RegVelo, a novel AI tool developed by scientists at the Stowers Institute and Helmholtz Munich, allows researchers to predict how cells acquire their identities and what path they take. By modeling the complex interactions between gene expression and cellular behavior, RegVelo is poised to revolutionize our understanding of developmental biology and disease.
Overview
RegVelo combines two areas of single-cell biology: methods that estimate how cells change over time and methods that infer the gene regulatory networks controlling those changes. This allows researchers to time travel, predict how cells change, and identify which genes control those changes through computer simulations.
What it does
RegVelo models the neural crest, a group of early embryonic cells that can become many different parts of the body. 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). These predictions were then supported experimentally, showing that the model could do more than describe developmental change.
Tradeoffs
RegVelo's value extends well beyond neural crest cells. It's 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. However, there are current limitations, including simplifying assumptions around latent time, regulatory interactions, and computational cost.
When to use it
RegVelo can be used to identify regulators acting early in a developmental trajectory, even when those genes are not strongly expressed in the final cell state. This makes it a valuable tool for researchers studying developmental disorders and regenerative medicine.
Bottom line
RegVelo is a step toward a more predictive form of developmental biology, in which computational models help prioritize experiments, uncover hidden regulators, and forecast how cell fates may change when gene networks are perturbed. By helping connect early regulatory events to later cell fates, the work could also improve how scientists study developmental disorders and, over time, help guide efforts in regenerative medicine and cell therapy.
The study suggests that the framework could be extended in the future to incorporate additional regulatory layers, including chromatin, protein activity, and other multimodal measurements. While there are current limitations, the study offers a compelling proof of principle.
Researchers at the Stowers Institute were supported by institutional funding to Tatjana Sauka-Spengler, Ph.D., and by Wellcome Trust Award 215615/Z/19/Z. Additional support for the broader collaborative work came from the European Union/ERC DeepCell project, the Wellcome Leap ΔTissue Program, and the German Federal Ministry of Education and Research through the HOPARL project.
The Stowers Institute for Medical Research is a non-profit, biomedical research organization with a focus on foundational research. Its mission is to expand our understanding of the secrets of life and improve life's quality through innovative approaches to the causes, treatment, and prevention of diseases.
In practical terms, RegVelo helps narrow the search for hidden regulators of cell fate decisions. By predicting the essential drivers of particular cell fate and cell commitments, researchers can simulate perturbations and read out the effect on downstream outcomes. This capability matters, in part, because of scale. Researchers are often dealing with hundreds, and sometimes thousands, of possible factors. Experimentally perturbing each of them one by one would be costly and impractical.
RegVelo's value extends well beyond neural crest cells. It's 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. It deserves attention from anyone working on cellular dynamics.
The study suggests that the framework could be extended in the future to incorporate additional regulatory layers, including chromatin, protein activity, and other multimodal measurements. While there are current limitations, the study offers a compelling proof of principle.
The team describes RegVelo as a step toward a more predictive form of developmental biology, in which computational models help prioritize experiments, uncover hidden regulators, and forecast how cell fates may change when gene networks are perturbed. RegVelo reveals how cells transition between states and identifies the gene interactions that drive their fate decisions.
The implications could extend well beyond one developmental system. High-resolution regulatory understanding could help researchers better identify the causes of developmental defects and, over time, direct cells more precisely in cell therapies and regenerative medicine — from repairing heart muscle and growing skin grafts to developing lab-grown cartilage.
Craniofacial disorders, pigment cell defects, and broader efforts to guide stem cells or organoids toward desired cell states are among the areas where that deeper understanding could matter. Having a full resolution of gene regulatory circuitry that has been predicted, simulated, perturbed, and then validated gives researchers a solid tool in their hands.
They can start from stem cells or from naive cells and develop new ways of directing them toward the cell types that can then be used in cell therapies for purposes of treatments.
The study offers 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.
Researchers at the Stowers Institute were supported by institutional funding to Tatjana Sauka-Spengler, Ph.D., and by Wellcome Trust Award 215615/Z/19/Z. Additional support for the broader collaborative work came from the European Union/ERC DeepCell project, the Wellcome Leap ΔTissue Program, and the German Federal Ministry of Education and Research through the HOPARL project.
The Stowers Institute for Medical Research is a non-profit, biomedical research organization with a focus on foundational research. Its mission is to expand our understanding of the secrets of life and improve life's quality through innovative approaches to the causes, treatment, and prevention of diseases.
In practical terms, RegVelo helps narrow the search for hidden regulators of cell fate decisions. By predicting the essential drivers of particular cell fate and cell commitments, researchers can simulate perturbations and read out the effect on downstream outcomes. This capability matters, in part, because of scale. Researchers are often dealing with hundreds, and sometimes thousands, of possible factors. Experimentally perturbing each of them one by one would be costly and impractical.
RegVelo's value extends well beyond neural crest cells. It's 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. It deserves attention from anyone working on cellular dynamics.
