Human T Cell Response to Substrate Rigidity for Design of Improved Expansion Platform

Sarah E. De Leo

Submitted in partial fulﬁllment of the

requirements for the degree of

Doctor of Philosophy

in the Graduate School of Arts and Sciences

COLUMBIA UNIVERSITY

Cells have long been known to sense and respond to mechanical stimuli in their environment. In the adoptive immune system particularly, cells are highly specialized and responsible for detecting and eliminating pathogens from the body. T cell mechanosensing is a relatively new ﬁeld that explores how force transmission in cell- cell interaction elicits both inter- and intra-cell signaling. Owing to recent advances in genetic manipulation of T cells, it has emerged as new tool in immunotherapy. We recently demonstrated human T cell activation in response to mechanical rigidity of surfaces presenting activating antibodies CD3 and CD28. The work in this dissertation highlights new progress in the basic science of T cell mechanosensing, and the utilization of this knowledge toward the development of a more specialized expansion platform for adoptive immunotherapies.

Human T cells are known to trigger more readily on softer PDMS substrates, where Young’s Modulus is less than 100 kPa as compared to surfaces of 2 MPa. While the range of effective rigidities has been established, it is important to explore local differences in substrates that may also contribute to these findings. We have isolated the rigidity-dependence of cell-cell interactions apart from material properties to optimize design for a clinical cell expansion platform. Though PDMS is a well understood biomaterial and has found extensive use in cellular engineering, a PA gel substrate model allows for rigidity to be tuned more closely across this specific range of rigidities and provides control over ligand density and orientation. These rigidity-based trends will be instrumental in adapting models of mechanobiology to describe T cell activation via the immune synapse. In what is generally accepted as the clinical gold-standard for T cell expansion, rigid (GPa) antibody-coated polystyrene beads provide an increase in the ratio of stimulating surface area-per-volume, over standard culture dishes. Herein we describe the development of a soft-material fiber-based system with particular focus on maintaining mechanical properties of PDMS to exploit rigidity-based expansion trends, investigated through atomic force microscopy. This system is designed to ease risks associated with bead-cell separation while preserving a large area-to-volume ratio. Exposing T cells to electrospun mesh of varying rigidities, fiber diameters, and mesh densities over short (3 day) and long (15 day) time periods have allowed for this system’s optimization. By capitalizing on the mechanisms by which rigidity mediates cell activation, clinical cell expansion can be improved to provide greater expansion in a single growth period, direct the phenotypic makeup of expanded populations, and treat more patients faster. This technology may even reach some cell populations that are not responsive to current treatments. The aims of this work are focused to identify key material properties that drive the expansion of T cells and optimize them in the design of a rigidity-based cell expansion platform. Contents

List of Figuresv

List of Tables vii

Acknowledgments viii

1 Introduction1

1.1 The advent of personalized medicine...... 1

1.2 The immune system functions to ﬁght disease...... 2

1.3 Harnessing the immune system through adoptive immunotherapies...7

1.4 Bead-based cell expansion technologies...... 8

1.5 Improving ex vivo T cell expansion through mechanobiology...... 12

1.5.1 T cell mechanosensing...... 12

1.5.2 Adaptation of the motor clutch model may explain rigidity response 15

1.6 Impact and signiﬁcance...... 18

2 Polyacrylamide Gels for Delineation of T Cell Response to Surface Rigidity 20

i 2.1 Abstract...... 20

2.2 Introduction...... 21

2.3 Materials and Methods...... 24

2.3.1 Preparation of gel substrates...... 24

2.3.2 Preparation of antibodies...... 26

2.3.3 Fluorescence analysis of antibody presentation...... 26

2.3.4 Atomic force microscopy...... 27

2.3.5 Cell isolation and culturing...... 27

2.3.6 Interleukin 2 (IL-2) secretion assay...... 28

2.3.7 Data acquisition and analysis...... 28

2.4 Results...... 29

2.4.1 Polyacrylamide gels with bis-acrylamide crosslinker limited in rigidity spectrum...... 29

2.4.2 Hertzian analytical model employed for AFM indentation studies 30

2.4.3 Polyacrylamide gels with piperazine-diacrylamide crosslinker can be made from 100 kPa to 1 MPa...... 35

2.4.4 Human CD4+ T cells activate across rigidity range...... 42

2.5 Discussion...... 45

3 Electrospun Mesh Platform for Enhanced T Cell Expansion 47

3.1 Abstract...... 47

3.2 Introduction...... 48

3.2.1 Electrospun mesh...... 51

3.3 Materials and Methods...... 57

ii 3.3.1 Atomic force microscopy...... 57

3.3.2 Surface preparation...... 57

3.3.3 Confocal microscopy...... 58

3.3.4 Cell culture...... 58

3.3.5 Assessment of initial cell-mesh binding...... 59

3.3.6 Long-term cell expansions in conjunction with CFSE assays.. 59

3.3.7 Flow cytometry...... 61

3.3.8 Statistical analysis...... 61

3.4 Results...... 61

3.4.1 Characterization of rigidity and protein adhesion on electrospun mesh...... 61

3.4.2 T cell penetration into and interaction with mesh topology... 65

3.4.3 T cell stimulation and expansion on electrospun mesh at short and long time points...... 67

3.5 Discussion...... 74

4 Conclusions and Future Directions 78

5 Perspective on Personalized Medicine in a Changing Healthcare Land- scape 82

5.1 The Current Healthcare Climate...... 82

5.2 The Role of Personalized Medicine...... 84

5.2.1 Reserach and Development...... 87

5.2.2 The FDA’s Regulatory Strategies...... 89

iii 5.2.3 Economic and Ethical Considerations...... 92 5.3 Patients and Advocates Guide Medical Research Eﬀorts...... 96

Bibliography 98

Appendix 113

A 113 A.1 Electrospinning mesh...... 113 A.2 Mesh characterization...... 114

iv List of Figures

1.1 Schematic of T Cell Activation...... 5

1.2 Adoptive Immunotherapy Method...... 10

1.3 PDMS Bead Coating Methodology for T Cell Activation...... 11

1.4 Motor-Clutch Model...... 17

2.1 Atomic Force Microscope System...... 32

2.2 Chemical Structure of Polyacrylamide Crosslinkers...... 36

2.3 Human CD4+ T Cell Cytokine Secretion on Rigidity-Dependent Poly- acrylamide Gels...... 44

3.1 Schematic of Electrospinning...... 52

3.2 SEM images of PMDS-PCL mesh...... 55

3.3 Schematic of AFM Tip Interaction with Substrate...... 63

3.4 AFM Indentation Measurements...... 64

3.5 Confocal Images of Mesh and Cell Interaction...... 66

3.6 Human CD4-CD8 T Cell Activation and Proliferation Study Comparing Antibody Linkers...... 69

v 3.7 Human CD4-CD8 T Cell Activation and Proliferation Study Comparing Unaligned and Aligned Fibers with Varying Diameters...... 71 3.8 Human CD4-CD8 T Cell Activation and Proliferation Study with PCL Controls...... 73

5.1 Integration of molecular, clinical, and genetic factors can be assessed by personalized diagnostics and impact therapeutic options [Bottinger, 2007]. 85 5.2 Probablility of drug success to market by stage of develpment [Arrow- smith, 2012]...... 90

vi List of Tables

2.1 Polyacrylamide gel compositions with bis-acrylamide crosslinker.... 30 2.2 AFM indentation measurements...... 38 2.3 PDA-polyacrylamide gels...... 39 2.4 Antibody presentation on PA gels...... 41 2.5 Streptavidin Acrylamide Concentrations...... 41 2.6 Streptavidin Acrylamide Concentrations...... 42

3.1 Morphological data of mesh...... 56

vii Acknowledgments

I would like to thank Professor Kam, whose dedication to science is inspiring and commitment to his graduate students is invaluable. I would not have completed this work without his selﬂess time and patient guidance. I am also appreciative of members of the MBL group past and present who have been a sounding board for ideas and a source of constant support and encouragement, particularly Keenan, Haoqian, Alex, Debjit, Geri, Susie, Ed, Lester and Helen.

I would like to thank our collaborators, without whom this work would not have been possible; special thanks to Dr. Lu for her advice and Danielle for her work in mesh fabrication. Thanks to Corina for her assistance with the AFM. I am also indebted to my committee for their helpful guidance and insightful feedback.

I am grateful to my family for supporting whenever and wherever this dream to ‘tackle some big questions’ takes me. They encouraged my curiosity from the beginning and imparted unto me a strong work ethic to make it possible. To my father, for his constant support and wake up calls at odd hours of the morning; and to my mother, for always helping me to see what’s truly important. And to my sister, my biggest cheerleader and sender of care packages.

To my friends, fellow doctoral students, and post-doctoral scholars. I’ve thoroughly

viii enjoyed exploring and eating our way through NYC. I am especially indebted to Ofer for his patience, support, endless coﬀee supply, and late night dinners in lab - without which I’m sure I’d still be there.

ix Chapter 1

Introduction

1.1 The advent of personalized medicine

Markets for wide-spread therapeutic targets have long enjoyed large focus and invest- ments over the years. Recently academic researchers, large pharmaceutical companies, and the government have directed their attention to diseases with smaller affected populations, termed personalized medicine. Still regulatory, scientific, and financial challenges must be addressed before personalized medicine can become widely available.

Cancer is known to be a heterogeneous disease, complex in developmental stages and effected by innumerable and often unknown genetic and environmental factors. Can- cers also widely varying in target and manifestation, making current blanket treatment avenues ineffective and the death rate by cancer relatively constant [Roger et al., 2012]. Even so, cancer accounts for approximately $100 billion in direct healthcare costs, second only to cardiovascular disease. Major research in the basic sciences has uncovered genetic and other molecular mechanistic foundations for more personalized diagnostic and treatment plans to present the patient with the most appropriate treatment at the optimal time [Hamburg and Collins, 2010]. The advent of personalized medicine will see the investment of resources in developing ways in which small factions of broad diseases like cancer can be addressed more directly, with more appropriate and effective treatments.

1.2 The immune system functions to ﬁght disease

In a healthy immune system, both adaptive immunity and innate immunity are have the capacity to launch a response against potential pathogens. While innate immunity is immediately available as a first line of defense to combat a range of pathogens; it is non-specific, thereby less efficient, and incapable of granting the benefits of immunological memory. For these reasons we rely on adaptive immunity. An adaptive immune response is developed to combat an infection caused by a specific pathogen that could not be successfully fought off by an innate immune response. Adaptive immune responses develop over a longer time scale (days rather than hours) but may confer life- long protective immunity, defense against reinfection by the same pathogen [Janeway et al., 2001].

Immune cells and other cellular elements of blood originate from the pluripotent hematopoietic stem cells in the bone marrow. Here a common lymphoid progenitor gives rise to antigen-speciﬁc T and B lymphocytes, central players in adaptive immunity. B cells mature in the bone marrow while T cells migrate to the thymus for development.

2 After maturation, nave forms of both B and T lymphocytes enter the bloodstream, bearing a single type of receptor with a unique speciﬁcity.

Once a lymphocyte finds an antigen to which its receptor binds, it is then activated to proliferate and differentiate in a process known as clonal expansion. An adaptive immune response is initiated when an antigen-presenting cell (APC), often a dendritic cell, expresses both antigen and co-stimulatory molecules to a nave T cell. Upon the surface of the APC, specialized host-cell glycoproteins, termed major histocompati- bility complexes (MHCs), present a small number of foreign peptides among a larger number of self-peptides. Peptides are so much an integral part of a pMHC’s molecular structure that they are comparatively unstable when peptides are not bound [Janeway et al., 2001]. T cell receptors (TCRs) are specific for a unique combination of peptide antigen and the MHC to which it is bound. These peptide-loaded MHCs are then de- tected and bound by TCRs to form an interface termed the immune synapse [Monks et al., 1998, Grakoui et al., 1999, Huppa and Davis, 2003, van der Merwe and Dushek, 2010] (Figure 1.1). The engagement of TCR ligation is accompanied by a co-receptors, such as CD4, CD8, and CD28. In particular, CD8+ and CD4+ T cells are further differentiated by the surface proteins they express that confers their ability to recognize different classes of MHC molecules, class I and II respectively. These interactions prompt the recruitment of Src family kinase lymphocyte-specific protein tyrosine kinase (LcK), which phosphorylates CD3 on at least two immunoreceptor tyrosine-based activation motifs (ITAMs). Zap70 then binds double-phosphorylated ITAM residues and is phosphorylated and activated LcK. Once LAT is recruited to the TCR complex, and phosphorylated by Zap70, further signaling is initiated along an extended protein

3 network [Rossy et al., 2012]. In brief, the immune synapse the site where extensive information is relayed between a T cell and its activating APC. Its highly regulated and structurally complex method for directing T cells towards one of its multifarious fates is mediated by the synchronization of interrelated proteins. Depending on the interaction of other surface receptors, the accepting T cell is programmed to either target this antigen, as is the case with positive costimulation by CD28, or to ignore this antigen, which occurs with CTLA-4 mediated antagonism.

4 Figure 1.1: Essential signaling components in APC activation of nave CD4+ T cells. Reprinted from (Murphy 2012).

5 Naive T lymphocytes once stimulated undergo diﬀerentiation into cytotoxic, helper, or regulatory T cells. Helper T cells, marked by surface protein CD4+, are the focus of much of these genetic manipulation and expansion eﬀorts in adoptive immunotherapy. Unlike CD8+ T cells which are known as cytotoxic T cells because of their role in directly engaging pathogens; helper T cells instead focus on tasks such as cytokine secretion and immune cell interactions to direct and hone the response of other immune cells.

Each effector cell will bear receptors specific to the initial offending antigen [Janeway et al., 2001]. B lymphocytes encounter an antigen and differentiate into antibody- secreting plasma cells. Immunoglobulins (Ig) are highly specific recognition proteins produced by B cells that may be surface-bound or secreted to tag or neutralize foreign antigens. Though a B cell’s antigen receptors are capable of stimulation by free antigens directly, an optimal response is achieved in cooperation with T cells. A T cell’s antigen- recognition molecules exist solely as membrane bound proteins. T cell plasticity enables numerous viable pathways but requires antigen fragment presentation on the surface of APCs for recognition. If the APC is indeed a B cell, a T cell will activate it to produce antibody. Likewise if the APC is a macrophage, the T cell can stimulate it to engulf or destroy the pathogen of interest. This wide range of T cell functionalities can be usurped and manipulated to the fight against cancer driving the body’s own immune system.

6 1.3 Harnessing the immune system through adop-

tive immunotherapies

The immune system employs a number of processes to detect and neutralize pathogens. The precarious balance of recognizing foreign pathogens while not collaterally targeting native tissues comes to light when the immune system fails in either of these two tasks. Autoimmunity occurs when an immune response is launched targeting native tissues. Alternatively, proliferation of an undesirable cell population may occur if the foreign pathogen goes undetected. In eﬀorts to treat autoimmunity and chronic infections, adoptive immunotherapy, the redirection of immune system resources to address these insuﬃciencies, has been explored.

