bioRxivImmunologyPreprint
Lung Resident T Cells Across the Spectrum of TB Infection and Disease
Dylan C. KainiD, GW McElfreshiD, Katherine H. RottiD, Meghan E. Cansler, Gwendolyn M. SwarbrickiD, Gerhard WalzliD, Nelita Du PlessisiD, Deborah A. LewinsohniD, Benjamin N. BimberiD, David Michael LewinsohniD
Abstract
Tuberculosis (TB) remains the leading infectious cause of death worldwide, yet immunological protection against Mycobacterium tuberculosis (Mtb) remain poorly understood. We performed integrated single-cell transcriptomics and functional T-cell cloning on paired bronchoalveolar lavage (BAL) and peripheral blood samples from those exposed to TB, using IGRA and PET-CT to study potentially protective responses across the spectrum of TB infection/disease. Recent Mtb exposure alone was sufficient to remodel the pulmonary T-cell compartment. Longitudinal follow-up demonstrated that all participants who progressed to active TB had baseline PET-CT abnormalities, showing that PET-CT scan can be used to find those at high risk of progression. Furthermore, those who were PET-positive, but did not progress to active TB provide a natural model for protection, and exhibited enrichment of pulmonary cytotoxic CD8-associated T cells, identifying a candidate protective immune program. TAR-seq identified a discrete population of clonally expanded Mtb-responsive T cells and enabled direct linkage of antigen-responsive TCRs to transcriptional state through T cell cloning. Functional T-cell cloning further established a framework connecting antigen specificity, TCR sequence, and pulmonary cell state. Together, these findings provide a comprehensive atlas of human pulmonary T-cell immunity across the spectrum of TB, identify pulmonary immune features associated with durable control of infection, and establish an integrated platform for defining protective T-cell responses to inform next-generation TB vaccine development.
- organism: human
- assay: scRNA-seq
- assay: proteomics
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
- Citations
- 0
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