. B) Frequency of VAMP2-pHluorin mediated exocytosis for basal plasma membrane of cortical neurons over developmental time. (*=P<0.05, n = 17 cells per condition, 464, 455, 466 exocytic events per condition). See Movie 3. C-D) Frequency of

VAMP2-pHluorin mediated exocytosis in the soma (C) and neurites (D) of cortical neurons. E)

Frequency of VAMP7-pHluorin mediated exocytosis for basal plasma membrane was not significantly different between time points (n = 14 cells per condition). See Movie 4. F)

Schematics of Ripley’s L(r) function in spatial and temporal dimensions. Red dots represent exocytic events. G) Example of Ripley’s L(r) analyses. The mean L(r) value of the aggregated data from multiple replicates (Lobs(r), black line) and standard error of the data (Lerr(r), pink) is compared to the expected L(r) value of completely random exocytic events (Ltheo(r), red dashed line). H) Spatial Ripley’s L(r) function analysis revealed that events were randomly distributed in the soma at 24 hours, whereas exocytosis occurred in spatial clusters in the soma at 48 hours and 72 hours. Exocytosis followed a similar pattern in the neurites. I) Temporal Ripley’s L(r) function of exocytic events over time in mouse cortical neurons, yet temporal bursts of exocytosis at 48 and 72 hours in vitro. See Movie 3.

Figure 2.3 A mathematical estimation of membrane expansion

A) Hierarchical diagram of the Bayesian linear model of neuronal surface area increases.

Probability distributions (blue) of surface areas at 24 and 48 hours represent priors imposed, with mean μ and standard deviation σ for the normal distributions, and a uniform distribution for the model error. B) Posterior predictions of surface area expansion by the model (orange lines) were compared to actual data (black line) to check for model fit. C) Credible regression lines

(orange) and mean (blue line) regression line of predicted plasma membrane expansion. D)

Estimates of membrane addition between 24 and 48 hours. The black line is the measured surface area at 24 hours in vitro. The blue line and blue shaded area represent predicted amount of plasma membrane expansion using a Bayesian linear model (A-C). Green lines represent predicted membrane added by VAMP2-mediated exocytosis. Orange lines represent predicted net membrane addition from VAMP2-mediated exocytosis after accounting for clathrin-mediated membrane removal. E) Example transmission electron micrograph and histogram of measured diameters of non-clathrin coated vesicles with density line overlaid

(black solid line), 25th % (red dotted line), and 75th % (black dotted line) of the interquartile range. F) Frequency of clathrin-mediated endocytosis G) Example transmission electron micrograph and histogram of measured diameters of clathrin coated vesicles with a density line overlaid (black solid line), The 25th % of the interquartile range (red dotted line, 30nm), and 75th

% (black dotted vertical line, 40nm) of the interquartile range.

Figure 2.4 Netrin and TRIM9-dependent changes in the distribution of exocytosis

A-C) Individual data points and boxplot of peak ΔF/F (A), t1/2 (B), and major/minor axis (C) of

VAMP2-mediated exocytosis in Trim9+/+ and Trim9-/- cortical neurons, treated or untreated with netrin-1. (n = 562,632,643,673 exocytic events per condition, respectively) D) Heatmaps of density of exocytosis. E) Individual data points and boxplot of frequency of VAMP2-pHluorin- mediated exocytosis (n = 17 cells per condition). F-G) Frequency of VAMP2-pHluorin mediated exocytic events in the soma (F) and neurites (G). H) Ripley’s L(r) function applied to the soma and neurites. The data line and pink surrounding SEM represent untreated, the data line and surrounding blue SEM represent netrin-1 treated neurons. I) 1D Ripley’s L(r) function of exocytic events over time.

Figure 2.5. Different domains of TRIM9 modulate specific parameters of exocytosis in neurites.

A) Domain organization of murine TRIM9 (top) and domain mutants that lack the ubiquitin ligase containing RING domain (TRIM9ΔRING), the DCC binding SPRY domain (TRIM9ΔSPRY), or the coiled-coil motif (TRIM9ΔCC), which mediates TRIM9 multimerization and interaction with

SNAP25. B-D) Frequency of VAMP2-pHluorin mediated exocytosis across basal plasma membrane of neurons (B), neurites (C), or soma (D) in Trim9+/+ and Trim9-/- neurons expressing

the indicated TRIM9 domain mutants (n = 17 for each condition, * = P < 0.05, ** = P < 0.005).

See Figure 2S5.

Figure 2.6. Distinct spatiotemporal organization of fusion of different vesicles pools in melanoma cells. A) Frequency of VAMP7-pHluorin and VAMP3-pHluorin mediated exocytosis in

VMM39 melanoma cells (n = 9 cells/condition). B) Heatmaps of density of VAMP7- and VAMP3- mediated exocytosis in representative VMM39 melanoma cells. Solid white line demarcates longest axis of the cell. Dashed perpendicular lines indicate minor axes 10 μm from the tips of the longest axis. The area encompassed from the edge of the cell to the dotted lines were measured to define a larger and smaller end of the cell. C) Number of exocytic events normalized to the larger, middle, and smaller end of the cell. VAMP7-mediated exocytosis was polarized to the larger end of the cell. D) 2D Ripley’s L(r) function applied to the spatial occurrence of VAMP7- and VAMP3-mediated exocytic events. VAMP7-mediated events exhibited non-random clustering at all distances measured, whereas VAMP3-mediated events were dispersed at larger distances in regularly-spaced clusters 1.5-2.5 μm in size. E) 1D

Ripley’s L(r) function applied to the temporal occurrence of VAMP7- and VAMP3-mediated exocytic events. Both VAMP7 and VAMP3-mediated events exhibited random temporal occurrence. See Movie 5.

Figure 2.S1 Widefield and TIRF imaging of VAMP2-pHluorin and VAMP7-pHluorin

Single images from widefield and TIRF time lapse microscopy of cortical neurons expressing either VAMP2-pHluorin or VAMP7-pHluorin 48 hours in vitro. B) Frequency of VAMP2-pHluorin and VAMP7-pHluorin exocytosis measured from TIRF and widefield imaging. C) Background plasma membrane fluorescence levels measured from widefield images of VAMP2-pHluorin and

VAMP7-pHluorin expressing neurons. D) Heatmaps of density of VAMP2-pHluorin exocytosis observed by TIRF (left) and widefield microscopy at bottom (middle) and top (right) focal planes.

Black arrows demarcate how apparent exocytic frequency detection changed with the focal plane. Pink arrows denote clusters apparent by TIRF and at the lower focal plane in widefield, that were absent in the higher focal plane in widefield images.

Figure 2.S2 Kalman filter algorithm and parameter selection. A) Schematic of the Kalman filter used for linking potential exocytic events between frames. X is defined as a state vector of the object (position and velocity), P is the covariance matrix of X, A is the prediction matrix, B is the control matrix, u is the control vector, Q is an added noise covariance matrix, H is the measured position of an object, R is the covariance modeling the uncertainty of the measured position of an object, Z is the measured position of an object, and K is the Kalman gain. “c” represents the

Euclidean distance “cost” between two objects in the cost matrix. To recognize whether objects detected in multiple frames are the same or different objects, the Kalman filter predicts the location of current objects in the subsequent frame. Fluorescent objects were detected in the first frame (t0). Using a prediction function, which took into account velocity and current location of the detected fluorescent object, the location of each object was predicted for the next frame

(t+1). The prediction function was initialized with an experimentally determined set of

parameters. After the prediction was made, fluorescent objects were detected in the next frame

(t+1). These detected objects were then assigned as either a new object, or as a previously detected object based on a cost matrix, which assigned a cost between the distance of predicted locations of objects and detected objects. The penalties assigned to this cost matrix were determined experimentally (B,C). After the newly detected objects were assigned, the prediction function was corrected based upon the error between predicted object locations and detected object locations. The cycle was then repeated, predicting the next location of objects in frame t+2. Cost matrices of the false negative (B) and false positive (C) rates of event detections with using varying intensity thresholds for detecting events. This indicates how high the signal- to-background ratio must be for pixels to be recognized as a potential exocytic event. False positives rates were computed as 100*(1-M/D) and false negative rates as 100*(1-M/G), where

D is the number of computer-detected exocytic events, G is the number of user-defined detected exocytic events, and M are the number of matches between the two, as described in

(Matov et al., 2010).

Figure 2.S3 Confirmation of VAMP7-pHluorin exocytosis A) Example minimal projections of

TIRF time-lapse images of VAMP7-pHluorin before and after treatment with NH4Cl. B) The detected frequency of exocytosis of VAMP7-pHluorin, VAMP7-pHluorin + VAMP7ΔLD, or

VAMP7-pHluorin + VAMP7-LD at different acquisition rates and different movement parameter thresholds. C) The detected frequency of VAMP7-pHluorin exocytosis at baseline (10 Hz), at 2

Hz, and at 2 Hz with the movement parameter threshold of detection relaxed (p = 0.02 between

baseline and 2 Hz, p = 0.001 between baseline and 2 Hz + relaxed movement parameter). D)

Change in peak fluorescence (left) and half-life (right) of VAMP2-pHluorin and VAMP7-pHluorin mediated events. E). The Mean Squared Displacement (MSD) of potential exocytic events. The red line indicates the experimentally-produced threshold, based on the diffraction-limited resolution of the microscope.

Figure 2.S4 Estimation of membrane surface area of neurons. A) Boxplot of surface area calculated from both VAMP2-pHluorin and CAAX-GFP expressing neurons at 24 hours in vitro.

B) Left: Fluorescence intensity, and local standard deviation of cells with and without visible membrane folding. Right: histogram of local standard deviation of pixels of each of cell.

Standard deviation of the spread of local standard deviations of the cell is noted. C) Scanning electron micrograph of neuron at 48 hours in vitro. D) Boxplot of the measured height of neuron somas at 48 hours in vitro. E) Graphic depicting surface area estimation of neurons, with separate estimations for the neurite and soma.

Figure 2.S5 Specific domains of TRIM9 modulate distinct parameters of exocytosis in neurites. A-B) 1D Ripley’s L(r) function of spatial distribution in neurites (A) and temporal distribution (B) of exocytosis of Trim9-/- mouse cortical neurons expressing the indicated TRIM9 domain mutant. Expression of TRIM9 rescued netrin-1 dependent spatial clustering in neurites and netrin-1 dependent temporal relaxation of exocytic clustering lost in Trim9-/- neurons. The

RING and CC domain of TRIM9 were required to rescue the netrin-dependent spatial clustering.

C) SDS-PAGE and western blot of inputs and GFP-SNAP25 immunoprecipitates from TRIM9+/+ and TRIM9-/- HEK293 cells immunoblotted for GFP and ubiquitin. High molecular weight ubiquitin in the IP is considered SNAP25 ubiquitination (arrowheads). Plot shows quantification of SNAP25 ubiquitination. Ubiquitination of SNAP25 did not change significantly in the absence of TRIM9 or the presence of netrin-1. P-values calculated from Kruskal Wallis ANOVA followed by LSD post-hoc correction compared to TRIM9+/+ control.

REFERENCES

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CHAPTER 3: TRIM67 REGULATES EXOCYTIC MODE AND NEURONAL MORPHOGENESIS VIA SNAP47 1 Introduction

Membrane bound compartments define eukaryotic cells. The transfer of material between compartments requires fusion of lipid bilayers. A fusion pore between bilayers is initiated by assembly of SNARE proteins into a complex (Fig 3.1A). Specific SNAREs mediate fusion between different compartments: for example, vesicle-associated membrane protein 2

(VAMP-2) and plasma membrane (PM)-associated syntaxin-1 and synaptosomal-associated protein 25 (SNAP25) facilitate docking and fusion of synaptic vesicles (Sudhof and Rothman,

2009; Schoch, 2001; Söllner et al., 1993). Evoked exocytosis of neurotransmitters involves multiple fusion events spatio-temporally clustered at the synapse, a diffraction-limited area. This restriction renders visualization of individual fusion events difficult. During neuronal development, however, constitutive exocytosis mediated by these same SNARE proteins is not spatially restricted and is sufficiently infrequent to resolve individual vesicle fusion events (Bello et al., 2016; Urbina et al., 2018).

Our previous work suggested that prior to synaptogenesis, VAMP2-mediated fusion inserted excess membrane material into the expanding PM, contributing to neuronal morphogenesis (Urbina et al., 2018). However, potentially not all exocytic events add membrane material. At the synapse, two modes of exocytosis have been described, yet their contribution remains controversial (Alabi and Tsien, 2013; Albillos et al., 1997; He and Wu,

2007; Elhamdani et al., 2006). During full vesicle fusion (FVF), the fusion pore is suggested to dilate and the vesicle membrane incorporates into the PM. In contrast, during kiss and run

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fusion (KNR), the fusion pore opens transiently, secretes lumenal cargo, and then reseals, retaining vesicular identity (Alabi and Tsien, 2013; Albillos et al., 1997; Bowser and Khakh,

2007; Holroyd et al., 2002; Wang et al., 2003). As presumably only FVF donates membrane material, regulating the mode of exocytosis may fine tune neuronal PM expansion during development.

Fusion pore size and kinetics vary with fusion mode. How the fusion pore and thus mode of exocytosis are regulated is debated. In vitro, the number of SNARE complexes regulate fusion pore dilation (Bao et al., 2018; Bello et al., 2016). The composition of SNARE complexes may also regulate the fusion pore. The SNARE family contains more than 60 members (Burri and Lithgow, 2004). SNAP47 is an atypical SNAP25 family member that can substitute for

SNAP25 in complexes with VAMP2 and syntaxin-1 in vitro. SNARE interacting proteins, such as

TRIM9, synaptotagmins, complexins, and -synuclein may also regulate fusion pores (Archer et al., 2002; Logan et al., 2017; Wang et al., 2003; Winkle et al., 2014). We found that an interaction between SNAP25 and the brain-enriched TRIpartite Motif (TRIM) E3 ubiquitin ligase

TRIM9 prevents SNARE complex formation and consequently attenuates the rate of exocytosis and axon branching in developing neurons (Winkle et al., 2014). Like SNAREs, the mammalian

TRIM family of ubiquitin ligases contains ~ 70 members, with many TRIMs enriched in neurons

(Napolitano and Meroni, 2012). The TRIM9 paralog TRIM67 is enriched in the developing cortex and regulates axonal projections, filopodia, and spatial learning and memory (Boyer et al., 2018,

2020), but thus far has not been implicated in exocytosis. The roles that TRIM67 and SNAP47 play in VAMP2-mediated fusion in developing neurons have not been explored.

Here, we use VAMP2-pHluorin expression to explore modes of single exocytic events in developing neurons. We develop computer vision classifiers to surprisingly reveal four distinct modes of fusion. This includes two classes within both FVF and KNR, which exhibit distinct fluorescence behavior following fusion pore opening. Experimental manipulations and

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simulations agree that membrane provided by FVF-like modes of VAMP2-mediated exocytosis approximate PM expansion of developing neurons. The E3 ubiquitin ligase TRIM67 regulates exocytic mode by reducing SNAP47 protein and limiting SNAP47 incorporation into SNARE complexes. We show that SNAP47 co-localizes with a subset of VAMP2-mediated exocytic events and alters fusion mode.

