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

Autor:	Leckie-Harre A; Division of Nephrology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO., Silverman I; Division of Nephrology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO., Wu H; Division of Nephrology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO., Humphreys BD; Division of Nephrology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO.; Department of Developmental Biology, Washington University in St. Louis School of Medicine, St. Louis, MO., Malone AF; Division of Nephrology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO.
Jazyk:	angličtina
Zdroj:	Transplantation [Transplantation] 2024 Feb 01; Vol. 108 (2), pp. 421-429. Date of Electronic Publication: 2024 Jan 19.
DOI:	10.1097/TP.0000000000004762
Abstrakt:	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. (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)
Databáze:	MEDLINE
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