bioRxivImmunologyPreprint
Insights from Comparison of the Renal and Skin Single Cell Transcriptomes in Lupus Nephritis
E. Der, H. Suryawanshi, P. Morozov, M. Kustagi, B. Goilav, S. Ranabathou, P. Izmirly, R. Clancy, H. M. Belmont, M. Koenigsberg, M. Mokrzycki, H. Rominieki, J. Graham, J. Rocca, N. Bornkamp, N. Jordan, E. Schulte, M. Wu, J. Pullman, K. Slowikowski, S. Raychaudhuri, J. Guthridge, J. James, Acclerating Medicine Partnership (AMP), J. Buyon, , C. Putterman
Abstract
Lupus nephritis (LN) occurs in up to 50% of patients with systemic lupus erythematosus (SLE), and is a major contributor to mortality and morbidity. LN presents as a highly heterogeneous disease both in histopathology and response to therapy. The molecular and cellular processes leading to renal damage and to the heterogeneity of the disease are not well understood. To elucidate the processes underpinning the heterogeneity of LN, we applied singlecell RNA-sequencing (scRNA-seq) to renal biopsies from LN patients. Skin biopsies were evaluated as a source of biomarkers for monitoring kidney disease. Type-I interferon (IFN) response signatures were identified in tubular cells and keratinocytes, differentiating LN patients from healthy controls. Non-responders associated with higher IFN signatures in both tissue compartments. Moreover, non-response was also associated with a fibrotic signature in the tubular cells. Receptor-ligand interaction analysis indicated that the fibrotic process is likely mediated by FGF receptors with the initiating signal originating from infiltrating leukocytes. Differential expression analysis of tubular cells between proliferative and membranous LN pointed to several fibrosis-relevant pathways, which may offer insight into their histological differences. In summary, scRNA-seq was applied to LN to deconstruct its heterogeneity and provide novel targets for personalized approaches to therapy.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- approach: deep learning
- approach: statistical model
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