GBD Chronic Kidney Disease Collaboration Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 395, 709–733 (2020).
Kidney disease: a global health priority. Nat. Rev. Nephrol. 20, 421–423 (2024).
Kalantar-Zadeh, K., Jafar, T. H., Nitsch, D., Neuen, B. L. & Perkovic, V. Chronic kidney disease. Lancet 398, 786–802 (2021).
Muntner, P. Longitudinal measurements of renal function. Semin. Nephrol. 29, 650–657 (2009).
Zuk, A. & Bonventre, J. V. Acute kidney injury. Annu. Rev. Med. 67, 293–307 (2016).
Sethi, S. et al. Mayo clinic/renal pathology society consensus report on pathologic classification, diagnosis, and reporting of GN. J. Am. Soc. Nephrol. 27, 1278–1287 (2016).
Schreibing, F. & Kramann, R. Mapping the human kidney using single-cell genomics. Nat. Rev. Nephrol. 18, 347–360 (2022).
Lake, B. B. et al. An atlas of healthy and injured cell states and niches in the human kidney. Nature 619, 585–594 (2023).
Sikkema, L. et al. An integrated cell atlas of the lung in health and disease. Nat. Med. 29, 1563–1577 (2023).
Rood, J. E., Maartens, A., Hupalowska, A., Teichmann, S. A. & Regev, A. Impact of the human cell atlas on medicine. Nat. Med. 28, 2486–2496 (2022).
Robinson, N. B. et al. The current state of animal models in research: a review. Int J. Surg. 72, 9–13 (2019).
Zhou, J. et al. Unified mouse and human kidney single-cell expression atlas reveal commonalities and differences in disease states. J. Am. Soc. Nephrol. 34, 1843–1862 (2023).
Luecken, M. D. & Theis, F. J. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019).
Heumos, L. et al. Best practices for single-cell analysis across modalities. Nat. Rev. Genet. 24, 550–572 (2023).
Squair, J. W. et al. Confronting false discoveries in single-cell differential expression. Nat. Commun. 12, 5692 (2021).
Zhao, K. & Rhee, S. Y. Interpreting omics data with pathway enrichment analysis. Trends Genet. 39, 308–319 (2023).
Abedini, A. et al. Single-cell multi-omic and spatial profiling of human kidneys implicates the fibrotic microenvironment in kidney disease progression. Nat. Genet. 56, 1712–1724 (2024).
Kirita, Y., Wu, H., Uchimura, K., Wilson, P. C. & Humphreys, B. D. Cell profiling of mouse acute kidney injury reveals conserved cellular responses to injury. Proc. Natl Acad. Sci. USA 117, 15874–15883 (2020).
Wu, H. et al. Mapping the single-cell transcriptomic response of murine diabetic kidney disease to therapies. Cell Metab. 34, 1064–1078 (2022).
Abedini, A. et al. Single-cell transcriptomics and chromatin accessibility profiling elucidate the kidney protective mechanism of mineralocorticoid receptor antagonists. J. Clin. Invest. 134, e157165 (2024).
Balzer, M. S. et al. Treatment effects of soluble guanylate cyclase modulation on diabetic kidney disease at single-cell resolution. Cell Rep. Med. 4, 100992 (2023).
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053–1058 (2018).
Fischer, S. & Gillis, J. How many markers are needed to robustly determine a cell’s type? iScience 24, 103292 (2021).
Chen, Y. et al. Structural basis of ALDH1A2 inhibition by irreversible and reversible small molecule inhibitors. ACS Chem. Biol. 13, 582–590 (2018).
Chen, A., Liu, Y., Lu, Y., Lee, K. & He, J. C. Disparate roles of retinoid acid signaling molecules in kidney disease. Am. J. Physiol. Ren. Physiol. 320, F683–F692 (2021).
Uhlen, M. et al. Towards a knowledge-based Human Protein Atlas. Nat. Biotechnol. 28, 1248–1250 (2010).
Safizadeh Shabestari, S. A. et al. Overlapping pathogenic de novo CNVs in neurodevelopmental disorders and congenital anomalies impacting constraint genes regulating early development. Hum. Genet. 142, 1201–1213 (2023).
Shi, W., Le, W., Tang, Q., Shi, S. & Shi, J. Regulon analysis identifies protective FXR and CREB5 in proximal tubules in early diabetic kidney disease. BMC Nephrol. 24, 180 (2023).
Crow, M., Paul, A., Ballouz, S., Huang, Z. J. & Gillis, J. Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor. Nat. Commun. 9, 884 (2018).
Kiselev, V. Y., Andrews, T. S. & Hemberg, M. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat. Rev. Genet. 20, 273–282 (2019).
Grabski, I. N., Street, K. & Irizarry, R. A. Significance analysis for clustering with single-cell RNA-sequencing data. Nat. Methods 20, 1196–1202 (2023).
Hill, R. Z. et al. Renal mechanotransduction is an essential regulator of renin. Preprint at bioRxiv https://doi.org/10.1101/2023.11.04.565646 (2023).