The concept of adoptive immunotherapy was ﬁrst validated in 1957 but did not see FDA approval for therapeutic use until 2010 [Spyridonidis and Liga, 2012]. Early immunotherapy was based primarily in the introduction of chemical immunomodulators, including cytokines, interleukins, and chemokines, to stimulate or suppress systemic immune response. In later studies APCs were introduced as ex vivo vaccinations. Of the APCs, dendridic cells proved to be a promising vector due in part to their high plasticity, capacity to activated both CD4+ and CD8+ T cells, established markers for immature and mature states, and ability of cross-penetrations. However, the complexities of the many considerations associated with dendritic cell use, including but not limited to the maturation state of the cell, the method of isolation, and the route of administration, has complicated implementation. Ultimately the complexity of APC manipulation has led to conceptually sound but practically ineﬀective therapies, yielding only marginal

7 clinical success.

Current achievements in adoptive immunotherapy techniques have been propelled by technologies such as genetic engineering which have enabled the activation of T cells in a process that does not require APCs. In fact, throughout a number of clinical trials T cells have been shown to be responsive to genetic manipulation and expansion for the targeted treatment of cancers [June et al., 2007, Porter et al., 2011]. This therapy exploits the body’s own defense mechanism: the innate ability of T cells to detect and respond to foreign antigens with high speciﬁcity and sensitivity. Central to this methodology is the ex vivo expansion of T lymphocytes, wherein cells may be manipulated in ways not possible in an in vivo scenario. For instance, transfecting cells with viral vectors or subjecting cells to direct viral infection can be used to encode production of chimeric antigen receptors (CARs) or other proteins which confer targeting abilities [June et al., 2007,Brentjens et al., 2011,Porter et al., 2011,Restifo et al., 2012]. Thus an expansion step is critical to the current implementation of this approach and stands to greatly beneﬁt from strategic biomaterial design.

1.4 Bead-based cell expansion technologies

In what is considered the clinical gold-standard for cell expansion, patient’s cells are mixed with small (4.5 mm in diameter) rigid polystyrene beads with magnetic cores, coated with activating antibodies that induce growth and proliferation, see Figure 1.2.[Kim et al., 2004, June et al., 2007]. In accordance with early clinical trials, a treatment typically requires puriﬁcation of T cells from donor or patient blood before

8 cells are transduced with virus for expression of specific antigen expression. These cells are activated and expanded to reach counts of 3x108 cells (5% transduced) before in- fusion into the patient over a three day period. Preliminary Phase 1 studies suggest a patient would require just one such treatments before achieving remission status [Porter et al., 2006,Porter et al., 2011]. The primary advantage of this cell culture platform is the increase in surface area afforded by coated beads, over standard culture dishes, to achieve sufficient expansion. However, the current expansion platform requires removal of beads from expanded cells through magnetic separation, which is potentially imper- fect and may lead to the injection [Levine et al., 1998]. An improved ex vivo expansion platform has the capacity to expand cells from a small or weak population to clinically relevant numbers quickly, be separated from the culture system easily, and offer better control over phenotypic output.

9 Figure 1.2: In a basic adoptive immunotherapy process, immune cells are isolate from a patient or another suitable source, expanded ex vivo, and infused back into the patient.

Work by our lab and others has established the effect of substrate rigidity on T cell activation and expansion; notably, that human T cells activate more readily on softer (∼100 kPa) PDMS substrates than stiffer substrates (2 MPa). Please see 1.5.1 T cell mechanosensing for a more indepth discussion [Judokusumo et al., 2012, OConnor et al., 2012]. To improve upon the current clinical model, our lab successfully produced beads from both PDMS and NuSil elastomers through an emulsion technologies. These elastomers possess distinct bulk rigidities ranging from 25 kPa to 2 MPa. NuSil formulations were employed as they have associated FDA master files to streamline regulatory considerations if implemented for clinical use. These beads were prepared for expansion efforts through coating with capture proteins for antibody attachment.

10 Protein A and goat-anti-mouse IgG were chosen as early candidates mounted to the aldehyde on the bead’s surface (Figure 1.3).

Figure 1.3: Elastomer beads are fabricated via curing submerged in albumin. They are then treated with glutaraldehyde and a capture, or linker, protein before activating antibodies are mounted.

Protein A was selected for its physiological relevance, a bacterial surface protein known to bind immunoglobins and is used as an affibody molecule in therapeutic and diagnostic applications. Goat-anti-mouse IgG offers higher specificity to antibodies

11 of interest but is less economically sound. Though goat-anti-mouse IgG bears little physiological founding, its usefulness is exploited by Miltenyi Biotec’s Anti-Mouse IgG Microbeads for cell separation. Though beads fabricated from the NuSil polymers were shown to increase the average number of cell divisions as compared to Dynabeads, no diﬀerence was observed between the elastomer compositions. Recent AFM eﬀorts on large scale beads fabricated under the same conditions suggest that at a local level all formulations appear rigid (data not shown). Thus it is desirable to devise a platform that maintains a large surface area-to-volume ratio but can be cured in air to preserve rigidity properties during the curing process.

1.5 Improving ex vivo T cell expansion through mechanobi-

ology

1.5.1 T cell mechanosensing

T cells have been shown to sense and respond to their extracellular environment. Recent investigations have provided direct evidence of force interactions as mediators of TCR signaling [Kim et al., 2009a, Li et al., 2010]. In particular, the TCR is an aniosotropic mechanosensor, making it sensitive to tangential or shear, but not normal, forces. Ex- ternal torque on the TCR following pMHC ligation during immune surveillance was shown to be necessary for T cell activation; a stop signal was met with a change in quanternary structure [Kim et al., 2009b]. An increased number TCR-pMHC interactions in a single cell has also been shown to convey a greater degree of T cell response

12 speciﬁcity and sensitivity [Kim et al., 2009b]. In another study, ITAM in the CD3e chain was demonstrated to be activated by conformational changes in the ITAM interaction with the inner leaﬂet of the plasma membrane, providing one possible mechanism by which force may mediate TCR signaling [Xu et al., 2008].

In an alternative model for the study of force-mediated TCR activation, APCs have been successfully simulated artificially by mounting super-agonists CD3 and CD28 antigens on a rigid surface. Anti-CD3 on the surface binds with the approaching cell, indirectly triggering the TCR complex, while anti-CD28 provides costimulatory signals [Shen et al., 2008, Linsley, 2005, Riley et al., 2002]. CD28 co-stimulation in particular is known to modulate proliferation, differentiation, and effector function in T cells by regulation of gene transcription, cell cycle progression, survival, secretion of cytokines and chemokines, metabolism, and mobility [Riha and Rudd, 2010].

Work in our lab shows T cells respond to the mechanical rigidity, here considered the Young’s Modulus (E), of a substrate presenting anti-CD3 and anti-CD28 [Shen et al., 2008, Judokusumo et al., 2012]. Recently work published with our collaborators has demonstrated cytokine secretion, a measure of cell activation, of primary human T cells correlated with the underlying substrate stiﬀness. They show that in this cases softer substrates (E < 100 kPa) stimulate a four-fold increase in cytokine secretion as compared to stiﬀer substrates (E > 2 MPa) [OConnor et al., 2012]. Here cytokine secretion is regarded as a marker of cell activation. These experiments were carried out on planar Sylgard 184 (Dow Corning) polydimethysiloxane (PDMS) elastomer surfaces. Mechanical rigidity was altered by adjusting the ratio of the crosslinking agent to elastomer base during substrate preparation. Surfaces were coated in activating antibodies

13 prior to cell seeding. Cell viability and activating antibody presentation were shown to be unaﬀected by changes in the concentration of crosslinking agent in this rigidity range.

T cell stimulated on softer substrates not only exhibited higher IL-2 cytokine secretion but also met with greater short term (3 day) proliferation, as measured through a CFSE assay. These short term proliferation studies are indicators of longer prolifera- tive performance; and indeed cells stimulated on softer substrates grew over many more days and incurred a larger number of doublings than those stimulated on more rigid substrates. Additionally, these studies revealed that softer substrates enhance nave

+ CD4 T cell Th1-like differentiation. Rigidity-dependent preferential proliferation and differentiation of a certain T cell population could be a useful alternative to cytokine- directed differentiation to meet the needs of clinical applications. Together these data indicted enhanced activation and expansion as substrate rigidity decreases from 2 MPa to <100 kPa.

Aside from the direct role of force in TCR/CD3 complex signal-transduction, many proteins involved in in TCR and costimulatory receptor signal transduction interact with the actin cytoskeleton, either directly or indirectly [Burkhardt et al., 2008]. More- over, the structure and dynamics of the actin cytoskeleton, known to respond to changes in substrate stiﬀness, have also exhibited an important role at the immune synapse, supporting and regulating signal transduction [Gomez et al., 2006,Campi et al., 2005]. In fact, tuning of mechanoreceptor function has been attributed to inducible actin cytoskeletal interactions between TCR and pMHC because the torque will be greatest in their presence [Yokosuka et al., 2008]. With these early ﬁndings, the role of the

14 acto-myosin cytoskeleton together with TCR/CD3 complex in force-signal transduction remains largely uncharacterized. Greater investigation is necessary into the underlying interactions that between the TCR complex and actin cytoskeleton that produce the observed rigidity response.

1.5.2 Adaptation of the motor clutch model may explain rigid-

ity response

IThe response of human CD4+ T cells to changes in substrate rigidity was unexpected, as parallel studies conducted by our lab with murine CD4+ T cells showed a different trend [Judokusumo et al., 2012]. On gels of Young’s moduli ranging from 10 to 200 kPa activation of primary mouse CD4+ T cells decrease with decreasing rigidities, as quantified by the IL-2 secreted over six hours and cells attached. The low cytokine secretion and cell attachment characterizing the softest gel suggest that at this low range, cell activation is very limited. Additional results in that report of note include the association of rigidity sensing with CD3 (not CD28) and the inhibition of actomyosin contractility that through the use of blebbestatin nullified this response. These results are suggestive of a biphasic response, but inconclusive. More work should be done to characterize the activation of murine T cells on stiffer substrates (E>200 kPa).

Work in our lab (unpublished data) and others suggest large whole-cell systems are at work in cytoskeleton remodeling after T cell triggering [Kumari et al., 2014]. We seek here to establish if functional activation of CD4+ are biphasic, as a part of an investigation to characterize the response of cell traction forces to substrate rigidity. The biphasic function of rigidity has been described in a motor-clutch-model put forth

15 by Bangesser et al for integrin-ECM interaction [Bangasser et al., 2013]. It is our goal to determine if this model is adaptable to T cell activation and traction forces are likewise related to rigidity. The basic components of the motor-clutch model are illustrated in Figure 1.4, top. Cell traction forces are generated by a set of motors, typically myosin, that pull on the actin cytoskeleton, which in turn is linked to transmembrane receptors by molecular clutches. The biphasic response in this model is produced as the bond oﬀ-rate increases with load applied to the system, scaled by a bond rupture force. The substrate rigidity comes into play as its compliancy may present some elastic resistance to applied forces, critical for oﬀsetting the traction force and dictating how rapidly forces are applied to the transmembrane receptors by the moving cytoskeleton.

16 Figure 1.4: We can see that for (A) migration and (B) traction forces, the optimal rigidity range may be cell-type dependent (C) Motor clutch model developed by Odde and colleauges at the University of Minnesota to explain cell response to rigidity, unlike other models of its kind it allows for this biphasic response linking the actin cytoskeleton to transmembrane proteins; (D) Substrate rigidity infuences developing traction and migration forces with traction force as a function of clutch number, nc, and with number of motors present nm (from Bangasser 2013). If this model could be applied to T cell behavior, it would help to reconcile the mechanobiology of the immune system with other systems. 17 As described through this model, at high rigidities bonds load rapidly, increasing off-rate and decreasing the number of receptors that are engaged at a given time. In between these extremes, receptors are loaded, yet have sufficient lifespan to generate strong traction forces. The specific spring constant of the substrate (related to rigidity) at which this maximum is observed is a function of both motor and clutch parameters, as well as combinations of these parameters; increasing the number of clutches shifts the peak to higher spring constant, until a plateau is reached (Figure 1.4, bottom). In the model adapted to T cell activation, the clutch components are proposed to represent intermediate adapter and coupling proteins between the TCR complex and the actin cytoskeleton; it is currently unclear which proteins serve in this mechanical role.

1.6 Impact and signiﬁcance

Recent advances in genetic engineering have expanded the applicability of adaptive immunotherapy techniques. Excitingly, immunotherapy offers an important alternative for the treatment of diseases that have thus far eluded small molecule pharmacology or surgical techniques, as is the case for many cancers. Adoptive immunotherapy offers an alternative treatment strategy by harnessing the high specificity of immune cells. As it has been shown that these cells can be ‘trained’ ex vivo to meet their physiological targets, the expansion of these trained cells into clinically relevant quantities is the next major hurdle. Many cell-based therapies in development could benefit from improved expansion efforts. Immunotherapy comprises a quarter of ongoing clinical trials of cellular therapies. In 2010, the FDA approved Provenge, a dendritic cell-based

18 treatment for prostate cancer that requires a similar ex vivo expansion process. In 2012, the market for prostate cancer therapeutics reached approximately $17 billion worldwide. The American Cancer Society estimated more than 240,000 new diagnosis worldwide [BCCResearch, 2012]. The primary target of this therapy discussed herein is chronic lymphoid leukemia (CLL), the second most common type of leukemia in adults. The market size of CLL therapeutics was estimated to be $437 million in 2010 with 123,379 new cases reported by the World Health Organization [MarketsandMarkets, 2013]. Several recent publica- tions from our lab and others demonstrate that the activation of T cells is dependent on the stiﬀness of the prepared substrate. A critical barrier to the understanding of T cell mechanosensing is the lack of knowledge of lymphocyte activation on soft substrates. Elucidating the observed rigidity-based trend from a substrate-dependent response, considering the implications of a biphasic trend, and exploring the activation of T cells on rigidity-tuned electrospun mesh has the potential to improve the design of the clinical expansion platform. We seek to use this improved understanding of T cell mechanosensing to provide more cells from a single round of expansion and ultimately to exert control over phenotypic makeup of the expanded populations and rescue exhausted T cells which would otherwise have little therapeutic potential.

19 Chapter 2

Polyacrylamide Gels for Delineation of T Cell Response to Surface Rigidity

2.1 Abstract

A recent publication by our lab illustrates that murine T cells activate more readily on polyacrylamide (PA) hydrogels as rigidities from 10 – 200 kPa [Judokusumo et al., 2012]. Work by a collaborating lab indicates that human T cells trigger on softer PDMS substrates, where Young’s Modulus is less than 100 kPa as compared to surfaces of 2 MPa [OConnor et al., 2012]. While the range of effective rigidities has been established, it is important to explore local differences in substrates that may also contribute to these findings. We must isolate the rigidity-dependence of cell-cell interactions apart from material properties to optimize design for a clinical cell expansion platform. Though PDMS is well understood and has found extensive use in cellular engineering, a PA gel substrate model allows rigidity to be tuned more closely across this specific range of rigidities and provides control over ligand density and orientation. The objective of these studies is to delineate a narrow range of optimal substrate rigidities for human CD4+ T cell activation as a basis for biomaterial design. The approach involves designing PA gels to meet the rigidity specifications, while incorporating streptavidin acrylamide to directly bind antibodies in functional positions, required for cell activation. We seek to determine if human T cell activation is a biphasic response where TCR triggering, as measured by cytokine secretion, will peak at an intermediate value of substrate rigidity in the range of 10 kPa to 2 MPa. These rigidity-based trends will be instrumental in adapting models of mechanobiology to describe T cell activation via the immune synapse

2.2 Introduction

Isolating human T cell rigidity response is central to determining the effect of material properties on lymphocyte activation and assessing the propensity and implications of a biphasic trend. High-level cellular functions have been linked to biphasic responses with respect to substrate stiffness. In a recent article, Bangasser et al. provides a concise overview of biphasic responses in mechanobiology and presents a motor-clutch mechanism as the foundation for this trend. Briefly, this framework captures a biphasic relationship between traction forces and the rigidity of the underlying substrate. This

21 model is based on coupling of transmembrane receptors to retrograde ﬂow of actin cytoskeleton through a set of clutch connections that show force-dependent changes in bond lifetime [Bangasser et al., 2013].

