D-SPIN constructs regulatory network models from scRNA-seq that reveal organizing principles of perturbation response.

Abstract

Gene regulatory networks modulate the expression of the genome in response to signals and environmental conditions. Reconstructions of such networks can reveal the control principles cells use to maintain homeostasis and execute cell-state transitions. Here, we introduce a computational framework, dimension-scalable single-cell perturbation integration network (D-SPIN), that infers mechanistically interpretable and generative models of gene regulatory networks from single-cell mRNA-seq datasets collected across thousands of perturbation conditions. The models explain how perturbations modulate cell-state proportions by reconfiguring underlying regulatory interactions. Using large Perturb-seq and drug response datasets, D-SPIN models reveal key regulators of cell fate decisions and the coordination of distant cellular pathways in response to gene knockdowns and drug treatments, elucidate how combinations of immunomodulatory drugs induce combinatorial cell states through additive recruitment of gene expression programs, and simulate shifts in immune cell population structures across unobserved drug dosage combinations. D-SPIN provides a computational framework for revealing principles of cellular information processing and physiological control.