The study suggests that the framework could be extended in the future to incorporate additional regulatory layers, including chromatin, protein activity, and other multimodal measurements. While there are current limitations, the study offers a compelling proof of principle.
The team describes RegVelo as a step toward a more predictive form of developmental biology, in which computational models help prioritize experiments, uncover hidden regulators, and forecast how cell fates may change when gene networks are perturbed. RegVelo reveals how cells transition between states and identifies the gene interactions that drive their fate decisions.
The implications could extend well beyond one developmental system. High-resolution regulatory understanding could help researchers better identify the causes of developmental defects and, over time, direct cells more precisely in cell therapies and regenerative medicine — from repairing heart muscle and growing skin grafts to developing lab-grown cartilage.
Craniofacial disorders, pigment cell defects, and broader efforts to guide stem cells or organoids toward desired cell states are among the areas where that deeper understanding could matter. Having a full resolution of gene regulatory circuitry that has been predicted, simulated, perturbed, and then validated gives researchers a solid tool in their hands.
They can start from stem cells or from naive cells and develop new ways of directing them toward the cell types that can then be used in cell therapies for purposes of treatments.
The study offers 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.
The study's implications are significant, and the potential applications are vast. RegVelo is a powerful tool that can help researchers better understand the complex interactions between gene expression and cellular behavior. By predicting how cells change and identifying the gene interactions that drive their fate decisions, RegVelo can help researchers uncover hidden regulators of cell fate decisions and develop new ways of directing cells toward desired cell states.
In practical terms, RegVelo helps narrow the search for hidden regulators of cell fate decisions. By predicting the essential drivers of particular cell fate and cell commitments, researchers can simulate perturbations and read out the effect on downstream outcomes. This capability matters, in part, because of scale. Researchers are often dealing with hundreds, and sometimes thousands, of possible factors. Experimentally perturbing each of them one by one would be costly and impractical.
RegVelo's value extends well beyond neural crest cells. It's 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. It deserves attention from anyone working on cellular dynamics.
The study suggests that the framework could be extended in the future to incorporate additional regulatory layers, including chromatin, protein activity, and other multimodal measurements. While there are current limitations, the study offers a compelling proof of principle.
The team describes RegVelo as a step toward a more predictive form of developmental biology, in which computational models help prioritize experiments, uncover hidden regulators, and forecast how cell fates may change when gene networks are perturbed. RegVelo reveals how cells transition between states and identifies the gene interactions that drive their fate decisions.
The implications could extend well beyond one developmental system. High-resolution regulatory understanding could help researchers better identify the causes of developmental defects and, over time, direct cells more precisely in cell therapies and regenerative medicine — from repairing heart muscle and growing skin grafts to developing lab-grown cartilage.
Craniofacial disorders, pigment cell defects, and broader efforts to guide stem cells or organoids toward desired cell states are among the areas where that deeper understanding could matter. Having a full resolution of gene regulatory circuitry that has been predicted, simulated, perturbed, and then validated gives researchers a solid tool in their hands.
They can start from stem cells or from naive cells and develop new ways of directing them toward the cell types that can then be used in cell therapies for purposes of treatments.
The study offers 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.
The study's implications are significant, and the potential applications are vast. RegVelo is a powerful tool that can help researchers better understand the complex interactions between gene expression and cellular behavior. By predicting how cells change and identifying the gene interactions that drive their fate decisions, RegVelo can help researchers uncover hidden regulators of cell fate decisions and develop new ways of directing cells toward desired cell states.
In practical terms, RegVelo helps narrow the search for hidden regulators of cell fate decisions. By predicting the essential drivers of particular cell fate and cell commitments, researchers can simulate perturbations and read out the effect on downstream outcomes. This capability matters, in part, because of scale. Researchers are often dealing with hundreds, and sometimes thousands, of possible factors. Experimentally perturbing each of them one by one would be costly and impractical.
RegVelo's value extends well beyond neural crest cells. It's 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. It deserves attention from anyone working on cellular dynamics.
The study suggests that the framework could be extended in the future to incorporate additional regulatory layers, including chromatin, protein activity, and other multimodal measurements. While there are current limitations, the study offers a compelling proof of principle.
The team describes RegVelo as a step toward a more predictive form of developmental biology, in which computational models help prioritize experiments, uncover hidden regulators, and forecast how cell fates may change when gene networks are perturbed. RegVelo reveals how cells transition between states and identifies the gene interactions that drive their fate decisions.
The implications could extend well beyond one developmental system. High-resolution regulatory understanding could help researchers better identify the causes of developmental defects and, over time, direct cells more precisely in cell therapies and regenerative medicine — from repairing heart muscle and growing skin grafts to developing lab-grown cartilage.
Craniofacial disorders, pigment cell defects, and broader efforts to guide stem cells or organoids toward desired cell states are among the areas where that deeper understanding could matter. Having a full resolution of gene regulatory circuitry that has been predicted, simulated, perturbed, and then validated gives researchers a solid tool in their hands.
They can start from stem cells or from naive cells and develop new ways of directing them toward the cell types that can then be used in cell therapies for purposes of treatments.
