bioRxivImmunologyPreprint
Multimodal memory T cell profiling identifies a reduction in a polyfunctional Th17 state associated with tuberculosis progression
A. Nathan, J. I. Beynor, Y. Baglaenko, S. Suliman, K. Ishigaki, S. Asgari, C.-C. Huang, Y. Luo, Z. Zhang, K. Lopez Tamara, J. Jimenez, R. I. Calderon, L. Lecca, I. van Rhijn, B. Moody, M. B. Murray, S. Raychaudhuri
Abstract
Mycobacterium tuberculosis (M.tb) results in 10 million active tuberculosis (TB) cases and 1.5 million deaths each year1, making it the worlds leading infectious cause of death2. Infection leads to either an asymptomatic latent state or TB disease. Memory T cells have been implicated in TB disease progression, but the specific cell states involved have not yet been delineated because of the limited scope of traditional profiling strategies. Furthermore, immune activation during infection confounds underlying differences in T cell state distributions that influence risk of progression. Here, we used a multimodal single-cell approach to integrate measurements of transcripts and 30 functionally relevant surface proteins to comprehensively define the memory T cell landscape at steady state (i.e., outside of active infection). We profiled 500,000 memory T cells from 259 Peruvians > 4.7 years after they had either latent M.tb infection or active disease and defined 31 distinct memory T cell states, including a CD4+CD26+CD161+CCR6+ effector memory state that was significantly reduced in patients who had developed active TB (OR = 0.80, 95% CI: 0.73-0.87, p = 1.21 x 10-6). This state was also polyfunctional; in ex vivo stimulation, it was enriched for IL-17 and IL-22 production, consistent with a Th17-skewed phenotype, but also had more capacity to produce IFN{gamma} than other CD161+CCR6+ Th17 cells. Additionally, in progressors, IL-17 and IL-22 production in this cell state was significantly lower than in non-progressors. Reduced abundance and function of this state may be an important factor in failure to control M.tb infection.
- organism: human
- assay: scRNA-seq
- assay: flow cytometry
- research focus: cell type annotation
- research focus: differential expression
- approach: deep learning
- approach: experimental assay
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