2 Results

2.1 Heterogeneity in single vesicle fusion event kinetics

VAMP2-pHluorin labeled exocytic events on the basal surface of embryonic murine cortical neurons were imaged at two days in vitro (2 DIV) by total internal reflection fluorescence

(TIRF) microscopy. VAMP2 is the most highly expressed v-SNARE during neuronal development (Grassi et al, 2015; Urbina and Gupton, 2020) and present on both FM4-64 dye labeled recycling vesicles and VSV-G-labeled post Golgi vesicles (Fig 3S1A-B). pHluorin is a pH-sensitive variant of GFP that is quenched in the vesicular lumen and fluorescent at extracellular pH (Miesenböck et al., 1998) (Fig 3.1A). Fusion events were characterized by a rapid fluorescence increase when fusion pores open (Fig 3.1B) and a slower fluorescent decay.

Exocytic events were automatically detected (Urbina et al., 2018). To evaluate heterogeneity in the population of events, we examined the normalized peak change in fluorescence per event

(peak ∆F/F, Fig 3.1B,3.1C), an estimate of relative VAMP2-pHluorin per vesicle, and the event half-life (t1/2, Fig 3.1B,1.C’), which describes fluorescence decay. Unlike peak ∆F/F, t1/2 exhibited a bimodal distribution, suggesting two populations of fusion events. We hypothesized these peaks could represent two well-described modes, full-vesicle fusion (FVF) and kiss-and-run fusion (KNR, Fig 3.1A)(Alabi and Tsien, 2013; Holroyd et al., 2002; Wang et al., 2003). FVF in astrocytes exhibits diffusion of VAMP2-pHluorin from the fusion site, whereas KNR events

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remain fluorescent until pore closure and re-acidification or retreat from the PM (Bowser and

Khakh, 2007). Events with longer t1/2 exhibited fluorescence spreading, consistent with diffusion, whereas events with a rapid t1/2 did not (Fig 3.1D). These two responses aligned with the bimodality of t1/2 distribution (Fig 3.1E). Thus, there is sufficient information to differentiate

VAMP2-pHluorin fusion events.

Vesicles that fuse by KNR retain their identity; yet re-acidification prevents visualization of VAMP2-pHluorin. To visualize vesicles before, during, and after fusion, we imaged neurons expressing both VAMP2-pHluorin and VAMP2-tagRFP (Fig 3.1F). Exponential decay of both markers confirmed diffusion of v-SNAREs during putative FVF events (Fig 3.1F’, top, blue).

Events without VAMP2-pHluorin diffusion maintained VAMP2-tagRFP fluorescence after fusion

(Fig 3.1F’, bottom, orange), indicating vesicles persisted following putative KNR events. Thus

VAMP2-tagRFP fluorescence behavior agreed with pHluorin-based classification (Fig 3.1G), suggesting that FVF and KNR were discernable in the pHluorin-based assay.

2.2 Multiple unbiased classifiers converge on four exocytic modes

To explore fusion heterogeneity in an unbiased fashion, we turned to classification and clustering approaches. Most clustering algorithms rely on a single classification method. In contrast, we employed an unbiased three-pronged approach to rigorously classify vesicle fusion. Detected events were first temporally aligned to peak ∆F/F (Fig 3.2A, red dots). As a ground truth method, we selected features that discern known differences between KNR and

FVF. This includes ∆F/F and t1/2, among other parameters (Fig 3.2B, see methods). Principal

Component Analysis (PCA) was performed on all features and the five principal components

(PC) capturing 85% of the variance were kept (Fig 3.2B’). Second, we utilized agglomerative and divisive hierarchical cluster analysis (Fig 3.2C). Hierarchical clustering summated a

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Euclidean distance cost (∑) between each pair of events starting at peak ∆F/F (Fig 3.2Ci) and either merged the lowest-cost events iteratively (agglomerative) or iteratively divided events

(divisive), creating a cluster dendrogram (Fig 3.2Cii, see methods). Euclidean distance matrices were constructed to visualize the relationship and groupings of events (Fig 3.2Ciii). Finally, we utilized Dynamic Time Warping (DTW, Fig 3.2D) to measure spatio-temporal dissimilarity between all pairs of ∆F/F curves. DTW allowed non-linear temporal matching between events

(Fig 3.2Di) to find the optimal path of smallest distance (lowest cost). DTW created a time warp matrix where each square was the Euclidean distance between respective timepoints of two

∆F/F time series (Fig 3.2Dii). The optimal warp path for a pair of exocytic events was the lowest cost path through the matrix, which may be distinct from the ∑ Euclidean distance (Fig 3.2Dii).

The sum of the warp path, or the DTW cost, was plotted on a distance matrix, where each pixel represents the warp path sum for a pair of exocytic events (Fig 3.2Diii).

Using the output of each classifier, we took the plurality rules decision from a committee of the most common and best performing clustering indices (Charrad et al., 2014) to confidently determine the number of exocytic classes or clusters (k). Among the indices used were the gap statistic, which measured within-cluster dispersion, and the elbow method, which examined percent variance explained as a function of the number of clusters (Fig 3.2E, see methods).

Unexpectedly, the committee converged on a k of four classes for each classification method instead of the two, as predicted by the literature, with eight of 20 indices selecting four classes

(Fig 3.2F). 96.3% of 733 exocytic events were classified unanimously by the different methods, indicating variations between events and clusters were sufficiently robust to be discerned via multiple methods (Fig 3.2G). Inspection of events with conflicting classification revealed edge cases, with low signal-to-noise or occurrence close to or on the cell periphery (Fig 3.2G, inset).

To confirm that none of the unexpected classes were an artefact of false-positive identification of exocytic events, we treated neurons with tetanus neurotoxin (TeNT, Fig 3.2H), which cleaves

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VAMP2 and blocks VAMP2-mediated fusion (Link et al., 1992), including VAMP2-pHluorin mediated events (Urbina et al, 2018). All four classes were sensitive to TeNT, indicating that all four classes contained bona fide exocytic events.

2.3 Distinguishing features of four modes of exocytosis

We next characterized each exocytic class (Fig 3.3). Class averages and representative image sequences revealed distinct fluorescence profiles (Fig 3.3A, Movie 1). Class 1 and 2 exhibited an instantaneous fluorescence decay after peak ∆F/F (fusion pore opening). In contrast, class 3 and 4 exhibited a delay between peak ∆F/F and the onset of fluorescent decay, observed as a plateau in ∆F/F prior to decay. A single exponential decay fit well to the fluorescence decay in classes 1 and 2 (Fig 3.3B), but was a poor fit for classes 3 and 4 (Fig

2 3.3C). Fitting sequential exponentials resulted in a higher R (Fig 3.3D). t1/2 for class 3 and 4 were calculated from the second exponential, which revealed no differences in t1/2 between class 1 and 3 or class 2 and 4 (Fig 3.3E).

Fluorescence decay following FVF is due to VAMP2-pHluorin diffusion into the bordering PM

(Fig 3.3F). The PM surrounding class 1 and 3 events exhibited a fluorescent peak followed by an exponential decay with the same t1/2 as center pixels (Fig 3.3G,H, Movie 1), consistent with diffusion and FVF. In contrast, VAMP2-pHluorin fluorescence did not increase in the PM surrounding class 2 and 4 events (Fig 3.3F,G), suggesting these may be KNR. Loss of fluorescence following KNR is due to either vesicle-reacidification or vesicle retreat from the evanescent wave. The t1/2 of class 2 and 4 did not change after increasing the evanescent wave penetration (Fig 3S1C), indicating significant fluorescence loss was not due to vesicle retreat from the evanescent wave. In contrast, pH buffering the media with cell-impermeable HEPES slowed fluorescence decay of class 2 and 4 events, but not class 1 and 3 (Fig 3.3E). This

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suggested that HEPES entered the fusion pore of class 2 and 4 vesicles before resealing and retarded reacidification, consistent with KNR. The t1/2 of HEPES-sensitive events were similar to single-vesicle KNR events measured in astrocytes (Bowser and Khakh, 2007), but faster than reported at synapses (Pradeep and Ryan, 2006). These results suggest class 2 and 4 were

KNR and that reacidification rates may differ in developing and mature neurons. Based on the mechanisms of fluorescence decay and either the instantaneous (i) or delayed (d) onset of fluorescence decay after peak ∆F/F, we named the classes FVFi, (class 1), KNRi (class 2),

FVFd (class 3), and KNRd (class 4).

Both VSV-G and FM4-64 localized to vesicles that fused by different modes, indicating fusion mode did not correspond to vesicle class (Fig 3S1A,B). Surface levels of VAMP2- pHluorin varied between cells and between experiments, but did not alter the distribution exocytic modes (Fig 3S1D,E). The consistency in the distribution of modes regardless of

VAMP2 expression level or biological replicate suggested that cellular VAMP2 levels did not alter exocytic modes. There was a stratification of peak ∆F/F between modes (Fig 3S1F). FVFi and KNRi events had a higher peak ΔF/F than FVFd and KNRd, suggesting vesicular VAMP2 levels may influence fusion mode.

To further ensure the four exocytic modes were not an artifact of VAMP2-pHluorin, we employed alternative fusion reporters. Transferrin receptor (TfR) attached to a pH-sensitive red fluorophore pHuji showed 100% overlap with VAMP2-pHluorin fusion events (Fig 3S2A). We thus detected and analyzed exocytic events based of TfR-pHuji fluorescence and diffusion behavior (see Methods). Using all three classifiers with the committee of indices, four classes of exocytosis were detected for TfR-pHuji with identical ratios to VAMP2-pHluorin (Fig 3S2B,C).

The t1/2 of KNRi and KNRd for TfR-pHuji was the same as VAMP2-pHluorin (Fig 3S2D,E), consistent with the similar pH sensitivity of the fluorophores (Martineau et al, 2017), supporting that decay was due to reacidification. In contrast, the t1/2 of FVFi and FVFd were different

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between the two exocytic markers (Fig 3S2D,E), explained by the distinct size and diffusion behaviors of VAMP2 and TfR (Di Rienzo et al., 2013; Fujiwara et al., 2002, 2016; Jaqaman and

Grinstein, 2012; Lenne et al., 2006). This further supports diffusion-dependent fluorescence decay.

As an additional marker of exocytosis, we turned to the v-SNARE VAMP7, which mediates exocytosis of a discrete population of vesicles in developing neurons, albeit at lower frequencies than VAMP2 (Gupton and Gertler, 2010; Urbina et al.,2018). VAMP7-mediated fusion is autoinhibited by the longin doman of VAMP7 (Burgo et al., 2013; Martinez-Arca et al.

2001); a VAMP7-pHluorin construct lacking the longin domain (VAMP7∆LD) increased the frequency of exocytosis (Urbina et al, 2018) sufficiently to identify exocytic modes. Four modes of VAMP7∆LD exocytosis were detected by the three classifiers for this discrete vesicle population. Interestingly the relative abundance of each fusion mode was distinct from VAMP2- pHuorin (Fig 3S2F). Together the VAMP7∆LD and TfR data confirmed the existence of four exocytic modes.

2.4 Expression of a truncated VAMP2 alters exocytic mode

The VAMP2 transmembrane domain catalyzes fusion initiation and pore expansion

(Dhara et al., 2016). In astrocytes, expression of a VAMP2 mutant lacking the transmembrane domain (VAMP21-96, Fig 3S3A) in the presence of endogenous VAMP2 was shown by capacitance measurements to stabilize narrow fusion pores and block full fusion events, but not transient KNR events (Guček et al., 2016). To validate our classification and determine if fusion pore behavior distinguished exocytic mode, we expressed tagRFP-VAMP21-96 along with full length VAMP2-pHluorin, which is capable of and reports fusion. VAMP21-96 reduced the frequency of exocytic events (Fig 3S3B, Movie 2). Similar to the report in astrocytes, VAMP21-96

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specifically reduced the frequency of FVFi and FVFd events (Fig 3S3C). This shifted the distribution of exocytic events toward KNRd and KNRi (Fig 3S3D), aligning with capacitance measurements (Guček et al., 2016) and suggesting that the FVF-like classes may be mechanistically distinct from KNR. These data suggest endogenous VAMP2 and VAMP2- pHluorin still mediate fusion, but the mode is affected by the presence of VAMP21-96.

2.5 TRIM67 biases exocytic mode towards full-vesicle fusion

Experiments with TeNT (Fig 3.2H), HEPES (Fig 3.3E), VAMP2-tagRFP (Fig 3.1F),

VAMP21-96 (Fig 3S3), VAMP7∆LD-pHluorin (Fig 3S2F), and TfR-pHuji (Fig 3S2B-E) support the conclusion that each exocytic class harbors bona fide and distinct exocytic events, yet the biological relevance of and molecular mechanisms influencing exocytic modes are unclear. Our previous work identified the E3 ubiquitin ligase TRIM9 as a novel regulator of neuronal exocytosis (Urbina et al., 2018; Winkle et al., 2014). Although deletion of Trim9 elevated the frequency of exocytosis (Urbina et al., 2018; Winkle et al., 2014), this was not associated with a change in the ratio of classes (Fig 3.4A, Movie 3). TRIM67 is a TRIM9 paralog enriched in developing neurons (Boyer et al., 2018). Unlike Trim9, deletion of Trim67 altered the mode but not the frequency of exocytosis; KNRi and KNRd increased two-fold, whereas FVFi and FVFd decreased (Fig 3.4A,B). These data indicate that exocytic frequency and mode are independently regulated by distinct E3 ligases. FVFd events were enriched in neurites of both

Trim67-/- and Trim67+/+ neurons, and the distribution of exocytic classes within the soma was not distinct from the whole neuron (Fig 3.4C).

During morphogenesis neuronal surface area increases (Fig 3.4D). Our previous experimental and modeling work suggested that VAMP2-mediated exocytosis provided excess material for PM expansion, which was partially balanced by clathrin-mediated endocytosis

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(Urbina et al., 2018) (Fig 3.4). The model used empirically measured surface areas of neurons, non-coated, and clathrin-coated vesicles, along with the frequencies of exocytosis and endocytosis to predict PM expansion. A caveat was the assumption that all exocytic events supplied PM material. Upon considering that KNRi and KNRd comprise ~22% of events (Fig

3.4A), we updated the model with exocytic modes (Fig 3.4F) and new starting surface area measurements. This demonstrated that accounting for KNRi and KNRd improved the similarity of predictions to measured values of surface area (Fig 3.4H vs 3.4E). We extended the simulation to 3 DIV (Fig 3.4I). The modeled neurons matched the measured surface area well, however a population of neurons with larger surface areas arose that were not reflected in simulations.

Since deletion of Trim67 altered the distribution of exocytic mode, we simulated growth of Trim67-/- neurons (Fig 3.4J,K). Incorporation of increased KNR and less FVF predicted a reduction in surface area expansion in Trim67-/- neurons at both 2 DIV and 3 DIV.

Experimentally measured surface area of Trim67-/- neurons at 2 and 3 DIV agreed (Fig 3.4J,K).

These data suggested a bias in exocytic mode was sufficient to alter surface area. Consistent with decelerated morphogenesis, callosal axons in Trim67-/- mice were delayed in midline crossing (Boyer et al, 2020). Simulations using the increased exocytic frequency of Trim9-/- neurons predicted increased neuronal surfaces areas, which was confirmed empirically at 2 and

3 DIV (Fig 3.4L). Trim9-/- simulations did not fit as well, overestimating the increase in neuron size, suggesting compensatory mechanisms exist that were not captured in our model. Despite this, the remarkable fit between simulated and empirically measured data suggested that we have captured relevant factors responsible for developmental neuronal growth.

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2.6 The t-SNARE SNAP47 interacts with TRIM67 and localizes to VAMP2-mediated exocytic events Time-lapse TIRF microscopy revealed that TRIM67 did not localize to VAMP2-mediated exocytic events (Fig 3.5A; Movie 4). The distance between VAMP2-pHluorin exocytic events and TRIM67 were compared to theoretically randomly distributed points using Ripley’s L analysis, which indicated they were below the threshold for colocalization (red dotted line).

