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Review

Advances in Single-Cell Sequencing for Understanding and Treating Kidney Disease

1
Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
2
Instituto EPOMEX, Universidad Autónoma de Campeche, San Francisco de Campeche 24062, Mexico
*
Author to whom correspondence should be addressed.
Computation 2026, 14(1), 6; https://doi.org/10.3390/computation14010006
Submission received: 4 August 2025 / Revised: 15 December 2025 / Accepted: 22 December 2025 / Published: 2 January 2026

Abstract

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.
Keywords: nephrology; kidney disease; scATAC-seq; scRNA-seq; multiomic; transcriptomics nephrology; kidney disease; scATAC-seq; scRNA-seq; multiomic; transcriptomics

Share and Cite

MDPI and ACS Style

Agraz, J.L.; Verma, A.; Agraz, C.M. Advances in Single-Cell Sequencing for Understanding and Treating Kidney Disease. Computation 2026, 14, 6. https://doi.org/10.3390/computation14010006

AMA Style

Agraz JL, Verma A, Agraz CM. Advances in Single-Cell Sequencing for Understanding and Treating Kidney Disease. Computation. 2026; 14(1):6. https://doi.org/10.3390/computation14010006

Chicago/Turabian Style

Agraz, Jose L., Amit Verma, and Claudia M. Agraz. 2026. "Advances in Single-Cell Sequencing for Understanding and Treating Kidney Disease" Computation 14, no. 1: 6. https://doi.org/10.3390/computation14010006

APA Style

Agraz, J. L., Verma, A., & Agraz, C. M. (2026). Advances in Single-Cell Sequencing for Understanding and Treating Kidney Disease. Computation, 14(1), 6. https://doi.org/10.3390/computation14010006

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