Background/Purpose: Renal histology remains a primary tool for classification and treatment decisions in lupus nephritis (LN). Single-cell RNA-seq (scRNA-seq) analysis may provide mechanistic insights into the genesis; more precisely categorize different pathologic subtypes; and better inform treatment decisions and prognosis. Here we characterized single-cell transcriptome profiles of the renal cellular landscape using scRNA-seq.

Methods: ScRNA-seq was performed on ~2 mg cryostored kidney tissue collected from clinically indicated renal biopsies in 9 SLE patients and 2 healthy transplant donors. A droplet-based 10X Chromium platform (10X Genomics) was used to capture single cells in emulsion followed by cDNA synthesis. The cDNA library was sequenced on Ilumina HiSeq 2500. Cell-barcode and unique molecular identifiers (UMIs) were tagged in read1, an adapter was trimmed from 5’ of read2 and poly(A) sequences of length 6 or more were removed. The resulting reads were aligned to the human (hg38) reference genome and a single cell gene expression matrix was generated. Using a graph-based method embedded in the Seurat package, cell clustering analysis was performed. Cells with > 25% mitochondrial content were considered poor quality and filtered out as were any cells with < 100 genes detected.

Results: We obtained 16,192 and 3,104 high-quality scRNA-seq profiles from 9 LN patients (class I, II, IV/V and IV) and 2 donor controls respectively. Graph-based clustering analysis revealed several cell clusters as visualized by t-distributed stochastic neighbor embedding (t-SNE). Differential gene expression analysis along with expression of established lineage markers revealed these clusters as loop of Henle (LH), proximal convoluted tubule (PCT), distal convoluted tubule (DCT), intercalated cells (IC), mesangial cells (MC), podocytes (PC), T cells (TC), macrophages (MAC), B cells (B cell), plasmablasts (PB), endothelial cells (EC) and fibroblasts (FB). Differential gene expression analysis revealed elevated levels of interferon response genes such as IFI6, IFITM1, IFITM3 and ISG15 in CD, DCT and EC of LN samples. In addition, WFDC2, recently described as an LN biomarker, was upregulated in CD and DCT of LN samples. PCT in the LN samples revealed increased expression of COL4A1, COL4A2 and COL18A1 genes whose proteins form the structural component of the basement membrane. FB detected in the LN samples expressed COL1A1, COL1A2, and COL3A1 as the most abundant collagen genes.

Conclusion: ScRNA-seq derived from excess and limited cryostored renal biopsy tissue in LN can be used to generate a cellular atlas invaluable to the study of heterogeneity in disease. These data provide important insights into potential pathogenic mechanisms, particularly with regard to tissue fibrosis so highly relevant to the ultimate renal prognosis.

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/10x-genomics-based-single-cell-rna-seq-analysis-identifies-a-transcriptional-landscape-of-inflammation-and-fibrosis-in-lupus-nephritis%e2%80%8b/