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4.1 INTRODUCTION

Stem cell fate is known to be sensitive to its local microenvironment, both chemical and physical in nature. In the previous chapter we showed that the pancreatic differentiation of hPSC was highly sensitive to alginate capsule stiffness. However, there could be other factors affecting cell fate as well. Given the complexity of stem cell response to environmental parameters, it will be important to evaluate cell response to multiple combinatorial perturbations, in isolation and conjugation. Such combinatorial perturbations are best enabled by high throughput screening platforms, which also minimize the cost of materials, time of experimentation, and physical space. The sensitivity of stem cells to various environmental factors has inspired the development of high throughput platforms. This approach has been used in 2D for screening physical stimuli such as material stiffness [148], topography [149, 150], and ECM protein or peptide composition [151-153] on stem cell attachment, growth and differentiation. However the complexity of stem cell response limits the information gathered from single perturbations and necessitates combinatorial perturbations. For example, Gobba et al. developed a microengineered hydrogel microarray which can vary substrate stiffness while being functionalized with protein combinations, which was used to test MSC differentiation [154]. The advent of 3D culture of

stem cells has initiated the development of array platforms supportive of 3D cell culture. Ranga et al. utilized nanoliter-dispensing technology to synthesize over 1000 unique environments to simultaneously probe the effect of matrix elasticity, degradability, and signaling proteins on mESC self-renewal and proliferation [155]. Yang et al. developed a 3D combinatorial ECM hydrogel platform to identify optimal ECM combinations which support linage specific differentiation of hESCs [156]. Thus, the use of 3D array platforms permits the analysis of the effects of combinatorial stimuli on cells in an environment which can closely mimic in vivo organogenesis. However, many of these high-throughput techniques mentioned above require special robotic setups, which cannot be achieved commonly in the laboratory setting. While these techniques provide a rich multitude of data, our primary interest in this study was to conduct a small number of specific perturbations in a laboratory set-up. The Jonathan Dordick group developed a simple alginate-based array platform, originally for the purpose of toxicological studies in 3D [157], and later utilized to track mESC fate to quantify the expression of key proteins in 3D [158]. While this platform also utilized robotic techniques, the simplicity is particularly attractive. We have adapted this platform for bench-scale screening without the use of robotics, and further modified it to perturb the physical microenvironment. This array platform was originally built for chemical perturbations, while in our work we have modified the system to induce combinatorial physical perturbations, and was achieved using simple laboratory tools.

Encapsulation for 3D culture of hPSCs seems to be a promising avenue to meet both biomanufacturing goals and immune protection in a single platform. However, the signals used to drive differentiation not only include soluble chemical cues, but also include insoluble physical cues. Previous work has clearly indicated that stem cell fate is highly sensitive to its

chemical, as well as physical microenvironment. More specifically for pancreatic differentiation, we have shown in the first part of Aim2 (Chapter 3) that encapsulation will influence stem cell fate. Microenvironment can constitute soluble chemicals/growth factors, interactions with extracellular matrix (ECM) proteins, cell-cell contact, or physical stimuli such as stiffness or tension. In 2006, Engler et. al. showed that culture of mesenchymal stem cells (MSC) on substrates of stiffness matching those of tissues in the body resulted in tissue stiffness specific differentiation of the MSCs [57]. In our earlier work from our lab, we demonstrated the effect of substrate properties using fibrin and alginate on the differentiation of mouse embryonic stem cells (mESCs) [117, 118]. More recently, covered in the preceding chapter of this dissertation, we demonstrated the feasibility of pancreatic differentiation of hESCs within alginate capsules;

and as expected, pancreatic maturation within the capsules was sensitive to alginate capsule properties [159]. Cell-cell contact, especially in 3D cellular aggregates, is another important insoluble cue which can influence hPSC differentiation. Lee et al. showed controlling hPSC colony size could control specification to mesoderm (1200 μm in diameter) or endoderm in the presence of soluble differentiation factors, linking cellular organization to hPSC differentiation [160]. Recently, Toyoda et al. have shown that the pancreatic induction of hPSCs was correlated with increasing cell density in 2D, and was further improved in aggregate culture [161]. Thus clinical translation of encapsulated hPSCs will require a thorough evaluation of optimum parameters supporting hPSC growth and differentiation [10, 162, 163]. In this chapter, which constitutes the second part of Aim 2, we evaluated the sensitivity of proliferation and differentiation of encapsulated hPSCs to both cell culture configuration and substrate mechanical properties, through the development and use of a 3D alginate array platform.

Conceptually, the alginate array immobilizes individual alginate beads to the culture surface, enabling convenient imaging and analysis of the same. Our objective here was to investigate the effect of alginate properties and encapsulated culture configurations on the propagation and pancreatic differentiation of hPSCs. This simple alginate array platform allowed for multiparametric perturbations and quantitative imaging. As this alginate array generated a rich complex data set, the use of a simple linear statistical model allowed us to decouple the complex interactions between the stem cells and the effect of their microenvironment. Thus, in combination with statistical modeling, this allowed us to identify the sensitivity of stem cell proliferation and fate to multiparametric modulation.