This motor-clutch model has been primarily explored in the context of integrin- based adhesion to extracellular matrix proteins; however, as a complementary system we believe it may be adapted to describe T cell response to TCR complex ligands. As a first step, we have explored here whether T cell activation is biphasic with respect to rigidity. In order to do so, we must separate T cell activation in response to the mechanical rigidity of the extracellular environment apart from substrate-dependent drivers. It has long been known that T cells have the capacity to distinguish between soluble and surface bound agonist antibodies [Geppert and Lipsky, 1987]. O’Connor et al. showed human lymphocytes activate more readily on softer PDMS substrates, reporting a four- fold increase in IL-2 cytokine secretion by cells stimulated on surfaces measuring E < 100 kPa over cells stimulated on surfaces measuring E > 2 MPa. PDMS elastomer is hydrophobic and at a local level comprised of a cross-linked matrix with silica filler that acts as reinforcement, making fine-tuning of the structural properties that determine rigidity difficult [Ratner, 2004]. Alternatively, polyacrylamide hydrogels are hydrophilic and exhibit loose network entanglements at the local level making them less uniform. Hydrogels are by nature more porous, subject to swelling, and by varying copolymer ratio rigidity can be controlled down to the tens of kilopascals. Polyacrylamide gels with a bis-acrylamide crosslinker can be fabricated at 10 kPa to more than 200 kPa [Yeung et al., 2005,Judokusumo et al., 2012]. Likewise, using a piperazine diacrylamide (PDA) crosslinker in polyacrylamide gels has been shown to increase the hydrogel rigidity range

22 to 200 - 1100 kPa [Yeung et al., 2005,Judokusumo et al., 2012].

The propensity for protein adsorption to a surface is recognized to be highly dependent on both the substrate’s hydrophobicity and electrostatic properties [Nakanishi et al., 2001]. While PDMS readily adsorbs proteins, early in the study of cell-substrate interactions it was determined that polyacrylamide hydrogel surfaces do not and therefore proteins must be covalently bound to promote cell attachment [OConnor et al., 2012,Pelham and Wang, 1997]. Initially sulpho-SANPAH was relied upon as a protein crosslinker for this purpose: covalently linking to the hydrogel under UV exposure and revealing a N-hydroxysuccinimide ester for primary amine reaction [Tse and Engler, 2010]. Yet modiﬁed surfaces used to investigate the immune synapse rely on proper orientation of antibodies to preserve their functionality [Groves and Dustin, 2003,Moss- man et al., 2005,Doh and Irvine, 2006,Shen et al., 2008]. In a recent publication, our lab has developed a system to orient antibodies on a hydrogel surface [Judokusumo et al., 2012]. Polyacrylamide hydrogels are polymerized from a mixture of acrylamide, copolymer bisacrylamide, and a small percentage of functionalized streptavidin-acrylamide.

The avidin biotin linkage (kd ∼10 - 15) is recognized as one of the strongest noncovalent

1 bonds, with a lifetime ( /koff ) on the order of ∼109 seconds. Once the hydrogel cures, streptavidin is exposed on the surface and will bind to biotinylated goat-anti-mouse IgG. Then mouse-anti human CD28.6 and OKT3 activating antibodies can link to the surface with proper orientation, maintaining their functionality. Antibody density-dependence and rigidity-dependence have been documented in the study of T cell activation and proliferation [Judokusumo et al., 2012,OConnor et al., 2012]. Therefore, the concentration of streptavidin-acrylamide was normalized for each hydrogel formulation to yield

23 a standardized surface density of tethered proteins across all substrates.

The aim of this chapter is to examine the rigidity response of human CD4+ T cells apart from substrate-dependent eﬀects and to use this trend toward reconciling human and mouse biphasic responses. We developed a polyacrylamide hydrogel platform, with substrate rigidities ranging from 10 kPa to 1000 kPa. These rigidities were conﬁrmed through AFM indentation measurements and antibody presentation via streptavidin- biotin linkages was normalized across all hydrogel formulations. Finally, we examined the rigidity-dependent activation of nave human CD4+ T cells across prepared hydrogel substrates.

2.3 Materials and Methods

2.3.1 Preparation of gel substrates

The preparation of polyacrylamide gels, acrylamide solution with a bis-acrylamide crosslinker, has been previously demonstrated in both this lab and others [Yeung et al., 2005, Judokusumo et al., 2012]. In accordance with Judokusumo et al. the concentration of bis-acrylamide can be varied between 0.02% and 0.8% (w/v), to achieve gels with rigidities ranging from 10 kPa to 200 kPa as conﬁrmed by atomic force microscopy. In brief, acrylamide and the bis-acrylamide crosslinker are combined and degassed for 1 hour. With the addition of catalyst 10% (w/v) ammonium persulfate (APS) at a 1:100 dilution, polymerization is initiated. Tetramethylethylenediamine (TEMED) is added at a 1:1000 dilution to enhance bond formation. Streptavidin acrylamide is incorporated just before gels are sandwiched between coverslips, to limit exposure to oxygen

24 which is known to inhibit polymerization, and cured for 2 hours.

These coverslips underwent chemical activation prior to their use. One was functionalized to promote adhesion and the other was salinized for easy release. To function- alize, 12 mm glass coverslips were treated with base piranha solution (1:1:5 dilution of NH4OH:H2O2:diH2O) (Fisher Scientific) and heated at 120oC for one hour to remove organics. Glassware was then rinsed well with deionized water and submerged in a solution of 5% 3-Aminopropyltriethoxysilane (3-APTS) (Sigma Aldrich) to add primary amines to the surface. These amine groups were reacted with a 5% glutaraldehyde solution (Sigma Aldrich), leaving a functional group exposed on the glass to which the gel binds [Aplin and Hughes, 1981]. Salinization follows a similar procedure, where the glass was rendered hydrophobic and the gel did not adhere. Large 5 x 7.5 cm glass slides were first cleaned with base piranha and treated with dimethyldichlorosilane in 5% heptane (Sigma Aldrich), rinsed twice with 100% ethanol, and washed again with deionized water as a final step. Through the curing process, polyacrylamide binds to the aldehyde on the surface of the functionalized glass while the salinized surface is easily removed. To achieve stiffer rigidities as desired, we turned to a new polyacrylamide gel platform, an acrylamide solution with a piperazine diacrylamide (PDA) crosslinker. PDA is known to have a higher molecular weight than bis-acrylamide and produce stiffer gels for use in SDS-PAGE. Adapting a protocol put forth by Hansma et al., the concentration of PDA crosslinker is varied from 0.01% to 3% (w/v) to yield polyacrylamide gels with rigidities ranging from 100 kPa to 1 MPa [Hansma et al., 2009]. Production of polyacrylamide gels using PDA as a crosslinker can be carried out in the manner described above; however, 10% APS and TEMED were instead added at 1:200

25 and 1:2000 dilutions, respectively. All polyacrylamide gels can be made up to two days in advance and stored at 4oC in 1x phosphate buﬀered saline (PBS).

2.3.2 Preparation of antibodies

To assess the uniformity of antibody presentation on the gels, goat-anti-mouse IgG was biotinylated and fluorescently labeled. A minimum of 100 mL of IgG was vigorously combined in a 1:8 molar ratio with Biotin X (Life Technologies) and left to react for 1 hour. The solution was dialyzed in PBS for at least 6 hours up to a maximum of 18 hours. It was then removed and combined likewise with a fluorophore, in this case Alexa Fluor 647 succinimidyl carboxylic acid to label the amines (Life Technologies) and dialyzed again. For IL-2 surface preparation, the goat-anti-mouse IgG used was biotinylated but not labeled with a fluorophore, to maximize antibody functionality and minimize fluorescence channel interference.

2.3.3 Fluorescence analysis of antibody presentation

To assess uniformity of antibody presentation, polyacrylamide gels were incubated with 30µg/mL labeled biotinylated-goat-anti-mouse IgG for 1 hour at room temperature. To better identify the focal plane of the gel’s surface, samples were then incubated with 200 nm-diameter biotinylated polystyrene microspheres ﬂuorescent at 488 nm; stock solution 2.8x1012 beads/mL diluted 1:500,000 (Invitrogen).

26 2.3.4 Atomic force microscopy

AFM was performed on all gel compositions submerged in 1x PBS. 4.5 µm-polystyrene spherical tips mounted on 0.35 N/m cantilever silicon nitride probes were purchased from Novascan (Ames, Iowa) and used in conjunction with a BioScope Catalyst microscope (Bruker Corporation) for data collection. Probes were calibrated to determine deflection sensitivity, and in turn spring constant, at the start of each experiment. On average, three indentations per region were performed over four to six regions per substrate. Analysis was performed primarily in NanoScope Analysis v1.40 (Bruker Corporation) but code was also written in MatLab (Mathworks, Inc.) for analysis ver- ification. The Nanoscope analysis provided a consistent and unbiased determination of the contact point. Other employed functions included a smoothing filter, baseline adjustment, and accounting for adhesion. The model used to fit indentation curves was primarily based on the Hertz model, estimating a linear elastic infinite solid and a round probe that does not undergo deformation.

2.3.5 Cell isolation and culturing

Human CD4+ T cells isolated by Human CD4 T cell Enrichment Cocktail (Rosette SepTM, STEMCELL TechnologiesTM) from a leukopak (New York Blood Center) are frozen down until ready for use. In preparation for experimentation, CD4+ T cells were recovered for 6 to 24 hours at 3x106 cells/mL in RPMI 1640 media supplemented with 10% FBS, 10 mM HEPES, 50 U/mL Penicillin & Streptomycin, 2 mM L-glutamine and

50 µM β-Mercaptoethanol at 37°C and 5% CO2.

27 2.3.6 Interleukin 2 (IL-2) secretion assay

In preparation for experimentation, polyacrylamide gel substrates were incubated in 30 µg/mL goat-anti-mouse IgG for 1 hour at room temperature. However, human T cells were stimulated with a mixture of monoclonal antibodies targeting the CD3e chain of TCR and CD28. Therefore, OKT3 and CD28.6 (gift of the Nanomedicine Center for Mechanobiology) were attached to the IgG linker in a 1:1 ratio to achieve a total activating antibody concentration of 30 µg/mL. A clean glass coverslip functioned as a rigid substrate control and was also coated with this antibody mix via direct adsorption. Nave CD4+ T cells were seeded onto prepared substrates at 3,400 cells/mm2 in supplemented RPMI and maintained under standard cell culture conditions, as described above, for six hours.

In accordance with the manufacturer’s protocol, cytokine assessment begins at six hours, a sufficient length of time for cells to initiate IL-2 production. Cells adhered to their respective substrates were briefly incubated with a catch reagent, which functions to capture IL-2 yield over the next hour. An APC-conjugated detection antibody was then added to fluorescently tag secreted IL-2 on the surface of cells. Finally, cells were fixed with 2% paraformaldehyde. In some cases, fixed cells were stained with Oregon- green 488 phalloidin (Life Technologies) to facilitate image processing.

2.3.7 Data acquisition and analysis

All images were collected via an Olympus IX81 inverted microscope equipped with 20x, 60x, and 100x objectives. MetaMorph for Olympus software was used to acquire images. Fluorescence intensity, for both antibody presentation and IL-2 secretion experiments,

28 was captured at 20x magnification and quantified via Fiji ImageJ processing package. For cytokine secretion studies, images were taken in two separate channels: one to register cell area and locale and the other for the measurement of discrete cytokine secretion. Analysis of cytokine secretion data was conducted primarily through ImageJ. Functions of ImageJ used include, but are not limited to: bleach correction of cell localization channel, threshold, analyze particles, and measure average fluorescence. In some cases, images of cytokine secretion were analyzed for fluorescence intensity at cell locations using MetaMorph Software for Olympus (Molecular Devices, LLC), using brightfield images to determine cell position. For IL-2 cytokine secretion assays, fluorescence intensity was measured from at least 500 cells per condition, excluding glass control. The average fluorescence intensity from these cells was averaged.

2.4 Results

2.4.1 Polyacrylamide gels with bis-acrylamide crosslinker lim-

ited in rigidity spectrum

It has been demonstrated by our lab and others that polyacrylamide gels with a bis- acrylamide crosslinker can be fabricated at rigidities of 10 kPa to more than 200 kPa [Yeung et al., 2005,Judokusumo et al., 2012]. Based on early observations set forth by Yeung et al., attempts were made to design stiﬀer gels by altering the bis-acrylamide concentration. However AFM indentation measurements prove the limits of this model and show it unﬁt for achieving rigidities in the mega Pascal-range (Table 2.1).

29 Table 2.1: Polyacrylamide gel compositions with bis-acrylamide crosslinker (n = 8)

2.4.2 Hertzian analytical model employed for AFM indenta-

tion studies

AFM has emerged as a valuable nano-indentation tool to determine the elastic modulus of both natural and synthetic biological materials [Nitta et al., 2000, Matzelle et al., 2003, Engler et al., 2004, Solon et al., 2007]. The Young’s modulus for biomaterials ranging from E < 1 kPa to 100kPa (cells) to E ∼ 1-10 GPa (collagen) has been successfully measured using this technology [Dimitriadis et al., 2002, Alonso and Goldmann, 2003, Wenger et al., 2007]. In operation, a laser is focused onto the back face of the cantilever, on which the tip is mounted, and reﬂected onto a photodiode detector (Fig- ure 2.1). To generate a force-distance curve, the position of the scanner is changed in

30 the z-direction with repect to the sample, which causes the cantilever to deflect. This deflection is measured by the change in the laser position on the detector. Collected data relies on Hooke’s law (F = kh) for interpretation; where the force, F, is calculated as a product of the deflection h [nm] (h = Z - D, as defined in the schematic) and the spring constant, k [N/m]. This deflection, h, in practice is a product of the measured cantiever’s deflection [V] and the deflection sensitivity [nm/V]. Deflection sensitivity is an important component of the calibration process. It can be computed by pressing a tip against a rigid surface to relate laser movement on a position-sensitive split photodiode detector to probe motion. This constant allows future deflections (on surfaces of unkown rigidities) to be measured directly from the laser’s position on detector.

