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In a new study published in Cell titled, “RegVelo: gene-regulatory-informed dynamics of single cells,” researchers from Stowers Institute of Medical Research have developed a new AI model that connects two areas of single-cell biology that have often remained separate: estimating how cells change over time and inferring the gene regulatory networks controlling those changes.  

“You can imagine if you had a very early set of cells, having a particular set of instructions could allow you to reproduce, in vitro, some of these cell types in a very natural way. These cells could then be used in cell therapies in regenerative medicine,” said Tatjana Sauka-Spengler, PhD, Stowers Institute Investigator and co-senior author of the study.  

While development is often described as a series of static snapshots of cell states, RegVelo models how these fate decisions are encoded in gene regulatory networks over time and space, and what drives cell state transitions. In zebrafish neural crest development, RegVelo identified an early driver of pigment cell formation (tfec) and revealed a previously unknown regulator of pigment cell fate (elf1). The neural crest is a developmental system that gives rise to many different cell types, including pigment cells, craniofacial tissues, and parts of the peripheral nervous system. 

CRISPR/Cas9-mediated knockout and single-cell Perturb-seq supported predictions, showing that the model could do more than describe developmental changes and generate biologically meaningful hypotheses that held up in living systems. 

Alejandro Sánchez Alvarado, PhD, Stowers President and chief scientific officer says RegVelo’s value “extends well beyond” neural crest cells and is applicable to any system in which cells change over time, from basic developmental biology to modeling tumor trajectories and the cellular outcomes that may inform treatment. 

“Sauka-Spengler and her collaborators have developed a meaningfully different way to process this kind of data,” said Sánchez Alvarado. “It allows us to infer the most likely path of each component through space and time, and to use deep learning to predict those dynamics and test them experimentally.” 

Single-cell biology research has made it possible to build increasingly detailed maps of development. RNA velocity methods can help researchers estimate how cells move through developmental landscapes, while gene regulatory network approaches can identify relationships among genes. However, these methods have typically been used in parallel rather than together.  

“For a long time, cellular dynamics and gene regulation have largely been modeled separately,” said Fabian Theis, PhD, the study’s co-senior author and director of the institute of computational biology at Helmholtz Munich. “RegVelo brings those pieces together, allowing us to ask not only how cells are changing, but which regulatory interactions are helping drive those changes.”  

The framework jointly models splicing kinetics and gene regulatory relationships, allowing researchers to map the hidden timeline of cell development, predict how cells shift from one state to another, and test what might happen when specific regulators are perturbed. 

The framework can incorporate additional regulatory layers, including chromatin, protein activity, and other multimodal measurements. While the study’s limitations include simplifying assumptions around latent time, regulatory interactions, and computational cost, the results demonstrate a compelling proof of principle.

“When dynamic cell-state modeling is linked directly to gene regulation, it becomes possible to move closer to mechanism and then discovery,” Sauka-Spengler said. 

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