Expression of RFP-tagged TRIM67 in Trim67-/- neurons (Fig 3S4A) rescued the ratio of exocytic modes (Fig 3S4B) without changing the frequency of exocytosis (Fig 3S4C). These data confirmed that although TRIM67 did not localize with exocytic events, it biased the mode of exocytosis toward FVFi and FVFd and away from KNRi and KNRd, suggesting TRIM67 biased exocytic mode remotely.

Because few interaction partners of TRIM67 are known, we used the BioID method

(Roux et al., 2012) to identify candidate TRIM67 interacting partners that may regulate exocytosis (Menon et al, 2020). One interesting candidate and the only SNARE protein significantly enriched (~4 fold) over the negative control (Myc-BirA*) was SNAP47, a t-SNARE in the SNAP25 family. SNAP47 forms thermally stable SNARE complexes and competes with

SNAP25 in SNARE complex formation in vitro (Holt et al., 2006). Although SNAP47-containing

SNARE complexes are capable of fusion, they are significantly less efficient in liposomal fusion assays (Holt et al., 2006). Immunoprecipitation of Myc-SNAP47 co-precipitated GFP-TRIM67, but not GFP-TRIM9 from HEK293 cells (Fig 3.5B), confirming that TRIM67 and SNAP47 interact. Live-cell TIRF microscopy revealed that TRIM67-GFP puncta colocalize with TagRFP-

SNAP47, particularly at the periphery of neurons (Fig 3.5C, arrows; Movie 5). Time-lapse TIRF microscopy also revealed that SNAP47 co-localized with a subset (28%) of VAMP2-pHluorin exocytic events (Fig 3.5D; Movie 6). Although we did not perform three color imaging, these data suggested that SNAP47 and TRIM67 interacted at sites distal from exocytic events.

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2.7 Multiple domains of TRIM67 modulate the interaction with SNAP47 and exocytic mode

SNAP47 was a promising candidate to be the intermediary between TRIM67 and the fusion apparatus in regulating exocytic mode. To determine domains of TRIM67 important for interacting with SNAP47, we performed co-immunoprecipitation assays using domain mutants expressed in TRIM67-/- HEK293 cells (Boyer et al., 2018)(Fig 3.5E). Similar structure:function experiments were performed in Trim67-/- neurons to examine exocytic mode (Fig 3S4B,C).

TRIM67ΔFN3 failed to precipitate detectable levels of SNAP47 (Fig 3.5E) or rescue the ratio of exocytic modes (Fig 3S4B). TRIM67ΔSPRY precipitated similar amounts of SNAP47 compared to TRIM67 and rescued exocytic mode (Fig 3.5E,3S4B). In contrast, more SNAP47 was detected in precipitates of constructs lacking the ligase containing RING domain

(TRIM67∆RING) or multimerization coiled-coil domain (TRIM67ΔCC), yet these mutants failed to rescue the ratio of exocytic modes. Mutation of conserved Zn-coordinating cysteines required for ligase activity (TRIM67LD,(Boyer et al., 2020)) also failed to rescue exocytic mode (Fig

3S4B). None of these mutants altered the frequency of exocytosis (Fig 3S4C). Deletion of the

COS domain (TRIM67ΔCOS) however exhibited a unique phenotype; it decreased the frequency of exocytosis, but did not rescue the ratio of exocytic modes (Fig 3S4B,C).

Interestingly, TRIM67ΔCOS precipitated much more SNAP47 (Fig 3.5E). These data fit a

Goldilocks principle, where the interaction strength between TRIM67 and SNAP47 must fall within certain margins to rescue exocytic mode, i.e. mutants that showed a stronger interaction than full length TRIM67 or mutants that did not bind TRIM67 at all were unable to rescue exocytic mode.

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2.8 TRIM67 alters exocytosis via modulated SNAP47 protein levels

Analysis of cell lysates revealed a 1.5x fold increase in SNAP47 protein in Trim67-/- neurons (Fig 3.6A), suggesting that TRIM67 regulated SNAP47 expression or stability. A cycloheximide chase revealed that SNAP47 was degraded at the same rate in Trim67+/+ and

Trim67-/- cortical neurons (Fig 3S5A). Bortezomib or chloroquine treatments indicated SNAP47 degradation was proteasome-dependent and lysosome independent (Fig 3S5A) in both

Trim67+/+ and Trim67-/- cortical neurons. To assess SNAP47 ubiquitination, we performed denaturing immunoprecipitation of GFP-SNAP47 in TRIM67-/- HEK293 cells co-expressing HA- ubiquitin and either Myc or Myc-TRIM67 (Fig 3S5B). The ratio of SNAP47-Ub:SNAP47 was not altered by loss of TRIM67. Altogether these data indicated that SNAP47 ubiquitination and degradation were not detectably altered by loss of TRIM67. Currently the precise mechanism by which loss of TRIM67 results in elevated SNAP47 protein is unknown. However, we hypothesized that the elevated SNAP47 in Trim67-/- neurons may alter the ratio of exocytic modes. Like deletion of Trim67 (Fig 3.4B), overexpression of SNAP47 in Trim67+/+ neurons did not alter the frequency of exocytosis (Fig 3.6B). However, overexpression of SNAP47 increased

KNRd and decreased FVFi (Fig 3.6C). This change was more pronounced in events with detectable tagRFP-SNAP47. SNAP47 fluorescence intensity was highest at FVFd and KNRd events (Fig 3.6D), further suggesting SNAP47 presence influences mode. Strikingly, the delay time before onset of fluorescent decay increased for KNRd and FVFd, which was most evident in events where TagRFP-SNAP47 was detectable (Fig 3.6E). Once fluorescence decay initiated, overexpression of SNAP47 did not affect half-life (Fig 3.6F). These findings indicated increased SNAP47 promoted KNRd and was sufficient to shift exocytic mode. Cell surface area was reduced at 3 DIV after SNAP47 overexpression, further supporting that reducing membrane delivery by exocytosis impacts plasma membrane expansion (Fig 3.6G). To determine if increased SNAP47 was required to shift exocytic mode in Trim67-/- neurons, we reduced

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SNAP47 protein levels using siRNA (Fig 3.6H,H’). siRNA resulted in ~80% knockdown of

SNAP47 (Fig 3.6H,H’). Knockdown of SNAP47 in Trim67-/- neurons did not alter the frequency of exocytosis (Fig 3.6I), but decreased KNRd and KNRi and increased FVFi and FVFd (Fig

3.6J). In contrast, knockdown of SNAP47 in Trim67+/+ neurons did not alter exocytic frequency or mode (Fig 3.6I,J). Together these data suggest that physiological levels of SNAP47 were insufficient to alter exocytic mode at this developmental stage, whereas experimentally increasing SNAP47 levels by overexpression or deletion of TRIM67 revealed a role for SNAP47 in exocytic mode regulation.

2.9 SNAP47 forms more SNARE complexes in Trim67-/- neurons

The increased SNAP47 protein in Trim67-/- neurons altered the ratio of SNAP47 to

SNAP25 protein (Fig 3.7A). Endogenous immunoprecipitation of SNAP47 from membrane fractions of Trim67+/+ and Trim67-/- neurons showed that SNAP47 interacted with syntaxin-1 and

VAMP2 (Fig 3.7B). We thus hypothesized that SNAP47 altered exocytosis by forming more

SNARE complexes in Trim67-/- neurons. We thus hypothesized that SNAP47 altered exocytosis by forming more SNARE complexes in Trim67-/- neurons. To test this, we treated Trim67+/+ and

Trim67-/- cortical neurons with NEM to prevent SNARE disassembly and compared SDS- resistant SNARE complexes at 37˚C, which run at higher molecular weights than their monomeric constituents. These high molecular weight SNARE proteins were not present upon simultaneous NEM,DTT treatment or at 100˚C incubation, which allow and promote disassembly, respectively (Banerjee et al., 1996). VAMP2, SNAP25, and syntaxin-1 exhibited similar proportions of complex:monomer between genotypes. In contrast, SNAP47 exhibited increased amounts of complex:monomer in Trim67-/- neurons (Fig 3.7C). This suggested that

TRIM67 inhibited the incorporation of SNAP47 into SNARE complexes and thus, SNAP47-

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mediated fusion events. Therefore in the absence of TRIM67, increased SNAP47 protein levels may compete with SNAP25 to alter SNARE complex composition and exocytic mode.

3 Discussion

Here we developed an unsupervised set of classifiers based on VAMP2-pHluorin fluorescence to identify four distinct exocytic classes in developing cortical neurons. These four classes were independent of cellular VAMP2 levels, occurred with both post Golgi vesicles and recycling vesicles, and were detected with other markers and vesicle types. Distinguishable mechanisms of VAMP2-pHluorin fluorescence decay that were either pH-sensitive or diffusion- dependent suggested that two classes were KNR-like and two classes were FVF-like. The delayed or instantaneous onset of fluorescence decay after fusion pore opening further distinguished exocytic classes into KNRd, KNRi, FVFd, and FVFi. Even though the diffraction-limited resolution of our microscope obfuscates visualization of fusion pore properties other than opening, the alignment of our experiments using VAMP21-96 with capacitance measures supports the hypothesis that the visualized delay in fluorescence decay is either a delay in fusion pore dilation (FVFd) or delay in fusion pore closure (KNRd). Deletion of Trim67 increased KNRi and KNRd, decreased FVFi and

FVFd, and reduced neuronal surface area. Our data support the hypotheses that TRIM67 altered exocytic mode in part by regulating SNAP47 incorporation into SNARE complexes, and that SNAP47 pauses the fusion pore in an open state.

3.1 Distinguishing exocytic modes with novel classifiers and protein perturbation

Distinguishing exocytic modes is complicated by the fast and diffraction-limited nature of vesicle fusion and fusion pores (Albillos et al., 1997; Bao et al., 2018). In contrast to semi- automated, supervised classification algorithms (Diaz et al., 2010; Yuan et al., 2015), our classifier requires no a priori information. Instead automated exocytic detection coupled with three classifiers

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and clustering indices repeatedly delivered the surprising finding that four classes of exocytic events exist in developing cortical neurons. This three pronged approach was robust: altering the half-life of a subset of events with HEPES did not alter classification or frequency of different exocytic modes.

Further, classification was identical between VAMP2 and TfR at fusion events, even though the vesicular cargo TfR exhibited distinct diffusion dynamics in FVFi and FVFd.

Distinguishing KNR and FVF at the synapse remains controversial, relying on indirect and often population-based evidence to infer single vesicle behavior (He and Wu, 2007; Alabi and Tsien,

2013; Karatekin, 2018). Using developing neurons and their unique spatial distribution and temporal frequency of exocytosis, we resolved individual events, allowing identification of not two, but four discrete classes. Some classes exhibited a surprising delay after fusion, in which fluorescence does not decay instantaneously. This delay may occur if the fusion pore stabilized in an open state, which could facilitate the release of larger cargo. This is supported by the expression of a VAMP2 peptide known to stabilize the fusion pore (Guček et al., 2016). Alternatively, the delay may represent a

“decision point” for the fusion state, prior to the vesicle undergoing FVF or KNR. Finally, the delay could occur due to an inefficiency in fusion, as the opening and dilation of the fusion pore represents an energy barrier that SNARE machinery must overcome (Chernomordik and Kozlov, 2008).

Previous work in mature neurons supported the existence of FVF and KNR, which influenced our interpretation of the four classes of exocytosis in developing neuron (Alabi and Tsien, 2013;

Albillos et al., 1997; He and Wu, 2007; Elhamdani et al., 2006). Previous work in chromaffin cells suggested additional exocytic modes differing from FVF and KNR (Chiang et al, 2014; Shin et al,

2018; Shin et al, 2020). Exocytic events previously interpreted as KNR were argued to be omega- shrink fusion, in which a vesicle fuses and adds up to 80% of its vesicle membrane before retreating from the plasma membrane, with slowed or absent diffusion of VAMP2-pHluorin (Chiang et al, 2014).

Although we cannot rule out that omega-shrink fusion occurs in developing neurons, vesicles in chromaffin cells are much larger compared to the diameter of vesicles in developing cortical neurons

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and fluorescent decay an order of magnitude slower in chromaffin cells compared to fusion events in developing neurons. Therefore, fusion events in these two systems may not be directly comparable.

3.2 Exocytic mode and morphogenesis

Prior to synaptogenesis, exocytosis provides membrane material to the expanding PM

(Pfenninger, 2009; Urbina et al., 2018), which would suggest FVF predominates in developing neurons. Whether KNR-like modes of exocytosis carry cargo that contribute to morphogenesis remains to be determined. Although vesicular cargo is unidentified at this point, we did find that

VAMP2 may be common exocytic machinery for both post Golgi vesicles and recycling vesicles, consistent with other studies (Kubo et al., 2015; Oishi et al., 2006), and that both vesicle types fused by multiple modes. Whether these VAMP2-containing vesicles represent a shared sorting compartment with both newly synthesized cargo and recycled cargo is not known, but such a compartment has been observed in neuroendocrine cells (Park et al. 2011). In a mature neuron, secretion via KNR may be more relevant to maintain a steady-state surface area. Supporting a role for KNR in the mature neuron, protein levels of TRIM67, which promotes FVF, drop in the adult cortex (Boyer et al., 2018). In developing neurons, FVFi events comprised most soma events, whereas FVFd predominated in neurites, with overall events more frequent in soma. Both FVFi and

FVFd provide membrane material; functional differences between these modes of fusion are not yet known. Perhaps different membrane curvatures and tension in the soma and neurites or distinct membrane composition trigger spatial differences in fusion mode. However, whether these different modes represent distinct vesicles with distinct cargo remains a possibility. The increased KNRi and

KNRd along with decreased FVFi in the soma of Trim67-/- neurons were associated with reduced neuronal surface area, supporting a role for FVFi events as a mechanism for providing new membrane required for morphogenic PM expansion. The fluid nature of the PM may account for how changes in the soma affect morphogenesis. Membrane remodeling may also be affected by actin

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dynamics in the axonal growth cone. Since TRIM67 regulates filopodia in the growth cone (Boyer et al., 2020), TRIM67-mediated coordination of both exocytosis and actin dynamics likely contribute to morphogenesis, yet relative contributions are not known. The reduction in PM surface area associated with SNAP47 overexpression however suggest that altering exocytic mode is sufficient to alter PM expansion.

Simulations based on frequency and mode of exocytosis, frequency of endocytosis, and respective vesicle size remarkably recapitulated developmental increases in neuronal surface area, fitting better than simulations assuming all fusion events were FVF. Altering exocytic mode or frequency in model simulations to mimic Trim67-/- or Trim9-/- neurons accurately predicted altered surface areas. Although the simple model makes a number of assumptions, including that vesicles fusing via different modes are the same size and that vesicles sizes and rates of endocytosis are not altered by deletion of Trim9 or Trim67, the accurate fit to empirical data suggests that our model captures relevant parameters to accurately predict surface area expansion. However, simulations in

Trim9-/- neurons overestimated expansion, suggesting possibly that endocytosis was also elevated in these neurons. Consistent with this, we recently identified several candidate TRIM9 interactors associated with clathrin coats (Menon et al., 2020). We conclude that VAMP2-mediated fusion is the primary source of membrane addition in developing neurons, and that manipulating exocytic frequency or mode alters neuronal growth. The accuracy of our model decreased somewhat at 3

DIV, suggesting additional parameters in membrane remodeling were not captured. Perhaps analogously, in vivo Vamp2-/- neurons have a largely normal morphology, suggesting compensatory mechanisms such as membrane added via ER-PM contact sites or exocytosis mediated by other

SNARE proteins like VAMP7 (Fuschini et al., 2018; Galli et al., 1998) participate in membrane remodeling.