Determination of the spring constant is also required for proper calibration. Hutter and Bechhoefer determined that the spring constant of a cantilever was related to its

1 1 2 thermal energy: 2 kBT = 2 k hzc i, where kB is the Boltzmann constant, T is the absolute 2 temperature, and hzc i is the mean squared displacement of the cantilever’s thermal motion [Hutter and Bechhoefer, 1993,Ohler, 2007]. The spring constant is then applied to Hooke’s law, as previously mentioned, and when given a deﬂection a force can be calculated.

31 Figure 2.1: A) Basic schematic of AFM indentation system. (B) Resulting ‘force-curves’ where cantilever deﬂection is plotted against probe position. (C) Example of a curve (blue) measuring approximately 10 kPa by Hertzian approximation (green); red lines denote range of measurement and dashed blue vertical line approximates contact point. (D) Example of a curve measuring approximately 1000 kPa by Hertzian approximation. Reprinted from Costa 2006.

The Hertz model is often used to approximate indentations in biological samples. It assumes a linear elastic solid and a spherical, not deformable tip. The Hertzian model

E h a2+R2 R+a i is given as F = 1−ν2 2 ln R−a − aR , where R is the radius of the sphere, E is the Young’s Modulus, a is the radius of the contact circle, and n is Poisson’s ratio (value of 0.5 for most soft biological samples) [Lin et al., 2007]. This model assumes inﬁnitesimal sample thickness and homogeneity of substrate material which, though widely implemented, may not be the case for biological samples such as cells which can be thin and heterogenic.

Alternative models have been developed to better account for the variability of AFM

32 data collected from soft materials and biological samples. Interestingly, the Derjaguin, Muller, Topov (DMT) model, published in 1975, may be applied to tips with a small curvature radius and high stiffness, in the investigation of samples with low adhesive properties. It differs from the Hertzian model primarily through its consideration of the Van Der Waals forces acting along the perimeter of the contact area. These forces increase the level of attraction between the probe and sample; thereby weakening elastic repulsion forces [Cappella and Dietler, 1999]. Additionally, Costa et al. devised a new theory to address cell indentation experiments based on finite element modeling to account for indenter geometry [Costa and Yin, 1999]. This model may be simplified for √ 4 16 3 the case of linear hyperelastic materials to read F = 3 Eπφ (D) = 9 E RD . Costa’s model facilitates the computation of elastic modulus as a function of indentation depth which reveals information regarding nonlinearity and heterogeneity of the sample.

Results from the following AFM indentation studies were analyzed primarily via the Hertzian model, the most widely used in studies of biological samples. Again this model assumes inﬁnite sample thickness and homogeneity of substrate material indented with a spherical, non-deformable tip. Polyacrylamide hydrogels are considered homogenous, with small pore sizes. Samples for both indentation analysis and cell activation studies were prepared on 12 mm-diameter coverslips with a minimal volume of 15 mL. Basic mathematical computations lead to a calculated total volume of 15 mm3 and a conservative gel height of approximately 130 mm. In order for a sample to be considered inﬁnitely thick, indentation testing must occur in less than the top 10% of the entire sample depth [Costa and Yin, 1999]. Typical AFM measurements performed indent 300 nm – 2 mm into the surface of the gel, well within this restriction, so we may

33 therefore consider the sample inﬁnite. Finally, measurements were performed with a spherical polystyrene tip, much more rigid than the hydrogels with which they interact and may therefore be considered non-deformable.

Exploring these different analytical models across the same data set, as we will see later in Table 2, the theory detailed in Costa et al. gave similar results to the Hertzian model at low rigidities and increasingly disparate values at higher rigidities. This may be explained by the Costa model’s accounting indentation depth for small biological samples. This work examined indentations up to 80% of the thickness for thin samples (< 2 µm). It states that for small values of indention depth, the model performs well; however, at deeper indentations artifacts from an underlying rigid substrate appear. Our samples have not been indented to the same degree, nor do they suffer from finite depth. For more rigid substrates we can assume a smaller indentation depth. As a comparison, indentation depths hold a polynomial relationship to the Young’s Modulus through Costa’s formula in which at low indentation depths only slightly higher rigidities are observed. Alternatively as indentation depth decreases in the Hertz model, deflection increases leading to a proportional increase in force and thereby Young’s Modulus.

Differences in the Hertz model conducted through the Nanoscope software and via MatLab code can primarily be explained by the identification of the contact point for each curve. The contact point may be most simply defined as the explicit point where the approaching tip makes contact with the substrate. Though this may seem simple enough to identify in an ideal scenario, the selection of this point may be complicated in reality by a drifting baseline, a noisy signal, and intramolecular forces between the

34 tip and the substrate, as is the case for DMT. The Nanoscope software provides for smoothing the curve, adjusting for baseline trajectory, and employs a sophisticated algorithm for selecting the contact point. Here we saw that the Hertzian modeling carried out through MatLab was slightly lower (by a factor of two or less) than that via Nanoscope, but may be presumed, for these reasons, to be less accurate. Like- wise results from DMT analysis showed gels appear less rigid; this is undoubtedly due to DMT’s attributing some of the force to surface energy. The DMT model assumes forces acting outside of the contact region create a ﬁnite area of contact between tip and sample for the duration of the indentation. Though polyacrylamide gels exhibit low adhesive properties, the large spherical tip employed here does not ﬁt well with the DMT model.

2.4.3 Polyacrylamide gels with piperazine-diacrylamide crosslinker

can be made from 100 kPa to 1 MPa

As polyacrylamide gel compositions with bis-acrylamide crosslinker failed to meet the desired rigidity range (10 kPa to 1000 kPa), an alternative crosslinker was chosen. Hansma et al, 2009 describes employment of piperazine diacrylamide (PDA), a crosslinker that can be substituted for bis-acrylamide directly, replacing mass for mass, without changing polymerization protocols (Figure 2.2) (Bio-Rad bulletin). PDA has been substituted for bis-acrylamide in electrophoresis work to increase gel strength and reduce background in silver staining. It has also been used to increase resolution and improve sensitivity in protein sequencing gels.

35 Figure 2.2: The stable piperazine ring characteristic of PDA eliminates hydrogen atoms of amide groups and contributes to the increased rigidity of polyacrylamide gels. MW PDA 194.23 and MW bis 154.17

Importantly, this crosslinker has been shown to achieve hydrogels in a rigidity range from 200 to 1100 kPa, as measured by nanoindentation [Hansma et al., 2009,Allen et al., 2012]. It has been previously observed that AFM indentation will yield slightly lower rigidity measurements than those via nanoindentation [Hansma et al., 2009]. PDA gels were fabricated in accordance with Hansma et al. and measured via AFM indentation

36 (Table 2.2). It is evident from these early studies that through the use of PDA as a crosslinker polyacrylamide gels can be produced at suﬃciently high rigidities, in the mega-Pascal range. Alternatively it also establishes a need for designing PDA-based gels at the softer end of the rigidity spectrum of interest (10-100 kPa).

37 Table 2.2: Early AFM indentation measurements analyzed via distinct prescribed model. All results expressed in kilopascals. Three measurements were taken from six regions per sample. Average are representative of successful indentations.

38 Compositions described in Table 2.3 were designed to elicit softer rigidities. Decreas- ing the percent of crosslinker does produce less rigid gels; however, PDA concentration and rigidity do not hold a linear relationship. These results point to the lower limit of the PDA-polyacrylamide gel system: at low concentrations of crosslinker, approximately 100 kPa gels are the softest achievable.

Table 2.3: Early PDA-polyacrylamide gel formulations to establish lower end of rigidity range

As previously mentioned, it would be most desirable to establish a hydrogel substrate system with a singular crosslinker to span the desired rigidity range (10 kPa – 1000 kPa); however, neither polyacrylamide gels formulated with bis-acrylamide nor those formulated with PDA span the desired range. Therefore a system in which the two types of preparations overlap to map the entire range is utilized. The ﬁnal working compositions of hydrogels used throughout this study are given in Table 2.6.

While we have put a hydrogel system in place that allows for cell seeding across the desired range of rigidities, we must also consider that the presentation of antibodies on the surface will also play a causal role in T cell activation. As previously mentioned, polyacrylamide hydrogel substrates do not readily adsorb proteins. Our

39 lab has developed a system that not only covalently binds proteins to the surface but also ensures their proper orientation, an important stipulation in antibody presentation where functional binding sites are paramount to cell activation [Judokusumo et al., 2012,Groves and Dustin, 2003,Mossman et al., 2005,Doh and Irvine, 2006,Shen et al., 2008]. In this system, streptavidin-acrylamide replaces a portion of the deionized water concentration in the gel solution, as streptavidin-acrylamide is primarily composed of streptavidin protein not acrylamide. Streptavidin-acrylamide present at the surface of the gel binds and thereby properly orients biotinylated goat-anti-mouse IgG.

To ensure uniform antibody presentation, biotinylated goat-anti-mouse IgG was labeled with a fluorophore and incubated on the surface of several different rigidities across both gel-types. To improve proper identification of the hydrogel surface upon imaging, these samples were also incubated with biotinylated polystyrene microspheres, fluorescent in an alternate channel. In following previously established protocols by Judokusumo et al., a total concentration of 667 mg/mL streptavidin-acrylamide was used for all gels with expected rigidities greater than or equal to 100 kPa. For lower rigidities, 267 µg/mL streptavidin-acrylamide was employed. Early work at these concentrations showed low expression rates with non-uniform antibody presentation for PDA-crosslinked gels (Table 2.4).

40 Table 2.4: Fluorescence measurements denoting antibody presentation on polyacrylamide hydrogels’ surfaces. Average intensity calculated from twelve images with measurements across nine distinct regions in each image. Standard error represents variation over one sample.

Antibody presentation on PDA gels was deemed adequate due to fluorescence intensity measurements and preliminary cell cytokine secretion studies (data not shown here). Therefore streptavidin acrylamide concentration was lowered in bis-acrylamide hydrogels to approximate the fluorescence antibody presentation in PDA hydrogels. Adjusted streptavidin acrylamide concentrations are given in Table 2.5, along with their measured fluorescence intensity.

Table 2.5: Adjusted streptavidin acrylamide concentrations to yield uniform ﬂuores- cence intensities across gels. Average intensity calculated from twelve images with measurements across nine distinct regions in each image. Standard error represents variation over one sample.

Similar ﬂuorescence intensity values across all samples reﬂect equivalent antibody

41 presentation across all substrates. However, PDA-based hydrogels have a less uniform presentation pattern than their bis-acrylamide counterparts. To account for such variation, 100 kPa and 200 kPa samples from both bis-acrylamide and PDA-crosslinker formulations were included in future studies.

The incorporation of streptavidin acrylamide into polyacrylamide does not signiﬁ- cantly alter measured gel rigidity, evident in Table 2.6. PDA-crosslinker concentrations were determined by interpolating measurements displayed in Table 2.2. Streptavidin acrylamide was incorporated in place of deionized water in each of the formulations.

Table 2.6: Adjusted streptavidin acrylamide concentrations to yield uniform ﬂuores- cence intensities across gels

These are the ﬁnal working compositions of hydrogels used in human nave CD4+ T cell cytokine secretion studies.

2.4.4 Human CD4+ T cells activate across rigidity range

IL-2 secretion assay (Miltenyi Biotech) was performed to detect cytokine secretion. IL-2 cytokine secretion is a key indicator of early CD4+ T cell activation and regulates and functionally mediates the activity of other leukocytes [Fraser et al., 1991, Street et al.,

42 1990].

Human nave CD4+ T cells were seeded on prepared hydrogel substrates presenting a 1:1 mix of anti-CD3 and anti CD28.6. After cells have been incubated on substrates for six hours, IL-2 cytokine secretion is measured to quantify the rate at which it is produced. This cytokine secretion kit indirectly captures IL-2 production via the abun- dant transmembrane protein CD45, which enables discrete measurement of secretion on an individual cell level. The cytokine secretion assay is a fluorescence-based surface capture kit and experimental results are quantified through fluorescence microscopy (Figure 2.3).

43 Figure 2.3: Rigidity dependent activation of T cells measured by IL-2 secretion at 6 hour time point. (n = 1).

Cells exhibit higher IL-2 secretion on softer substrates nearest to 100 kPa over more rigid substrates. T cells also evince lower cytokine secretion on substrates softer than 100 kPa, trends characteristic to a biphasic response. Cells secrete higher levels of cytokine on bis-acrylamide formulations as compared to PDA formulations, which may be due to a combination of material properties unique to each or the uniformity of antibody presentation on the surface. However the trend across rigidities fabricated from each crosslinker formulation is consistent.

44 2.5 Discussion

Studies examining rigidity-based activation of T lymphocytes have been vital to the increased understanding of immune cell function and are taking a central role in to the development of improved therapeutics for clinical trials [OConnor et al., 2012]. The work in this chapter demonstrates a new platform for T lymphocyte rigidity-based activation studies, based on polyacrylamide hydrogels formed with a PDA crosslinker. PDA was selected as a crosslinker because it is known to be biocompatible and produce stiﬀer hydrogel substrates than the use of a bisacrylamide crosslinker. Moreover PDA can be easily integrated into the original hydrogel system; the manufacturer recom- mends replacing bisacrylamide with PDA on a gram-by-gram basis.

These polyacrylamide gels with PDA crosslinker were tested alongside previously characterized polyacrylamide gels with bisacrylamide crosslinked [Judokusumo et al., 2012]. AFM indentation measurements were primarily analyzed through the Hertz model. This model assumes a linear elastic substrate indented by a spherical tip that is not deformed under contact. Futhermore it assumes inﬁnitesimal sample thickness an homogeneity of the substrate material. These assumptions hold up well within this system: a rigid polystyrene spherical tip interacts with a comaratively soft homogeneous polyacrylamide gel substrate, never indenting to a depth more than 10% of the total height of the gel so that it may be considered inﬁnitely thick.

This new platform stimulated T cell activation in agreement with results published by O’Connor et al. Cells secrete higher levels of cytokine on soft substrates (100 kPa) as compared with rigid substrates (1000 kPa). These results provide clear evidence of the importance of a substrate’s mechanical properties independent of material type.

45 One explanation set forth suggests that these forces impact the formation and stability of the immune synapse and in turn modify signal transduction downstream, but much of T cell mechanosensing pathways remain unknown. Though changes in the stiffness of the substrate on which the antigens are presented would be expected to dampen the forces applied to individual adhesive receptor-ligand interactions, the range of force required to induce activation or disrupt the mechanical linkage between the TCR and APC remains unknown [OConnor et al., 2012]. Softer substrates may benefit from smaller applied loading forces. Lower applied forces translate into extended bond lifetime and prolongation of signaling [Evans, 2001]. Unfortunately, the technology to examine dynamic signaling in live cells on such a small spatial scale is severely limited. The forces generated on the T cell or by the immune synapse itself directly impact T cell activation. In fact, the cytoskeleton is known to exhibit mechanical coupling with the TCR complex and mechanical forces applied to the TCR complex through an anchored pMHC or anti-CD3 activating antibody initiate TCR signaling [Nguyen et al., 2008, DeMond et al., 2008, Yu et al., 2010, Ma et al., 2008, Li et al., 2010]. Through the work in this chapter we have determined that human T cell activation is biphasic with respect to rigidity. These results will facilitate future efforts in reconciling the trend exhibited by mouse cells into a single and unified framework adapted from the motor-clutch model. This model will aid ultimately in identifying specific molecular pathways that mediate T cell mechanobiology.