Tefft, J. B. et al. Notch1 and Notch3 coordinate for pericyte-induced stabilization of vasculature. Am. J. Physiol. Cell Physiol. 322, C185–C196 (2022).
Kuppe, C. et al. Decoding myofibroblast origins in human kidney fibrosis. Nature 589, 281–286 (2021).
Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium Nat. Genet. 25, 25–29 (2000).
Onoda, N. et al. Spatial and single-cell transcriptome analysis reveals changes in gene expression in response to drug perturbation in rat kidney. DNA Res. 29, dsac007 (2022).
Su, H., Lei, C. T. & Zhang, C. Interleukin-6 signaling pathway and its role in kidney disease: an update. Front. Immunol. 8, 405 (2017).
Meier, M., Menne, J. & Haller, H. Targeting the protein kinase C family in the diabetic kidney: lessons from analysis of mutant mice. Diabetologia 52, 765–775 (2009).
Hewitson, T. D. & Smith, E. R. A metabolic reprogramming of glycolysis and glutamine metabolism is a requisite for renal fibrogenesis—why and how? Front. Physiol. 12, 645857 (2021).
Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M. & Ishiguro-Watanabe, M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 51, D587–D592 (2023).
Bonventre, J. V. & Yang, L. Cellular pathophysiology of ischemic acute kidney injury. J. Clin. Investig. 121, 4210–4221 (2011).
Yan, L. J. Folic acid-induced animal model of kidney disease. Anim. Model Exp. Med. 4, 329–342 (2021).
Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Cao, H. et al. Tuberous sclerosis 1 (Tsc1) mediated mTORC1 activation promotes glycolysis in tubular epithelial cells in kidney fibrosis. Kidney Int. 98, 686–698 (2020).
Salcher, S. et al. High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer. Cancer Cell 40, 1503–1520 (2022).
Lau, S. C. M., Pan, Y., Velcheti, V. & Wong, K. K. Squamous cell lung cancer: current landscape and future therapeutic options. Cancer Cell 40, 1279–1293 (2022).
Mullen, N. J. & Singh, P. K. Nucleotide metabolism: a pan-cancer metabolic dependency. Nat. Rev. Cancer 23, 275–294 (2023).
Friedlaender, A., Drilon, A., Banna, G. L., Peters, S. & Addeo, A. The METeoric rise of MET in lung cancer. Cancer 126, 4826–4837 (2020).
Herbst, R. S., Morgensztern, D. & Boshoff, C. The biology and management of non-small cell lung cancer. Nature 553, 446–454 (2018).
Bansal, A. & Simon, M. C. Glutathione metabolism in cancer progression and treatment resistance. J. Cell Biol. 217, 2291–2298 (2018).
Hayes, J. D. & Ashford, M. L. Nrf2 orchestrates fuel partitioning for cell proliferation. Cell Metab. 16, 139–141 (2012).
Leitner, B. P. et al. Multimodal analysis suggests differential immuno-metabolic crosstalk in lung squamous cell carcinoma and adenocarcinoma. npj Precis. Oncol. 6, 8 (2022).
Muto, Y. et al. Defining cellular complexity in human autosomal dominant polycystic kidney disease by multimodal single cell analysis. Nat. Commun. 13, 6497 (2022).
Wilson, P. C. et al. Multimodal single cell sequencing implicates chromatin accessibility and genetic background in diabetic kidney disease progression. Nat. Commun. 13, 5253 (2022).
Liu, H. et al. Kidney multiome-based genetic scorecard reveals convergent coding and regulatory variants. Science 387, eadp4753 (2025).
Xu, C. et al. Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models. Mol. Syst. Biol. 17, e9620 (2021).
Lai, K. N. et al. IgA nephropathy. Nat. Rev. Dis. Prim. 2, 16001 (2016).
Yang, X. et al. Genome-wide linkage and regional association study of blood pressure response to the cold pressor test in Han Chinese: the genetic epidemiology network of salt sensitivity study. Circ. Cardiovasc. Genet. 7, 521–528 (2014).
Hanukoglu, I. & Hanukoglu, A. Epithelial sodium channel (ENaC) family: phylogeny, structure-function, tissue distribution, and associated inherited diseases. Gene 579, 95–132 (2016).
Crowley, S. D. & Coffman, T. M. The inextricable role of the kidney in hypertension. J. Clin. Invest. 124, 2341–2347 (2014).
Russo, C. J. et al. Association of NEDD4L ubiquitin ligase with essential hypertension. Hypertension 46, 488–491 (2005).
Persu, A. & Devuyst, O. Transepithelial chloride secretion and cystogenesis in autosomal dominant polycystic kidney disease. Nephrol. Dial. Transplant. 15, 747–750 (2000).
