Journal of the American Society of NephrologyGenomicsPeer-reviewed journal article
Harnessing Expressed Single Nucleotide Variation and Single Cell RNA Sequencing to Define Immune Cell Chimerism in the Rejecting Kidney Transplant
Andrew F. MaloneiD, Hao WuiD, Catrina C. FronickiD, Robert S. FultoniD, Joseph P. GautiD, Benjamin D. HumphreysiD
Abstract
In solid organ transplantation, donor derived immune cells are assumed to decline with time after surgery. Whether donor leukocytes persist within kidney transplants or play any role in rejection is unknown, however, in part because of limited techniques for distinguishing recipient and donor cells. To address this question, we performed paired whole exome sequencing of donor and recipient DNA and single cell RNA sequencing (scRNA-seq) of 5 human kidney transplant biopsy cores. Exome sequences were used to define single nucleotide variations (SNV) across all samples. By analyzing expressed SNVs in the scRNA-seq dataset we could define recipient vs. donor cell origin for all 81,139 cells. The leukocyte donor to recipient ratio varied with rejection status for macrophages and with time post-transplant for lymphocytes. Recipient macrophages were characterized by inflammatory activation and donor macrophages by antigen presentation and complement signaling. Recipient origin T cells expressed cytotoxic and pro-inflammatory genes consistent with an effector cell phenotype whereas donor origin T cells are likely quiescent expressing oxidative phosphorylation genes relative to recipient T cells. Finally, both donor and recipient T cell clones were present within the rejecting kidney, suggesting lymphoid aggregation. Our results indicate that donor origin macrophages and T cells have distinct transcriptional profiles compared to their recipient counterparts and donor macrophages can persist for years post transplantation. This study demonstrates the power of this approach to accurately define leukocyte chimerism in a complex tissue such as the kidney transplant coupled with the ability to examine transcriptional profiles at single cell resolution.
- organism: human
- assay: scRNA-seq
- assay: whole-exome sequencing
- research focus: cell type annotation
- approach: statistical model
- Citations
- 116
- FWCI
- 14.02
- Field percentile
- top 1%
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