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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 5

Preprocess effect.

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Preprocess effect.
Using pseudo-bulk mRNA analysis, a gene set that was ...
Using pseudo-bulk mRNA analysis, a gene set that was upregulated in postoperative tissue procurement method compared with percutaneous needle biopsies was identified. LD (n = 9) and TN (n = 9) samples were acquired by postoperative biopsy procedure and HC (n = 12), DM-R (n = 18), early DKD (n = 9), and DKD (n = 17) samples were by percutaneous needle biopsies. (A) Violin density plot showing the score calculated at cell level for the 25 genes that were upregulated in postoperative tissue biopsies. (B) Violin plot showing the elevated expression of the score in LD and TN samples compared with needle biopsy samples. (C) Dot plot showing the expression of preprocess effect score calculated based on the expression of the 25 genes in the cell types identified. (D) String interaction network showing direct interaction of 19/25 genes. (E) Top 5 enriched Gene Ontology biological processes for the gene set. (F) Violin plot showing preprocess effect score in Visium data from LD (n = 15), DM-R (n = 15), and DKD (n = 13).

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