Designing signaling environments to modulate neural progenitor cell differentiation with regulatory network models

Abstract

During development, combinatorial signals modulate progenitor differentiation to build populations of diverse cell types. Classical deterministic fate-control circuits focus on stable commitment but do not explain how a diverse cell population is generated and tuned by signals. Here, using single-cell RNA-seq profiling of neural progenitor cells (NPCs) upon combinatorial signal inputs, we identify a candidate circuit-level mechanism for controlling cell type compositions through a noise-gated toggle switch. With 40 signaling conditions, we show that combinatorial signal inputs modulate the neuronal-glial population proportions following a simple log-linear model. Regulatory network models built by D-SPIN quantitatively capture cell state distribution shifts induced by signal combinations, and indicate a circuit model for the probabilistic fate specification by a toggle switch with controlled transcriptional heterogeneity. Noise levels drive the system from highly plastic to committed states, allowing the relative level of signal inputs to shift population structures. The model further predicts an early bipotent state expressing lineage-specifying factors of both fates, which we verify in our single-cell profiling experiment and a mouse embryonic visual cortex development dataset. Our work identifies a design principle of noise-driven additive regulation in the logarithmic cell-fate probability space, providing a new strategy for population-level stem cell control.