46 Chapter 3

Electrospun Mesh Platform for Enhanced T Cell Expansion

3.1 Abstract

The ex vivo manipulation of T cells and introduction, or at times re-introduction, into a patient has emerged as a new tool in immunotherapy. A major challenge associated with this transition is producing a highly controlled and expedited expansion of these cells to reach a clinically relevant population size. The current clinical gold-standard for T cell expansion utilizes rigid (Giga Pascal) antibody-coated Dynabeads® CTS polystyrene beads in combination with an in-bag format, WAVE bioreactor (GE LifeSciences) to provide increased surface area per cell volume as opposed to standard culture dishes. This system evolved from a system for lymphocyte isolation and puriﬁcation where cells attach to magnetized beads which can then be separated from solution. Though widely accepted and implemented, this expansion system presents diﬃculties in manufacturing and use. Notably, the separation of cells from beads prior to reinjection into the body is known to be incomplete and the implications of the accumulation of these plastic particulates in the blood stream are unclear.

Here we describe a soft-material, fiber-based system with particular focus on maintaining the mechanical properties of polydimethylsiloxane (PDMS) to exploit rigidity- based expansion trends presented in the previous chapter, while easing risks associated with bead-cell separation and maintaining a large area-to-volume ratio. Exposing T cells to electrospun mesh of varying rigidities, fiber diameters (on the order of micro- to nano-meter), and mesh densities over short (3 day) and long (15 day) time periods have allowed for an improved understanding of cell-fiber interaction and contributed to the iterative design of this expansion platform. Results from this study provide a new insight into how micro- and nano-scale topography features of a biomaterial affect T cell activation and functional differentiation. Furthermore the outcome of this work provides clues into the feasibility and advantages of a stable rigidity-based expansion platform for clinical use.

3.2 Introduction

Recent advancements in adoptive immunotherapy have generated a need for a platform with which to expand cells from a limited population to reach clinically relevant numbers. These naive T cells are initially isolated from the whole blood of the patient or other source and exposed ex vivo to activating antibodies, typically over three days’

48 time. During this incubation period, T cell surface receptors interact with antibodies on the surface which initiaties cell activation. As the T cells activate they lift off of the coated substrate, undergo division, and differentiate. It is through this dividing and differentiating that T cells are commissioned to recruite other players in the immune response. Depending on the successfulness of the initial activaiton, these cells may continue to divide over several days or weeks in culture.

Dynabeads, the current clinical gold-standard, are mixed with purified T cells and typically achieve 6 doublings in 10 days. The beads are then collected with a magnet before cells are infused into the patient, a process which is known to be incomplete [Levine et al., 1998]. Additionally, human lymphocytes have been shown to achieve greater expansion on softer (<100 kPa) PDMS substrates [OConnor et al., 2012]. Work by our lab has successfully produced beads from both PDMS and NuSil elastomers through an emulsion technology (unpublished data). NuSil formulations have associated FDA master files to streamline regulatory considerations. These elastomers possess distinct bulk rigidities ranging from 25 kPa to 2 MPa. Though these beads were shown to increase the average number of cell divisions as compared to Dynabeads, no difference was observed between the elastomer compositions. Recent AFM efforts on large scale beads fabricated under the same conditions suggest that at a local level all formulations appear rigid (data not shown). Thus it is desirable to devise a platform that does not require an emulsification agent for submerged curing. Please refer to 1.4 Bead-based cell expansion for a more detailed overview of developing technologies for T cell expansion. Within this chapter we describe the characterization of a stationary expansion system comprised of an electrospun platform that maintains a high surface area-to-volume

49 ratio. Electrospun polymer mesh networks have been produced with extensive surface area for cell expansion [Kim et al., 2009b, Kim et al., 2012]. Current electrospinning techniques enable customization of fiber diameter and porosity that, together with chemical functionalization, tailor extracellular matrix conditions to support growth and activation for a variety of cell types [Yoo et al., 2009,Bhardwaj and Kundu, 2010,Saino et al., 2011]. This platform not only capitalizes on the observed rigidity response exhibited by human T cells but also simplifies the process of substrate removal from the system. This electrospun mesh format has allowed for control over the modification of particular parameters targeting polymer composition, fiber diameter, and mesh density to effect local rigidity and small-scale topography, conditions known to influence cell activation. We are primarily interested in the local rigidity perceived on a cellular level and the influence of topology, including fiber diameter and pore-size, in determining the success of cell expansion. Mesh was designed from the following polymer blends of PDMS and polycaprolactone (PCL): 10:1 PDMS blended 1:1 with PCL (10:1/1:1), 10:1 PDMS blended 4:1 with PCL (10:1/4:1), 50:1 PDMS blended 1:1 with PCL (50:1/1:1), and 50:1 PDMS blended 4:1 with PCL (50:1/4:1). Mesh was also spun from PCL without PDMS. In order to gain a better understanding of T cell response to topology, in some instances both nanometer- and micrometer-diameter fibers were spun from the same compositions. Likewise, aligned and unaligned fibers were also procured from the same blends.

In order to assess mesh performance, we began by characterizing mesh rigidity on a local level. Using a 4.5 µm-diameter polystyrene tip to approximate the size of a 7 µm-diameter T cell, we gathered indentation measurements via AFM. These rigidity

50 measurements do not reﬂect classical material properties, but rather describe the surface as perceived by the T cell. We are also particularly interested in exploring how topology of the mesh inﬂuences cell expansion; therefore work was undertaken to characterize cell-mesh interaction through confocal microscopy. Finally, both short (3 day) and long (15 day) term cell proliferation studies were conducted on prepared mesh in order to assess their overall propensities for T cell expansion.

3.2.1 Electrospun mesh

Developed initially for the fabrication of non-woven textiles, electrospinning technology has more recently been adapted to a variety of natural and synthetic polymers for use in tissue scaﬀolding [Friess, 1998, Li et al., 2002, He et al., 2007], dressings for wound healings [Jin et al., 2002,Khil et al., 2003], drug delivery [Kenawy et al., 2002,Zong et al., 2002, Zhang et al., 2006], immobilization of enzymes [Jia et al., 2002, Ye et al., 2005], biosensing [Haiying et al., 1996,Zhang et al., 1998,Zhang, 1998], and ﬁltration [Endres et al., 2003,Burger et al., 2006]. These applications utilize a basic electrospinning system that consists of a high voltage power supply, a spinneret, and a grounded collector plate [Liang et al., 2007,Sill and von Recum, 2008]. In this process, a polymer-solvent mixture is extruded through this charged syringe, linked to the high voltage power supply (Figure 3.1 ). When the repulsive electrical forces overcome the surface tension forces in the stream, a charged jet of polymer solution is ejected from the tip of the Taylor cone and accelerated towards a grounded plate. As the stream of solution is rapidly whipped in the space between the syringe tip and the collector, the solvent is evaporated and the polymer is collected as a randomized mesh network [Taylor,

51 1969,Adomaviˇci¯ut˙eand Milaˇsius,2007]. Importantly, processing parameters, including but not limited to ﬂow rate, applied voltage, and makeup of the polymer solution, may be manipulated to generate particular geometrical and structural properties within the scaﬀold [Demir et al., 2002, Zeng et al., 2003, Sill and von Recum, 2008, Zhang et al., 2005,Zhao et al., 2005].

Figure 3.1: Schematic of electrospinning technique. Fibers are extruded onto a grounded collector by applying a high voltage to the tip of a syringe ﬁlled with a polymer solution. Reprinted with permission from [Bhardwaj and Kundu, 2010].

Several groups have demonstrated that immune murine cells can be isolated and/or activated on electrospun ﬁbers. Interestingly, Saino et al. observed that macrophages’

52 secretion of proinflammatory molecules depended on PLLA fiber diameter but not fiber orientation [Saino et al., 2011]. However, no available platforms have been optimized based on T cell substrate rigidity and they do not fully meet the needs of long term clinical human T cell expansion. Kim et al. have successfully isolated and activated CD3+ T lymphocytes with magnetized nanoparticles spun into polymer fiber [Kim et al., 2012]. However, as the fibers must be collected via magnet post-expansion, this technology does not ensure the complete elimination of polymer particulates and therefore presents many of the same concerns at the current Dynabead cell expansion model.

For substrate production we have collaborated with the Biomaterials and Interface Tissue Engineering Laboratory under the guidance of Dr. Helen H. Lu at Columbia University to electrospin mesh from a blend of PCL and PDMS elastomers. We as- sert that the rigidity of electrospun mesh will depend on polymer composition, fiber diameter, and mesh porosity. To address this first point, perceived surface rigidities were targeted by formulating polymer solutions with a range of bulk stiffnesses that we aim to preserve in fiber format. It is challenging to electrospin PDMS fibers owing to PDMS’s low molecular weight [Yang et al., 2009]. However, it has been successfully electrospun after blending with copolymers including poly(methyl methacrylate) (PMMA) [Yang et al., 2009] and polyvinylpyrrolidone (PVP) [Srivastava et al., 2007], which not only increases the chain entanglements of the polymer but also confers other attractive properties such as thermal stability, mechanical strength, and surface properties. Known for its biocompatibility, PCL was chosen as a copolymer for electrospun nanofibers [Chong et al., 2007]. PCL is widely used in drug delivery applications owing

53 to its slow biodegradation by hydrolysis of its ester linkages [Sinha et al., 2004].

As mentioned above, a variety of PDMS-PCL fiber formulations have been developed and tested. PDMS is made in-house by our collaborators combining a polymer and crosslinker at two particular ratios. The two formulations have been chosen for their range of bulk rigidities: 1:10 PDMS exhibits a bulk rigidity of approximately 2 MPa while the bulk rigidity of 1:50 PDMS measures >100 kPa [OConnor et al., 2012]. Both of these PDMS ratios are blended in either 4:1 or 1:1 ratios with PCL, in efforts to preserve their individual rigidities, and spun into mesh. These polymers are combined in a solvent phase together with a 3:1 ratio of DCM and DMF, to achieve a final polymer concentration of 19% w/v in solvent for the production of fibers with nanometer-scale diameters and of 35% w/v for the production of fibers with micrometer-scale diameters. FTIR spectroscopy confirms proper incorporation of both PCL and PDMS polymers in fiber formulations (data not shown). PCL, known to have very stiff bulk properties, was also electrospun without PDMS to achieve a broader range of rigidities.

Scanning electron microscopy (SEM) is employed for surface mapping and morphology assessment. Additionally, mesh stiﬀness and protein penetration are measured to project expansion capacity of fabricated scaﬀolds. The surface topography of a number of mesh compositions is captured via SEM and illustrated in Figure 3.2 Images depict a randomized (unaligned) mesh network for each formulation.

54 55

Figure 3.2: SEM images of samples from a number of PDMS-PCL mesh, with diameters ranging from nanometer- to micrometer- scale diameters. Morphological data from electrospun mesh can also be collected from SEM images (Table 3.1). Notably, PCL spun alone under the same conditions as PDMS-PCL formulations presents wider ﬁber diameters and higher bulk rigidities, as expected. Addition- ally, by increasing the percentage of polymer in solvent, larger, micrometer-scale ﬁber diameters are achieved. However none of the pore sizes appear large enough to foster T cell penetration, as the diameter of a lymphocyte is known to measure approximately 7 µm.

Table 3.1: Morphological data of mesh formulations collected via SEM images together with mechanical properties derived from Instron measurements, p < 0.05.

In addition to an investigation of morphological properties, electrospun mesh also underwent mechanical testing. Samples were tested to failure under uniaxial tension on an Instron mechanical testing device. Tensile testing showed inherently rigid bulk properties, in the mega Pascal range (Table 3.1). A one-way ANOVA was conducted across each material property using a Tukey-Kramer post-hoc test for pair-wise comparison of each group. Formulations comprised of a blend of PDMS and PCL gives softer moduli and thinner ﬁbers than PCL alone, as denoted by the asterisk. For more details regarding mesh fabrication and material characterization, please see appendix.

56 3.3 Materials and Methods

3.3.1 Atomic force microscopy

AFM was performed on all mesh compositions. Small pieces of mesh were cut with foil backing and secured foil-side down to a petri dish. Mesh was incubated for 2 hours at ambient temperature in 2-5% bovine serum albumin for two hours to prevent adhesive interactions during indentation. The samples were rinsed thoroughly before being submerged in 1x PBS for measurement. 4.5 mm-polystyrene spherical tips mounted on 0.35 N/m cantilever silicon nitride probes were purchased from Novascan (Ames, Iowa) and used in conjunction with a BioScope Catalyst microscope (Bruker Corporation) for data collection. Probes were calibrated to determine deflection sensitivity, and in turn spring constant, at the start of each experiment. On average, three indentations per region were performed over four to six regions per substrate. Analysis was performed primarily in NanoScope Analysis v1.40 (Bruker Corporation). Functions used in analysis include smoothing filter, baseline adjustment, and accounting for adhesion. The model used to fit indentation curves was primarily based on the Hertz model, estimating a linear elastic infinite solid and a round probe that does not undergo deformation.

3.3.2 Surface preparation

Electrospun mesh was removed from foil, mounted on 12 mm coverslips with 1:3 PDMS (crosslinker: polymer ratio), and left to cure at ambient temperature for >1 day. After curing was complete, excess mesh was trimmed from the edge of the coverslip. Prior to the experiment, both sides of the mounted mesh were sterilized in ethanol under UV

57 and then dried completely in a sterile culture hood. Mounted scaﬀolds are compatible with 24-well polystyrene dishes for long-term cell expansion experiments. For confocal imaging, mesh was taped to glass coverslip backings to allow for removal and improve ease of handling.

3.3.3 Confocal microscopy

Images were collected via a LEICA TCS SPS Confocal and Multiphoton Microscope with 20x objective at zoom 0 or 5, for a total magniﬁcation of 20 – 100x. Leica Application Suite Advanced Fluoresence (LAS AF) software was used to acquire images. For confocal imaging, electrospun mesh was taped to coverslips and labeled with Alexa Fluor 647 donkey-anti-mouse IgG (Life Technologies) and unlabeled OKT3 and CD28.6, while cells were labeled with CFSE CellTrace (Life Technologies) prior to seeding. For mesh visualization studies, images were taken in two separate channels: one to visualize penetration of antibodies in the scaﬀolding and the other to locate and identify the penetration of T cells into the mesh. Analysis of images and reconstruction of stacks were primarily performed through Fiji ImageJ software package.

3.3.4 Cell culture

Human CD4+/CD8+ T cells were isolated from a leukopak source (New York Blood Center) via Rosette Sep Human T cell Enrichment Cocktail (Stemcell Technologies) and were frozen down until ready for use. In preparation for experimentation, CD4+/CD8+ T cells were recovered for 6 hours at 3x106 cells/mL in RPMI 1640 media supplemented with 10% FBS, 10 mM HEPES, 50 U/mL Penicillin & Streptomycin, 2 mM L-glutamine

58 o and 50 mM b-Mercaptoethanol at 37 C and 5% CO2.

