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Sequencing of Physically Interacting Cells in Human Kidney Allograft Rejection to Infer Contact-dependent Immune Cell Transcription. | LitMetric

Background: Rejection requires cell-cell contact involving immune cells. Inferring the transcriptional programs of cell-cell interactions from single-cell RNA-sequencing (scRNA-seq) data is challenging as spatial information is lost.

Methods: We combined a CD45 pos enrichment strategy with Cellular Indexing of Transcriptomes and Epitopes by sequencing based quantification of leukocyte surface proteins to analyze cell-cell interactions in 11 human kidney transplant biopsies encompassing a spectrum of rejection diagnoses. scRNA-seq was performed using the 10X Genomics platform. We applied the sequencing physically interacting cells computational method to deconvolute the transcriptional profiles of heterotypic physically interacting cells.

Results: The 11 human allograft biopsies generated 31 203 high-quality single-cell libraries. Clustering was further refined by combining Cellular Indexing of Transcriptomes and Epitopes by sequencing data from 6 different leukocyte-specific surface proteins. Three of 6 doublet clusters were identified as physically interacting cell complexes; macrophages or dendritic cells bound to B cells or plasma cells; natural killer (NK) or T cells bound to macrophages or dendritic cells and NK or T cells bound to endothelial cells. Myeloid-lymphocyte physically interacting cell complexes expressed activated and proinflammatory genes. Lymphocytes physically interacting with endothelial cells were enriched for NK and CD4 T cells. NK cell-endothelial cell contact caused increased expression of endothelial proinflammatory genes CXCL9 and CXCL10 and NK cell proinflammatory genes CCL3 , CCL4 , and GNLY .

Conclusions: The transcriptional profiles of physically interacting cells from human kidney transplant biopsies can be inferred from scRNA-seq data using the sequencing physically interacting cells method. This approach complements previous methods that estimate cell-cell physical contact from scRNA-seq data.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10798591PMC
http://dx.doi.org/10.1097/TP.0000000000004762DOI Listing

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