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3.3 Regulation of Exocytic Mode

Regulation of fusion pore kinetics and mode of exocytosis by regulatory proteins and number of SNAREs are well-documented (Bao et al., 2018; Bretou et al., 2014; Gauthier et al., 2011; Wen et al., 2016; Logan et al., 2017; Segovia et al., 2010; Archer et al., 2002). Altering SNARE complex composition adds another layer of regulation, as shown by our results with both VAMP21-96 and

SNAP47. Effects of VAMP21-96 expression on exocytic mode possibly support a role for the number of functional SNARE complexes, by suggesting that altering the number of SNARE complexes with fusion-competent endogenous VAMP2 and VAMP2-pHluorin affects exocytic mode. Analogously, exocytic mode was altered by increased incorporation of SNAP47, a member of the SNAP25 family of Qb/Qc t-SNAREs, into SNARE complexes. SNAP47 is capable of substituting for SNAP25 to form

SNARE complexes with VAMP2 and syntaxin-1. These complexes however are inefficient in liposomal fusion assays, suggesting that fusion is slow to proceed in comparison to SNAP25 containing complexes (Holt et al., 2006). This is consistent with our finding of increased SNAP47 at

FVFd and KNRd events, and in increased delay time between fusion pore opening and fluorescence decay. We hypothesize that the SNAP47-containing complexes may be the primary SNARE complex involved in KNRd. Interestingly SNAP47 has many unique features that may be responsible for altered fusion kinetics: it lacks cysteine palmitoylation-mediated membrane targeting, has a long N- terminal extension, and has a linker region between the Qb and Qc SNARE domains (Holt et al.,

2006). Structure-function experiments and observations that TRIM67 interacted with and colocalized with SNAP47 distal to exocytic sites, and that SNAP47 co-localized with a subset of VAMP2-pHluorin events with distinct fusion behavior, fit with a hypothesis that TRIM67 regulated exocytic mode via a weak interaction with SNAP47. Future studies will examine the mechanisms by which TRIM67 regulates SNAP47 protein level and incorporation into SNARE complexes, and how these may be related. SNAP47 also localizes to the ER, Golgi, and post-Golgi compartments (Kuster et. al, JBC,

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2015), suggesting that TRIM67 and SNAP47 potentially mark secretory vesicle release sites in addition to regulating modes of exocytosis.

SNAP47 knockdown did not completely rescue the distribution in modes of exocytosis in

Trim67-/- neurons, suggesting that TRIM67 regulates exocytic fusion by multiple mechanisms. Myosin

II, actin remodeling at the PM, and membrane tension are all suggested to alter exocytosis (Aoki et al., 2010; Wen et al., 2016). TRIM67 interacts with the actin-polymerase VASP at filopodia tips in the growth cone, a highly dynamic region with local changes in membrane tension and actin remodeling

(Boyer et al., 2020; Staykova et al., 2011; Wen et al., 2016), but whether VASP alters exocytosis is not known. Proximity biotinylation approaches revealed several candidate TRIM67 interactors that are poised to regulate exocytosis, including the exocyst complex, tomosyn, and Munc18 (Menon et al., 2020). Separate pools of vesicles with distinct fusion characteristics and cargo (Hua et al., 2011;

Rizzoli and Jahn, 2007) may fuse with different modes of fusion. Future work must define how

TRIM67 interfaces with these vesicle populations and distinct mechanisms regulating fusion, and whether this unique regulation occurs at the synapse or is specific to earlier developmental time points.

4 Materials and Methods

4.1 Plasmids and antibodies

Plasmid encoding human-VAMP2-pHluorin was acquired from James Rothman (Yale,

New Haven, CT). VAMP2-tagRFP was generated by cloning from VAMP2-pHluorin into the tagRFP plasmid (Clontech). VAMP21-96-tagRFP was created by PCR of VAMP2 (AA 1-96) into a tagRFP vector. VAMP7∆LD-GFP and Myc-SNAP47 was obtained from Thierry Galli (University of Paris, France). VAMP7∆LDwas cloned into pCax-pHluorin. TagRFP-SNAP47 was generated by PCR of SNAP47 from Myc-SNAP47 followed by cloning into a tagRFP vector. VAMP2-pHuji

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was generated by cloning VAMP2 into a pHuji backbone (Addgene, Plasmid #61505). TRIM67 constructs were generated as described in (Boyer et al., 2020). Flag-tagged Ubiquitin was obtained from Ben Philpot (University of North Carolina at Chapel Hill, Chapel Hill, NC). GFP-

CAAX was obtained from Richard Cheney (University of North Carolina at Chapel Hill, Chapel

Hill, NC). TfR-pHuji was obtained from David Perrais (Université de Bordeaux, France, Addgene

Plasmid #61505). Antibodies included rabbit polyclonal against TRIM67 (Boyer et al., 2018); mouse monoclonal against TRIM9 (H00114088-M01, Abnova); mouse monoclonal against c-

Myc (9E10); mouse monoclonal against human β-III-tubulin (TujI SCBT); ubiquitin (sc-8017,

SCBT); GAPDH (sc-166545, SCBT); GFP-Trap® Agarose (Chromotek); VAMP2 (D6O1A)

Rabbit mAb #13508 (Cell Signaling); Syntaxin-1 (sc-12736, Santa Cruz); SNAP25, Mouse monoclonal (Synaptic Systems 111 011); SNAP47, polyclonal rabbit (Synaptic Systems

111403); HA, from the lab of Patrick Brennwald (UNC: Chapel Hill); IRDye® 800CW Donkey anti-Mouse IgG Secondary Antibody and IRDye® 680LT Goat anti-mouse Secondary Antibody

(Licor); rabbit polyclonal against HA (71-5500, Thermo Fisher Scientific); sepharose beads

(Sigma) siRNAs pool targeting SNAP47 were purchased from Dharmacon (target sequence 5’-

CGTACGCGGAATACTTCGA-3’). FM 4-64 Dye was purchased from ThermoFisher (T13320).

4.2 Animals

All mouse lines were on a C57BL/6J background and bred at UNC with approval from the Institutional Animal Care and Use Committee. Mouse colonies were maintained in specific pathogen-free environment with 12-12 hr light and dark cycles. Timed pregnant females were obtained by placing male and female mice together in a cage overnight; the following day was designated as embryonic day 0.5 (E0.5) if the female had a vaginal plug. Trim9-/- mice were described in Winkle et al., 2014 and Trim67-/- mice were described in (Boyer et al., 2018). Mice

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were genotyped using standard procedures. For all culture experiments, embryos from time- matched homozygous WT, homozygous Trim9-/-, or homozygous Trim67-/- were used.

4.3 Culture, transfection, and treatment of primary neurons and HEK293 cell lines

Male and female embryos were used to generate cultures and were randomly allocated to experimental groups. Cortical neuron cultures were prepared from day E15.5 embryos as previously described (Viesselmann et al., 2011). Briefly, cortices were micro-dissected and neurons were dissociated with 0.25% trypsin for 15 min at 37˚C followed by quenching with neurobasal media supplemented with 10% FBS and 0.5 mM glutamine. Cortices were gently triturated 15x and cells were counted by hemocytometer. Cells were spun at 0.1xg for 7 min at room temperature. Pelleted neurons were resuspended in neurobasal media supplemented with

B27 (1:50 of manufacturer stock (Invitrogen)) and plated on cover glass, Mattek dishes, or tissue culture plastic coated with 1 mg/ml poly-D-lysine (Sigma-Aldrich). This same media was used for all time-lapse experiments. For transfection of plasmids and siRNA, neurons were resuspended after disassociation in Lonza Nucleofector solution (VPG-1001) and electroporated with Amaxa Nucleofector according to manufacturer protocol. A pool of 4 siRNAs targeting mouse SNAP-47 and control Luciferase siRNA (Target Sequence: 5'-

CGTACGCGGAATACTTCGA-3') (Dharmacon, Thermofisher Scientific) were electroporated along with GFP into E15.5 cortical neurons (2 µg GFP + 50 pmol siRNAs). Neurons were fixed and immunostained at 3 DIV. For TeNT treatment of VAMP2-pHluoring expressing neurons, 50 nM of TeNT (Sigma) was added to the neuronal media 30 min prior to imaging. For HEPES treatment, VAMP2-pHluorin exocytic events were imaged immediately after addition of 60 mM

HEPES to the imaging media. For the TeNT and HEPES treatment, neurons were electroporated with VAMP2-pHluorin and imaged at 2 DIV. as described in “Live imaging and image analysis”. For assays investigating SNAP47 protein stability, 2 DIV neurons were treated

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with cycloheximide (50 μg/μl) for 0, 2, 4, 8 hrs or cycloheximide (50 μg/μl) and bortezomib (200 nM) for 8 hrs or cycloheximide (50 μg/μl) and chloroquine (50 μM) for 8 hrs prior to lysis.

HEK293 cells (female) were obtained from Rothenfußer (Klinikum der Universit¨at

München, München, Germany). HEK293 cells were maintained at 5% CO2 at 37˚C in DMEM with glutamine (Invitrogen) supplemented with 10% FBS (Hyclone). TRIM67-/- HEK293 cells were generated from this line by CRISPR/Cas9 editing as described in (Boyer et al., 2020).

HEK293 cells were transfected with Lipofectamine 2000 (Invitrogen) or Polyplus jetPRIME® reagent according to manufacturer protocol.

4.4 Imaging

4.4.1 Live and fixed cell imaging.

All live cell time-lapse imaging was performed on an inverted Olympus microscope

(IX81-ZDC2) with MetaMorph acquisition software, an Andor electron-multiplying charge- coupled device (iXon), and a live cell imaging chamber (Tokai Hit Stage Top Incubator INUG2-

FSX). A UAPON 100x/1.49 NA DIC TIRF objective (Olympus) was used for all live cell TIRF imaging assays. GFP-CAAX images of basal surface area were acquired with an Olympus 40x,

1.4 NA Plan Apo DIC objective. The live cell imaging chamber-maintained humidity, temperature at 37°C, and CO2 at 5%. Fixed cells were imaged using Zeiss LSM 780 confocal laser scanning microscope using a 20x/0.75 U-Plan S-Apo objective at room temperature in glycerol and n-propyl-gallate-based mounting medium.

For all exocytosis assays, E15.5 murine cortical neurons expressing VAMP2-pHluorin,

VAMP7∆LD-pHluorin, VAMP2-pHuji, FM 4-64 dye, or Tfr-pHuji and/or tagRFP expressing constructs were imaged at the 2 DIV with a 100x 1.49 NA TIRF objective and a solid-state 491-

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nm laser illumination and a solid-state 561-nm laser, both at 35% power at 110-nm penetration depth, unless indicated otherwise. Images were acquired using stream acquisition, imaging at

10 Hz for 2 min with 100 ms exposure time. For all colocalization experiments and dual-color live cell imaging, a Hamamatsu W-VIEW GEMINI image splitter optic (A12801-01) was used for simultaneous imaging of both lasers by splitting and projecting the beams, by wavelength, side- by-side onto an electron-multiplying charge-coupled device (iXon).

For spontaneous FM 4-64 dye loading, neurons were incubated in a 10 µM solution of

FM 4-64 dye in Hank’s Balanced Salt Solution (HBSS) for 30 min at 37°C. The media was then replaced with non-dye containing HBSS for 15 min to wash the cells. This washing step was performed 3 times. After the 3rd wash, cells are re-incubated in Neurobasal media and imaged.

4.5 Image analysis

ImageJ software was used for viewing of images and frames.

4.5.1 Detection of exocytic events

To identify the soma and neurites, the soma was segmented from the neurites as a rough ellipsoid encompassing the body of the neuron. All VAMP2-pHluorin, tfR-pHuji, and

VAMP7∆LD-pHluorin exocytic events were detected using the automated detection software as described (Urbina et al., 2018) in Matlab and R with Rstudio. Briefly, the algorithm defines exocytic events as transient, non-motile (mean-square displacement <0.2 m2), gaussian shaped objects that reached peak fluorescent intensity four deviations above the local background intensity.

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4.5.2 Analysis of exocytic events

Once events were detected, a region of interest (ROI) of 25x25 pixels surrounding the gaussian-shaped exocytic event and the bordering plasma membrane 10 frames before (1 second) and 100 frames following (10 seconds) was used for subsequent event analysis. The border plasma membrane was defined as follows: A Difference-of-Gaussian (Marr and Hildreth,

1979) filter was performed on each ROI around an exocytic event to segment the gaussian shaped fluorescent signal at timepoint 1. The border pixels of exocytic events were defined as the average fluorescence of a 15 pixel-wide perimeter surrounding the segmented gaussian shaped fluorescence.

Several parameters were measured within this ROI (Table S2). This includes the normalized change in fluorescence (ΔF/F) of the gaussian, defined as the (average intensity of an ROI – background fluorescence)/background fluorescence. The background fluorescence was calculated as the average of the region of the gaussian in the first 10 frames (1 second) before an exocytic event is detected. To ensure all relevant fusion information was obtained, fluorescence was tracked for 10 seconds following an exocytic event because a subset of exocytic events have an extended decay; this empirically determined time-frame ensured fluorescence returned to baseline (Urbina et al., 2018).

To determine the rate of decay of fluorescence, an exponential decay model was used for FVFi and KNRi:

First we estimate the decay constant, λ, from the data:

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In which V(t) is the fluorescence intensity at each timepoint and t is time in seconds. To ensure that the exponential decay constant is a good fit, we take the natural log of the data to estimate the R2 value:

After ensuring a good fit, we determine the half-life (t1/2) from the decay constant.

To estimate the rate of decay of FVFd and KNRd, two sequential exponential decay models were fit to the gaussian. The log of fluorescence from peak ΔF/F until the end of decay were fit using segmented regression, with the breakpoint between the two regressions decided by iteratively moving the breakpoint one time-step, fitting segmented regression, and minimizing the sum of squared error. The breakpoint that has the lowest total error was considered the true breakpoint. The first regression represents the “delay” before decay starts, and second regression’s t1/2 represents the fluorescence decay.

4.6 Classification of exocytosis

Exocytic events were classified using three independent methods of classification:

Feature extraction and principle component analysis, hierarchical clustering, and dynamic time warping (DTW).

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4.6.1 Feature selection and principal component analysis

Feature selection was performed using both Matlab and R. In Matlab, image features were extracted from the ROI. In R, features were extracted from the the gaussian shaped exocytic event, and the bordering plasma membrane (example in Fig 3.1B). A full list of features extracted is appended in Table S2. After features were extracted from all exocytic events, principal component analysis was performed using the R package “FactoMineR” (Lê et al. 2008) using the singular value decomposition approach. The principal components that captured the top 85% of variance were kept and used for the committee of indices.

4.6.2 Hierarchical clustering

Hierarchical clustering was performed using R’s base “stats” package and the “cluster” package.

Agglomerative: This function performed a hierarchical cluster analysis using a set of dissimilarities for the n objects being clustered. Initially, each object was assigned to its own cluster and then the algorithm proceeds iteratively, at each stage joining the two most similar clusters, continuing until there was a single cluster. At each stage distances between clusters were recomputed by the Lance–Williams dissimilarity update formula. In hierarchical cluster displays, a decision was needed at each merge to specify which subtree should go on the left and which on the right. Since, for n observations there are n−1 merges, there are 2(n−1) possible orderings for the leaves in a cluster tree, or dendrogram. The algorithm iordered the subtree so that the tighter cluster was on the left (the last, i.e., most recent, merge of the left subtree was at a lower value than the last merge of the right subtree). Single observations were the tightest clusters possible, and merges involving two observations were ordered by observation sequence number.

