In a groundbreaking advancement at the intersection of developmental biology and artificial intelligence, researchers from the Stowers Institute for Medical Research, Helmholtz Munich, the Technical University of Munich, and the University of Oxford have unveiled RegVelo, an innovative AI-driven framework designed to unravel the complex dynamics that steer cellular fate decisions. Published in Cell on May 11, 2026, this collaborative study introduces a paradigm shift in how scientists model cellular development by simultaneously integrating gene regulatory networks with cellular dynamics, enabling unprecedented predictive power over cell fate transitions.
Traditional methods in single-cell biology have offered increasingly detailed developmental maps by tracing cellular trajectories using RNA velocity approaches. These methods estimate the direction of a cell’s progression based on immature and mature RNA ratios, effectively capturing the velocity vector of cellular states within developmental landscapes. However, until now, the molecular underpinnings that mechanistically dictate these trajectories—particularly the gene regulatory networks (GRNs) that intricately control gene expression—have largely been studied in isolation. RegVelo bridges this critical gap by marrying the dynamics of RNA velocity with the regulatory circuitry governing gene interactions, forming a holistic computational framework.
The core innovation of RegVelo lies in its ability to treat genes not as isolated entities but as components of an interconnected network, where transcription factors and other regulatory genes can exert activating or repressive influences on each other. By embedding these regulatory relationships within a deep learning model, RegVelo simultaneously deciphers how cells transition from one developmental state to another and identifies the molecular drivers that orchestrate those transitions. This dual insight enables predictions about cellular fate that are both trajectory-aware and regulatory-informed—a feat that previous methods, limited to either trajectory or regulation alone, could not accomplish.
At the helm of this cutting-edge work, Prof. Fabian J. Theis, Director of the Computational Health Center at Helmholtz Munich and Professor at the Technical University of Munich, emphasizes the transformative nature of RegVelo. “Our framework does more than chart cellular pathways,” Theis explains. “It illuminates the regulatory relationships that actively shape those paths, providing a dynamic map of gene interactions in action as cells develop. This allows us not only to observe but to interrogate and simulate the genetic ‘engines’ driving development.”
The genesis of RegVelo is itself a testament to scientific synergy, arising from the union of high-resolution experimental data and sophisticated computational modeling. Tatjana Sauka-Spengler, an Investigator at the Stowers Institute who transitioned from the University of Oxford, contributed richly detailed gene regulatory circuits from her laboratory’s pioneering research on cranial neural crest cells—an embryonic cell population integral to the formation of facial features, heart structures, nervous systems, and pigmentation. Coupled with Theis’s computational neuroscience expertise and the deep learning acumen of doctoral researcher Weixu Wang, RegVelo represents a seamless integration of experimental precision and algorithmic innovation.
To rigorously evaluate RegVelo’s predictive capacity, the team applied their model to multiple biological contexts, including classic developmental archetypes like the cell cycle, hematopoiesis (blood cell formation), and pancreatic organogenesis. The most exhaustive application targeted zebrafish neural crest cells, chosen for their versatility and well-characterized developmental fate decisions. Crucially, RegVelo pinpointed the transcription factor tfec as an early, pivotal regulator of pigment cell development, a finding concordant with prior knowledge. More strikingly, the model identified elf1, a previously unrecognized regulator, as a key driver in melanocyte differentiation. These insights transcended computational predictions, as subsequent CRISPR/Cas9 knockouts and single-cell Perturb-seq experiments validated these regulatory roles, underscoring RegVelo’s robustness.
“Development has too often been depicted as static snapshots of cellular states,” reflects Sauka-Spengler. “RegVelo changes this narrative by capturing the continuous decision-making process—a cell’s journey through transient regulatory landscapes that dictate its fate. This temporal and mechanistic clarity paves the way to decipher how switching a single gene on or off can reroute a developmental path entirely.” The ability to simulate genetic perturbations in silico anticipates transformative impacts on experimental design, enabling researchers to prioritize hypotheses with real-time, predictive insights.
From a translational perspective, RegVelo heralds a significant leap toward the realization of ‘virtual cell’ models that can forecast cell behavior under genetic manipulations with unprecedented accuracy. Such models hold immense promise for disease modeling, especially in understanding how disruptions in regulatory networks contribute to developmental disorders, oncogenesis, or regenerative processes. The framework’s predictive precision opens opportunities for identifying novel therapeutic targets by revealing hidden nodes within gene regulatory landscapes that might be amenable to intervention.
Furthermore, the integration of gene regulatory networks with dynamic velocity modeling exemplifies a blueprint for multi-modal computational biology frameworks, harmonizing discrete data types into cohesive, interpretable models. This approach exemplifies the power of AI to detect and model emergent properties of biological systems that elude traditional analysis, guiding science toward predictive and prescriptive biology rather than merely descriptive.
Looking ahead, Prof. Theis envisions RegVelo as a cornerstone technology for the next generation of stem cell research and cellular engineering. “By simulating and validating gene regulatory perturbations, we are edging closer to being able to rationally engineer cell fates, steering naïve cells into desired lineages with medical relevance. This has profound implications for cell therapy and regenerative medicine, where controlling differentiation pathways is essential for successful outcomes.”
In sum, the advent of RegVelo signifies a critical inflection point in developmental biology research, combining the temporal dimension of single-cell trajectories with the mechanistic context of regulatory networks within a unified AI framework. This synergy not only augments our fundamental understanding of cellular decision-making but also accelerates the translation of single-cell omics data into actionable biological insights, potentially revolutionizing therapeutic strategies across diverse biomedical fields.
Subject of Research: Animals
Article Title: RegVelo: gene-regulatory-informed dynamics of single cells
News Publication Date: 11-May-2026
Web References:
- DOI: 10.1016/j.cell.2026.04.022
- Stowers Institute for Medical Research: www.stowers.org
- Helmholtz Munich: www.helmholtz-munich.de/en
- University of Oxford MRC Weatherall Institute of Molecular Medicine: imm.ox.ac.uk
- Radcliffe Department of Medicine: www.rdm.ox.ac.uk
References:
Sauka-Spengler T. et al., “RegVelo: gene-regulatory-informed dynamics of single cells,” Cell, 2026, DOI: 10.1016/j.cell.2026.04.022.
Image Credits: Stowers Institute for Medical Research
Keywords
Gene regulatory networks, single-cell RNA velocity, developmental biology, AI modeling, computational biology, neural crest cells, zebrafish, transcriptomics, CRISPR/Cas9, Perturb-seq, cell fate prediction, virtual cell models
