T cell populations are negatively correlated with natural killer and macrophage cell populations in aspirate samples of peripheral lymphadenopathies

We employed single-cell RNA sequencing (scRNA-seq) of fine needle aspirates (FNAs) to describe the cells and communication networks characterizing granulomatous lymph nodes of TB patients. We uniformly identified several cell types known to characterize granulomas. Overall, we found the T cell cluster to be the most abundant. Other cell clusters that were uniformly detected, but that varied in abundance amongst the individual patient samples, were the B cell, plasma cell and macrophage/dendritic and NK cell clusters. When we combined all our scRNA-seq data from our current 19 patients, we distinguished T, B, macrophage, dendritic and plasma cell subclusters. The sizes of these subclusters also varied dramatically amongst the individual patients. In comparing FNA composition we noted trends in which T cell populations were negatively correlated with NK cell populations and with macrophage/dendritic cell populations. In addition, we discovered that the scRNA-seq pipeline detects Mtb RNA transcripts and associates them with their host cell’s transcriptome, thus identifying individual infected cells. The number of infected cells also varies in abundance amongst the patient samples. CellChat analysis identified predominating signaling pathways amongst the cells comprising the various granulomatous lymph nodes, identifying several pathways involved in immune cell maturation, migration and adhesion. The research conducted describes the cellular composition and communication networks within granulomatous lymph nodes of tuberculosis (TB) patients, employing a single-cell RNA sequencing (scRNA-seq) approach. By analyzing individual patient samples and clustering cells based on their transcriptome similarities, the study reveals several consistent cell types described to be present in both human and non-human primate granulomas. Notably, T cells emerge as abundant in most samples. Additionally, variations in the abundance of B cells, plasma cells, macrophages/dendritic cells, and Natural Killer (NK) cells among patient samples are observed. Pooling scRNA-seq data from 19 patients enabled the identification of T, macrophage, dendritic, and plasma cell subclusters, each displaying distinct signaling activities. A trend identified in the sample set inversely correlates T cell abundance with that of NK cells. Also detected was a negative correlation between T cell populations and cells of monocytic and macrophage lineages. Moreover, the study uncovers a surprising capability of the scRNA-seq pipeline to detect Mtb RNA transcripts within host cells, providing insights into individual infected cells and Mtb burden. CellChat analysis reveals predominant signaling pathways within granulomas, highlighting interactions between stromal/endothelial cells and the immune cell components. Selective communication pathways involving molecules such as MHC-I, MHC-II, MIF, CD45, galectin, APP, CD99, LCK, ICAM, NEGR, NCAM, CD6, TGFβ, PECAM and prostaglandins are identified, shedding light on the intricate interplay of cells within granulomatous lymph nodes during TB infection.