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Review

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

by
Jose L. Agraz
1,
Amit Verma
1 and
Claudia M. Agraz
2,*
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
(This article belongs to the Special Issue Integrative Computational Methods for Second-and Third-Generation Sequencing Data)
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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.

1. Introduction

Kidney disease remains a major cause of morbidity and mortality worldwide. In the U.S., chronic kidney disease (CKD) affects 35 million people, roughly 14% of the population [1]. Traditional bulk sequencing approaches obscure critical cell specific gene expression patterns that are necessary for precise disease characterization, complicating accurate diagnosis and effective treatment strategies [2]. Single-cell sequencing of kidney biopsies offers visual insights into the disease mechanisms at the individual cell level, further advancing personalized nephrology [3]. We discuss single-cell sequencing in nephrology by showing state-of-the-art methods in spatial or histology whole-slide-image (WSI) integration, evaluating emerging research, and discussing future applications for kidney disease. Together, these advances promise to refine kidney disease classification, enable targeted therapeutics, and ultimately improve patient outcomes.
In the nineteenth century, Richard Bright and William Bowman established the kidney’s role in maintaining bodily homeostasis [4,5,6]. Subsequent studies clarified functions in filtration, metabolism, electrolyte balance, and blood pressure regulation, positioning the kidney as a key checkpoint for detoxification and nutrient reclamation [5,6]. Each day, the kidneys generate roughly 180 liters of filtrate, removing waste and excess fluid to preserve physiological balance [7]. This microenvironment, comprising podocytes, tubular epithelial cells, endothelial cells, and diverse immune populations, supports the kidney’s compartmentalized structure and specialized function [8,9,10]. However, a fuller understanding of kidney disease requires single-cell resolution. Genomic variation can arise across cells within a population, and bulk assays can mask rare cell types, transitional states, and context specific programs, motivating single-cell sequencing approaches [11]. The literature on the subject has expanded rapidly. PubMed searches on (‘kidney’ AND ‘single-cell’) climbed from 74 articles in 2018 to 580 in 2024, redefining kidney cell research. More specifically, 1210 publications on single-cell RNA sequencing (scRNA-seq) in kidney disease were indexed from 2015 to 2024 in the Web of Science Core Collection (WoSCC) [12]. Nevertheless, information remains fragmented across papers and disease specific studies.
Early reviews emphasized that single-cell sequencing can reveal disease mechanisms by resolving distinct cellular populations. These reviews also noted the need for high quality genotypic data and persistent technical constraints [11,13,14,15]. Subsequent reviews broadened the scope to multimodal strategies integrating transcriptomic [16,17] and chromatin data [18]. More recent work summarized major assay formats and implementations, including droplet based, plate based, and hydrogel based scRNA-seq, alongside spatial transcriptomics, and documented gains in throughput and sequencing performance across studies [17,19,20,21,22,23]. Looking ahead, the priorities include computational innovation and expanded multiomic applications that integrate single-cell, spatial, and clinical data to support earlier detection, improved patient stratification, and more targeted therapeutic interventions [24,25,26]. This review begins with an overview of kidney disease, citing clinical burden and biological complexity as motivations for high resolution cellular profiling. The subsequent sections review core single-cell sequencing methods, including scRNA-seq and single-cell assay for Transposase Accessible Chromatin using sequencing (scATAC-seq), and summarize the major platforms that support these analyses. The discussion then examines advances in multiomic single-cell sequencing and emerging spatial technologies that preserve tissue architecture and microenvironmental context. The final sections survey complementary genomic approaches, including whole-genome and whole-exome sequencing, expression quantitative trait loci (eQTL) mapping, and single-nucleus RNA sequencing (snRNA-seq), and summarize state-of-the-art applications in diabetic nephropathy (DN), acute kidney injury (AKI), and IgA nephropathy.

