Populations of cells create local environments that lead to emergent heterogeneity. This is particularly evident in human pluripotent stem cells (hPSCs) where microenvironmental heterogeneity limits cell fate control. We have developed a high-throughput platform to screen hPSCs in configurable micro-environments, enabling the optimization of colony size, cell density, and additional parameters for rapid and robust cell fate responses. Single-cell protein expression profiling revealed that Oct4 and Sox2 co-staining discriminate pluripotent, neuroectoderm, primitive streak, and extraembryonic cell fates, allowing dose responses of 27 developmental factors to simultaneously delineate lineage-specific concentration optima. This platform also enabled quantification of endogenous signaling pathway activation and differentiation bias (fingerprinting). Short-term (48 h) fingerprinting is predictive of definitive endoderm induction efficiency across 12 cell lines and was used a priori to rescue long-term (>18 day) differentiation of a cell line reticent to cardiac induction. These findings facilitate high-throughput hPSC-based screening and quantification of lineage induction bias.

SummaryHuman pluripotent stem cells (hPSCs) exist in heterogeneous micro-environments with multiple subpopulations, convoluting fate-regulation analysis. We patterned hPSCs into engineered micro-environments and screened responses to 400 small-molecule kinase inhibitors, measuring yield and purity outputs of undifferentiated, neuroectoderm, mesendoderm, and extra-embryonic populations. Enrichment analysis revealed mammalian target of rapamycin (mTOR) inhibition as a strong inducer of mesendoderm. Dose responses of mTOR inhibitors such as rapamycin synergized with Bone Morphogenetic protein 4 (BMP4) and activin A to enhance the yield and purity of BRACHYURY-expressing cells. Mechanistically, small interfering RNA knockdown of RAPTOR, a component of mTOR complex 1, phenocopied the mesendoderm-enhancing effects of rapamycin. Functional analysis during mesoderm and endoderm differentiation revealed that mTOR inhibition increased the output of hemogenic endothelial cells 3-fold, with a concomitant enhancement of blood colony-forming cells. These data demonstrate the power of our multi-lineage screening approach and identify mTOR signaling as a node in hPSC differentiation to mesendoderm and its derivatives.

A growing body of evidence highlights the importance of the cellular microenvironment as a regulator of phenotypic and functional cellular responses to perturbations. We have previously developed cell patterning techniques to control population context parameters, and here we demonstrate Context-explorer (CE), a software tool to improve investigation of microenvironmental variables through colony level analyses. We demonstrate the capabilities of CE in the analysis of human and mouse pluripotent stem cells (hPSCs, mPSCs) patterned in colonies of defined size and shape in multi-well plates.CE employs a density-based clustering algorithm to identify cell colonies within micropatterned wells. Using this automatic colony classification methodology, we obtain accuracies comparable to manual colony counts in a fraction of the time. Classifying cells according to their relative position within a colony enables statistical analysis of radial spatial trends in protein expression within multiple colonies in the same treatment group. When applied to colonies of hPSCs, our analysis reveals a radial gradient in the expression of the pluripotency inducing transcription factors SOX2 and OCT4, and a similar trend in the intra-colony location of different cellular phenotypes. We extend these analyses to colonies of different sizes and shapes and demonstrate how the metrics derived by CE can be used to asses the patterning fidelity of micropatterned plates.We have incorporated a number of features to enhance the usability and utility of CE. To appeal to a broad scientific community, all of the software's functionality is accessible from a graphical user interface, and convenience functions for several common data operations 2 of 15 May 15, 2018Manuscript Context-explorer are included. CE is compatible with existing image analysis programs such as CellProfiler and extends the analytical capabilities already provided by these tools. Taken together, CE facilitates investigation of spatially heterogeneous cell populations in fundamental research and drug development validation programs.

A growing body of evidence highlights the importance of the cellular microenvironment as a regulator of phenotypic and functional cellular responses to perturbations. We have previously developed cell patterning techniques to control population context parameters, and here we demonstrate context-explorer (CE), a software tool to improve investigation cell fate acquisitions through community level analyses. We demonstrate the capabilities of CE in the analysis of human and mouse pluripotent stem cells (hPSCs, mPSCs) patterned in colonies of defined geometries in multi-well plates. CE employs a density-based clustering algorithm to identify cell colonies. Using this automatic colony classification methodology, we reach accuracies comparable to manual colony counts in a fraction of the time, both in micropatterned and unpatterned wells. Classifying cells according to their relative position within a colony enables statistical analysis of spatial organization in protein expression within colonies. When applied to colonies of hPSCs, our analysis reveals a radial gradient in the expression of the transcription factors SOX2 and OCT4. We extend these analyses to colonies of different sizes and shapes and demonstrate how the metrics derived by CE can be used to asses the patterning fidelity of micropatterned plates. We have incorporated a number of features to enhance the usability and utility of CE. To appeal to a broad scientific community, all of the software’s functionality is accessible from a graphical user interface, and convenience functions for several common data operations are included. CE is compatible with existing image analysis programs such as CellProfiler and extends the analytical capabilities already provided by these tools. Taken together, CE facilitates investigation of spatially heterogeneous cell populations for fundamental research and drug development validation programs.