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Divisive: fully described in chapter 6 of Kaufman and Rousseeuw (1990). The algorithm constructed a hierarchy of clusterings, starting with one large cluster containing all n observations. Clusters were divided until each cluster contains only a single observation. At each stage, the cluster with the largest diameter were selected. (The diameter of a cluster was the largest dissimilarity between any two observations.) To divide the selected cluster, the algorithm first selected the most disparate observation (i.e., the largest average dissimilarity to the other observations of the selected cluster). This observation initiated the "splinter group". In subsequent steps, the algorithm reassigned observations that were closer to the "splinter group" than to the "old party". The result was a division of the selected cluster into two new clusters.

4.6.3 Dynamic Time Warp

Dynamic time warping was implemented using the ‘dtw’ package in R (Giorgino, 2009) to compare two time series, X = (x1,..., xN) and Y = (y1,..., yM). DTW assumed a nonnegative, local dissimilarity function f between any pair of elements xi and yj, with the shortcut:

d(i; j) = f(xi; yj) ≥ 0 (1) d, the cross-distance matrix between vectors X and Y, was the only input to the DTW algorithm.

The warping functions φx and φy remapped the time indices of X and Y respectively. Given φ, the average accumulated distortion between the warped time series X and Y was computed:

where mφ(k) was a per-step weighting coefficient and Mφ was the corresponding normalization constant, which ensured that the accumulated distortions were comparable along different

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paths. To ensure reasonable warps, constraints were imposed on φ. Monotonicity was imposed to preserve their time ordering and avoid meaningless loops:

φx(k + 1) ≥ φx(k)

φy(k + 1) ≥ φy(k)

The goal of DTW was to find the optimal alignment φ such that

Which represented the warp path cost of X and Y. The warp path cost for each exocytic event pairing was built into a matrix.

4.6.4 Clustering

Each of the classifiers were independently clustered by a committee of indices.

Numerous clustering validity algorithms have been proposed (Rousseeuw, 1987; Pelleg et al.,

2000; Sugar and James, 2003). These algorithms combine information about intracluster compactness and intercluster isolation, as well as other factors, such as geometric or statistical properties of the data, the number of data objects, and the dissimilarity or similarity measurements. However, different algorithms lead to different clusters, and even in a single algorithm, choice of parameters can lead to different clusters. To perform classification, a plurality-rules decision of 20 well-used algorithms for determining clustering was used. This committee of indices includedA full list of the 20 methods used in this committee of indices can be found in Charrad et al, 2014.

The analysis described above was developed for VAMP2-pHluorin, and exploited for

VAMP7∆LD-pHluorin and TfR-pHuji. Adjustments were made in TfR-pHuji analysis because it was larger (~980 amino acids) than VAMP2-pHluorin (~350 amino acids) and had distinct

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diffusion characteristics (Di Rienzo et al., 2013; Fujiwara et al., 2002; Fujiwara et al., 2016) TfR undergoes hop diffusion, a model of transmembrane protein diffusion characterized by various transmembrane proteins anchored to the actin-based membrane skeleton meshwork acting as rows of pickets that temporarily confine diffusion. To capture this information, an additional parameter measuring goodness-of-fit to linear or exponential decay was added. The goodness- of-fit was defined as the R2 of fitting a linear regression to the fluorescent decay divided by the

R2 of fitting an exponential curve for the half-life as described under “analysis of exocytic events”. A linear fit will have a high linear goodness-of-fit, while an exponential curve will have a lower value.

4.6.5 Colocalization

To perform co-localization analysis, TRIM67tagRFP and SNAP47tagRFP puncta were identified by difference-of-gaussian filter. The x,y centroids of VAMP2-pHluorin events, TagRFP-

SNAP47 puncta, or TRIM67-tagRFP puncta were fit to a marked point process model, which allowed comparisons of groups of points, and Ripley’s L value distances were measured.(Grantham, 2012). Ripley’s L value measures the average distance between points normalized to the number of puncta and total cellular area. This average distance was statistically compared to what distances expected if points were “randomly“ localized, allowing a measure of colocalization. Ripley’s L values here were compared to theoretically random marked points, setting a threshold of 0.05% chance to fall below the theoretical Ripley’s L value.

All values were normalized to the theoretical Ripley’s L value for comparisons.

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4.7 Surface area measurements and modeling predictions

4.7.1 Empirical Surface Area Estimation

To estimate the surface area of neurons during DIV 1-3, TIRF images of GFP-CAAX expressing cells at indicated time points were used. The soma was identified and segmented from the neurites, and the basal surface area of each were calculated separately. Total neurite area was estimated by doubling the measured basal membrane area. The average height of the soma (~11 μm) was obtained from confocal image z stacks through cortical neurons at 2 DIV.

The major and minor axis of the soma were measured, and used with the average height of the soma to calculate the surface area using the following calculation for a truncated ellipsoid:

4.7.2 Model Selection

A Bayesian linear model based on Urbina et al., 2018 was adapted to predict neuronal surface area increases from DIV1 to DIV 2 and 3. Surface area at 2 and 3 DIV, the predicted and independent variables, are a positive, continuous, right skewed distribution, which has non- uniform standard deviation over time. Several models were constructed, selecting for probability distributions that would fit the criteria of positive, continuous, and right-skewed data: log-normal,

Skew-normal, and Gamma. Models using the t-distribution and the normal distribution as a base model were also constructed, based on their well-characterized distributions. Previously published data were used as priors. Namely, the surface area of neurons at 1 DIV, the surface

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area of vesicles, the frequency of exocytosis, the frequency of clathrin-mediated endocytosis, and the size of clathrin-coated vesicles were used. This was combined with newly measured surface area of neurons at 1 DIV (described under “Surface area estimation”) to model membrane addition. This provided confidently constructed priors that were estimated from previously measured neuronal surface area, instead of subjectively chosen priors. From this, four hierarchical models of surface area expansion were constructed: Gamma with a log-link, log-normal, skewed-normal, and log-normal with a lower bound truncation of 0. Given the difference in the standard deviation between the observed surface area at 1 and 2 DIV (Urbina et al., 2018), a fifth model was added, in which the standard deviation was also modeled based on the measured standard deviation at 1 and 2 DIV. Four chains were run with 100,000 samples after a burn-in period of 10,000 steps for a total of 110,000 steps to ensure chain convergence and enough coverage of the posterior distribution. Model evaluation using Rhat and ESS suggested the chains were well-mixed and converged properly. Each of these models were then compared by computing Watanabe-Akaike information criteria and using leave-one-out validation to compute the expected log pointwise predictive distribution for the difference in each of the models. Model selection using these criteria indicate that using the log-normal family results in the best fit for these data, followed by the gamma distribution. Taking all of these terms into account, an equation for the hierarchical Bayesian linear model was created, using the log-normal distribution:

Y ~ N(B0 + B1xi, sigma ~ N(0,1))

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4.8 Western blot

4.8.1 Analysis of protein lysates

To investigate SNAP47, SNAP25, and GAPDH protein levels, cultured E15.5 cortical neurons of indicated genotypes were lysed in immunoprecipitation (IP) buffer (10% glycerol, 1%

NP-40, 50 mM Tris pH 7.5, 200 mM NaCl, 2 mM MgCl2, 1 mM NEM, and protease and phosphatase inhibitors at 2 DIV. Lysates were collected and centrifuged at 19,745 xg for 10 min.

Supernatants were diluted in 4x sample buffer (200 mM Tris-Cl, pH 6.8, 8% SDS, 0.4%

Bromophenol Blue, 40% glycerol, 20% 2-Mercaptoethanol), boiled for 10 min at 100 °C, and resolved by SDS-PAGE. For SNAP47 half-life measurements, cycloheximide treated cortical neurons were lysed (300 mM sucrose, 1% NP-40, 50 mM Tris pH 7.5, 200 mM NaCl, 2 mM

MgCl2 and protease, phosphatase and deubiquitinase inhibitors). Lysates were collected and centrifuged at 19,745 xg for 10 min. Protein concentrations were estimated, and lysates were boiled with 2x sample buffer and resolved by SDS-PAGE.

4.8.2 Co-immunoprecipitation assay

For coimmunoprecipitation assays in HEK293 cells expressing GFP-SNAP47 and either

MycTRIM9, MycTRIM67, or MycTRIM67 domain deletion mutants. were lysed in IP buffer and lysates collected and centrifuged at 19745xg for 10 min. 500–1,000 µg of protein per sample was used per IP (the same amount of protein was maintained across all conditions per experiment). Myc-tagged proteins were precipitated with anti-Myc antibody (9E10) overnight, followed by Protein A coupled agarose beads. Beads were washed twice with IP buffer, pelleted, and boiled with 2X sample buffer. For endogenous SNAP47 immunoprecipitation from cultured cortical neurons, E15.5 cortical neurons at 2 DIV were treated with 1mM NEM (15 min) prior to lysis. Membrane proteins were enriched through ultra-centrifugation of neuronal lysates,

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as described in (Shimojo et al., 2015). Briefly, cortices of wild-type mice were homogenized in extraction buffer (20 mM HEPES-NaOH (pH 7.4), 320 mM sucrose, 5 μg/ml leupeptin, 2 μg/ml aprotinin, and 1 μg/ml pepstatin) post NEM treatment. Membrane fractions were isolated by two sequential steps of centrifugation at 3,000 and 100,000 × g and solubilized for 30 min in 20 mM

HEPES-NaOH (pH 7.4), 150 mM NaCl, and 1% Triton X-100. Protein extracts were cleared by centrifugation at 100,000 × g and incubated overnight with sepharose beads coated with anti-

SNAP47, or control IgG antibodies. Attached complexes were then washed five times with extraction buffer, eluted from the beads with an SDS gel loading buffer, and resolved by SDS-

PAGE.

4.8.3 SNARE complex assay

SNARE complex assays were performed as described in (Hayashi et al., 1994) with modification. E15.5 cortical neurons at 2 DIV were treated with either 1mM NEM (15 min) or

1mM NEM+ 2mM DTT (15 min) prior to lysis with homogenization buffer (10mM HEPES-NaOH, pH 7.4, 150mM NaCl, 1mM EGTA, 1mM NEM, and protease inhibitors). After collection of lysates, Triton X-100 was added to a final composition of 1% Triton x-100 followed by trituration

10x and solubilization for 2 min on ice. Lysates were then centrifuged at 10min, 6000 g.

Samples were diluted in 4x sample buffer (200mM Tris-Cl, pH 6.8, 8% SDS, 0.4% Bromophenol

Blue, 40% glycerol, 20% 2-Mercaptoethanol) into two replicates, one replicate incubated at 100

°C for 10 min and the second incubated at 37 °C for 10 min. SNARE complexes and monomers were then resolved by SDS-PAGE.

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4.8.4 Ubiquitination experiment

For ubiquitination assay, TRIM67-/- or rescued (transfected with Myc or Myc-TRIM67 respectively) HEK cells were co-transfected with HA-tagged Ubiquitin and control GFP or GFP-

SNAP47 using Lipofectamine 2000. GFP-Trap beads (Chromotek) were used to enrich GFP-

SNAP47 and GFP according to manufacturer protocol. Briefly, cells were treated with MG132 for 4 h then lysed in ubiquitination immunoprecipitation (Ub-IP) buffer (50 mM Tris-Cl, 150 mM

NaCl, 1 mM EDTA, 0.5% Triton X, 0.7% N-ethylmaleimide, and protease and phosphatase inhibitors, pH 7.3–7.4). Lysates were collected and centrifuged at 19745 xg for 10 min 4˚C. 500–

1,000 µg of protein per sample was used in each assay (the same amount of protein was maintained across all conditions per experiment). 15 µl GFP-TRAP beads were incubated with the lysate for 2.5 hrs followed by three washes. The first wash was performed using 10mM Tris-

Cl, pH7.5, 150 mM NaCl, 0.5 mM EDTA, 0.7% NEM. This was followed by two washes using first a stringent buffer (8 M Urea, 1% SDS) and then SDS wash buffer (1% SDS). The beads were then boiled with 2X sample buffer. HA-ubiquitin that co-immunoprecipitated with GFP-

SNAP47 and migrated at a higher apparent molecular weight was interpreted as ubiquitinated

SNAP47 (SNAP47-Ub)

4.8.5 Immunoblotting

All protein samples were resolved by SDS-PAGE followed by transfer onto 0.45μM

(0.22μM for the SNARE complex assay) nitrocellulose paper and analyzed by immunoblotting.

Blots were probed with indicated primary antibodies, followed by IRDye® 680LT Goat anti-

Rabbit Secondary Antibody and/or IRDye® 800CW Donkey anti-Mouse IgG Secondary

Antibody and imaged on an Odyssey Licor. Western blots were analyzed using Fiji (ImageJ) or

Li-Cor Image Studio Lite. Total fluorescence of labeled bands representing relative protein were

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normalized to control conditions for each experiment as indicated. For all western blot comparisons, the relative band intensity or relative ratios (indicated for each blot) were log- transformed followed by paired-t-test.

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4.9 Mass Spectrometry

To identify potential interacting partners for TRIM67, we performed three biological replicates using the Proximity-Dependent Biotin Identification (BioID) approach (Roux et al. JCB

2012). Briefly, the negative control Myc-BirA* and Myc-BirA* TRIM67ΔRING were packaged into

Short Term Herpes Simplex Virus (HSV)(MGH) and driven under a IE 4/5 promoter. GFP expression is driven in tandem downstream of a mCMV promoter. Cortical neurons from wildtype and Trim67-/- E15.5 litters were dissociated and plated on PDL coated tissue culture dishes. Approximately 40 hours post-plating the neurons were transfected with HSV (MOI = 1.0) carrying either the negative control Myc-BirA* or Myc- BirA* TRIM67ΔRING. 6 hrs post-infection neurons were treated with 50 µM Biotin for 24 hrs. After incubation the cells were lysed using

RIPA buffer (150 mM NaCl, 25 mM Tris-HCl, pH 7.5, 0.1% SDS, 1.0% NP-40, 0.25%

Deoxycholic acid, 2 mM EDTA, 10% glycerol, protease and phosphatase inhibitors). Biotinylated proteins were then enriched from the lysate using streptavidin-conjugated sepharose beads.

The enriched proteins were digested with trypsin and eluted using the RapiGEST SF Surfactant protocol (Waters). C18 column and Ethyl acetate extraction protocols were employed to prepare the peptides for mass spectrometry.