2. Understanding Kidney Disease

Accurate characterization of kidney disease remains challenging due to pronounced cellular heterogeneity. Traditional bulk sequencing can mask cell-type specific gene expression programs. Single-cell sequencing addresses this limitation by profiling individual cells, strengthening biomarker discovery and therapeutic targeting [14,27]. In the kidney, these approaches have delineated immune and epithelial populations central to disease pathogenesis, thus uncovering previously unrecognized features of fibrosis and inflammation and supporting the development of targeted therapies [17,21,28].
Recent work in single-cell technologies has enabled increasingly detailed maps of kidney cellular landscapes. These studies have identified novel and transient cell states, offering insight into cellular plasticity and differentiation programs implicated in disease progression [29,30]. Recent analyses have also revealed cell–cell signaling pathways that clarify intercellular communication within the kidney microenvironment and have yielded markers that better distinguish healthy from diseased states [31,32,33,34].
Single-cell sequencing has further clarified inflammatory dynamics by resolving interactions between immune cells and kidney parenchyma that inform anti-inflammatory therapeutic design. Beyond kidney disease, these methods have refined understanding of the tumor microenvironment, linking molecular states with treatment response and informing cancer therapy [32,35,36,37]. By resolving pathway-level programs at cellular resolution, single-cell sequencing approaches can accelerate tailored therapies and improve outcomes through precision medicine.

3. Single-Cell Sequencing Techniques

scRNA-seq and scATAC-seq are the primary single-cell sequencing methods. Consumable and instrument costs, plus computational burdens from large, sparse datasets, limit broader adoption [38]. Sensitivity limits—especially for low-expression genes—hinder reliable characterization and constrain utility in kidney research [14,39].

3.1. Single-Cell RNA Sequencing

scRNA-seq begins by isolating individual cells from heterogeneous populations [40,41,42,43,44]. Isolation can be performed by manual pipetting, microfluidic devices, or flow cytometry, with method selection guided by tissue type and study requirements (e.g., throughput, cell size, and viability). These approaches aim to minimize cellular damage and preserve RNA integrity for accurate profiling [45]. After isolation, cellular RNA is extracted, amplified, and sequenced [46]. Extraction typically involves cell lysis followed by purification to remove proteins and other contaminants. Amplification commonly relies on reverse transcription to generate complementary DNA (cDNA) libraries, which are then prepared for sequencing. Sequencing is most often performed on Illumina instruments, while 10x Genomics workflows are widely used for single-cell barcoding and high-throughput library generation. Together, these steps enable high-resolution analysis of cellular heterogeneity, reconstruction of differentiation trajectories, and investigation of disease mechanisms in nephrology [14]. The resulting data support kidney cell atlas construction and the identification of previously unrecognized cell types, expanding understanding of renal cellular composition and function [47].
scRNA-seq also generates transcriptomic profiles that capture gene expression programs relevant to kidney disease and personalized nephrology [34,48,49]. These data can pinpoint aberrant gene expression patterns linked to renal pathology, providing insight into the genetic basis of conditions including CKD and acute AKI [50]. However, scRNA-seq is affected by dropout and limited sensitivity, which can obscure low-abundance transcripts and bias downstream analyses. Computational approaches, including imputation methods, help to mitigate these effects by reducing information loss and improving interpretability [51]. Bioinformatics tools remain essential for processing and analyzing large scRNA-seq datasets, enabling more precise therapeutic targeting and patient-specific treatment strategies in nephrology [52].

3.2. Single-Cell ATAC Sequencing

scATAC-seq profiles chromatin accessibility in individual cells, enabling inference of gene regulatory programs [53]. The workflow starts with single-cell isolation, followed by permeabilization or nuclei isolation to allow transposase mediated tagmentation. Tagmentation fragments DNA at accessible genomic regions and supports efficient library preparation [54,55].
Recent work has broadened scATAC-seq applications. Integrated scRNA-seq and scATAC-seq workflows jointly capture transcriptomic and epigenomic profiles, strengthening cell-state and cell-identity resolution [56]. In parallel, the Human scATAC Corpus provides a large-scale reference that expands tissue coverage and biological diversity in chromatin accessibility datasets [57]. These resources have been applied to dissect disease-associated regulatory programs, including in sarcomatoid clear cell renal cell carcinoma [56].
Analytical progress has addressed key challenges in scATAC-seq interpretation by introducing hierarchical and count based models that improve resolution in accessibility patterns [58]. These methods strengthen inference linking transcription factor binding sites to gene expression and facilitate discovery of regulatory elements [59,60].
In nephrology, scATAC-seq refines understanding of kidney cellular architecture by generating chromatin accessibility maps across nephron cell types, providing a framework to study regulatory dynamics in renal pathologies [61]. These epigenetic insights support nomination of candidate therapeutic targets and advance prospects for personalized interventions in kidney disease [62].