3.3.5 Assessment of initial cell-mesh binding

Cleaned mesh were incubated in 2 µg/mL of goat-anti-mouse IgG or 100 µg/mL Protein A, a protein linker which binds to the prepared substrate by direct adsorption for two hours at room temperature. In early studies, mesh were directly subjected to a mixture of antibodies, but as the system was reﬁned, a 5% bovine serum albumin incubation (BSA) step was added. After a thorough washing, scaﬀolds were then coated with OKT3 and CD28.6 which bind to the IgG linker. These antibodies were used in a 1:4 ratio, respectively, to achieve a total activating antibody concentration of 100 µg/mL. Following thaw and 6 hour recovery, CD4+/CD8+ cells were seeded at 1,000,000 cells/mL in a 1 mL volume per well. At 2 hours post-seeding or at 12 hours post-seeding, unbound cells were gently removed from the substrate and counted via hemacytometer to determine the percentage of cells initially adhered to the substrate, with the potential for stimulation and activation, at these early time points.

3.3.6 Long-term cell expansions in conjunction with CFSE as-

says

Mesh were prepared with protein linker, BSA block, and activating antibodies as described previously. Following thaw and 4.5 hour recovery, CD4+/CD8+ nave T cells are stained with carboxyfluorescein succinimidyl ester (CFSE), a fluorescent dye that is retained stably within the cells for long periods, even through several divisions. Cell proliferation can be measured by assessing the halving of conferred fluorescence within

59 daughter cells after each division. Therefore, cell populations were assessed for the percentage of cells divided and proliferation index, the average number of divisions each original cell underwent.

After staining, cells were seeded onto prepared mesh at 1,000,000 cells/mL, a density equivalent to approximately 5,670 cells/mm2. The positive control was comprised of an equivalent number of cells incubated with 3x106 Dynabeads (Life Technologies), considered the clinical gold standard of T cell activation. This concentration was calculated to equate the available surface area on PDMS beads in other rigidity-based expansion studies. A negative control consisting of unstimulated cells was also included.

After three days, cells were washed from the mesh, counted, and diluted to 800,000 cells/mL for optimal growth conditions. A portion was removed for CFSE analysis via ﬂow cytometry. Cells were counted and suspended at the prescribed density every other day thereafter. CFSE analysis was carried out on both Day 3 and Day 5.

Cell population doublings were calculated using the following equation,

D T1 = T0e (3.1)

where T0 is the initial cell count before the growth period, T1 is the ﬁnal cell count after the growth period, and D is the number of doublings. To account for large cell volumes, we considered D to be the increase factor and did factor in dilution by considering the

Vc D total volume, VT , and the volume kept in culture Vc as T1 = T0 ( /VT ) e . The total number of doublings was calculated by taking the log of the product of the increase factors for each day up until that day.

60 3.3.7 Flow cytometry

After three days, cells were washed from the mesh and a small portion was analyzed through ﬂow cytometry (BDFACS Canto II; Excitation 494 nm) Populations from 80,000 – 100,000 cells are considered an adequate sample. Data was then analyzed using FlowJo software (Tree Star, Inc.). Gates were established to select the cell population. Additional FlowJo tools allow for mapping cell divisions across scaﬀold types, enable determination of the percentage of cells divided, and calculate proliferation index, the average number of cell divisions a single cell undergoes. Flow cytometry analysis was repeated on Day 5 (data not shown).

3.3.8 Statistical analysis

Data collected from bi-daily cell counts, as well as proliferation index and percent divided terms derived from ﬂow cytometry data, was analyzed using standard ANOVA techniques in conjunction with the Tukey test for multiple means comparison. Statis- tical analysis was performed using OriginPro 9 Software.

3.4 Results

3.4.1 Characterization of rigidity and protein adhesion on elec-

trospun mesh

Fibrous electrospun mesh provides us with a platform the desired high surface-to-volume ratio and ﬂexibility in the end form. It is durable, easy to handle, and mimics the

61 structure of an extracellular matrix. Mesh was spun from diﬀerent compositions of PDMS and PCL blends, but it is imperative for us to determine if this rigidity response is preserved as PDMS mesh is cured in air. AFM techniques have been applied to the topographic imaging and mechanical measurement of electrospun ﬁbers [Cronin- Golomb and Sahin, 2013,Nandakumar et al., 2013].

To this end, using a 4.5 µm-diameter polystyrene tip on the order of the size of a T cell, we have gathered rigidity measurements based on what a cell-sized particle may actually sense, see Figure 3.3. These measurements do not reflect the stiffness of each solid individual fiber, rather, they reflect the resistance produced by a woven porous substrate. As we expect the tip to be much stiffer than the substrate, and the thickness of the mesh is known to be 50 – 100 µm (sufficiently thick to be considered infinite for indentation measurements), we can assume the substrate is homogeneous and employ the Hertz method for curve analysis. Again examining stiffness on the size scale of individual fibers (300 nm - 1 µm), that is to say indenting with a tip smaller than this size, we would expect AFM measurements to reflect the rigidity of the homogeneous bulk material. In this case, we conduct indentation with a larger AFM tip because are interested in what the whole-cell system perceives.

62 Figure 3.3: Schematic of AFM probe 4.5 µm in diameter interacting with electrospun mesh for size comparison.

AFM indentation measurements were conducted over a wide range of PCL-PDMS blends, as shown in Figure 3.3. PDMS mixed 10:1 with its crosslinker blended with PCL produced mesh of more varied and often stiffer regions. Interestingly, though PCL is known to exhibit the most rigid bulk properties, in this format the local rigidity is less stiff than in the PDMS-PCL blends. Furthermore, in cases where PDMS is mixed at a ratio of 50:1 with its crosslinker, softer rigidities were achieved when blended with a greater ratio of PCL (1:1 as opposed to 4:1). It may be thereby speculated that PCL incorporated into the mesh solution lowers the local rigidity. This may be due to an improved ease of spinning and a more stable curing of fibers resulting in increased porosity or increased mesh resilience. The proportion of PCL in solution does not appear to correlate with pore size but it may be worthwhile to examine porosity, or the prevalence of pores (Table 3.1).

63 Figure 3.4: AFM indentation measurements across all mesh spun from PDMS-PCL blends with PCL-only and PDMS sputter controls. Each point represents up to three indentations at a single region. Standard deviation is shown.

64 3.4.2 T cell penetration into and interaction with mesh topol-

ogy

Cells have long been known to sense and respond to mechanical stimuli in their environment. The rigidity-based and topographical implications of the PDMS-PCL blend mesh on cell activation and expansion are characterized here. Mesh of sufficiently large pore size also encourage cell infiltration and ensure expansion efforts stimulate a greater number of cells. In order to better visualize cell penetration into the mesh, mesh was incubated with a fluorescently labeled antibody linker on to which activating antibodies were mounted. Cells stained with carboxyfluorescein succinimidyl ester (CFSE), a fluorescent dye that is retained stably within the cells for long periods, even through several divisions, were seeded onto mesh for 12 - 18 hours to ensure sufficient time to penetrate the mesh. Confocal images show cells penetrating deep into the mesh across all mesh blends. Cells may also be seen interacting with individual fibers, an advantage of the increased surface area-to-volume ratio.

65 Figure 3.5: Confocal images across mesh blends show cell penetration into mesh and interaction with ﬁbers.

66 3.4.3 T cell stimulation and expansion on electrospun mesh at

short and long time points

To assess the capacity of PDMS-based fibrous scaffolds for T cell expansion, substrates were prepared accordingly. Mesh were first treated with protein linkers, either goat- anti-mouse IgG or Protein A, to improve orientation and binding of antibodies. A 4:1 mixture of activating antibodies CD28.6 and OKT3 was then presented via linkers on the surface of fibrous mesh structures. In the previous chapter as in rigidity studies on planar PDMS, successful activation was achieved with nave human CD4+ T cells [OConnor et al., 2012]. We chose to measure proliferation of a CD4+/CD8+ population, more representative of the starting population of T cells currently utilized in many adoptive immunotherapy studies. Cytotoxic CD8+ T cells directly engage pathogens, while helper CD4+ T cells secrete cytokines and interact with immune cells to hone and drive the response of the immune system.

The uniformity of initial adhesion of cells across varied substrates directly impacts quantiﬁcation of cell activation and expansion at later (Day 3 and Day 15) time points. In eﬀorts to address this discrepency, we measured initial cell adhesion onto mesh at 2 and 12 hour time points. The number of cells adhered onto mesh varied between 60 - 70% of cells initially seeded, at both time points. Alternative coating protocols and mesh of higher porosity could be implemented to increase initial cell adhesion.

For expansion studies, cells are stained with CFSE; thereby cell proliferation can be measured by assessing the halving of conferred ﬂuorescence within daughter cells after each division. Cells were analyzed for the percentage of cells divided and proliferation index, the average number of divisions each original cell underwent, on both Day 3 and

67 Day 5 after seeding. Cell populations were counted to assess doublings on Day 3 and every other day thereafter for the lifetime of the population.

As both Protein A and goat-anti-mouse IgG have been shown to adequately bind antibodies to polymer substrates, we began with an investigation into which linker is best suited to this platform (Figure 3.6). 10:1/4:1 and 10:1/1:1 polymer mixtures were spun into mesh and then coated with either Protein A or goat-anti-mouse IgG linkers, to which activating antibodies attached. ANOVA and Multiple Means Comparison Tukey testing at the three day time point showed statistically significant higher percentages of cells divided on 10:1/4:1 mesh treated with IgG over the same mesh treated with Protein A (p = 0.04114). Additionally a significantly higher percentage of cells divided with the Dynabead control as compared to both blends of mesh treated with Protein A (for 10:1/1:1, p = 0.02046; for 10:1/4:1, p = 0.00597; n=3) . No statistically significant differences were observed in terms of the proliferation index. Two-way ANOVA statistical methods applied across the maximum number of divisions for each sample and across flow cytometry data did not yield additional insights into the effect of protein linkers on cell expansion. Additional studies will need to be completed to optimize antibody presenation on the mesh platform.

68 Figure 3.6: Expansion data from 1:10 PDMS-PCL blends with Protein A or IgG antibody linkers (A) Doublings measured at Day 3 and every other day thereafter, standard error shown; (B) Representative CFSE data at Day 3; (C) Flow cytometry analysis including the percentage of cells divided and the proliferation index. Standard error denoted, n = 3.

Comparing the peak doubling value for each sample across all days, the Dynabead control and both PDMS-PCL blends treated with goat-anti-mouse IgG showed a statistically signiﬁcant diﬀerence over unstimulated cells. Owing to the superior performance of goat-anti-mouse IgG over Protein A, this linker was employed in the following studies described here.

These early studies shaped the next stage of experimental design and mesh iterations. AFM indentation studies described in Figure 3.4 show that PDMS mixed 50:1

69 with its crosslinker, blended with PCL, and then spun into mesh produce less rigid more uniform substrates. This, together with fabrication parameters that allowed for more diverse iterations of this particular fiber form, led us to produce more varied fibers mainly from blends containing 50:1 PDMS. The following study explored the activation and stimulation of CD4+/CD8+ T cells across 50:1 PDMS – PCL polymer blends (Figure 3.7). Blends were tested in nanometer- and micrometer-diameter fiber formats. Additionally, both aligned and unaligned formats of meshwere procured. Aligned fibers may be more easily manipulated by T cell interaction, thus encouraging greater penetration depth.

70 Figure 3.7: Expansion data from multiple 50:1/1:1 and 50:1/4:1 with PCL in both aligned and unaligned forms. (A) Doublings measured at Day 3 and every other day thereafter with standard error bars; (B) Representative CFSE data at Day 3 ; (C) Flow cytometry analysis yields the percentage of cells divided and the proliferation index with standard error, double asterisks denote statistical diﬀerence from Dynabead control p < 0.05, n = 3.

The proliferation index of 50:1/4:1 aligned nanofibers is significantly higher the Dynabead control (p=0.00929). Additionally the proliferation index of the 50:1/1:1 aligned microfibers is higher than that of the Dynabead control (p= 0.08804). Though we presume values of proliferation index and percent of cells divided at Day 3 will predict later expansion success, assessing for peak doubling values across all samples

71 we find there is no statistical difference in terms of the percent cells divided across all stimulated samples. Moreover there is no apparent correlation between alignment of mesh and expansion success, by any of the three metrics. Two-way ANOVA statistical analysis conducted across the maximum number of divisions for each sample and across flow cytometry data also did not yield any additional insight into mesh performance. Additional studies will need to be completed to optimize this mesh platform.

We also compared T cell stimulation and expansion across 50:1 PDMS blended 1:1 with PCL to form mesh with a range of diameters and aligned and unaligned PCL controls (Figure 3.8).

72 Figure 3.8: Expansion data from 50:1 PDMS-PCL blends with PCL-only mesh controls in both aligned and unaligned forms. (A) Doublings measured at Day 3 and every other day thereafter, standard error shown here where n=3; (B) Representative CFSE data at Day 3; (C) Peak doubling of each sample over expansion period, single asterisk denotes statistical diﬀerence from Dynabead control p < 0.1; (D) Flow cytometry analysis yields percentage of cells divided and the proliferation index with standard error, double asterisks denote statistical diﬀerence from Dynabead control p < 0.05, n = 3.

The proliferation index measured at Day 3 for 50:1/1:1 aligned micrometer-diameter mesh is signiﬁcantly higher than that of Dynabeads® (p = 0.04788). Comparing the peak doubling value for each sample, 50:1/1:1 unaligned micrometer-diameter mesh is yields a greater number of doublings overall than the Dynabeads® control (p = 0.0937). Furhter investigation via two-way ANOVA techniques across the maximum

73 number of divisions for each sample and across flow cytometry data did not yield additional insights into mesh performance. Though PCL has the stiffest bulk rigidities and the softest local rigidities measured via AFM indentation, it does not stimulate cells as well as unaligned 50:1/1:1 micrometer-diameter mesh. Future experimentation will establish an optimal mesh design for cell expansion efforts.

3.5 Discussion

The results in this chapter highlight a number of key findings. Firstly, mesh with soft local rigidities can be produced by electrospinning polymers with rigid bulk properties. Secondly, in terms of scaffold topology, alignment of fibers does not appear to influence T cell activation; rather the diameter of spun fibers may impart a greater effect. Lastly, based on our most recent findings we conclude that 50:1/1:1 spun into micrometer- diameter mesh is the most effective formulation for inducing T cell activation.

Work in the previous chapter attests to the role of rigidity sensing in T cell activation. In this chapter we were able to assess the local rigidity across a number of blends for the PDMS-PCL scaﬀold expansion platform. Despite being characterized by relatively stiﬀ bulk properties, polymers spun in pure and blended forms exhibit much softer rigidities in mesh form. This may be explained in part by the porosity element of the mesh material and by the give or resiliency of the woven platform.

However there was no trend observed relating the bulk rigidity of the contributing polymer and its local rigidity in mesh form. PCL spun alone, possessing the most rigid bulk properties, showed the softest local rigidities; while 1:10 PDMS blended with PCL

74 had comparatively less rigid bulk properties but showed the stiffest local rigidities and greatest variation region-to-region. As there are no appreciable differences in pore size to explain the differences in rigidity, the porosity of the substrate, the frequency of pores in a given volume, may account for this discrepancy. To this end porosity of the scaffold may be measured using mercury porosimetry as a part of future investigations [Lu et al., 2003].

