{"id":3078,"date":"2025-08-16T18:27:16","date_gmt":"2025-08-16T18:27:16","guid":{"rendered":"https:\/\/www.europesays.com\/ie\/3078\/"},"modified":"2025-08-16T18:27:16","modified_gmt":"2025-08-16T18:27:16","slug":"analysis-of-individual-patient-pathway-coordination-in-a-cross-species-single-cell-kidney-atlas","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ie\/3078\/","title":{"rendered":"Analysis of individual patient pathway coordination in a cross-species single-cell kidney atlas"},"content":{"rendered":"
  • \n

    GBD Chronic Kidney Disease Collaboration Global, regional, and national burden of chronic kidney disease, 1990\u20132017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 395<\/b>, 709\u2013733 (2020).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Kidney disease: a global health priority. Nat. Rev. Nephrol. 20<\/b>, 421\u2013423 (2024).<\/p>\n<\/li>\n

  • \n

    Kalantar-Zadeh, K., Jafar, T. H., Nitsch, D., Neuen, B. L. & Perkovic, V. Chronic kidney disease. Lancet 398<\/b>, 786\u2013802 (2021).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Muntner, P. Longitudinal measurements of renal function. Semin. Nephrol. 29<\/b>, 650\u2013657 (2009).<\/p>\n

    PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Zuk, A. & Bonventre, J. V. Acute kidney injury. Annu. Rev. Med. 67<\/b>, 293\u2013307 (2016).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Sethi, S. et al. Mayo clinic\/renal pathology society consensus report on pathologic classification, diagnosis, and reporting of GN. J. Am. Soc. Nephrol. 27<\/b>, 1278\u20131287 (2016).<\/p>\n

    PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Schreibing, F. & Kramann, R. Mapping the human kidney using single-cell genomics. Nat. Rev. Nephrol. 18<\/b>, 347\u2013360 (2022).<\/p>\n

    PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Lake, B. B. et al. An atlas of healthy and injured cell states and niches in the human kidney. Nature 619<\/b>, 585\u2013594 (2023).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Sikkema, L. et al. An integrated cell atlas of the lung in health and disease. Nat. Med. 29<\/b>, 1563\u20131577 (2023).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Rood, J. E., Maartens, A., Hupalowska, A., Teichmann, S. A. & Regev, A. Impact of the human cell atlas on medicine. Nat. Med. 28<\/b>, 2486\u20132496 (2022).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Robinson, N. B. et al. The current state of animal models in research: a review. Int J. Surg. 72<\/b>, 9\u201313 (2019).<\/p>\n

    PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/b>, 1843\u20131862 (2023).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Luecken, M. D. & Theis, F. J. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15<\/b>, e8746 (2019).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Heumos, L. et al. Best practices for single-cell analysis across modalities. Nat. Rev. Genet. 24<\/b>, 550\u2013572 (2023).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Squair, J. W. et al. Confronting false discoveries in single-cell differential expression. Nat. Commun. 12<\/b>, 5692 (2021).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Zhao, K. & Rhee, S. Y. Interpreting omics data with pathway enrichment analysis. Trends Genet. 39<\/b>, 308\u2013319 (2023).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/b>, 1712\u20131724 (2024).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/b>, 15874\u201315883 (2020).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Wu, H. et al. Mapping the single-cell transcriptomic response of murine diabetic kidney disease to therapies. Cell Metab. 34<\/b>, 1064\u20131078 (2022).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Abedini, A. et al. Single-cell transcriptomics and chromatin accessibility profiling elucidate the kidney protective mechanism of mineralocorticoid receptor antagonists. J. Clin. Invest. 134<\/b>, e157165 (2024).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Balzer, M. S. et al. Treatment effects of soluble guanylate cyclase modulation on diabetic kidney disease at single-cell resolution. Cell Rep. Med. 4<\/b>, 100992 (2023).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15<\/b>, 1053\u20131058 (2018).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Fischer, S. & Gillis, J. How many markers are needed to robustly determine a cell\u2019s type? iScience 24<\/b>, 103292 (2021).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Chen, Y. et al. Structural basis of ALDH1A2 inhibition by irreversible and reversible small molecule inhibitors. ACS Chem. Biol. 13<\/b>, 582\u2013590 (2018).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/b>, F683\u2013F692 (2021).<\/p>\n

    CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Uhlen, M. et al. Towards a knowledge-based Human Protein Atlas. Nat. Biotechnol. 28<\/b>, 1248\u20131250 (2010).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/b>, 1201\u20131213 (2023).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/b>, 180 (2023).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/b>, 884 (2018).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Kiselev, V. Y., Andrews, T. S. & Hemberg, M. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat. Rev. Genet. 20<\/b>, 273\u2013282 (2019).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Grabski, I. N., Street, K. & Irizarry, R. A. Significance analysis for clustering with single-cell RNA-sequencing data. Nat. Methods 20<\/b>, 1196\u20131202 (2023).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/a> (2023).<\/p>\n<\/li>\n

  • \n

    Tefft, J. B. et al. Notch1 and Notch3 coordinate for pericyte-induced stabilization of vasculature. Am. J. Physiol. Cell Physiol. 322<\/b>, C185\u2013C196 (2022).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Kuppe, C. et al. Decoding myofibroblast origins in human kidney fibrosis. Nature 589<\/b>, 281\u2013286 (2021).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium Nat. Genet. 25<\/b>, 25\u201329 (2000).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/b>, dsac007 (2022).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Su, H., Lei, C. T. & Zhang, C. Interleukin-6 signaling pathway and its role in kidney disease: an update. Front. Immunol. 8<\/b>, 405 (2017).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Meier, M., Menne, J. & Haller, H. Targeting the protein kinase C family in the diabetic kidney: lessons from analysis of mutant mice. Diabetologia 52<\/b>, 765\u2013775 (2009).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Hewitson, T. D. & Smith, E. R. A metabolic reprogramming of glycolysis and glutamine metabolism is a requisite for renal fibrogenesis\u2014why and how? Front. Physiol. 12<\/b>, 645857 (2021).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M. & Ishiguro-Watanabe, M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 51<\/b>, D587\u2013D592 (2023).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Bonventre, J. V. & Yang, L. Cellular pathophysiology of ischemic acute kidney injury. J. Clin. Investig. 121<\/b>, 4210\u20134221 (2011).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Yan, L. J. Folic acid-induced animal model of kidney disease. Anim. Model Exp. Med. 4<\/b>, 329\u2013342 (2021).<\/p>\n

    CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Mootha, V. K. et al. PGC-1\u03b1-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34<\/b>, 267\u2013273 (2003).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102<\/b>, 15545\u201315550 (2005).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Cao, H. et al. Tuberous sclerosis 1 (Tsc1) mediated mTORC1 activation promotes glycolysis in tubular epithelial cells in kidney fibrosis. Kidney Int. 98<\/b>, 686\u2013698 (2020).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/b>, 1503\u20131520 (2022).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Lau, S. C. M., Pan, Y., Velcheti, V. & Wong, K. K. Squamous cell lung cancer: current landscape and future therapeutic options. Cancer Cell 40<\/b>, 1279\u20131293 (2022).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Mullen, N. J. & Singh, P. K. Nucleotide metabolism: a pan-cancer metabolic dependency. Nat. Rev. Cancer 23<\/b>, 275\u2013294 (2023).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Friedlaender, A., Drilon, A., Banna, G. L., Peters, S. & Addeo, A. The METeoric rise of MET in lung cancer. Cancer 126<\/b>, 4826\u20134837 (2020).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Herbst, R. S., Morgensztern, D. & Boshoff, C. The biology and management of non-small cell lung cancer. Nature 553<\/b>, 446\u2013454 (2018).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Bansal, A. & Simon, M. C. Glutathione metabolism in cancer progression and treatment resistance. J. Cell Biol. 217<\/b>, 2291\u20132298 (2018).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Hayes, J. D. & Ashford, M. L. Nrf2 orchestrates fuel partitioning for cell proliferation. Cell Metab. 16<\/b>, 139\u2013141 (2012).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Leitner, B. P. et al. Multimodal analysis suggests differential immuno-metabolic crosstalk in lung squamous cell carcinoma and adenocarcinoma. npj Precis. Oncol. 6<\/b>, 8 (2022).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Muto, Y. et al. Defining cellular complexity in human autosomal dominant polycystic kidney disease by multimodal single cell analysis. Nat. Commun. 13<\/b>, 6497 (2022).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Wilson, P. C. et al. Multimodal single cell sequencing implicates chromatin accessibility and genetic background in diabetic kidney disease progression. Nat. Commun. 13<\/b>, 5253 (2022).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Liu, H. et al. Kidney multiome-based genetic scorecard reveals convergent coding and regulatory variants. Science 387<\/b>, eadp4753 (2025).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Xu, C. et al. Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models. Mol. Syst. Biol. 17<\/b>, e9620 (2021).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Lai, K. N. et al. IgA nephropathy. Nat. Rev. Dis. Prim. 2<\/b>, 16001 (2016).<\/p>\n

    PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/b>, 521\u2013528 (2014).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Hanukoglu, I. & Hanukoglu, A. Epithelial sodium channel (ENaC) family: phylogeny, structure-function, tissue distribution, and associated inherited diseases. Gene 579<\/b>, 95\u2013132 (2016).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Crowley, S. D. & Coffman, T. M. The inextricable role of the kidney in hypertension. J. Clin. Invest. 124<\/b>, 2341\u20132347 (2014).<\/p>\n

    PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Russo, C. J. et al. Association of NEDD4L ubiquitin ligase with essential hypertension. Hypertension 46<\/b>, 488\u2013491 (2005).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Persu, A. & Devuyst, O. Transepithelial chloride secretion and cystogenesis in autosomal dominant polycystic kidney disease. Nephrol. Dial. Transplant. 15<\/b>, 747\u2013750 (2000).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    M\u00fcller, 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<\/b>, 825\u2013839 (2022).<\/p>\n

    PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/b>, 242\u2013244 (2012).<\/p>\n

    PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Li, X. et al. A tumor necrosis factor-\u03b1-mediated pathway promoting autosomal dominant polycystic kidney disease. Nat. Med. 14<\/b>, 863\u2013868 (2008).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Song, Y., Miao, Z., Brazma, A. & Papatheodorou, I. Benchmarking strategies for cross-species integration of single-cell RNA sequencing data. Nat. Commun. 14<\/b>, 6495 (2023).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Rosen, Y. Towards universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN. Nat. Methods 21<\/b>, 1492\u20131500 (2024).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Swamy, V. S., Fufa, T. D., Hufnagel, R. B. & McGaughey, D. M. Building the mega single-cell transcriptome ocular meta-atlas. GigaScience 10<\/b>, giab061 (2021).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Bakken, T. E. et al. Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature 598<\/b>, 111\u2013119 (2021).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Li, H. et al. Cross-species single-cell transcriptomic analysis reveals divergence of cell composition and functions in mammalian ileum epithelium. Cell Regen. 11<\/b>, 19 (2022).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Chen, D. et al. Single cell atlas for 11 non-model mammals, reptiles and birds. Nat. Commun. 12<\/b>, 7083 (2021).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/b>, i571\u2013i579 (2025).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Andreatta, M. & Carmona, S. J. UCell: robust and scalable single-cell gene signature scoring. Comput. Struct. Biotechnol. J. 19<\/b>, 3796\u20133798 (2021).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/b>, e71994 (2022).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Tomfohr, J., Lu, J. & Kepler, T. B. Pathway level analysis of gene expression using singular value decomposition. BMC Bioinformatics 6<\/b>, 225 (2005).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Ozerov, I. V. et al. In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development. Nat. Commun. 7<\/b>, 13427 (2016).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Barbie, D. A. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462<\/b>, 108\u2013112 (2009).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Levy, O. et al. Age-related loss of gene-to-gene transcriptional coordination among single cells. Nat. Metab. 2<\/b>, 1305\u20131315 (2020).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Yu, T. & Bai, Y. Capturing changes in gene expression dynamics by gene set differential coordination analysis. Genomics 98<\/b>, 469\u2013477 (2011).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Leote, A. C., Lopes, F. & Beyer, A. Loss of coordination between basic cellular processes in human aging. Nat. Aging 4<\/b>, 1432\u20131445 (2024).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Fischer, S., Crow, M., Harris, B. D. & Gillis, J. Scaling up reproducible research for single-cell transcriptomics using MetaNeighbor. Nat. Protoc. 16<\/b>, 4031\u20134067 (2021).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Maan, H. et al. Characterizing the impacts of dataset imbalance on single-cell data integration. Nat. Biotechnol. 42<\/b>, 1899\u20131908 (2024).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Beckerman, P. et al. Transgenic expression of human APOL1 risk variants in podocytes induces kidney disease in mice. Nat. Med. 23<\/b>, 429\u2013438 (2017).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/b>, 329\u2013337 (2019).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Young, M. D. & Behjati, S. SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. GigaScience 9<\/b>, giaa151 (2020).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Martin, F. J. et al. Ensembl 2023. Nucleic Acids Res. 51<\/b>, D933\u2013D941 (2023).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Cakir, B. et al. Comparison of visualization tools for single-cell RNAseq data. NAR Genom. Bioinform. 2<\/b>, lqaa052 (2020).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19<\/b>, 15 (2018).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9<\/b>, 5233 (2019).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44<\/b>, W90\u2013W97 (2016).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Szklarczyk, D. et al. The STRING database in 2023: protein\u2013protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 51<\/b>, D638\u2013D646 (2022).<\/p>\n

    PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Korotkevich, G. et al. Fast gene set enrichment analysis. Preprint at bioRxiv https:\/\/doi.org\/10.1101\/060012<\/a> (2021).<\/p>\n<\/li>\n

  • \n

    Gillis, J. Protocol data (Python version). figshare https:\/\/doi.org\/10.6084\/m9.figshare.13034171.v1<\/a> (2020).<\/p>\n<\/li>\n

  • \n

    Kleshchevnikov, V. et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat. Biotechnol. 40<\/b>, 661\u2013671 (2022).<\/p>\n

    PubMed<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    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<\/a> (2021).<\/p>\n<\/li>\n

  • \n

    Kl\u00f6tzer, K. A. et al. A cross-species single-cell kidney atlas: data repository. Zenodo https:\/\/doi.org\/10.5281\/zenodo.15007208<\/a> (2025).<\/p>\n<\/li>\n

  • \n

    KonstantinKltz. susztaklab\/SISKA: pre-release V0.1. Zenodo https:\/\/doi.org\/10.5281\/zenodo.15109635<\/a> (2025).<\/p>\n<\/li>\n

  • \n

    KonstantinKltz. kloetzerka\/CellSpectra: CellSpectra pre-release. Zenodo https:\/\/doi.org\/10.5281\/zenodo.15112987<\/a> (2025).<\/p>\n<\/li>\n","protected":false},"excerpt":{"rendered":"GBD Chronic Kidney Disease Collaboration Global, regional, and national burden of chronic kidney disease, 1990\u20132017: a systematic analysis…\n","protected":false},"author":2,"featured_media":3079,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[272],"tags":[2567,2569,2564,2566,18,3470,2568,910,458,2565,19,17,133,864,3471,3472,3473],"class_list":{"0":"post-3078","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-genetics","8":"tag-agriculture","9":"tag-animal-genetics-and-genomics","10":"tag-biomedicine","11":"tag-cancer-research","12":"tag-eire","13":"tag-experimental-models-of-disease","14":"tag-gene-function","15":"tag-general","16":"tag-genetics","17":"tag-human-genetics","18":"tag-ie","19":"tag-ireland","20":"tag-science","21":"tag-software","22":"tag-therapeutics","23":"tag-transcriptomics","24":"tag-translational-research"},"share_on_mastodon":{"url":"","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts\/3078","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/comments?post=3078"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts\/3078\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/media\/3079"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/media?parent=3078"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/categories?post=3078"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/tags?post=3078"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}