4. Sequencing Platforms

In academic research, 10x Genomics, Standard BioTools (Fluidigm), and Illumina remain leading providers of single-cell sequencing platforms [63,64]. Indeed, 10x Genomics recently introduced the Chromium Flex Assay, an automation-compatible plate-based multiplexing system that supports profiling up to 384 samples and 100 million cells per week, reducing cost and increasing throughput for large studies, including CRISPR screens [65]. The company also launched GEM-X Flex Gene Expression & Universal Multiplex to support mega-scale research at reduced cost and high throughput [65]. The Chromium Xo Instrument and Visium HD XL further expand capabilities in single-cell and spatial transcriptomics (Figure 1) [65].
Standard BioTools has expanded its portfolio with KREX Precision Antibody Profiling, a new addition to the SomaScan suite that complements single-cell analysis workflows [66]. Continued development of its microfluidic-based single-cell genomics platform supports scalable genomics research with high sensitivity [65].
Following its acquisition of Fluent Biosciences, Illumina has integrated PIPseq into its Single Cell 3’ RNA kits to streamline workflows [67]. The Illumina Single Cell 3’ RNA Prep Kit provides an end-to-end option for gene expression profiling, supporting broader adoption of single-cell sequencing [65]. The NovaSeq X Plus platform delivers consistent results with potential cost savings for large-scale projects [65]. These platforms increasingly emphasize scalability, multiparametric integration, and accessibility, reflecting major trends in the field (Table 1).

5. Multiomic Single-Cell Sequencing

Multiomic single-cell sequencing has advanced from single-modality assays to integrated platforms that jointly profile the transcriptome, epigenome, and proteome; spatial measurements can also be incorporated to better capture cellular function and disease progression [60,68,69]. Such coupling improves resolution of cell states and transitions, strengthening disease relevant inference [60]. Recent protocols enable paired measurement of molecular layers (e.g., RNA expression with chromatin accessibility), supporting regulatory inference and finer mapping of cellular heterogeneity in healthy and diseased kidneys [43,70]. By combining complementary data types, single-cell multiome assays can detect signals that single modality methods miss [71]. In kidney disease, multiomic profiling supports biomarker discovery and therapeutic target nomination in conditions including DN and lupus nephritis (LN) [72]. As these datasets expand, the resulting evidence base is clarifying disease mechanisms and informing pathway-directed therapeutic strategies aligned with the complex biology of kidney disorders [73,74,75].

6. Spatial Technologies

Spatial transcriptomics maps gene expression to kidney tissue architecture by coupling spatially resolved RNA measurements with histologic imaging, enabling localization of disease associated programs within intact tissue [76]. Recent work supports higher resolution characterization of cellular neighborhoods and microenvironments implicated in DN and LN [77]. By integrating high throughput expression profiling with spatial context, these methods connect histology to molecular readouts and advance nephrology research and precision medicine efforts [78] (Figure 2).
The spatial landscape is evolving rapidly, with vendors releasing platforms that align spatially indexed molecular profiles with tissue morphology. Bruker, for example, has introduced CellScape [79,80] and the GeoMx Digital Spatial Profiler, which combines high resolution microscopy with region based spatial transcriptomic and proteomic profiling to support complex tissue analysis and microenvironment interrogation [81,82].
Similarly, 10x Genomics offers Visium and Xenium. Visium integrates spatial context with broad transcriptome profiling, whereas Xenium emphasizes higher spatial resolution for more granular mapping of cellular organization [83,84]. Complementary spatial proteomic platforms extend multiplexed protein quantification within tissue sections. Standard BioTools’ Imaging Mass Cytometry enables high-plex, spatially resolved protein measurement using metal-tagged antibody imaging [85]. Akoya Biosciences’ PhenoCycler-Fusion and PhenoImager support detailed immunofluorescence imaging and spatial phenotyping, with detection and visualization of more than 100 biomarkers within tissue sections [86,87]. Collectively, these tools support cohort scale mapping, fine-grained cellular localization, and integrated multiomic interrogation, allowing selection based on study needs and constraints. Overall impact is substantial, supporting deeper definition of spatial molecular landscapes in kidney disease and informing biomarker discovery and therapeutic target nomination [88,89].

