The molecular and cellular signals that guide T-cell development from hematopoietic stem and progenitor cells (HSPCs) remain poorly understood. The thymic microenvironment integrates multiple niche molecules to potentiate T-cell development in vivo. Recapitulating these signals in vitro in a stromal cell-free system has been challenging and limits T-cell generation technologies. Here, we describe a fully defined engineered in vitro niche capable of guiding T-lineage development from HSPCs. Synergistic interactions between Notch ligand Delta-like 4 and vascular cell adhesion molecule 1 (VCAM-1) were leveraged to enhance Notch signaling and progenitor T-cell differentiation rates. The engineered thymus-like niche enables in vitro production of mouse Sca-1cKit and human CD34 HSPC-derived CD7 progenitor T-cells capable of in vivo thymus colonization and maturation into cytokine-producing CD3 T-cells. This engineered thymic-like niche provides a platform for in vitro analysis of human T-cell development as well as clinical-scale cell production for future development of immunotherapeutic applications.

Pluripotent stem cells (PSCs) exist in multiple stable states, each with specific cellular properties and molecular signatures. The mechanisms that maintain pluripotency, or that cause its destabilization to initiate development, are complex and incompletely understood. We have developed a model to predict stabilized PSC gene regulatory network (GRN) states in response to input signals. Our strategy used random asynchronous Boolean simulations (R‐ABS) to simulate single‐cell fate transitions and strongly connected components (SCCs) strategy to represent population heterogeneity. This framework was applied to a reverse‐engineered and curated core GRN for mouse embryonic stem cells (mESCs) and used to simulate cellular responses to combinations of five signaling pathways. Our simulations predicted experimentally verified cell population compositions and input signal combinations controlling specific cell fate transitions. Extending the model to PSC differentiation, we predicted a combination of signaling activators and inhibitors that efficiently and robustly generated a Cdx2+Oct4− cells from naïve mESCs. Overall, this platform provides new strategies to simulate cell fate transitions and the heterogeneity that typically occurs during development and differentiation.

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The increasing availability of single-cell RNA-sequencing (scRNA-seq) data from various developmental systems provides the opportunity to infer gene regulatory networks (GRNs) directly from data. Herein we describe IQCELL, a platform to infer, simulate, and study executable logical GRNs directly from scRNA-seq data. Such executable GRNs allow simulation of fundamental hypotheses governing developmental programs and help accelerate the design of strategies to control stem cell fate. We first describe the architecture of IQCELL. Next, we apply IQCELL to scRNA-seq datasets from early mouse T-cell and red blood cell development, and show that the platform can infer overall over 74% of causal gene interactions previously reported from decades of research. We will also show that dynamic simulations of the generated GRN qualitatively recapitulate the effects of known gene perturbations. Finally, we implement an IQCELL gene selection pipeline that allows us to identify candidate genes, without prior knowledge. We demonstrate that GRN simulations based on the inferred set yield results similar to the original curated lists. In summary, the IQCELL platform offers a versatile tool to infer, simulate, and study executable GRNs in dynamic biological systems.