Reverse-phase nano-high-performance liquid chromatography (nano-HPLC) coupled with a nanoACQUITY ultraperformance liquid chromatography (UPLC) system (Waters

Corporation; Milford, MA) was used to separate trypsinized peptides. Trapping and separation of peptides were performed in a 2 cm column (Pepmap 100; 3-m particle size and 100-Å pore size), and a 25-cm EASYspray analytical column (75-m inside diameter [i.d.], 2.0-m C18 particle size, and 100-Å pore size) at 300 nL/min and 35°C, respectively. Analysis of a 150 min gradient of 2% to 25% buffer B (0.1% formic acid in acetonitrile) was performed on an Orbitrap Fusion

Lumos mass spectrometer (Thermo Scientific). The ion source was operated at 2.4kV and the ion transfer tube was set to 300°C. Full MS scans (350-2000 m/z) were analyzed in the Orbitrap

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at a resolution of 120,000 and 1e6 AGC target. The MS2 spectra were collected using a 1.6 m/z isolation width and were analyzed either by the Orbitrap or the linear ion trap depending on peak charge and intensity using a 3 s TopSpeed CHOPIN method.32 Orbitrap MS2 scans were acquired at 7500 resolution, with a 5e4 AGC, and 22 ms maximum injection time after HCD fragmentation with a normalized energy of 30%. Rapid linear ion trap MS2 scans were acquired using an 4e3 AGC, 250 ms maximum injection time after CID 30 fragmentation. Precursor ions were chosen based on intensity thresholds (>1e3) from the full scan as well as on charge states

(2-7) with a 30-s dynamic exclusion window. Polysiloxane 371.10124 was used as the lock mass. All raw mass spectrometry data were searched using MaxQuant version 1.5.7.4. Search parameters were as follows: UniProtKB/Swiss-Prot human canonical sequence database

(downloaded 1 Feb 2017), static carbamidomethyl cysteine modification, specific trypsin digestion with up to two missed cleavages, variable protein N-terminal acetylation and methionine oxidation, match between runs, and label-free quantification (LFQ) with a minimum ratio count of 2. To rank candidate protein-protein interactions by likelihood of interaction, LFQ values for proteins identified in control and pulldown experiments from three biological replicates were input into SAINTq (version 0.0.4). Interactions were sorted by SAINT's interaction probability and a false discovery rate threshold of 25% was employed. A full list of identified proteins are reported elsewhere (Menon et al., 2020).

4.10 Statistics and data presentation

The software package R was used for statistical analysis of data. Both R and Adobe

Illustrator were used for the generation of figures. At least three independent biological replicates were performed for each assay, often more. Sample sizes for all experiments measuring frequency were estimated using power analysis (Power = 0.8) and the estimated effect size based off of previous work in Urbina et al, 2018. For two-sample comparisons of

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normally distributed data, Welch’s t-test was used for two independent samples, or paired t-test for paired samples. For multiple comparisons, Welch’s or paired t-tests corrected using

Benjamini-Hochberg method. For comparison of ratios for the four classes, multivariate linear regression was used, with the expected ratio of the four classes as the dependent variables and the conditions as the independent variables. For analysis of Fig 3.1G, a chi-squared expected ratio test was performed to determine if the pHluorin classification assigned FVF or KNR classes different than randomly. For analysis of non-normally distributed data, the was used to determine significance followed by the method described above.

4.10.1 Plots

All boxplots were graphed as follows: the box represents the median value (middle of boxplot) and the interquartile range, which is the distance between the first and third quartile of the data (with the first quartile being the middle value between smallest number and median of the dataset, and the third quartile representing the middle value between the largest number and median of the dataset). The whiskers extend out to the last data point within 1.5x the interquartile range.

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CHAPTER 3 FIGURES

Figure 3.1 Heterogeneity in single vesicle exocytic fusion events

. A) Schematic of exocytic fusion depicts quenching and fluorescence of VAMP2-pHluorin during full vesicle fusion (FVF) and Kiss-and-run fusion (KNR). B) Diagram of automated detection of exocytosis. Exocytic events (green) are detected over time. Features are extracted, including the peak ∆F/F and half-life of fluorescence. Frequency of C) peak ∆F/F and C’) fluorescence half-life (t1/2) of individual events. D) Schematic of region of exocytic event (black)

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and bordering plasma membrane (PM, blue). Plots of ∆F/F over time demonstrate an event with subsequent spreading of fluorescence into bordering PM (top, FVF), and an event that without spreading of fluorescence (bottom). E) Frequency of half-life categorized by ∆F in bordering PM.

Spreading fluorescence events (∆F, presumably FVF) have a slower fluorescence decay than those without (no ∆F, presumably KNR). F) Dual-color time lapse imaging of VAMP2-pHluorin and VAMP2-tagRFP reveals vesicle fusion events and vesicle fate, respectively. Example images and ∆F/F curves shows that VAMP2-tagRFP either exponentially decays after ∆F/F (top,

FVF) or remains in retained vesicle (bottom, KNR). G) Classification of fusion events based on

VAMP2-pHluorin behavior or VAMP2-tagRFP behavior agree. * represents P-value < 0.05 based on a chi-squared analysis of expected ratios if the classes were assigned randomly.

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Figure 3.2 Multiple unbiased classifiers converge on four exocytic modes. A) ∆F/F curves are temporally registered to peak ∆F/F (red dot). Registered ∆F/F plot showing average (black) and individual events (blue). B) Features that represent % of captured variance from PCA. B’) Three principal components plotted revealing four exocytic classes. C) Event classification by hierarchical clustering. i) ∆F/F plot depicting the summed (∑) Euclidean distance between two exocytic events, m and n. ii) Dendrogram of ∑ Euclidean distances between multiple events, linked by agglomerative or divisive hierarchical clustering revealing four exocytic classes. iii)

Each pixel in distance matrix is the ∑ Euclidean distance between two ∆F/F curves (n=733). D)

Event classification by Dynamic Time Warping (DTW). i) ∆F/F plot depicting time warped distance between two exocytic events, m and n. ii) A matrix of Euclidean distances measured at all time point comparing a pair of exocytic events, starting at peak ∆F/F (green square, coordinates 1,1). Unlike the ∑ Euclidean distance (gray), DTW uses the warp path with the lowest cost (red squares) to reach the ending point (blue square). The sum of the total warp path is the

DTW cost per pair of exocytic events. iii) Each pixel in distance matrix is the DTW cost between two ∆F/F curves (n = 733). E) Two example indices, silhouette and elbow methods, used in the plurality rules committee suggest four exocytic classes. F) Decision histogram of the committee of indices, with the plurality of indices choosing four exocytic classes (8/20). G) Classification comparison of each of the methods of clustering. 96.3% of 733 events were classified unanimously. Purple inset: representative unanimously classified exocytic event. Black inset: representative events with conflicting classification. Cell edge effects (top black inset) or low signal-to-noise ratio (bottom black inset) account for the majority of conflict. White arrows denote exocytic events. Red arrows denote peak ∆F/F. H) Frequency of all event classes drops to near zero with tetanus toxin (TeNT) treatment (Welch’s t-test, n = 12 cells per experiment).

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Figure 3.3 Distinguishing features of four modes of exocytosis. A) Mean fluorescent curves +/-

SEM (gray) and representative images from each class (right). B-C) Example single exponential

- λt decay fit (V(t) = V0e ) to a class 1 or 2 instantaneous (i) events (B) or to class 3 or 4 delay (d) events (C). The log of an exponential decay curve (ln(V(t))=- λt) is linear, and the linear regression fit (R2) is an indicator of how good of a fit the exponential decay curve is (center graph). Box plots (right) of R2 of class 1 (FVFi) and class 2 (KNRi) events show a good fit (B) whereas fit to class 3 (FVFd) and class 4 (KNRd) are poor. D) Sequential exponential decay curves better fit to FVFd and KNRd. E) t1/2 of each class untreated or treated with HEPES. Class

2 and 4 are HEPES sensitive (n = 14 cells per condition; Welch’s t-test followed by Benjamini-

Hochberg correction). F) Schematic of region of exocytic event (black) and bordering PM (blue).

Plots of border PM ∆F/F over time demonstrate an event with fluorescence spreading (top,

FVF), and an event without (bottom). G) Class means +/- SEM of ∆F/F in PM bordering exocytic events. Red dotted line indicates peak ∆F/F of center of event. Red dotted line indicates peak

∆F/F of the center of event. H) Half-life of fluorescence decay (t1/2) in PM bordering FVFi or

FVFd events (border pixels) is not different from the t1/2 of the exocytic event (center pixels) (n =

14 cells per condition; Welch’s t-test).

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Figure 3.4 TRIM67 biases exocytic mode towards full-vesicle fusion. A) Proportion of KNRd,

KNRi, FVFd, and FVFi in wildtype, Trim9-/-, or Trim67-/- neurons (n = 15 neurons per condition; n

= 4 biological replicates; multivariate linear regression). B) Frequency of exocytic events in the whole neuron, soma, and neurites of Trim67+/+ or Trim67-/- neurons (n = 15 neurons per condition; n = 3 biological replicates; Welch’s t-test followed by Benjamini Hochberg correction).

C) Proportion of KNRd, KNRi, FVFd, and FVFi VAMP2-pHluorin exocytic events in the whole neuron, soma, and neurites of Trim67+/+ or Trim67-/- neurons (n = 15 neurons per condition; multivariate linear regression). D) Schematic of neuronal morphogenesis in vitro (left) and empirically measured surface areas from GFP-CAAX expressing wildtype neurons at indicated time points (right). Surface area calculations were performed as described in (Urbina et al.,

2018). E) Simulated 1 and 2 DIV neurons using the model from (Urbina et al., 2018) that considers all exocytic events FVF. Empirically measured surface area of cortical neurons in vitro plotted in yellow. F) Overview of new cell model that includes the mode of exocytosis for simulating cell growth. Surface area at 2 DIV (SA2DIV) was estimated by first generating a population of neurons at 1 DIV (SA1DIV) based on empirical measurements of neurons at 1DIV.

The distribution of modes (mode), frequency of exocytosis (fexo), surface area of vesicles (SAves), frequency of endocytosis (fendo), and surface area of clathrin-coated vesicles (SAccv) were incorporated into estimating the surface area expansion. G) Simulated surface areas at 1 and 2

DIV. Surface areas at 1 DIV are simulated based on empirical measurements of surface area at that time point. Surface areas at 2 DIV use the 1 DIV simulation and model described in F. H)

Simulated surface areas from G, with empirically measured surface areas at 2 DIV overlaid in yellow demonstrate a better fit than the model that only considers FVF in E. I) Simulated neurons at 3 DIV (purple) with measured surface area at neurons 3 DIV overlaid in yellow. J-K)

Predicted PM growth simulated at 2 DIV (J) and 3 DIV (K) using the relative proportion of modes from Trim67-/- neurons with empirically measured Trim67-/- neurons overlaid in yellow. L-M)

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Simulations of PM growth at 2 (L) and 3 DIV (M) using the distribution of frequencies from

Trim9-/- cortical neurons as compared to empirically measured Trim9-/- neurons (yellow overlay).

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Figure 3.5 The t-SNARE SNAP47 interacts with TRIM67 and localizes to VAMP2-mediated exocytic events. A) Representative images and Ripley’s L Distance co-localization analysis of

TRIM67-tagRFP and VAMP2-pHluorin events (red circles) n = 15 cells; n = 4 biological replicates; value lower than theoretical random suggest not significantly colocalized. B)

Immunoprecipitation (IP:Myc) of Myc-SNAP47 from HEK293 cells expressing Myc-SNAP47 and either GFP-TRIM67 or GFP-TRIM9, blotted for GFP and Myc. C) Average of 10 frames of images and co-localization analysis of TagRFP-SNAP47 and TRIM67-GFP in cortical neurons at 2 DIV (n = 14 cells; n = 3 biological replicates; value above theoretical line are significantly colocalized (*); analysis described in methods). D) Example of VAMP2-pHluorin and TagRFP-

SNAP47 co-localization during an exocytic event. n = 16 cells; value above theoretical line are significantly colocalized (*). Scale bar = 1µm. E) Structure-function co-IP assays. MycTRIM67 construct IP blotted for GFP-SNAP47 and Myc. Graph represents relative GFP-SNAP47/Myc-

TRIM67 constructs normalized to Myc-TRIM67ΔRING; blots without a detectable band of GFP-

SNAP47 represented below graph break.

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Figure 3.6 TRIM67 alters exocytosis by modulating SNAP47 protein levels. A) Immunoblot of

SNAP47 and GAPDH from lysate of Trim67+/+ and Trim67-/- cortical neurons at 2 DIV.

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Quantification of percent increase of SNAP47/GAPDH quantified from 4 blots (right; log paired-t- test, analysis in methods). B) Frequency of VAMP2-pHluorin exocytosis +/- tagRFP or tagRFP-

SNAP47 (n = 16 neurons per condition, n = 3 biological experiments, Welch’s t-test). C) Relative proportions of exocytic modes in Trim67+/+ neurons, Trim67-/- neurons, or Trim67+/+ neurons expressing tagRFP-SNAP47 (n = 16 neurons per condition; n = 3 biological replicates; multivariate linear regression). D) tagRFP-SNAP47 normalized fluorescence intensity at fusion sites (n = 16 neurons, n = 3 biological replicates; Welch’s t-test with Benjamini-Hochberg correction). E) Quantification of time after peak ∆F/F before fluorescence decay onset of KNRd and FVFd events (n = 17 neurons; n = 4 biological replicates; Welch’s t-test). F) t1/2 of each exocytic class +/- TagRFP-SNAP47 (n = 17 neurons per condition; Welch’s t-test with

Benjamini-Hochberg correction). G) Measured neuronal surface areas at indicated times and conditions. H) Example SNAP47 immunocytochemistry images in neurons expressing siRNA for

SNAP47 knockdown (KD) (GFP+, arrow), or without (*). Cells were transfected with an eGFP expression plasmid and SNAP47 siRNA followed by immunostaining of SNAP47; GFP positive neurons did not stain for SNAP47, suggesting knockdown of SNAP47. H’) Immunoblot and quantification of SNAP47 and III tubulin protein levels in SNAP47 knockdown compared to control (n = 3 experiments; Welch’s t-test). I) Frequency of VAMP2-pHluorin exocytosis +/-

SNAP47 siRNA (n = 12 neurons per condition; Welch’s t-test). J) Relative proportions of exocytic classes in Trim67+/+ neurons and Trim67-/- neurons, with SNAP47 knockdown (n = 12 neurons per condition, n = 3 biological replicates; multivariate linear regression).

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Figure 3.7 TRIM67 reduces SNAP47 incorporation into SNARE complexes. A) Immunoblot and quantification of SNAP47 and SNAP25 from Trim67+/+ and Trim67-/- neuronal lysates at 2 DIV

(four biological replicates). Quantified is the ratio of SNAP47/SNAP25 intensity, log-ratio paired t-test. B) IP of endogenous SNAP47 in neuronal membrane fractions at 2 DIV immunoblotted for SNAP47, syntaxin-1, and VAMP2. C) Immunoblot of monomeric and high molecular weight

SNARE proteins, interpreted as SNARE complexes. Cortical neurons were treated with NEM

(15 min) to preserve SNARE complexes followed by DTT (15 min) to quench NEM, or

NEM+DTT as a negative control (30 min). Increased intensity of bands above 37kD (50kD for

SNAP47) in the presence of NEM-only at 37ºC indicate formed SNARE complexes. D)

Quantification of % of each SNARE protein in complex (intensity of lane above 37kD or

50kD/(monomer + intensity of lane); n = 4 blots, log-ratio paired t-test; see methods).

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Figure 3.S1 Distinguishing features of four modes of exocytosis. A) Example images of different exocytic modes demonstrating VAMP2-pHuji co-localized with VSV-G-mEmerald. White bar = 1

156

µm. B) Example images of different exocytic modes demonstrating VAMP2-pHuji co-localized with FM4-64 dye. White bar = 1 µm. C) t1/2 of VAMP2-phluorin fluorescent decay in KNRi and

KNRd events is the same at 110 nm and 200 nm evanescent wave TIRF penetration depth, suggesting fluorescence decay is not caused by vesicle retreat from the PM. D) Cellular

VAMP2-pHluorin fluorescence or E) biological replicate did not affect exocytic mode distribution.