Müller, R. U. et al. An update on the use of tolvaptan for autosomal dominant polycystic kidney disease: consensus statement on behalf of the ERA Working Group on Inherited Kidney Disorders, the European Rare Kidney Disease Reference Network and Polycystic Kidney Disease International. Nephrol. Dial. Transplant. 37, 825–839 (2022).
Jardine, M. J., Liyanage, T., Buxton, E. & Perkovic, V. mTOR inhibition in autosomal-dominant polycystic kidney disease (ADPKD): the question remains open. Nephrol. Dial. Transplant. 28, 242–244 (2012).
Li, X. et al. A tumor necrosis factor-α-mediated pathway promoting autosomal dominant polycystic kidney disease. Nat. Med. 14, 863–868 (2008).
Song, Y., Miao, Z., Brazma, A. & Papatheodorou, I. Benchmarking strategies for cross-species integration of single-cell RNA sequencing data. Nat. Commun. 14, 6495 (2023).
Rosen, Y. Towards universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN. Nat. Methods 21, 1492–1500 (2024).
Swamy, V. S., Fufa, T. D., Hufnagel, R. B. & McGaughey, D. M. Building the mega single-cell transcriptome ocular meta-atlas. GigaScience 10, giab061 (2021).
Bakken, T. E. et al. Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature 598, 111–119 (2021).
Li, H. et al. Cross-species single-cell transcriptomic analysis reveals divergence of cell composition and functions in mammalian ileum epithelium. Cell Regen. 11, 19 (2022).
Chen, D. et al. Single cell atlas for 11 non-model mammals, reptiles and birds. Nat. Commun. 12, 7083 (2021).
Song, Y., Hu, Y., Dow, J., Perrimon, N. & Papatheodorou, I. ScGOclust: leveraging gene ontology to find functionally analogous cell types between distant species. Bioinformatics 41, i571–i579 (2025).
Andreatta, M. & Carmona, S. J. UCell: robust and scalable single-cell gene signature scoring. Comput. Struct. Biotechnol. J. 19, 3796–3798 (2021).
Noureen, N., Ye, Z., Chen, Y., Wang, X. & Zheng, S. Signature-scoring methods developed for bulk samples are not adequate for cancer single-cell RNA sequencing data. eLife 11, e71994 (2022).
Tomfohr, J., Lu, J. & Kepler, T. B. Pathway level analysis of gene expression using singular value decomposition. BMC Bioinformatics 6, 225 (2005).
Ozerov, I. V. et al. In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development. Nat. Commun. 7, 13427 (2016).
Barbie, D. A. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462, 108–112 (2009).
Levy, O. et al. Age-related loss of gene-to-gene transcriptional coordination among single cells. Nat. Metab. 2, 1305–1315 (2020).
Yu, T. & Bai, Y. Capturing changes in gene expression dynamics by gene set differential coordination analysis. Genomics 98, 469–477 (2011).
Leote, A. C., Lopes, F. & Beyer, A. Loss of coordination between basic cellular processes in human aging. Nat. Aging 4, 1432–1445 (2024).
Fischer, S., Crow, M., Harris, B. D. & Gillis, J. Scaling up reproducible research for single-cell transcriptomics using MetaNeighbor. Nat. Protoc. 16, 4031–4067 (2021).
Maan, H. et al. Characterizing the impacts of dataset imbalance on single-cell data integration. Nat. Biotechnol. 42, 1899–1908 (2024).
Beckerman, P. et al. Transgenic expression of human APOL1 risk variants in podocytes induces kidney disease in mice. Nat. Med. 23, 429–438 (2017).
McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337 (2019).
Young, M. D. & Behjati, S. SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. GigaScience 9, giaa151 (2020).
Martin, F. J. et al. Ensembl 2023. Nucleic Acids Res. 51, D933–D941 (2023).
Cakir, B. et al. Comparison of visualization tools for single-cell RNAseq data. NAR Genom. Bioinform. 2, lqaa052 (2020).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).
Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).
Szklarczyk, D. et al. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 51, D638–D646 (2022).
Korotkevich, G. et al. Fast gene set enrichment analysis. Preprint at bioRxiv https://doi.org/10.1101/060012 (2021).
Gillis, J. Protocol data (Python version). figshare https://doi.org/10.6084/m9.figshare.13034171.v1 (2020).
Kleshchevnikov, V. et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat. Biotechnol. 40, 661–671 (2022).
Megill, C. et al. cellxgene: a performant, scalable exploration platform for high dimensional sparse matrices. Preprint at bioRxiv https://doi.org/10.1101/2021.04.05.438318 (2021).
Klötzer, K. A. et al. A cross-species single-cell kidney atlas: data repository. Zenodo https://doi.org/10.5281/zenodo.15007208 (2025).
KonstantinKltz. susztaklab/SISKA: pre-release V0.1. Zenodo https://doi.org/10.5281/zenodo.15109635 (2025).
KonstantinKltz. kloetzerka/CellSpectra: CellSpectra pre-release. Zenodo https://doi.org/10.5281/zenodo.15112987 (2025).