The proportion of PCL in the polymer blend may also have bearing on the local rigidity. If we consider 50:1 PDMS blended with PCL, it appears that with an increased ratio of PCL (4:1 to 1:1 PDMS:PCL), local rigidity is decreased. This may be due to some contributing stabilization factor imparted by the PCL during the curing process. Nevertheless local AFM indentation measurements of mesh spun from PCL alone and 50:1 PDMS with PCL blends show suﬃciently consistent and adequately soft rigidities to take advantage of the previously established rigidity response.

Deliberating design and implementation of a scaffold T cell activation platform, the role of antibody binding and mesh topography in modulating cell response is also important to consider. O’Connor et al demonstrated that that the amount of antibody adsorbed on PDMS substrates across a range of rigidities is equal despite changes in the ratio of base polymer to crosslinking agent. PCL substrate has been shown to appre- ciably bind proteins, which understandably decreases after BSA blocking; however, the concentration bound is at least in part dependent upon the particular protein [Oliveira et al., 2014, Yin et al., 2009]. Further work may be conducted to investigate uniformity of antibody presentation on electrospun mesh, as the concentration of antibody presented is known to have a significant effect on cell activation. Differentiating the

75 cell’s response to antibody presentation from its response to substrate rigidity can aid in the design and optimization of this platform. This assessment in itself has thusfar presented technical challenges. Antibodies bound to fibers would be difficult to assess through traditional fluorescence imaging methods. Mesh are by nature not flat and the variation in number and direction of fibers could not be adequately captured in a single plane. The top layer may be sufficiently captured and tiled to ensure proper characterization, but with the level of penetration that we hope to achieve, the depth of the mesh is difficult to assess. Confocal microscopy was employed to examine cell penetration and interaction with mesh fibers, but is not a suitable means for assessing antibody presentation owing to confounding factors such as sample bleaching.

Topography, especially that on the nano-scale, of electrospun mesh has also been shown to modulate cell adhesion and differentiation [Yim et al., 2007]. Saino et al. showed an increase in inflammation associated with planar PLLA film and electrospun PLLA microfibers over electrospun PLLA nanofibers [Saino et al., 2011]. This study showed higher levels of cytokine and chemokine secretion by macrophages seeded on microfibers over nanofibers. Additionally, they do not report any significant difference in cell activation dependent upon the alignment of the fibers. We also find that the alignment of fibers does not appear to influence T cell activation. Based on our findings we show that 50:1 PDMS blended 1:1 with PCL and spun into micrometer-diameter mesh is the most effective formulation for inducing T cell activation. Comparing the peak doubling value for each sample, this formulation exhibits a significant advantage in expansion of T cells over the Dynabeads® control (p < 0.1).

Proliferation index, the average number of divisions a cell in the population under-

76 goes, and the percentage of cells in a population that divide are assessed on Day 3 of culture and meant to predict the success of the expansion. In the second set of expansions, the proliferation index of the Dynabeads® control is statistically significant over 50:1 PDMS blended with 4:1 PCL aligned nanofibers (p < 0.05) and does go on to predict a less successful cell expansion. Additionally the proliferation index measured at Day 3 for 50:1 PDMS 1:1 PCL aligned micrometer-diameter mesh is significant over the Dynabeads® control (p < 0.05) and the same expansion goes on to be the most successful, even surpassing the positive control in the extended expansion (p < 0.1). Therefore our findings conclude that the alignment of fibers seems to be of little consequence to cell stimulation; however, larger fiber diameters (micrometer-scale) may induce greater activation than smaller (nanometer-scale) fibers. These results will in- form future iterations of this mesh platform and enhance adoptive immunotherapy by providing a new level of control over the ex vivo expansion process.

77 Chapter 4

Conclusions and Future Directions

The primary aim of this body of work is to explore the inﬂuence of substrate rigidity on the activation and expansion of human T cells and use this knowledge towards the development of more specialized, more successful clinical platforms for adoptive immunotherapy. A polyacrylamide gel system with rigidities spanning 10 kPa to 1 MPa showed a maximum human CD4+ T cell activation at rigidities between 100 and 200 kPa, with lower activity observed outside of this range. This is consistent with human CD4+ T cell activation on a PDMS elastomer system as reported by O’Connor et al, and supports that the rigidity response is substrate-independent [OConnor et al., 2012]. These results are also in line with a biphasic cell stimulation response. Early studies suggest this could also be true for the murine T cell model, but a more extensive range of rigidities should be tested to establish a biphasic trend and reconcile results with studies of human cells [Judokusumo et al., 2012]. In order to further understand the motor-clutch model’s applicability or adaptability to T cell mechanosensing, traction forces should be examined. Our group has successfully conducted single-cell live imaging studies to investigate traction forces on PDMS pillars. If pillar systems could be made to approximate the ideal local substrate rigidities of the designed gels, we would have the opportunity to better understand force generaiton at the TCR complex and its impact on T cell response to rigidity [Bashour et al., 2014]. Moreover, the biphasic response of both cell stimulation and traction forces would be considered supportive of a powerful link between immune biology and mechanobiology known in other systems. Alternatively, the absence of biphasic traction force response could signify that cells operate entirely in the extremes of the rigidity model, or that a diﬀerent model may be more informative. In particular, the stick-slip model suggests a mechanism for maximizing signaling at intermediate molecular strains, with lower activity observed outside of this range [Margadant et al., 2011].

The agreement and adaptation of the motor-clutch model notwithstanding, changes in cytoskeletal structure upon cellular activation signal the role of actomyosin in T cell mechanosensing. In fact, work in our lab (unpublished data) and others suggest large whole-cell systems are at work in cytoskeleton remodeling after T cell triggering [Kumari et al., 2014]. The wider rigidity range of this hydrogel system oﬀers the capability to examine previously unexplored cell behavior in the upper range of biological tissue stiﬀness [Levental et al., 2007]. In particular, it will aid our lab in the investigation of the actin as an indicator of TCR triggering. The development of a comprehensive model of T cell mechanosensing would enable a greater understanding of the underlying pathways driving activation and could be put toward the advancement of adaptive immunotherapy technologies.

79 Work delineating the response of T cells directed by substrate rigidity thus far has guided the development of a new platform for clinical cell expansion. Electrospun mesh with highly-tuned rigidities offers high surface area-to-volume ratio for improved cell activation and expansion. Though the system has not yet been optimized we can report that the alignment of fibers seems to be of little consequence to cell stimulation; however, larger fiber diameters (micrometer-scale) may induce greater activation than smaller (nanometer-scale) fibers. This observation requires additional investigation, and it likewise may be useful to examine changes in pore size and overall porosity of the substrate. To better project the success of stimulation future studies may also employ techniques to track cell size, an early indicator of the level of activation.

Aside from increased doublings, the electrospun mesh platform has the potential to direct cell differentiation. In results published by our collaborators, softer substrates where shown to enhance nave CD4+ T cell Th1-like differentiation [OConnor et al., 2012]. Physiologically, Th1 helper cells support the cellular immune system, maximizing the potency of macrophages and the proliferation of cytotoxic CD8+ T cells. They have also been shown to be important for efficacy in preclinical immunotherapy models [Nishimura et al., 2000]. The Th1-like differentiation of nave CD4+ T cells is promoted by the production of IFNg in helper T cells. To this end, work will be done to assess IFNg yield in culture, a measure of intercellular effector cytokine expression of stimulated CD8+ T cells. Rigidity-dependent preferential proliferation and differentiation of a certain T cell population could be a useful alternative to cytokine-directed differentiation to meet the needs of clinical applications.

Looking ahead to the potential commercialization of electrospun mesh for T cell

80 expansion, we must consider the following. To establish this platform as a superior expansion modality, the electrospun mesh must perform as well as or better than the current clinical gold-standard, Dynabeads®. Dynabeads offer a high surface area- to-volume ratio, as does the electrospun mesh, but does not take advantage of the T cell’s increased activation on softer surfaces, as we’ve shown here. Limits to the efficiency of separating Dynabeads from activated cells prior to injection are also known, making the mesh a potentially safer alternative [Levine et al., 1998]. In future iterations electrospun mesh can be spun from NuSil, a set of elastomers with master files at the FDA, mitigating regulatory challenges. As the target of adoptive immunotherapies shift, from dispatching dendritic cells migrating to the lymph node to induce T cell activation toward stimulating T cells in a process free of APCs, a more controlled approach is required. Exploring the underlying mechanisms of T cell activation and proliferation will lead to the development of more potent immunotherapies. Rigidity-tuned electrospun mesh, an early example of such, has met with success, though in its early stages. Future work in this direction will look to optimize this platform, yielding improved cell expansion results important in the implementation and efficacy of developing adoptive immunotherapies.

81 Chapter 5

Perspective on Personalized Medicine in a Changing Healthcare Landscape

5.1 The Current Healthcare Climate

It is no secret that our healthcare system is in the midst of signiﬁcant change. With the success of medical technologies and consequential lengthening of the average lifespan, the focus of the healthcare landscape is shifting from providing acute patient care to the treatment and prevention of chronic illnesses [Emanuel, 2014]. As new discoveries are made and new technologies produced, new levels of coordination and cooperation must be implemented to unite the work of hospitals, physicians, clinical care workers, patients, insurers, drug and device manufacturers, and regulatory agencies in adapting these scientiﬁc advancements.

A more rounded approach is being taken to the provision of medical services: oﬀering a wider range of healthcare services through a number of new outlets. After-hours clinics and some drug and retail stores, who have primarily dealt in pharmaceuticals for example, are now building to oﬀer a wide range of primary healthcare services. These new outlets together with established primary care physicians (PCPs), nurses, social workers, and hospital networks will provide a new and formidable system of healthcare providers. The overarching system seeks to serve patients at various wellness stages: through prevention, diagnosis and treatment of disease, as well as maintenance of remission state. In the midst of all of this, patients are being encouraged to take greater involvement in and responsibility for their health and well-being, aside from solely relying on their PCP [Bielamowicz, 2014]. In time we may see the PCP’s role shift to better informing the patient, motivating long-term behavior changes, addressing new needs, and helping to coordinate available care providers so that the patient can stay invested in their treatment plan and better maintain their lifestyle.

On this level of patient care provision, it is obvious that a great deal of effort will be required in coordinating the work of an expanding network of healthcare providers. Comprehensive patient profiles would allow information to be transferred between phar- macies, clinics, physician’s offices, etc. to offer a centralized source of patient information, treatment options, and pharmaceutical records to devise and maintain a consistent treatment plan.

Family health history forms an important foundation for comprehensive patient proﬁles. This history reﬂects an amalgamation of shared genetic, environmental, and

83 lifestyle factors that can be used to gauge a patient’s risk for certain diseases [Chan and Ginsburg, 2011]. Yet, for a variety of reasons, not everyone has a complete family health history and even if so, these histories may not provide a whole picture of the individual’s risks.

Instead evidence-based health risk assessments have been developed to evaluate a patient’s risk and prioritize clinical approaches to disease events, such as coronary heart disease and breast cancer; however they are highly underutilized [Kannel et al., 1961,Gail and Greene, 2000]. This low implementation rate may be due to insufficient standards for the clinical data required or algorithm employed [Liu et al., 2006]. These assessments also suffer, notably, from poor integration into most health information technology systems. However computerized clinical decision support systems have been developed and are finding greater success in practice, see Figure 5.1 [Osheroff et al., 2007].

These systems assimilate patient information into user-friendly interfaces to improve accessibility to patient information and knowledge for healthcare providers. Work is still underway to expand this technology and incorporate electronic health records which would provide additional support for diagnostic and treatment decisions [Bennett and Glasziou, 2003,Shea et al., 1996,Walton et al., 1999].

5.2 The Role of Personalized Medicine

A similar evolution of strategy is occurring in parallel with research and development drivers of the healthcare landscape. With the progress of biomedical research, no-

84 Figure 5.1: Integration of molecular, clinical, and genetic factors can be assessed by personalized diagnostics and impact therapeutic options [Bottinger, 2007].

85 tably successful mapping of the human genome in 2003, scientists have come to better understand the causation, contributing factors, and manifestations of many diseases. This insight is very valuable in that treatments can be preemptively designed based on genetic evidence, which can be obtained from a patient via a simple cheek swab. This technology may even one day see integration into clinical decision support systems; however, there are still challenges in implementation as such large data sets and complex analysis mandate trained users and substantial computing power. Personalized medicine is the evidenced-based tailoring of medical treatment to a particular patient at a particular disease state [Fackler and McGuire, 2009,Chan and Ginsburg, 2011]. This medical treatment is not conﬁned to pharmaceutical or genomic therapies; rather it can be expanded broadly to encompass one or a culmination of the following technologies that provide patients with targeted services: genetic testing, biomedical imaging for improved monitoring or design of treatment, prosthetics, and tissue regeneration [Fackler and McGuire, 2009]. Generally two medical products, a diagnostic device and therapeutic product, are developed for cooperative use, though not always through the same company [Fackler and McGuire, 2009]. Regardless of form, personalized medicine aims to treat the underlying cause of a disease rather than to simply appease the symptoms. This realization and subsequent integration of this new form of medicine demands that healthcare decisions be made based on a combination of a patient’s unique clinical, genetic, genomic, and environmental data [Chan and Ginsburg, 2011].

86 5.2.1 Reserach and Development

Ultimately the field of personalized medicine seeks to provide an optimal treatment for an individual at any stage of disease progression. As mentioned previously, the healthcare system is shifting toward aiding patients through prevention of disease and monitoring of their health, and away from its focus on acute medical treatments. Per- sonalized medicine has the potential to play a significant role in this transition. It has the capacity to streamline clinical decision making, lower treatment time, and severity of illness by pairing patients with effective treatments and avoiding ineffective ones that may come with harmful side effects. Today drugs are ineffective in 38 - 75% of patients who suffer from major illnesses including depression, diabetes, and Alzheimer’s. The failure of these treatments may be due to a wide range of factors: complex interactions at the interface of the drug and target, subjection of the drug other chemical processes in the body, inappropriate dosages, and even noncompliance. With personalized medicine, many of these considerations can be identified outright and decisions can be made in advance. Effective therapies may be prescribed more quickly and accurately, based on genomic evidence, rather than through trial-and-error.

Genotypes are often studied to understand what underlies certain disease pheno- types. Interestingly, there are some cases where patients demonstrate the same disease phenotype but distinct genetic profiles. In these instances the same treatment plan may work differently or fail when applied to both types of patients. Therefore drug interaction, chemical messengers, and metabolism processes in the body must be considered, which may lead to the development of new more personalized treatment options [Vo- genberg et al., 2010a, Janetos, 2009]. Some of the most notable benefits of what has

87 been termed pharmacodiagnostics or thereonostics, the use of molecular analysis to optimize disease management, include better medication selection, safer dosing options, and improved drug development in clinical trials.