No significant difference in mode of exocytosis was found when partitioning cells based on fluorescent signal or grouping by biological replicate. F) Peak ΔF/F of fusion events of each exocytic mode (t-test with Benjamini-Hochberg correction).

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Figure 3.S2 Four exocytic modes are also detected with TfR-pHuji and VAMP7∆LD-pHluorin.

A) Colocalization of VAMP2-pHluorin and TfR-pHuji. 100% of VAMP2-pHluorin detected events colocalized with TfR-pHuji events, and 100% of TfR-pHuji detected events colocalized with

VAMP2-pHluorin (n = 6 cells, n = 264 events, white scale bar in inset = 1μm). B) Decision histogram of the committee of indices for TfR-pHuji, with the plurality of indices choosing four classes. C) Proportion of FVFi, FVFd, KNRi, and KNRd in wildtype neurons expressing VAMP2- pHluorin or tfR-pHuji (n = 11 neurons, 4 biological replicates, multivariate linear regression). D)

Mean fluorescent curves +/- SEM (grey) of each class of VAMP2-pHluorin and TfR-pHuji.

(lower) representative images of ad VAMP2-pHluorin exocytic event with TfR-pHuji. E) Half-life of fluorescence decay (t1/2) of each class of VAMP2-pHluorin (green) and TfR-pHuji (n = 11 cell, n = 4 biological replicates, paired t-tests with Benjamini-Hochberg correction) F) Proportion of

FVFi, FVFd, KNRi, and KNRd in wildtype neurons expressing VAMP2-pHluorin or VAMP7ΔLD- pHluorin (n = 8 cells per condition, n = 3 biological replicates, multivariate linear regression)

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Figure 3.S3 Expression of truncated VAMP2 alters exocytic mode. A) VAMP2 and VAMP21-96 constructs and representative images of a neuron expressing both of VAMP2-pHluorin and

VAMP21-96-tagRFP. B) Frequency of exocytic events in the whole cell, soma, and neurites of neurons expressing VAMP2-pHluorin +/- VAMP21-96-tagRFP (n = 23 cells per condition; n = 3 biological replicates; Welch’s t-test followed by Benjamini-Hochberg correction). C) Frequency of exocytic events of each class of in whole neurons expressing VAMP2-pHluorin +/- VAMP21-

96-tagRFP (n = 17 cells per condition; Welch’s t-test followed by Benjamini Hochberg correction).

D) Relative proportions of each class of exocytosis in whole neurons expressing VAMP2- pHluorin +/- VAMP21-96-tagRFP (n = 17 cells per condition; multivariate linear regression).

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Figure 3.S4 Multiple domains of TRIM67 and ligase function are required for exocytic mode regulation. A) Domain architecture of TRIM67 constructs tagged with TagRFP for structure:function assays. TRIM67 is a TRIpartite Motif (TRIM) E3 ubiquitin ligase, characterized by a ubiquitin ligase RING domain, two BBox domains, a coiled-coil motif (CC) that mediates multimerization, a COS domain, FN3 domain, and SPRY domain Example fluorescence images of each mutant (below). B) Relative proportion of each exocytic class (n = 16 cells per condition; n = 4 biological experiments; multivariate linear regression. P-values from left to right: p =

0.0004; p = 0.49; p = 0.20; p = 0.024; p = 0.009; p = 0.029; p = 0.0073; p = 0.014) and C) exocytic frequency in Trim67+/+ neurons, Trim67-/- neurons, or Trim67-/- neurons expressing

VAMP2-pHluorin and TRIM67-tagRFP constructs (n = 14 cells per condition; n = 3 biological replicates; l Welch’s t-test with Benjamini-Hochberg correction).

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Figure 3.S5 Proteasomal degradation and ubiquitination of SNAP47 are TRIM67 independent.

A) Immunoblot of neuronal lysates at 2 DIV after treatment with cycloheximide (CHX,50 μg/μl) for 0, 2, 4, 8 hrs or cycloheximide (50 μg/μl) +/- bortezomib (Bort) (200 nM) for 8 hrs or cycloheximide (50 μg/μl) +/- chloroquine (ChQ) for 8 hours (log-ratio paired t-test (methods) performed between genotypes at each timepoint followed by Benjamini-Hochberg correction; n

= 7 blots, n =5 for CHX+ChQ in Trim67+/+, n = 6 for CHX+ChQ in Trim67-/-). B) Immunoblot of lysates and immunoprecipitation in TRIM67-/- HEK cells expressing HA- tagged ubiquitin (HA-

Ub) along with empty GFP or GFP-tagged SNAP47 (GFP-SNAP47) with empty-Myc or Myc- tagged TRIM67 (Myc-TRIM67). Cells overexpressing these constructs were treated with MG132 for 4 hrs prior to lysis to prevent proteasomal degradation (log-ratio paired t-test (methods), n =

3).

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CHAPTER 4: REMAINING QUESTIONS AND CONCLUSION

Our work in showing the distinct behavior of fusion events on both an individual and whole-cell level suggests a fine-tuned regulation of exocytosis during development. The spatiotemporal organization of exocytic events in the cell are distinctly regulated by guidance cues, and at least one of the modes of exocytosis distinguishes between soma and neurite.

Both TRIM9 and TRIM67 are suggested to act individually to regulate the mode of exocytosis:

TRIM9 by constraining SNARE complexes and responding to guidance cues, and TRIM67 by altering which SNAREs are incorporated into SNARE complexes. In chapter one, I provided an overview of SNARE-mediated exocytosis during neuronal development. Chapter two outlined a computer vision software to detect exocytic events and the spatiotemporal organization of exocytosis during the early stages of neuron development. In chapter three, we revealed the existence of four classes of exocytic events and their regulation by TRIM67 and SNAP47 through development of an unbiased classifier of exocytic events. In the appendix, an outline of how to perform automatic detection of exocytic events, spatiotemporal clustering, and classification of exocytic events is provided. Here in Chapter four, I will outline some unanswered questions and conclusions to my thesis work.

1 Where does membrane insertion occur during neuron development?

Neuronal growth requires insertion of new membrane material. The sites of membrane insertion have long been debated. The traditional view is that plasmalemmal precursor vesicles are transported to sites of growth, such as the distal end of a neurite, where the vesicles then

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fuse and membrane is inserted (Pfenninger, 2009). While this model has substantial evidence to support it, only recently has imaging resolution allowed us to directly observes single exocytic fusion even occurring on the plasma membrane. Our results imaging individual exocytic events suggest that the majority of exocytosis occurs at the soma, far from sites of neuronal growth.

This evidence suggests membrane is first inserted and then flows to sites of new growth. This calls into question of where the majority of membrane material is delivered during neuronal development, and requires further investigation to determine how surface area expansion is achieved.

2 Do four modes of exocytosis persist in later stages of development?

The revelation of new modes of exocytosis during early stages of neuron growth is accomplished by exploiting two advantages of developmental constitutive exocytosis: the low frequency of exocytosis and the large cell-surface are on which they reside. This methodology, however, cannot be used for studying exocytic events at the synapse, as the synapse is spatially small and events occur near-simultaneously, often in a diffraction limited area when evoked. The unanswered question remains, then, if four modes of exocytosis exist at the synapse, a subset of four modes, or entirely new classes of exocytosis exist. We can reason that the modes may not carry over to the synapse through several lines of evidence. First, developmental exocytosis occurs at timescales faster than synaptic exocytic release, suggesting a difference in regulation. Constitutive and evoked exocytosis also have considerable differences in the regulatory machinery involved. Evoked synaptic release is regulated by synaptotagmin-1, while dendritic exocytosis is thought to be constitutive and is regulated by a set of proteins distinct from synapses, such as Rab8, Rab11, and NEEP21

(Kennedy and Ehlers, 2011). As new imaging technology emerges, probing exocytic events at

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the synapse may reveal novel insights into evoked fusion pore behavior compared to developmental exocytic fusion pore.

3 What role do other SNAREs play in neuron development?

An interesting phenomena emerged when we investigated different SNARE expression levels during murine development in Chapter 1: Different SNAREs reach peak expression during different stages of brain development. SNAP25, VAMP2, SNAP47, and Syntaxin-1 are highly expressed in adult brain, while SNAP23 and VAMP3 dominate early brain expression.

The question that arises is what roles are each SNARE playing at different stages in development? This is not an easy task to answer. SNAREs are notorious for compensating for each other, making elucidating their distinct functional roles difficult. Our results suggest that

SNAP47 is present VAMP2 fusion sites and may substitute into SNARE complexes to regulate kinetics of exocytic events. Other SNAREs with distinct spatiotemporal distributions and kinetics may also form heterogenous SNARE complexes. Given the large SNARE family, SNARE substitution may be an elegant way to regulate the exocytic fusion and delivery of membrane materials and cargo

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4 Do the four modes of exocytosis correlated with cargo or other regulatory proteins?

A general question that arises with the discovery of four modes of exocytosis is whether they represent important biological functions by being distinct from each other. Each mode may correspond to a unique cargo or set of cargoes or may represent distinct regulatory mechanisms that determine exocytic kinetics.

5 Do TRIM67 and TRIM9 interact to regulate exocytosis during development?

Finally, our work over the past decade has revealed the two TRIM proteins, TRIM9 and

TRIM67, regulate exocytosis in distinct fashion. TRIM9 regulates the frequency of exocytosis while TRIM67 regulates the mode of exocytosis independently of the frequency. TRIM9 and

TRIM67 have been shown to interact and may heterodimerize, suggesting they may regulate each other, and that although our experiments indicate independence, there may be a co- dependent regulation yet to be revealed. Our work has generally been performed in single TRIM knockout mice, which leads to the question of how exocytosis is affected if both Trim67 and

Trim9 are deleted. TRIM9 and TRIM67 may work in concert to regulate exocytosis for both membrane expansion and cargo delivery, acting as fine tuners responding to developmental cues. Curiously, TRIM67 is expressed early in development, with expression levels decreasing after birth. TRIM9, however, shows an opposite increase in expression as neuron development continues. A temporal switch may occur during development in which TRIM67 no longer promotes FVF type exocytosis, whereas TRIM9 constrains the frequency of fusion to control mature neuronal exocytic events. Whether TRIM9 and TRIM67 co-regulate the developmental history of neuronal exocytosis is a discovery yet to be found.

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REFERENCES

Kennedy, M.J., and Ehlers, M.D. (2011). Mechanisms and Function of Dendritic Exocytosis. Neuron 69, 856–875. Pfenninger, K.H. (2009). Plasma membrane expansion: a neuron’s Herculean task. Nat. Rev. Neurosci. 10, 251–261.

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APPENDIX 1: AUTOMATED DETECTION AND ANALYSIS OF EXOCYTOSIS

Timelapse TIRF microscopy of pH-sensitive GFP (pHluorin) attached to vesicle SNARE proteins is an effective method to visualize single vesicle exocytic events in cell culture. To perform unbiased, efficient identification and analysis of such events, we developed a computer- vision based approach implemented in Matlab. The analysis pipeline consists of a cell- segmentation and exocytic-event identification algorithm. The computer-vision approach includes tools for investigating multiple parameters of single events, including the half-life of fluorescence decay and peak ΔF/F, as well as whole-cell analysis of frequency of exocytosis.

These and other parameters of fusion are used in a classification approach to distinguish distinct fusion modes. Here we demonstrate a newly built GUI to perform the analysis pipeline from start to finish. Further we demonstrate adaptation of Ripley’s K function in R Studio to distinguish between clustered, dispersed, or random occurrence of fusion events in both space and time.

INTRODUCTION

VAMP-pHluorin constructs or transferrin receptor (TfR)-pHuji constructs are excellent markers of exocytic events, as these pH-sensitive fluorophores are quenched within the acid vesicle lumen, and fluoresce immediately upon fusion pore opening between the vesicle and plasma membrane (Miesenböck et al., 1998). Following fusion pore opening, fluorescence decays exponentially, with some heterogeneity that reveals information about the fusion event.

Here, we have built a graphical user interface application to automatically detect and analyze exocytic events (Figure 1).This application allows the user to automatically detect exocytic events (Urbina et al. 2018) revealed by pH sensitive markers and generate features from each

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event that can be used for classification purposes (Urbina et al. 2020) (Figure 1A). In addition, analysis of clustering using Ripley’s K function is described.

We recently reported the classification of exocytic events into different exocytic modes

(Urbina et al., 2020). Four modes of exocytosis were identified in developing neurons, two related to full-collapse of the vesicle into the plasma membrane, which we call full-vesicle fusion

(FVF), and two involving transient opening of a fusion pore prior to resealing and the vesicle retaining its identity, which is known as kiss-and-run (KNR). Our work has shown that these classes can be further subdivided into fusion events that proceed immediate to fluorescence decay (FVFi and KNRi) after fusion pore opening, or exocytic events that exhibit a delay after fusion pore opening before fluorescence decay begins (FVFd and KNRd)(Figure 1B). The classifier identifies the mode of exocytosis for each fusion event. Currently this application is for

Windows operating systems only. All analysis files can be found at https://github.com/GuptonLab.

PROTOCOL

Choose datasets and directory

1. To select your datasets for analysis, navigate to that folder using the “Find Datasets” button

(Figure 2A). Your datafiles will automatically populate the Data Files as a list. There can be more than one dataset in the folder.

1.1 Click the “Choose Directory” button and select the directory where analyzed files will be deposited (Figure 2A). A set of folders and finished analysis files as well as intermediate temporary images will be created in this directory when the Analysis button is pushed. Errors will be produced if a Directory is not chosen.

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Set the pixel size and framerate

2. Input the appropriate frame rate and pixel size of the images the appropriate “Framerate” or

“pixel size” box (Figure 2B). If no values are provided (they are set to the default “0”), then the program will search the metadata associated with the file for the framerate and pixel size. If these values cannot be found, the program will return an error and require user to manually input the framerate and pixel size.

Choose or make masks

3. Use the “Mask Maker” button to automatically create cell masks for the data in the file data setlist (Figure 2C). Upon using the “Mask Maker” button, a new folder in the chosen directory will be created called MaskFiles. A mask for each file in the Data Files list will be created and deposited in the MaskFiles folder using the proper naming scheme (described below). The mask files will automatically populate the Mask Files list and the user may proceed directly to the analysis.

NOTE: Always check mask files and confirm they capture the entire cell of interest. To do so, navigate to the MaskFiles folder and open them in ImageJ along with raw data (Figure 3D). The

Mask Maker may produce errors in the case of low signal-to-noise, so validating that mask files are appropriate is critical for quality control.

3.1 As an alternative to using Mask Maker, the user can create their own masks in ImageJ.

First, open your raw data file in ImageJ. Next, click the “polygon selection” button and click to draw a mask around your cell (Figure 2E). Once finished, double-click on the last point to complete the polygon. Once finished, navigate to Edit -> Seletion -> Create Mask (Figure 2F). A new mask will be created based on your polygon drawing. Save masks in a designated

MaskFiles folder in the chosen directory. The mask file naming scheme must match the

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corresponding individual data files followed by “_mask_file”. For example, if a data file is named

“VAMP2_488_WT_1.tif”, the corresponding mask file must be named

“VAMP2_488_WT_1_mask_file.tif”. Use the “Find Maskfiles” button to navigate to the chosen folder of deposited mask files. The masks will populate the Mask Files as a list.

