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

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.