7. Other Approaches

7.1. Whole-Genome Sequencing and Whole-Exome Sequencing

Whole-genome sequencing (WGS) and whole-exome sequencing (WES) define the genetic basis of kidney disease by sequencing genomes or exomes to identify mutations and rare variants [90]. Diagnostic yield has improved, including high diagnostic rates in pediatric cohorts using trio-WES and the detection of variants in genes such as COL4A5 and ADCK4 [90]. In large cohorts (e.g., UK Biobank), WGS has expanded the catalog of genetic variants, supporting genetic risk scoring and precision medicine. These studies have also identified more than 1000 potential treatment targets, informing therapeutic strategies [90]. Integrated with single-cell sequencing, WGS and WES sharpen interpretation of kidney biopsy heterogeneity and provide a multidimensional view of molecular dysfunction [91]. Some genome and exome based tests have received FDA Breakthrough Device Designation, reflecting ongoing clinical translation. Together, these approaches support target discovery and enable more personalized targeted interventions for complex kidney disorders [92].

7.2. Expression Quantitative Trait Loci

eQTL analysis associates genetic variants with gene expression differences, clarifying regulatory mechanisms relevant to kidney disease [93]. In nephrology, eQTL mapping prioritizes candidate biomarkers and therapeutic targets by linking variants to expression shifts [94]. High resolution eQTL methods improve localization of variant gene signals and co localization with kidney related traits [95]. Integrating proteomic quantitative trait loci (pQTLs) with eQTLs expands biomarker discovery, including in hypertension related kidney disease [96]. Large scale genetic association studies continue to identify loci associated with kidney function, reinforcing the value of combining GWAS and eQTL evidence to strengthen genetic inference [94]. Coupling eQTL results with single cell sequencing adds cell type context, narrowing mechanistic interpretation in complex conditions such as CKD [94]. Challenges remain, including limited ability to move from associated variants to causal gene function and substantial genetic heterogeneity across kidney disease subtypes [97].

7.3. Single-Nucleus RNA Sequencing

snRNA-seq offers advantages over scRNA-seq, particularly for archived or difficult to dissociate tissues, reducing reliance on fresh specimens [98]. This feature supports retrospective studies of stored kidney biopsies. By profiling nuclear transcripts, snRNA-seq captures nascent RNA and regulatory programs that can be missed with scRNA-seq. Recent work has expanded snRNA-seq utility through multiomic integration, improved protocols for challenging cell types, and broader application in disease settings, including CKD and IgA nephropathy [99,100].
In IgA nephropathy, snRNA-seq has revealed mesangial cell transformation into myofibroblast like states during progression to CKD [99]. In CKD, snRNA-seq has resolved endothelial cell (EC) subpopulations and identified transcriptional changes linked to angiogenesis and immune responses [101]. In diabetic kidney disease (DKD), snRNA-seq has identified proximal tubule (PT) cell subpopulations and signaling pathways associated with inflammation and injury [102].
Collectively, these studies improve detection of cellular heterogeneity, expose rare disease-associated populations, and inform potential targeted therapies [82,103,104].

