Single-Cell Transcriptomics of a Human Kidney Allograft Biopsy Specimen Defines a Diverse Inflammatory Response

Abstract

Single-cell genomics techniques are revolutionizing our ability to characterize complex tissues. By contrast, the techniques used to analyze renal biopsy specimens have changed little over several decades. We tested the hypothesis that single-cell RNA-sequencing can comprehensively describe cell types and states in a human kidney biopsy specimen. We generated 8746 single-cell transcriptomes from a healthy adult kidney and a single kidney transplant biopsy core by single-cell RNA-sequencing. Unsupervised clustering analysis of the biopsy specimen was performed to identify 16 distinct cell types, including all of the major immune cell types and most native kidney cell types, in this biopsy specimen, for which the histologic read was mixed rejection. Monocytes formed two subclusters representing a nonclassical CD16+ group and a classic CD16- group expressing dendritic cell maturation markers. The presence of both monocyte cell subtypes was validated by staining of independent transplant biopsy specimens. Comparison of healthy kidney epithelial transcriptomes with biopsy specimen counterparts identified novel segment-specific proinflammatory responses in rejection. Endothelial cells formed three distinct subclusters: resting cells and two activated endothelial cell groups. One activated endothelial cell group expressed Fc receptor pathway activation and Ig internalization genes, consistent with the pathologic diagnosis of antibody-mediated rejection. We mapped previously defined genes that associate with rejection outcomes to single cell types and generated a searchable online gene expression database. We present the first step toward incorporation of single-cell transcriptomics into kidney biopsy specimen interpretation, describe a heterogeneous immune response in mixed rejection, and provide a searchable resource for the scientific community.

In situ multiplex immunofluorescence analysis of the inflammatory burden in kidney allograft rejection: A new tool to characterize the alloimmune response

“…However, some of the transcripts previously used are not specific for NK cells. In a single‐cell RNA sequencing study of a human kidney allograft biopsy with mixed rejection, CX3CR1 and GNLY were indeed overexpressed in monocytes and T lymphocytes respectively. Moreover, no NK cells were found in this study evaluating 8746 single‐cell transcriptomes .…”

“…In a single‐cell RNA sequencing study of a human kidney allograft biopsy with mixed rejection, CX3CR1 and GNLY were indeed overexpressed in monocytes and T lymphocytes respectively. Moreover, no NK cells were found in this study evaluating 8746 single‐cell transcriptomes . In view of the fact that we found many macrophages and T lymphocytes in both ABMR and TCMR, these “NK cell transcripts” may be difficult to interpret.…”

“…In our study, we used the membrane marker NKp46, which targets NK cells with a very high specificity and is evolutionary conserved in mammals . NKp46 is also expressed by innate lymphoid cells (ILC), especially ILC3, but ILC so far do not seem to be implicated in rejection …”

“…Moreover, no NK cells were found in this study evaluating 8746 single-cell transcriptomes. 21 In view of the fact that we found many macrophages and T lymphocytes in both ABMR and TCMR, these "NK cell transcripts" may be difficult to interpret. In our study, we used the membrane marker NKp46, which targets NK cells with a very high specificity and is evolutionary conserved in mammals.…”

Single-cell RNA-seq data analysis on the receptor ACE2 expression reveals the potential risk of different human organs vulnerable to 2019-nCoV infection

“…Here, we explored the published scRNA-seq datasets from various tissues and organs of different human body systems, including the respiratory system [4,5] (nasal mucosa, respiratory track, bronchus, and lung), the cardiovascular system [6] (heart), the digestive system [7][8][9][10] (esophagus, stomach, ileum, and liver), and the urinary system [11,12] (kidney and bladder). The lung scRNA-seq data were acquired from the Gene Expression Omnibus (GEO) database under the series number GSE122960; the nasal mucosa, respiratory track, bronchus scRNA-seq data were from GSE121600; the heart data were from GSE106118; the esophagus data were downloaded from https://www.tissuestabilitycellatlas.org/; the ileum data were from GSE134809 sample GSM3972018; the stomach data were from GSE134520 sample GSM3954949; the liver data were from GSE115469; the kidney data were from GSE109564; and the bladder data were from GSE129845 sample GSM3723358.…”