The work in this thesis primarily seeks to modulate an adoptive immunotherapy for the treatment of chronic lymphoid leukemia and has been adapted to acute lym- phobastic leukemia as well. This adoptive immunotherapy technique is founded in the genetic manipulation of a patient’s own T cells. Over the span of approximately 10 days, cells are subjected to chimeric antigen receptor (CAR) engineering. This cellular engineering adds a receptor to the cell’s surface to target leukemia cells, and once the antigen is found the leukemic cell presenting it is destroyed. The genetic modiﬁcation process also inserts a viral vector mechanism into the T cell which triggers lymphocyte expansion and proliferation upon encountering a leukemic cell to seek out and destroy the rest of the cancerous population. Early forms of this treatment have been successful in treating a small subset of the adult patients with CLL and of the adult and children with ALL that failed to respond to other available treatment options [Chustecka, 2013]. This type of personalized medicine is being applied to diﬀerent subpopulations of cancers and its use and development may be improved by additional screening practices, which can only be established as implementation increases.

Though the impact of genomic sequencing is widely cited as the preeminent driver of personalized medicine, a number of medical devices can be modiﬁed on an indi- vidualized basis to improve performance. Anatomy, physiology, and environment are all important variables to consider; moreover, we currently possess the technological capacity to directly quantify and utilize such characteristics for personalized device de-

88 velopment. Examples of personalized medical devices in various stages of development and manufacturing include a 3D printed tracheal splint, a tinnitus masker auditory aid, and an artificial pancreas device system [Fackler and McGuire, 2009,Zopf et al., 2013]. Similarly, this dissertation takes early steps in utilizing substrate rigidity in the design of an expansion platform that could one day be used to better expand and differentiate T cell populations to fight a particular form or stage of cancer.

5.2.2 The FDA’s Regulatory Strategies

Even in its early stages, personalized medicine has given way to more informed ways to predict risk, screen for carriers, and direct clinical diagnosis, prognosis, and management of disease based on genetic variation [Collins, 1999]. As a result of such promise, the FDA has worked extensively to evolve in response to and in anticipation of scientific progress [Fackler and McGuire, 2009]. First and foremost, the FDA focuses on ensuring the safety, efficacy, and security of healthcare products. Not only do they evaluate the benefits and risks of scientific evidence in deciding whether to approve new products but also strictly regulate manufacturing practices. However nearly 100,000 deaths annually in the US may be attributed to adverse treatments effects. The FDA recognizes personalized medicine for its prospective role in reducing this number of ca- sualties. Additionally, it sees the potential in a more solid understanding of patient response variability to speed the development of drugs and approval to market [Fackler and McGuire, 2009].

To better support the advancement of the ﬁeld of personalized medicine, the FDA has taken strides to facilitate the research and development surrounding new technolo-

89 Figure 5.2: Probablility of drug success to market by stage of develpment [Arrowsmith, 2012].

90 gies and providing assistance to streamline regulatory processes. In recent years, fewer and fewer drugs have endured the many phases of clinical trials to successfully enter the market, see Figure 5.2. Currently drugs and devices face a host of regulatory challenges which can only become more complex in the onslaught of newly developed genetically guided therapies. Given its relative infancy in the market and propensity for fast and continuous evolution, emerging biomarkers will be challenging for the FDA to validate both clinically and analytically. Furthermore, as diagnostic and therapeutic products are often co-developed and not necessarily by the same company, regulatory agencies must have the wherewithal to collaborate amongst each other and between production companies to coordinate product validation, reviews, and oversight [Fackler and McGuire, 2009].

The FDA has commissioned several regulatory agencies in efforts to evolve and guide policy decisions in the changing healthcare landscape. With the sequencing of the human genome, the Center for Biologics Evaluation and Research (CBER), alongside the FDA’s other medical product centers began the arduous task of creating and implementing regulatory processes, policies, and infrastructure to meet the complex challenges of this booming field. CBER in particular has integrated new genetic technologies into regulatory oversight to speed the development and availability of complex biological products, including the introduction of manipulated cells into the body to fight disease. It has developed a consortium of intramural research scientists who work with other researchers within a variety government agencies to ensure these new therapies are safe and effective [Fackler and McGuire, 2009].

Also of note, in 2002 the Center for Drug Evaluation and Research together with the

91 Drug Industry Association engaged pharmaceutical and biotechnology industry part- ners to discuss scientiﬁc development and regulatory policies surrounding pharmacoge- netics. From these workshops the Voluntary Exploratory Data Submission Program was formed as an opportunity for drug and device companies to discuss genetic information with the FDA outside of a formal review process. The Oﬃce of In Vitro Diagnostics and Radiological Health likewise stemmed from this collaboration, founding a centralized unit for the comprehensive regulation for all diagnostic device activity [Fackler and McGuire, 2009]. From this point, it is imperative that standards be establsihed to ensure testing reliance and uniformity [Vogenberg et al., 2010b].

These examples are part of a larger eﬀort by the FDA that have served to encourage an exchange of information between sponsors and regulatory agencies for the research and development of personalized medical treatments.

5.2.3 Economic and Ethical Considerations

In order for personal medicine to gain traction with market entry its many shareholders, including but not limited to patients, clinicians, drug and device manufacturers, IT specialists, healthcare providers, insurers, educators and regulators, must feel secure in their adaptation of new technologies and the invest the creation of a supportive infrastructure [Fackler and McGuire, 2009].

Economically it stands to reason that by preemptively selecting drugs and dosages based on genetic and metabolic testing rather than assuming the ﬁnancial and health risks of traditional trail-and-error prescribing, overall healthcare costs can be reduced. With escalating costs, healthcare payments are progressively being assumed by patients

92 themselves which lends them a greater voice in regulatory and policy decisions [Vogen- berg et al., 2010b]. It is paramount that affordable and effective healthcare be offered; however, for biotechnology and pharmaceutical companies this decrease in drug sales translates to a decline in profits. With the current one-size-fits-all model for pharmaceutical sales, blockbuster drugs occupy huge market shares even though they are not always effective and may cause harmful side effects. With the advent of personalized medicine, companies will need to adapt their business models to foster combination diagnostic-therapeutic products and comply with regulatory submissions. Biopharma- ceutical companies can also adjust their product portfolios and market approach to improve profitability through other avenues. In some instances, the government offers them incentives to develop drugs to treat smaller disease populations, especially where alternative therapies do not exist. Additionally, industry players have the opportunity to improve the rate of use of drugs currently limited by poor efficacy or safety issues, through aiding prescribers in identifying patient populations who could stand to benefit [Vogenberg et al., 2010b,Mancinelli et al., 2000].

Scientific advancements may only minimally impact the total cost of patient care, still antiquated reimbursement practices and legislation may create barriers to success [Vogenberg et al., 2010c]. Insurers consider many factors when formulating coverage policies for personalized medicine; however, many will likely to continue to resist supporting such claims until clinical evidence for efficacy strengthens [Hresko and Haga, 2012]. In the face of expensive diagnostic and therapeutic products, the return on investment of disease prevention has not been shown to offset the initial costs [Vogenberg et al., 2010b]. Payers will need to take the time to experiment with their approach to

93 personalized medicine, gathering data to aid them in calculating the cost beneﬁts of the services for their organization. This may include creating network and negotiating testing costs with laboratories to secure testing costs that can then result in savings passed along to customers. Ultimately insurers will be faced with deciding which medical expenses to cover in their policies which will depend on a diagnostic or therapeutic’s clinical utility and potential to impact physician decision-making [Vogenberg et al., 2010c]. Otherwise patient demand for personalized will most probably continue to be mitigated by their ability to pay. The introduction of new therapeutics and diagnostics, especially those that involve genetic information, requires the establishment of policies to protect patients, physicians, and other healthcare workers. The Health Insurance Portability and Accountability Act of 1996 (HIPPA) was put in place to protect the privacy of patient information as it is stored, accessed, or processed. Genetic information must be held under particularly stringent regulations as it can reveal facts not only about the individual but also about certain blood relatives [Vogenberg et al., 2010b]. The Genetic Information Nondiscrimination Act of 2015 further protects the privacy of genetic information from use as a foundation for health insurance underwriting decisions and as a basis for career decisions at the hands of an employer [of Human Genet- ics, 2007,Research!America, 2010]. The goal of these regulations is help patients to be more secure in accessing genetic testing and pave the way for personalized medicine [Vo- genberg et al., 2010b]. As the Department of Health and Human Services Secretary Kathleen Sebelius announced the HIPPA privacy rules she stated “. . . consumer conﬁ- dence in genetic testing can now grow and help researchers get a better handle on the genetic basis of diseases. Genetic testing will encourage the early diagnosis and treat-

94 ment of certain diseases while allowing scientists to develop new medicines, treatments, and therapies” [Zeidner, 2009]. Moreover, these policies together with new regulations passed under the Affordable Care Act ensure insurace coverage for people with genetic predispositions for disease as well as pre-existing medical conditions. In order to implement personalized medicine in the clinical space, physicians must be convinced of its accuracy and efficacy bringing no harm whatsoever to the patient. At this time physicians are not poised to support practices of personalized medicine. This is in part because organizations that support clinical standards have not yet offered their support of diagnostic and therapeutic strategies. At the same time as patient advocates they recognize that the implementation of personalized medicine may also present ethical dilemmas. For instance, individuals must never be coerced into participating in genetic testing owing to the amount of information it may reveal regarding other risks. Though policies have been put in place to protect a patient’s privacy, it is still possible for an insurer or a state government to require genetic testing to determine drug safety or efficacy in order to sidestep unnecessary cost burdens [Vogenberg et al., 2010b]. Nev- ertheless Patients stand to reap huge benefits and they themselves could be a driving proponent force of this new field of medicine. Knowing more about potential health risk and genetic predispositions could motivate people to maintain a healthy lifestyle and address health concerns early on. However advocacy groups have not yet been established and access to such treatments are not yet being demanded.

95 5.3 Patients and Advocates Guide Medical Research

Eﬀorts

Advocacy groups are taking a larger role in funding, organizing, and soliciting development eﬀorts for better treatment options. In the 1980s, AIDS treatment activists participated as credible players alongside scientists and government oﬃcials, changing the way data is validated and utilized in biomedical research [Epstein, 1995].

More recently, the drug Kalydeco which helps to restore function to a protein made by a mutated gene has successfully entered the market as a treatment for cystic fibrosis. Kalydeco was born out of a decade-long collaboration between the Cystic Fibrosis Foundation (CFF) and Vertex Pharmaceuticals. The CFF ultimately piloted the path to drug development by undertaking sizable financial and organizational responsibilities. They took a leading role by advocating for their cause, building the network necessary to investigate genetic markers characteristics of the disease, organizing participants to take part in clinical trials, as well as funding research and drug screening efforts. Additionally, the FDA granted the drug application “priority review” expediting the review process for drugs that offer major advancements in treatment or therapies where no alternatives exist [Fackler and McGuire, 2009]. This is a valuable example of the success that can be achieved when scientists, advocacy groups, and regulatory agencies work together.

Breast cancer advocates are seeking a similar response. The Artemis Project spon- sored by the National Breast Cancer Coalition has been recently featured in the news for its goal to end breast cancer by 2020 through innovative and strategic collabora-

96 tion by bringing together various stakeholders. As a preliminary target, the coalition is sponsoring development of vaccine over the next ﬁve years to prevent breast cancer [Harris, 2014]. It is no longer just a matter of coming up with funding for scientiﬁc research: patients and their advocates are going to greater lengths to set the focus of research and bring together experts to answer the questions they deem most important. With healthcare costs rising and traditional funding sources dwindling researchers will need to rethink their research strategies. Additionally, if successful, these early collab- orations will help to shape the way science is conducted in the future. The work of scientists will become less introspective and self-contained. It will be essential for scientists to reorganize and collaborate more broadly with the government and the public to address important medical issues.

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112 Appendix A

A.1 Electrospinning mesh

. Electrospun mesh is fabricated from a PDMS/poly(caprolactone) (PCL) solution, courtesy of Dr. Helen Lu at Columbia University. Early on, it was determined that PDMS could not be spun alone, owing to its low molecular weight. We have ex- perimented with polymer solutions of different polymer-to-crosslinker compositions of PDMS and varying ratios of PDMS to PCL, a biodegradable material that serves to increase the molecular weight of the solution. Polymer solutions were blended from both 1:10 PDMS (bulk rigidity ˜ 2 MPa) and 1:50 PDMS (bulk rigidity >100 kPa) (polymer to crosslinker ratio) either in 4:1 or 1:1 ratios with PCL and spun into mesh. These polymers are combined in a solvent phase together with a 3:1 ratio of dichloromethane (DCM) and N,N-dimethlyformadahide (DMF), to achieve a final polymer concentration of 19% w/v in solvent for the production of fibers with nanometer-scale diameters. Alternatively a final polymer concentration of 35% w/v in solvent is required for the production of fibers with micrometer-scale diameters. In either case, as the fibers are spun, the solvent evaporates. Fibers are extruded through a spinneret, charged by a high voltage power supply in a range of 10-12 kV, spaced approximately 10 cm from the grounded collector, covered in foil for easy removal.. For unaligned fibers, the collector plate is fixed while for aligned fibers the rotating mandrel turns at a rate of 2100 rpm. In efforts to address a wider range of rigidities, PCL-only mesh was spun at a concentration of 19% w/v in solvent. For aligned PCL fibers, the rotating mandrel turns at a rate of 2400 rpm. Alternatively PDMS alone cannot be spun, but was sputtered through an electrospinning apparatus. Importantly, processing parameters may be manipulated to generate particular geometrical and structural properties within the scaffold. For instance, fiber diameter may be decreased by increasing the applied voltage or decreasing the flow rate [Demir et al.,

113 2002, Zeng et al., 2003, Sill and von Recum, 2008]. Additionally, there is an optimal distance for uniform fiber production: overly long or short distances between extraction and collection components may produce undesirable beading [Zhang et al., 2005, Zhao et al., 2005]. Composition of the polymer solution also plays a large role in determining fiber morphology. Wider fiber diameters can be achieved by increasing the viscosity of a solution, increasing the polymer concentration of a solution, or decreasing conductivity [Zeng et al., 2003, Zhang et al., 2005, Zhao et al., 2005]. Selecting a polymer with a high molecular weight will reduce beading and improve fiber uniformity [Demir et al., 2002,Gupta et al., 2005]. By fine-tuning these parameters, electrospun fibers can be produced with diameters ranging from 2 nm to several micrometers and pore sizes ranging from a few nanometers to nearly a micrometer [Ahn et al., 2006, Hunley and Long, 2008,Reneker and Yarin, 2008].

A.2 Mesh characterization

The basic material properties of each mesh is characterized via scanning electron microscopy, Fourier transform infrared spectroscopy, and tension testing by the laboratory of Dr. Helen Lu at Columbia University. Scanning electron microscopy (SEM) is an imaging technology based in scanning a surface with a beam of electrons to determine topography, capable of resolution better than a single nanometer. In preparation, 5 - 7 nm of gold is sputtered on the fibrous substrate for clearer imaging at higher magnifi- cations. Fourier transform infrared (FTIR) spectroscopy measures the absorbance of a sample at each wavelength to determine the presence of certain functional groups in a molecule. Samples were tested to failure under uniaxial tension. Specifically, scaffolds cut from spun sheets (6 cm x 1 cm, n = 5 / group) were secured using custom clamps and mounted on an Instron mechanical testing device with an average gauge length of 3 cm. Samples were evaluated at a strain rate of 5 mm/min and elongation and elastic modulus were calculated from the stress-strain curve.

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