Clustering Mycobacterium tuberculosis-specific CD154+CD4+ T cells for distinguishing tuberculosis disease from infection based on single-cell RNA-seq analysis

BACKGROUND: Distinguishing between active tuberculosis disease (TBD) and latent tuberculosis infection (TBI) is crucial for TB control but remains challenging. METHODS: T cells. RESULTS: T cells, both TBD and TBI can be classified into different subtypes, and a further established random forest model can accurately differentiate TBD from TBI. Additionally, the key checkpoints of exhausted MTB-specific Th1 cells are identified and blocking ADORA2A efficiently restores their function. CONCLUSIONS: T cells from TBI to TBD, putting forward a new direction in the diagnosis and prognosis of disease.