# Single-Cell Multi-Omic Analysis of Cell Differentiation in HIV Infection

> **NIH NIH F31** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $42,341

## Abstract

Abstract
Single-cell multi-omic datasets, in which multiple molecular modalities are profiled within the same cell, provide
a unique opportunity to discover the temporal relationship between epigenome and transcriptome. To realize
this potential, we propose to develop a system consisting of differential equations that extends the RNA
velocity framework for gene expression to incorporate epigenomic data. By fitting pairs of jointly sequenced
RNA-seq and ATAC-seq data, this probabilistic latent variable model is able to estimate the switch time and
rate parameters of chromatin accessibility and gene expression from single-cell data, providing a quantitative
summary of the temporal relationship between epigenomic and transcriptomic changes. The parameters
inferred by the method quantify the length of time for which genes occupy each of the four distinct and
biological meaningful states, ranking genes by the degree of coupling between transcriptome and epigenome.
Similar to how transcriptomic data has been split into unspliced and spliced modalities to compute RNA
velocity, the chromatin accessibility data measured with ATAC-seq can be further split into enhancers and
promoters to model dynamics inside cis-regulatory networks during transcription. We will use this
comprehensive multi-layer velocity method to study human blood cell differentiation and mechanistic changes
during transcription due to HIV infection. We seek to uncover previously unknown markers of infection status
through the means of mathematical modeling on single-cell multi-omic sequencing techniques.

## Key facts

- **NIH application ID:** 10836723
- **Project number:** 1F31AI177258-01A1
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Chen Li
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $42,341
- **Award type:** 1
- **Project period:** 2024-01-01 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10836723

## Citation

> US National Institutes of Health, RePORTER application 10836723, Single-Cell Multi-Omic Analysis of Cell Differentiation in HIV Infection (1F31AI177258-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10836723. Licensed CC0.

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