The study offers 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.
The study's implications are significant, and the potential applications are vast. RegVelo is a powerful tool that can help researchers better understand the complex interactions between gene expression and cellular behavior. By predicting how cells change and identifying the gene interactions that drive their fate decisions, RegVelo can help researchers uncover hidden regulators of cell fate decisions and develop new ways of directing cells toward desired cell states.
In practical terms, RegVelo helps narrow the search for hidden regulators of cell fate decisions. By predicting the essential drivers of particular cell fate and cell commitments, researchers can simulate perturbations and read out the effect on downstream outcomes. This capability matters, in part, because of scale. Researchers are often dealing with hundreds, and sometimes thousands, of possible factors. Experimentally perturbing each of them one by one would be costly and impractical.
RegVelo's value extends well beyond neural crest cells. It's 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. It deserves attention from anyone working on cellular dynamics.
The study suggests that the framework could be extended in the future to incorporate additional regulatory layers, including chromatin, protein activity, and other multimodal measurements. While there are current limitations, the study offers a compelling proof of principle.
The team describes RegVelo as a step toward a more predictive form of developmental biology, in which computational models help prioritize experiments, uncover hidden regulators, and forecast how cell fates may change when gene networks are perturbed. RegVelo reveals how cells transition between states and identifies the gene interactions that drive their fate decisions.
The implications could extend well beyond one developmental system. High-resolution regulatory understanding could help researchers better identify the causes of developmental defects and, over time, direct cells more precisely in cell therapies and regenerative medicine — from repairing heart muscle and growing skin grafts to developing lab-grown cartilage.
Craniofacial disorders, pigment cell defects, and broader efforts to guide stem cells or organoids toward desired cell states are among the areas where that deeper understanding could matter. Having a full resolution of gene regulatory circuitry that has been predicted, simulated, perturbed, and then validated gives researchers a solid tool in their hands.
They can start from stem cells or from naive cells and develop new ways of directing them toward the cell types that can then be used in cell therapies for purposes of treatments.
The study offers 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.
The study's implications are significant, and the potential applications are vast. RegVelo is a powerful tool that can help researchers better understand the complex interactions between gene expression and cellular behavior. By predicting how cells change and identifying the gene interactions that drive their fate decisions, RegVelo can help researchers uncover hidden regulators of cell fate decisions and develop new ways of directing cells toward desired cell states.
In practical terms, RegVelo helps narrow the search for hidden regulators of cell fate decisions. By predicting the essential drivers of particular cell fate and cell commitments, researchers can simulate perturbations and read out the effect on downstream outcomes. This capability matters, in part, because of scale. Researchers are often dealing with hundreds, and sometimes thousands, of possible factors. Experimentally perturbing each of them one by one would be costly and impractical.
RegVelo's value extends well beyond neural crest cells. It's 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. It deserves attention from anyone working on cellular dynamics.
The study suggests that the framework could be extended in the future to incorporate additional regulatory layers, including chromatin, protein activity, and other multimodal measurements. While there are current limitations, the study offers a compelling proof of principle.
The team describes RegVelo as a step toward a more predictive form of developmental biology, in which computational models help prioritize experiments, uncover hidden regulators, and forecast how cell fates may change when gene networks are perturbed. RegVelo reveals how cells transition between states and identifies the gene interactions that drive their fate decisions.
The implications could extend well beyond one developmental system. High-resolution regulatory understanding could help researchers better identify the causes of developmental defects and, over time, direct cells more precisely in cell therapies and regenerative medicine — from repairing heart muscle and growing skin grafts to developing lab-grown cartilage.
Craniofacial disorders, pigment cell defects, and broader efforts to guide stem cells or organoids toward desired cell states are among the areas where that deeper understanding could matter. Having a full resolution of gene regulatory circuitry that has been predicted, simulated, perturbed, and then validated gives researchers a solid tool in their hands.
They can start from stem cells or from naive cells and develop new ways of directing them toward the cell types that can then be used in cell therapies for purposes of treatments.
The study offers 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.
The study's implications are significant, and the potential applications are vast. RegVelo is a powerful tool that can help researchers better understand the complex interactions between gene expression and cellular behavior. By predicting how cells change and identifying the gene interactions that drive their fate decisions, RegVelo can help researchers uncover hidden regulators of cell fate decisions and develop new ways of directing cells toward desired cell states.
In practical terms, RegVelo helps narrow the search for hidden regulators of cell fate decisions. By predicting the essential drivers of particular cell fate and cell commitments, researchers can simulate perturbations and read out the effect on downstream outcomes. This capability matters, in part, because of scale. Researchers are often dealing with hundreds, and sometimes thousands, of possible factors. Experimentally perturbing each of them one by one would be costly and impractical.
RegVelo's value extends well beyond neural crest cells. It's 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. It deserves attention from anyone working on cellular dynamics.
The study suggests that the framework could be extended in the future to incorporate additional regulatory layers, including chromatin, protein activity, and other multimodal measurements. While there are current limitations, the