8. Kidney Disease Research State of the Art

8.1. Lupus Nephritis

LN is an autoimmune kidney disease marked by renal inflammation and immune dysregulation, with interferon (IFN) signaling in renal cells as a major driver [36]. Recent work has generated a high-resolution transcriptomic atlas of the LN kidney using unbiased scRNA-seq and single-cell T cell receptor sequencing (scTCR-seq), identifying CD163+ dendritic cells as a participating immune population in LN [105].
By defining LN-specific biomarkers, including cytokine profiles and immune cell states, scRNA-seq supports discovery of early diagnostic markers and informs pathway-specific therapeutic strategies [36,106,107]. For example, increased IFN- γ and IL-6 expression in immune cells presented therapeutic targets in early studies [108]. More recently, integrative analysis of COL6A3 in LN, combining single-cell transcriptomics and proteomics, has clarified differential expression relative to healthy controls [109]. These findings support target nomination and biomarker development by linking disease-associated cell states to molecular pathways that track with progression.
Multiomic studies integrating single-cell, spatial transcriptomic, and epigenomic approaches further show how clonal hematopoiesis and inflammatory macrophages cooperate to drive glomerular injury in LN [105,110]. Spatial metabolomic profiling across cortical and medullary regions maps metabolic heterogeneity associated with IFN-driven damage, indicating region specific shifts in metabolic pathways in the setting of immune dysfunction [111]. Macrophages remain central in these datasets. More specifically, elevated LGALS9 expression in LN appears distinct from other nephropathies, including IgA nephropathy [112]. These observations nominate LGALS9 as a diagnostic marker and therapeutic target, supporting molecularly guided treatment selection [112]. In parallel, ontology-driven modeling of large patient cohorts narrows molecular signatures for LN subtype stratification and targeted interventions [113,114]. Mesenchymal stem cells (MSCs) have also been reported to attenuate renal injury by modulating region specific immunity defined by distinct macrophage populations, showing the potential of regenerative strategies that target immune pathways to reduce inflammation and promote tissue repair [115]. These works demonstrate the value of single-cell and multiomic integration for precision medicine in LN, enabling improved subtype classification and more targeted treatment strategies.

8.2. Diabetic Nephropathy

DN is the leading cause of CKD and is characterized by progressive fibrosis and inflammation driven by complex cellular interactions [116]. At the molecular level, DN is marked by increased expression of fibrosis-related and inflammatory genes that exacerbate renal injury [116,117]. Single-cell sequencing has clarified these processes by resolving disease-associated cell states, mapping cellular crosstalk, and highlighting potential therapeutic targets [118].
The key pathways in DN include transforming growth factor-beta (TGF- β ) signaling and the renin angiotensin aldosterone system (RAAS). TGF- β contributes to fibrosis by promoting extracellular matrix deposition [119], while RAAS signaling exacerbates renal inflammation and fibrosis [120].
Fibroblast growth factor 1 (FGF1) exerts renoprotective effects and may mitigate fibrosis and inflammation in diabetic settings [121]. This underscores the therapeutic potential of targeting core molecular pathways to slow DN progression. Recent single-cell omics studies have further advanced the understanding of DN by resolving cellular heterogeneity and identifying rare cell populations [122]. Integrative spatial metabolomics and transcriptomics have revealed abnormal lipid handling and fibroblast activation in diabetic kidneys, pinpointing regions of early fibrotic change [111,123].
Single-cell epigenomic analyses have also identified regulatory variants, including those affecting serine beta lactamase-like protein (LACTB) and ACSS2, which modulate fibrotic gene expression and lipid metabolism [124,125,126]. These findings support development of epigenetic therapies that target disease-associated regulatory elements. Multi-center cohort studies correlate these molecular signatures with clinical DN subtypes, supporting precision strategies that target both metabolic dysfunction and extracellular matrix deposition [114,127]. In parallel, recent work emphasizes the role of chromatin accessibility and genetic background, as resolved by single-cell sequencing, in DN pathogenesis [128]. Incorporating these features may improve risk stratification and inform prevention strategies.

