Spatial cell fate manipulation of human pluripotent stem cells by controlling the microenvironment using photocurable hydrogel.

Human pluripotent stem cells (hPSCs) dynamically respond to their chemical and physical microenvironment, dictating their behavior. However, conventional in vitro studies predominantly employ plastic culture wares, which offer a simplified representation of the in vivo microenvironment. Emerging evidence underscores the pivotal role of mechanical and topological cues in hPSC differentiation and maintenance. In this study, we cultured hPSCs on hydrogel substrates with spatially controlled stiffness. The use of culture substrates that enable precise manipulation of spatial mechanical properties holds promise for better mimicking in vivo conditions and advancing tissue engineering techniques. We designed a photocurable polyethylene glycol-polyvinyl alcohol (PVA-PEG) hydrogel, allowing the spatial control of surface stiffness and geometry at a micrometer scale. This versatile hydrogel can be functionalized with various extracellular matrix proteins. Laminin 511-functionalized PVA-PEG gel effectively supports the growth and differentiation of hPSCs. Moreover, by spatially modulating the stiffness of the patterned gel, we achieved spatially selective cell differentiation, resulting in the generation of intricate patterned structures.

RENGE infers gene regulatory networks using time-series single-cell RNA-seq data with CRISPR perturbations.

Single-cell RNA-seq analysis coupled with CRISPR-based perturbation has enabled the inference of gene regulatory networks with causal relationships. However, a snapshot of single-cell CRISPR data may not lead to an accurate inference, since a gene knockout can influence multi-layered downstream over time. Here, we developed RENGE, a computational method that infers gene regulatory networks using a time-series single-cell CRISPR dataset. RENGE models the propagation process of the effects elicited by a gene knockout on its regulatory network. It can distinguish between direct and indirect regulations, which allows for the inference of regulations by genes that are not knocked out. RENGE therefore outperforms current methods in the accuracy of inferring gene regulatory networks. When used on a dataset we derived from human-induced pluripotent stem cells, RENGE yielded a network consistent with multiple databases and literature. Accurate inference of gene regulatory networks by RENGE would enable the identification of key factors for various biological systems.