# Leveraging multi-species single cell omic datasets to study the evolution of cell type-specific gene regulatory networks

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2022 · $498,204

## Abstract

PROJECT SUMMARY
Comparative functional genomics offers a powerful framework to study the molecular underpinnings of species-
specific traits. Gene regulatory networks (GRNs) which control precise context-specific expression patterns of
genes play a significant role in diversifying phenotypes across species. These networks are central to cell type
specific function and are often disrupted in many diseases. However, comparison of gene regulatory networks
across species has been challenging because of the lack of sufficient number of samples across matched
biological contexts. Single cell omic technologies, such as single cell RNA-seq (scRNA-seq) and ATAC-seq
(scATAC-seq), are revolutionizing biology enabling researchers to profile the activity of nearly all genomic
regions in each individual cell. Single cell omic studies are quickly expanding to multiple species providing
unprecedented opportunities to define cell types and their underlying gene regulatory networks and study their
evolution. However, computational methods for defining cell-types and cell-specific GRNs across species are
in their infancy. In particular, samples in a multi-species scRNA-seq dataset are related by a phylogeny, however,
existing integration approaches do not model these relationships. Furthermore, existing approaches are
restricted to one-to-one relationships across species, which makes it difficult to study some of the major sources
of evolutionary innovation (e.g., duplications) in cell type identity. In this project, we will develop novel
computational methods to tackle two problems: (a) defining cell types and their lineage relationships across
species from scRNA-seq and scATAC-seq datasets, (b) inference and comparative analysis of cell type-specific
GRNs across species from single cell RNA-seq and ATAC-seq data. Our tools will be based on machine learning
methods, namely, probabilistic graphical models, multi-task and multi-view learning, and matrix factorization, that
offer principled frameworks to integrate information across species. We will first test these tools in human and
mouse scRNA-seq/ATAC-seq datasets from our collaborators and published studies. We will demonstrate the
full potential of our tools on a novel multi-species kidney scRNA-seq/scATAC-seq dataset that we will collect to
study normal kidney function as well as compensatory renal growth, which controls how one kidney recovers
after surgical removal of another kidney. We will identify conserved and diverged regulatory networks that will
be used to prioritize sequence and protein regulators for validation studies with CRISPR and siRNA. Our analysis
will reveal key insights into how GRNs evolve across species and how they establish different cell types. Our
approaches and novel datasets will provide critical insight into the molecular programs governing kidney
structure and function that could have a significant clinical impact for patients with kidney disease. Our methods
will constitute a sui...

## Key facts

- **NIH application ID:** 10595349
- **Project number:** 1R01HG012349-01A1
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Sushmita Roy
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $498,204
- **Award type:** 1
- **Project period:** 2022-09-26 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10595349

## Citation

> US National Institutes of Health, RePORTER application 10595349, Leveraging multi-species single cell omic datasets to study the evolution of cell type-specific gene regulatory networks (1R01HG012349-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10595349. Licensed CC0.

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