{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T00:23:21Z","timestamp":1771979001537,"version":"3.50.1"},"reference-count":165,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:00:00Z","timestamp":1767312000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"U.S. National Science Foundation","doi-asserted-by":"publisher","award":["1925001"],"award-info":[{"award-number":["1925001"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>The fields of medical diagnostics, nephrology, and the sequencing of cellular genetic material are pivotal for precise quantification of kidney diseases. Single-cell sequencing, enhanced by automation and software tools, enables efficient examination of biopsies at the individual cell level. This approach shows the complex cellular mosaic that shapes organ function. By quantifying gene expression following injury, single-cell analysis provides insight into disease progression. In this review, new developments in single-cell analysis methods, spatial integration of single-cell analysis, single-nucleus RNA sequencing, and emerging methods, including expression quantitative trait loci, whole-genome sequencing, and whole-exome sequencing in nephrology, are discussed. These advancements are poised to enhance kidney disease diagnostic processes, therapeutic strategies, and patient prognosis.<\/jats:p>","DOI":"10.3390\/computation14010006","type":"journal-article","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T12:23:44Z","timestamp":1767356624000},"page":"6","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Advances in Single-Cell Sequencing for Understanding and Treating Kidney Disease"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6582-2783","authenticated-orcid":false,"given":"Jose L.","family":"Agraz","sequence":"first","affiliation":[{"name":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2885-6803","authenticated-orcid":false,"given":"Amit","family":"Verma","sequence":"additional","affiliation":[{"name":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9717-4514","authenticated-orcid":false,"given":"Claudia M.","family":"Agraz","sequence":"additional","affiliation":[{"name":"Instituto EPOMEX, Universidad Aut\u00f3noma de Campeche, San Francisco de Campeche 24062, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"ref_1","unstructured":"Centers for Disease Control and Prevention (2023). 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