*Purpose: Transcriptome-based clustering of individual cells within the kidney allograft may help identify cell type-specific injury and has the potential to redefine the current histopathology-based classification of allograft rejection/injury.

*Methods: We obtained kidney tissue at the time of core needle biopsy from 3 subjects; 1 healthy kidney donor and 2 kidney allograft recipients (NK1-donor biopsy prior to transplantation, TK1-allograft biopsy with minimal/non-specific biopsy findings 9 mo after active antibody mediated rejection, and TK2-allograft biopsy with transplant glomerulopathy and fibrosis 42 months after transplantation). We digested the biopsy tissues and generated single cell suspensions. We used droplet-based 10X Chromium platform (10X Genomics) to capture single cells in emulsion, followed by cDNA synthesis, sequencing, and data analysis.

*Results: We obtained 2813 (NK1), 2657 (TK1), and 2207 (TK2) high-quality scRNA-seq profiles and identified 13, 13, and 11 major cell clusters, respectively (Figure 1)

In both biopsies, T cells (27% TK1 and 24% TK2) were abundant despite the differences in histopathology findings. In TK1, infiltrating immune cells constituted more than 50% of the cells. Compared to NK1, TK1 showed altered gene expression pattern in endothelial cells and T cells whereas TK2 showed altered gene expression in macrophages (Figure 2)

Analysis of ligand-receptor interactions revealed increased cellular activity in multiple cellular subtypes in TK1 (Figure 3)

*Conclusions: Our single cell expression atlas of human kidney allograft has deciphered the complex cellular microenvironment of kidney allograft. Single cell RNA sequencing is a valuable technique that provides a powerful step towards better understanding of the cellular basis of kidney allograft dysfunction.

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