Single Cell Analysis of Peripheral TB-Associated Granulomatous Lymphadenitis.
Philip J MoosAllison F CareyJacklyn JosephStephanie KialoJoe NorrieJulie M MoyarelceAnthony AmofHans NoguaAlbebson L LimLouis R BarrowsPublished in: bioRxiv : the preprint server for biology (2024)
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 cell clusters emerge as abundant in most samples. Additionally, variations in the abundance of B cells, plasma cells, macrophages/dendrocytes, and NK cells among patient samples are observed. Pooling scRNA-seq data from 23 patients enabled the identification of T, macrophage, dendrocyte, and plasma cell subclusters, each displaying distinct signaling activities. 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 unveils predominant signaling pathways within granulomas, highlighting interactions between stromal/endothelial cells and other immune cell components. Moreover, selective communication pathways involving molecules such as Collagen, FN1, Laminin, CD99, MIF, MHC-1, APP and CD45 are identified, shedding light on the intricate interplay within granulomatous lymph nodes during TB infection.
Keyphrases
- single cell
- rna seq
- induced apoptosis
- endothelial cells
- mycobacterium tuberculosis
- lymph node
- end stage renal disease
- cell cycle arrest
- ejection fraction
- signaling pathway
- genome wide
- high throughput
- chronic kidney disease
- newly diagnosed
- nk cells
- endoplasmic reticulum stress
- pulmonary tuberculosis
- gene expression
- adipose tissue
- epithelial mesenchymal transition
- hiv aids
- machine learning
- deep learning
- rheumatoid arthritis
- vascular endothelial growth factor
- electronic health record
- patient reported outcomes
- big data
- mesenchymal stem cells