RegVelo: Gene-regulatory-informed dynamics of single cells
Summary
Cell fate transitions are driven by regulatory circuitry, yet RNA velocity models cellular dynamics without explicitly accounting for gene regulatory interactions, limiting mechanistic insight. Conversely, gene regulatory network (GRN) inference methods largely neglect the dynamic nature of biological systems. To overcome this conceptual disconnect, we present RegVelo, a bottom-up, actionable, and interpretable deep learning framework that jointly models splicing kinetics and gene regulato
Content
# RegVelo: Gene-regulatory-informed dynamics of single cells
*Published: 2026 May 11*
Cell fate transitions are driven by regulatory circuitry, yet RNA velocity
models cellular dynamics without explicitly accounting for gene regulatory
interactions, limiting mechanistic insight. Conversely, gene regulatory network
(GRN) inference methods largely neglect the dynamic nature of biological
systems. To overcome this conceptual disconnect, we present RegVelo, a
bottom-up, actionable, and interpretable deep learning framework that jointly
models splicing kinetics and gene regulatory interactions. Across diverse
biological systems, RegVelo provides reliable predictive power for terminal
states, gene interactions, and perturbation simulations. By applying RegVelo to
zebrafish neural crest development using full-length Smart-seq3 and shared gene
expression and chromatin accessibility measurements, we delineate regulatory
programs underlying fate specification. Guided by in silico perturbations and
validated by CRISPR-Cas9 knockout and single-cell Perturb-seq, we establish tfec
as an early driver and elf1 as a regulator of pigment cell fate. RegVelo
establishes a quantitative framework for bridging gene regulation and cell fate
decisions.
DOI: 10.1016/j.cell.2026.04.022