Sushmita Roy
University of Wisconsin-Madison, WI·Computer Science
Every cell carries the same genes, yet a kidney cell and a neuron behave nothing alike. The difference comes from gene regulatory networks: the control proteins and DNA switches that decide which genes are on in each cell type.
- Fields
Computer Science
Genetics
Biology
- Topics
gene regulatory networks
single-cell genomics
scRNA-seq
- Grants
- 2 known grants, through Jul 2027
What they work on
- Develop machine-learning methods, including probabilistic graphical models and matrix factorization, that define cell types and their lineage relationships across species from scRNA-seq and scATAC-seq while modeling the species phylogeny.
- Infer and compare cell type-specific gene regulatory networks across species using human, mouse and a newly collected multi-species kidney dataset spanning normal function and compensatory renal growth.
- Prioritize conserved and diverged regulators from the kidney networks and test them experimentally with CRISPR gene editing and siRNA knockdown.
- Build a computational toolkit that integrates single-cell gene activity and DNA accessibility data to infer cell lineage structure and the regulatory proteins and DNA regions controlling each gene.
- Identify which network components rewire during cellular reprogramming, and refine causal networks through an active-learning loop that selects regulators for Perturb-Seq perturbation.
More about the research
These networks are frequently disrupted in disease, and changes to them help explain differences between species. Mapping them is hard because any gene has many possible regulators. Two single-cell technologies make it more tractable: single-cell RNA sequencing (scRNA-seq), which measures which genes are active in each individual cell, and single-cell ATAC-seq (scATAC-seq), which shows which stretches of DNA are open and therefore usable as switches. Both projects build machine-learning tools that combine these data to define cell types and their regulatory networks, one comparing networks across species and one tracking how they rewire as a cell changes identity.
Why it matters
Many diseases involve broken gene regulation, and these tools could help pinpoint which regulators set the identity of healthy and diseased cells. The new multi-species kidney dataset may reveal molecular programs behind kidney structure, function and recovery, which could matter for patients with kidney disease. The publicly released methods could also help other labs study gene control and cell fate in many organisms and tissues.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Wenpin Hou
Duke University, NC·Genetics
Develop statistical methods that infer static and dynamic gene regulatory networks, including time-lagged regulator effects, from single-cell multiomics data that combine gene activity and epigenomic measurements across many samples.
Yan Li
Case Western Reserve University, OH·Genetics
Map how gene activity changes, cell by cell, as 24 human pluripotent stem cell lines become beta cells, using pooled single-cell RNA sequencing that sorts cells back to their line of origin.
Yanming Li
Baylor College of Medicine, TX·Genetics
Identify the aortic cell types in which TAAD-associated SNPs act, by integrating GWAS results with single-cell ATAC-seq maps of accessible chromatin from human aortic tissue.
The school
University of Wisconsin-Madison: 91 labs on PI Pages, $1.9B a year on research, 854 research doctorates a year.
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