# Integrating single-cell omics and ancestry-adjusted eQTL mapping to characterize Tuberculosis immune response

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2024 · $272,202

## Abstract

Tuberculosis (TB) remains the leading infectious cause of death worldwide. A quarter of the
human population is exposed to TB of which 5-15% will progress to active disease. Despite its
extreme prevalence, prediction of disease progression is poor. To address this, our proposal
integrates whole genome sequencing and single-cell RNA sequencing (scRNA-Seq) on the
entire repertoire peripheral blood mononuclear cells (PBMCs) at three crucial TB disease states:
latent TB infection, recent Mtb infection, and post-TB treatment completion. We will be the first
to leverage this unique study design across TB states, for expression quantitative trait
loci (eQTL) mapping. Our study population in South Africa resides in a TB-endemic area
where we have over a decade of established research infrastructure, enabling us to efficiently
capture these critical TB phenotypes at a relatively low cost. Previous TB eQTL mapping
studies have been limited by inadequate phenotyping (e.g., samples from TB cases months-
years after clearing infection), bulk RNA-seq (aggregating cell-type specific effects), or
scRNAseq on one cell type. We are generating CITE-seq profiles from PBMCs, a cutting-edge
technology that enables simultaneous profiling of gene expression and cell surface protein
composition at the single-cell level [funded by CZI, co-PI Suliman]. This approach allows us to
capture the fine-scale heterogeneity of immune cell states. To identify the genetic variants that
regulate these identified cellular and transcriptomic changes, we propose to generate whole
genome sequencing data paired with the transcriptomic data for eQTL fine-mapping. South
African populations exhibits high levels of genetic heterozygosity and are multiway admixed,
amplifying statistical power for discovering eQTL variants. To characterize the unique genetic
diversity of our population we have optimized state-of-the-art ancestry estimation methods.
Outcomes of this grant include multiomic data from 225 individuals across three TB
states and eQTL identification of ancestry- and cell-specific variants which affect gene
expression in early TB infection.

## Key facts

- **NIH application ID:** 10871442
- **Project number:** 1R21AI183161-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Brenna M Henn
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $272,202
- **Award type:** 1
- **Project period:** 2024-08-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10871442

## Citation

> US National Institutes of Health, RePORTER application 10871442, Integrating single-cell omics and ancestry-adjusted eQTL mapping to characterize Tuberculosis immune response (1R21AI183161-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10871442. Licensed CC0.

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