Analysis and Feature Extraction

4. After the directory is chosen and the Data Files list and Mask files list are populated, click the

“Analysis” button (Figure 2G). The Analysis button will perform a series of automated tasks to analyze the data. It will create individual folders in the chosen directory to deposit analyzed data. While running, the “run indicator” will change from green to yellow. After the analysis is finished, this will change back to green. User can then find a DataFiles folder with complete set of analysis files (as well as feature extraction files, to be used in classification later) named according to each datafile (Figure 2H,I,J):

Classification using R and Rstudio

4. Open the provided “Features_all_extracted.R” in Rstudio. At the top of the program are two lines: file_path_name = “...”

Output_file_name = “...”

4.1 File_path_name must be changed to the file path of DataFiles folder (the folder where all of the results of user analysis files are located). An example of the file path is provided with the program, just replace the path with the correct one.

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4.2 Output_file_name must be changed to a path where to deposit full-features file. The output file name is user defined and will later be provided to the Classify_events.R folder, so remember it!

4.3 Once paths are set, run the entire program by going to the dropdown menu and selecting

Code -> Run Region -> Run All. Once complete, a new file will appear based with the user defined output filename.

4.4 Once run is completed, open up Classify_events.R.

At the top of the program are two lines, similar to the other file:

File_path_name = “...”

Output_file_name = “...”

4.5 Change the file_path_name to the location and name of the output_file_name from the

Features_all_extracted.R code.

4.6 Change the output_file_name to a user-defined classification file name.

4.7 Once paths are set, run the entire program by going to the dropdown menu and selecting

Code -> Run Region -> Run All. A new file should appear in the selected out_file_name path that contains x,y positions, frame number, and class (FVFi,FVFd,KNRi,KNRd) of each exocytic event.

Spatiotemporal Analysis of Exocytosis using Ripley’s K values

5. A separate “soma” and “neurite” mask must first be created. First, segment the soma from the neurite. There is no unbiased method for segmenting the soma from the neurites, so the user

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should be blinded to condition experiment and use best judgement; we suggest an ellipsoid with no obvious neurite extensions.

5.1 Open ImageJ/Fiji.

5.2 Drag-and-drop the mask file or use file->open and then select your mask file.

5.3 Use the color-picker tool and click on any of the black pixels in the background of the mask to set the color to black.

5.4 Use the polygon-selection or freehand to draw around the soma, separating it from the neurites.

5.5 Click edit -> selection -> make mask. A new image will open with the circled-soma segmented from the rest of the image. Save this soma mask file.

5.5 without moving the drawn region, click on the header of the original mask file you drew in.

5.6 click Edit -> Fill to fill-in the circled soma so that only the neurites remain the mask.

5.7 Save this as the neurites mask file.

5.8 Open “neurite_2D_network” in Matlab.

5.9 Change the “neurite_mask_name.m” to the name of the neurite mask.

5.10 click “run” to skeletonize the neurite mask file.

5.11 Open “CSV_mask_creator.m” in Matlab.

5.12 Change the “mask_file_name” to whichever neurite or soma masks you wish to use for

Ripley’s K analysis. Click run.

5.13 Repeat 1.12 for every mask file.

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RStudio setup

6. Open Rstudio.

6.1 install the package “spatstat” in RStudio by going to Tools -> Install Packages and typing in

“spatstat” followed by clicking Install.

Note: This only needs to be performed once per Rstudio Installation.

6.2 Run the library spatstat at the beginning of each session.

6.3 read in the .csv of the mask files for each of the neurons you wish to analyze.

6.4 read in a file with the x,y,t positions of the exocytic events, as well as the neurite-specific file of the x,y positions for the 2D network.

6.5 Convert the data into a point-process format: soma_data = ppp(exocytic_x_positions,exocytic_y_positions, mask = mask.csv).

Neurite_data = lpp(linear_x_positions,linear_y_positions, mask = mask.csv) time_data = lpp(time_position, mask = mask.csv)

6.6 use plot(density(soma_data,0.4)) to create a heat map to visualize clustering.

Note: The number “0.4” here represents how smoothed-out the density function should be. It can be changed to fit user data in a meaningful way, but if comparisons are to be performed between different heatmaps, the number must be the same between them.

6.7 Export or save image from Rstudio. If you wish to edit the heatmap further, be sure to choose an appropriate file type (SVG or EPS).

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Ripley’s Analysis

7.1 First, run the envelope function for each cell. This function simulated complete spatial randomness (CSR) to test the Ripley’s K value of your exocytic event point pattern against.

Data_envelope_1 = envelope(soma_data_1, Kest, nsim = 19, savefuns = TRUE)

Data_envelope_2 = envelope(soma_data_2, Kest, nsim = 19, savefuns = TRUE)

...

7.2 Next, pool these envelopes together and create one estimate of CSR for the group:

Pool_csr = pool(Data_envelope_1, Data_envelop_2,...)

Next, run the Ripley’s K function for all of your data points.

Data_ripleys_k_1 = Kest(soma_data_1, ratio = TRUE)

Data_ripleys_k_2 = Kest(soma_data_2, ratio = TRUE)

…

7.3 Once complete, pool the Ripley’s K values together and bootstrap their confidence intervals:

Data_pool = pool(Data_ripleys_k_1, Data_ripleys_k_2,...)

7.4 Bootstrap:

Final_Ripleys_K = varblock(fun = Kest, Data_pool)

7.5 Plot

7.6 If a hard-statistical difference is required, Studentised Permutation Test is included in the spatstat package to test for a difference between groups of point patterns:

Test_difference = studpermu.test(all_points_to_test, exocytic_events ~ group, nperm = np).

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REPRESENTATIVE RESULTS:

Here we utilize the GUI (Figure 3) to analyze exocytic events from three VAMP2- pHluorin expressing neurons at 3 DIV using TIRF (total internal reflection fluorescence) microscopy. We followed the protocol as outlined in Winkle et al., 2016 to dissect E15.5 cortical neurons followed by transfection with VAMP2-pHluorin and plating. Our methodology of imaging parameters is as outlined in Urbina et al, 2018. Briefly, we used TIRF microscopy to image the basal plasma membrane of neurons every 100ms for 2 min. Figure 3 shows a step-by-step guide to analysis of exocytic events. The folder where the neuron images are located is selected, and a directory to deposit the final analysis datafiles is chosen (Figure 2A,B). Using the MaskMaker function (Figure 2C), a mask is generated for the neurons, which is inspected in

ImageJ (Figure 2D). In this instance, the cell mask is of good quality and the analysis can proceed. Should a mask be insufficient, a mask can be created in ImageJ (Figure 2F). After using the MaskMaker function or creating a mask in ImageJ and selecting the directory where the mask files are located, the analysis is performed (Figure 2G). Results are generated in the

DataFiles folder when the analysis is finished (the yellow indicator changes back to green)(Figure 2H).

Datafiles are automatically generated and named according to the raw data files provided. Assuming the datafile is named X:

X_tracking: This file includes x,y position and frame number of each event as well as bounding boxes which can be used to draw boxes around each event. Age indicates the number of frames past the initial detection an event is visible (still a distinct puncta).

X_fluorescent traces: This file includes x,y position and frame number of each event. In addition, it includes fluorescent intensity measures in a region of interest around each event 2

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seconds before and 10 seconds following the peak ΔF/F for each event (indicated by the

Timepoint columns).

X_cell_statistics: This file includes the cell area,total image time, and automatically calculated frequency of exocytic events for each cell (in events/mm2/minute).

Feature extraction files include:

X_contrast: Contrast. A measure of the intensity contrast between a pixel and its neighbor over the whole image.

X_correlation: Correlation. A measure of how correlated a pixel is to its neighbor over the whole image.

X_energy Total energy. Defined as the squared sum of the pixel intensity.

X_homogeneity measures the closeness of the distribution of elements in the ROI to the ROI diagonal.

X_ring_fluorescence: the average fluorescence of border pixels.

X_SD: Standard Deviation. This is defined as the standard deviation of the ROI.

Using the Cell_statistics file, we plot the frequency of exocytosis for each neuron (Figure

4A). After the GUI analysis if performed, the Classification program in R is used to assign each exocytic event to a class, plotted in Figure 4B. Following classification, we next follow the

Ripley’s K analysis code to determine if exocytic events are random, clustered, or dispersed in space and time. We first generate density heatmaps of the localization of exocytic events

(Figure 4C). This reveals expected clustered “hotspots” in distinct regions of the neuron. Next, we perform Ripley’s K analysis for the soma, neurite, and clustering over time (Figure 4D). Here we see that the Ripley’s K value and SEM (black line and blue shaded region, respectively) rise

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about the line of complete spatial randomness (red dotted line), suggesting statistically significant clustering.

DISCUSSION:

User based analysis of exocytosis is a time-consuming process and subject to personal bias, as events are not always clearly separated from other fluorescence. The use of an automated analysis program to correctly identify and analyze exocytic events in an unbiased manner increases analysis efficiency and improves reproducibility and rigor. We have demonstrated that not only does this program work for accurately capturing pH sensitive fluorescence in developing neurons but other cell types as well (Urbina et al, 2018). The detection of exocytosis is remarkably sensitive, and we have accurately detected exocytic events at signal-to-noise ratios as low as a ΔF/F of 0.01. The sensitivity of the detection depends partially on the variance of the background fluorescence, and we suggest caution be taken if the analysis is used to detect events less than 4 standard deviations above the background signal.

The creation of the mask file relies on a high initial signal-to-noise of the cell. pH sensitive markers may not always perform well in illuminating a cell, depending on what protein the probe is attached to. Another option for making mask files if the signal to noise of the exocytic marker insufficiently highlights the cell border is to employ a second fluorescent channel/image for mask creation. If using a different fluorescence marker (tagRFP-CAAX, for example) to make masks, when choosing a dataset, first navigate to the folder with the images to make masks from (in our example, a folder containing the tagRFP-CAAX images). Use the

Mask Maker button here. Importantly, remember to rename the mask files to match the exocytic data file to be analyzed with the above naming scheme (following the example above, the mask

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files named “CAAX_1_mask_file.tif” will need to be renamed to

“VAMP2_488_WT_1_mask_file.tif” to match the VAMP2 image set to be analyzed). Once mask files are appropriately named, return to the “choose dataset” button again to navigate to exocytic dataset files to analyze.

Classification using R and Rstudio

Exocytic events can be classified into 4 different classes using three different methods:

Hierarchical clustering, Dynamic Time Warping, and PCA (Urbina et al, 2020). Here, we employ all three in a convenient to run R file. Running the analysis in Step 4 provides the majority of the features needed for this classification. The remaining features and subsequent exocytic classification are performed using the statistical language R in the integrated development environment RStudio. The classification is simple, and will either assign FVFi, KNRi, FVFd, or

KNRd to each exocytic event. This classification has only been used in developing neurons to date, and indeed we do not know if these classes exist in other cell types or at later developmental time points in neurons. Other classes of exocytosis have also been described in different cell lines, so the classification is only appropriate for developing neurons at this point.

Spatiotemporal Analysis of exocytosis using Ripley’s K values

To explore the spatial and temporal clustering of exocytic events, we turn to Ripley’s K analysis (Ripley et. Al,. 1976). Analyzing clustering for neurons involves three separate analyses: One for the soma, one for the neurites, and one for time. The reason for splitting the soma and neurites is to account for the extreme morphology of neurites, which are often thin enough that they can be treated as a 2D network, and apply a 2D variant of Ripley’s K analysis.

For time, we implement 1D Ripley’s K analysis for temporal clustering. Ripley’s K analysis is a robust method for detecting clustering of point processes and colocalization. The graph of

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Ripley’s K can be interpreted as such: the Ripley’s K value+confidence interval has a 5% chance to fall outside of the line of complete spatial randomness at any point, analogous to a p value of 0.05. Where the value falls outside of the line of complete spatial randomness, we can determine that exocytic events are clustered (above CSR) or are separated at regular intervals

(below CSR) at those distanced (x-axis).

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Figure 1 – Representation of exocytic analysis and classification. A) Outline of the analysis pipeline for the GUI. Cells are segmented from the background before exocytic events are identified and tracked. Parameters such as the peak ΔF/F and t1/2 are calculated from fluorescent traces of exocytosis in a ROI around the event pre and post fusion. B) illustration of the four modes of exocytosis and example image montages. After fusion, events may proceed instantaneous to FVF or KNR (FVFi and KNRi), or a delay may be present before onset of fusion fate to FVF or KNR (FVFd and KNRd).

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Figure 3 – Step by step example analysis. A) First, datasets are chosen (inset) and a directory is chosen to place analysis files (orange boxes). B) Next, the framerate and pixel size are specified (green box). Here, we use 0.08um and 100ms for our pixel size and framerate, respectively. C) The MaskMaker function button is pushed (blue box). A folder titled “MaskFiles” is automatically created in the chosen directory containing a mask file for each image file in the dataset. D) Using ImageJ to inspect the mask file next to the original image. Mask files may not be completely correct; this mask file can be corrected for errors, or a new mask file may be made manually. E) Creating a mask file manually in ImageJ. First, open your file you wish to make a mask of. The “Polygon Selections” button is outlined in red. By clicking around the edge of the cell, a polygon outline is created (right cell image). F) How to create a mask from the polygon outline. By selecting Edit, Selection, Create Mask, a black and white mask will be created from your polygon (right image). G) Demonstration of the analysis button (orange box) and a running analysis. Notice the run indicator turns yellow while an analysis is being performed. H) Example analysis files that are created in the chosen directory when the analysis is complete. DataFiles contain all analysis files of the exocytic event. I) Analysis files generated in the DataFiles Folder. J) Two open files, “X_fluorescent_traces.csv” (top) and

“X_Cell_statistics”(bottom). Fluorescent traced contain the x,y position and frame number of each event, as well as fluorescent intensity at each ROI

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Figure 2 – Image of the GUI. Here, the buttons corresponding to each step in the protocol as outlined.

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Figure 4 – Representative results. A) Frequency of events plotted for three murine cortical neurons. These data values were plotted directly from “X_Cell_statistics”. B) Distribution of the classes of exocytosis for the same three cells used in A). Here, the ratio of each mode is plotted. C) Density plot of where exocytic events are occurring as generated in the Ripley’s K

Analysis portion of the protocol. This can be interpreted as a “heat map” of the spatial likelihood of where events are occurring. D) Ripley’s K analysis of three cells used for A) and B). The red line indicates what value completely spatially random distribution of exocytic events would be.

The black line indicates the aggregate Ripley’s K value for the three cells in this example, and the blue shaded region represents the confidence interval. Here, the shaded region notably falls outside of the line of complete spatial randomness between ~0.25-1um, suggesting exocytic events are clustered at those distances.

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REFERENCES

Miesenböck, G., De Angelis, D.A., and Rothman, J.E. (1998). Visualizing secretion and synaptic transmission with pH-sensitive green fluorescent proteins. Nature 394, 192–195. Ripley, B.D. (1976). The second-order analysis of stationary point processes. Journal of Applied Probability 13, 255–266. Urbina, F.L., Gomez, S.M., and Gupton, S.L. (2018). Spatiotemporal organization of exocytosis emerges during neuronal shape change. J. Cell Biol. 217, 1113–1128. Urbina, Fabio L., Shalini, Menon, Dennis, Goldfarb, Reginald, Edwards, M. Ben, Major, Patrick, Brennwald, and Gupton, Stephanie L. (2020). TRIM67 Regulates Exocytic Mode and Neuronal Morphogenesis via SNAP47.bioRxiv. doi: https://doi.org/10.1101/2020.02.01.930404 Winkle, C.C., Hanlin, C.C., and Gupton, S.L. (2016). Utilizing Combined Methodologies to Define the Role of Plasma Membrane Delivery During Axon Branching and Neuronal Morphogenesis. J. Vis. Exp.

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