# New software tools for differential analysis of single-cell genomics perturbation experiments

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2024 · $662,862

## Abstract

Single-cell genomics technology has advanced at a blistering pace. The throughput of single-cell transcriptome
sequencing has increased by four orders of magnitude in the past five years alone, enabling our group and
others to catalog all of the cell types in a whole embryo within a single experiment. In parallel, assays for
diverse aspects of the epigenome, including chromatin accessibility, DNA methylation, and histone
modifications have been adapted to work in single cells and at scale. Furthermore, multiplexing techniques
have raised the prospect of using single-cell genomics not only to catalog cell types, but to comprehensively
study the effects of myriad perturbations of embryonic development, or to characterize the evolution of disease
pathogenesis at whole-animal scale and molecular resolution. In principle, single-cell genomics could serve as
an extraordinarily high-content means of phenotyping, but the volume and richness of datasets produced by
such experiments poses new, daunting computational and statistical challenges. A lack of software tools for
comparing specimens profiled as part of single-cell RNA-seq or ATAC-seq experiments constitutes a
critical gap in the field. This proposal aims to fill that gap with software tools that will allow users to
characterize how disease progression, genetic or chemical perturbations, or environmental effects alter the
proportions and molecular states of cells in complex tissues or whole embryos. In order to establish the
accuracy of our tools and the physiological relevance of their predictions, we will extensively validate their
output through analysis of existing and newly generated single-cell sequencing data using the very tractable
zebrafish embryonic development system. In our first Aim, we will develop software for detecting shifts within
cell populations across healthy and pathological molecular states. In our second Aim, we will develop software
for identifying genes that mediate or regulate cell-state transitions during development or disease
pathogenesis. In our third Aim, we will develop methods for defining how chromatin states at regulatory DNA
control transcriptional states. Upon completing these aims, we will have delivered new, widely applicable
software for analyzing single-cell genomics experiments. We will also have produced new datasets that will
serve both as a reference map for vertebrate embryogenesis and a platform for further development of tools for
genetic analysis by our group and others. Our experiments will also yield new insights as to how vertebrate
genomes encode developmental programs.

## Key facts

- **NIH application ID:** 10933558
- **Project number:** 5R01HG012761-02
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** David Kimelman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $662,862
- **Award type:** 5
- **Project period:** 2023-09-22 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10933558

## Citation

> US National Institutes of Health, RePORTER application 10933558, New software tools for differential analysis of single-cell genomics perturbation experiments (5R01HG012761-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10933558. Licensed CC0.

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