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Not all reference samples are equal in single-cell transcriptomics of human kidney tissue
Rajasree Menon, Paul L. Kimmel, Edgar A. Otto, Lalita Subramanian, Christopher L. O’Connor, Bradley Godfrey, Cathy Smith, Fadhl Alakwaa, Celine C. Berthier, Minnie M. Sarwal, E. Steve Woodle, Laura Pyle, Ye Ji Choi, Patricia Ladd, John R. Sedor, Sylvia E. Rosas, Sushrut S. Waikar, Abhijit S. Naik, Ricardo Melo Ferreira, Michael T. Eadon, Markus Bitzer, Petter Bjornstad, Jeffrey B. Hodgin, Matthias Kretzler, for the Kidney Precision Medicine Project (KPMP)
Rajasree Menon, Paul L. Kimmel, Edgar A. Otto, Lalita Subramanian, Christopher L. O’Connor, Bradley Godfrey, Cathy Smith, Fadhl Alakwaa, Celine C. Berthier, Minnie M. Sarwal, E. Steve Woodle, Laura Pyle, Ye Ji Choi, Patricia Ladd, John R. Sedor, Sylvia E. Rosas, Sushrut S. Waikar, Abhijit S. Naik, Ricardo Melo Ferreira, Michael T. Eadon, Markus Bitzer, Petter Bjornstad, Jeffrey B. Hodgin, Matthias Kretzler, for the Kidney Precision Medicine Project (KPMP)
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Research Article Endocrinology Nephrology

Not all reference samples are equal in single-cell transcriptomics of human kidney tissue

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Abstract

Identifying mechanisms of kidney disease commonly involves comparing diseased samples with healthy reference tissues; however, the effects of variability in tissue procurement, storage, and donor characteristics remain underexplored. In this study, we systematically evaluated 3 reference tissue types — tumor nephrectomy (TN), pretransplant biopsies from living donors (LD), and percutaneous biopsies from healthy control volunteers (HC) — to determine their impact on differential gene expression across 3 diabetic kidney disease states. We observed distinct injury markers, cell state proportions, and gene signatures associated with procurement method, sex, and donor age. Adjustment for these confounding factors significantly influenced pathway analysis results. Specifically, correcting for age and sex eliminated significant enrichment of IFN-γ response when comparing the diabetes mellitus–resilient group and HC group. Processes related to biological aging were enriched in older reference tissues, potentially confounding disease-specific interpretations. Importantly, TNF signaling via NF-κB remained enriched in LD and TN samples relative to HC, even after accounting for confounders. These results underscore the critical importance of selecting appropriate control tissues and rigorously adjusting for confounding variables to reliably discern the molecular mechanisms underlying kidney diseases.

Authors

Rajasree Menon, Paul L. Kimmel, Edgar A. Otto, Lalita Subramanian, Christopher L. O’Connor, Bradley Godfrey, Cathy Smith, Fadhl Alakwaa, Celine C. Berthier, Minnie M. Sarwal, E. Steve Woodle, Laura Pyle, Ye Ji Choi, Patricia Ladd, John R. Sedor, Sylvia E. Rosas, Sushrut S. Waikar, Abhijit S. Naik, Ricardo Melo Ferreira, Michael T. Eadon, Markus Bitzer, Petter Bjornstad, Jeffrey B. Hodgin, Matthias Kretzler, for the Kidney Precision Medicine Project (KPMP)

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Figure 2

Single-cell data.

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Single-cell data.
(A) UMAP showing the annotated cell types from the int...
(A) UMAP showing the annotated cell types from the integrated analysis of a total of 119,193 cells from 6 sample types: 20,000 cells each from LD (n = 9), TN (n = 9), DM-R (n = 18), early DKD (n = 9), and DKD (n = 17) and 19,193 cells from HC (n = 12). (B) UMAP of the integrated dataset showing successful integration of HC, LD, TN, DM-R, early DKD, and DKD. (C) Dot plot showing the specific markers for the annotated cell types. (D) Violin plots showing the quality control features: percentage of mitochondrial reads per cell (percent.mt) and number of features per cell (nFeature_RNA) in the 3 cohorts: CROCODILE/IMPROV-T2D, PRECISE, and KPMP and the 6 sample groups studied. The background color in the violin density plot of the sample groups indicates the cohort from which the samples were procured. POD, podocyte; PEC, parietal epithelial cell; PT, proximal tubule; aPT, adaptive/maladaptive; DTL, descending thin loop of Henle; ATL, ascending thin loop of Henle; TAL, thick ascending loop of Henle; aTAL, adaptive/maladaptive thick ascending loop of Henle; DCT, distal convoluted tubule; CNT, connecting tubule; PC, principal cell; IC-A, intercalated type A; IC-B, intercalated type B; tPC-IC, transient between PC and IC; EC, endothelial cell; EC-AEA, efferent and afferent arteriolar endothelial cells; EC-GC, glomerular endothelial cell; FIB, fibroblast; VSMC, vascular smooth muscle cell; P, pericyte; MC, mesangial cell; B, B cell; T, T cell; NK T/C, natural killer T cell.

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