Predicting how individual cells change over time and space using single-cell genomics
Predictive modeling of mammalian cell fate transitions over time and space with single-cell genomics
Researchers are building computer models that use single-cell gene and protein data to predict how stem cells and other cell types change over time to help conditions like blood disorders and diabetes.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Stanford University NIH-funded |
| Lab location | 1 site (Stanford, United States) |
| Project ID | NIH-11250073 on NIH RePORTER |
What this research studies
The team will combine advanced single-cell experiments (including metabolic labeling and CRISPR-based perturbations) with new mathematical models and machine-learning algorithms to map how cells move between states over time and across tissues. They will extend tools such as the dynamo framework and least-action path methods to identify regulators that stabilize or push cells toward specific fates, focusing on blood-forming and pancreatic cell systems among others. Predicted regulators and trajectories will be tested in lab collaborations using 10x single-cell sequencing and perturb-seq experiments to validate the models. The broader aim is to produce predictive atlases linking gene activity, epigenetics, and proteins to real cellular transitions.
Who could benefit from this research
Good fit: Patients with blood disorders or conditions affecting pancreatic cell function (for example some forms of diabetes) could be eventual beneficiaries or donors of cells for related validation studies.
Not a fit: People with conditions unrelated to blood or pancreatic cell biology, or those seeking an immediate clinical treatment, are unlikely to receive direct benefit from this basic research.
Why it matters
Potential benefit: If successful, this work could help clinicians predict and eventually steer cell behavior to improve treatments for blood diseases, diabetes, and regenerative medicine.
How similar studies have performed: Related single-cell and computational approaches have shown promise for mapping cell trajectories, but integrating metabolic labeling, perturb-seq, and predictive dynamical models across multiple molecular layers is relatively novel.
Where this research is happening
Stanford, United States
- Stanford University — Stanford, United States (Active)
Researchers
- Principal investigator: Qiu, Xiaojie — Stanford University
- Study coordinator: Qiu, Xiaojie
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.