8.3. Acute Kidney Injury

AKI is defined as an abrupt decline in kidney function that increases the risk of disease progression and is a precursor to CKD [129]. In AKI, PT gene expression shifts point to impaired protein transport and profibrotic molecular signatures [130]. In COVID-19-related AKI, intensified inflammation and epithelial injury reflect interactions between virus-induced damage and host immune defenses [131]. When PT repair fails, fibrosis advances alongside increased expression of proinflammatory markers, highlighting opportunities for targeted therapies to reduce progression from AKI to CKD [132,133,134].
Recent single-cell work has expanded the understanding of AKI. scRNA-seq integrated with spatial omics maps transcriptional changes at high resolution, defining pathways and cell types involved in injury and repair [135]. Single-cell studies also identify shared epithelial response programs that inform strategies to limit injury and promote recovery [136]. Combining scRNA-seq with epigenomic profiling further nominates biomarkers and therapeutic candidates implicated in the AKI–CKD transition, including CLCNKB, KLK1, and PLEKHA4 [137].
Joint single-cell transcriptomic and chromatin accessibility analyses indicate that clonal hematopoiesis exacerbates AKI through hyperactive inflammatory macrophages and yields early injury biomarkers [105]. Integrated “genetic scorecards” that combine transcriptomic, epigenomic, and clinical features pinpoint epigenetic marks that predict incomplete tubular repair and long-term fibrosis risk [124,125]. The EGF/EGFR axis is a central regulator of renal repair. However, prolonged activation can promote fibrosis, positioning it as a mediator of both recovery and disease progression [138]. Dickkopf-3 (DKK3), a Wnt pathway modulating glycoprotein, also shows promise as an early AKI biomarker [139]. Lastly, large cohort studies that link molecular readouts with psychosocial and treatment variables improve risk stratification and support patient-specific therapeutic approaches [140].

8.4. IgA Nephropathy

IgA nephropathy (IgAN) is the most prevalent form of glomerulonephritis worldwide, characterized by IgA deposition in the glomerular mesangium that drives renal inflammation and scarring [141]. Recent scRNA-seq studies have refined the understanding of IgAN pathogenesis by resolving immune renal cell interactions and highlighting candidate therapeutic targets. Immune profiling in IgAN shows increased activation of B cells and T helper (Th) cells, depletion of cytotoxic T cells, and impaired NK cell function [142]. Monocytes and macrophages also exhibit enhanced antigen-presenting programs, with transcriptional changes consistent with monocyte-to-macrophage differentiation and enrichment of profibrotic pathways, including epithelial-to-mesenchymal transition (EMT) and TGF- β signaling [143].
scRNA-seq has also supported biomarker discovery in IgAN. Immune-cell-derived candidate biomarkers have been proposed using scRNA-seq [144], and additional reports nominate markers such as CYP27B1 and PCK1 [145]. In parallel, single-cell analyses have identified disease-associated immune subtypes and signaling programs that may inform diagnostic refinement and target prioritization [143].
At higher resolution, scRNA-seq and spatial multiomic approaches have clarified immune dysregulation in IgAN, including T cell subpopulations associated with reduced renal function and heightened inflammation [146]. Integrative single-cell and spatial analyses further implicate immunoregulatory gene networks linked to IgA deposition and mesangial injury [147,148]. Chromatin accessibility studies highlight mesangial fibrotic circuits, including targets such as PRSS23 and pathways involving JCHAIN upregulation that may promote local IgA1 dimerization and deposition [43]. The identification of inflammatory and proliferative mesangial cell (MC) subtypes also suggests the Cxcl12/Cxcr4/C3 axis as a candidate pathway for reducing inflammation and fibrosis [149].
Pathway-level analyses using scRNA-seq nominate several signaling routes implicated in IgAN progression, including the Slit–Robo and TGF- β pathways [150,151], as well as Toll-like receptor and cytokine signaling programs [152]. Cross-species integrative studies identify conserved pathological cell states and intercellular crosstalk, including complement and EMT signaling, which support IgAN progression [148]. Cohort-scale integration of these molecular profiles with clinical outcomes can refine patient stratification and accelerate biomarker development for personalized IgAN therapy [153].
These single-cell transcriptomic landscapes [99,143] are increasingly being used to interpret treatment mechanisms and to inform development of more personalized therapeutic strategies for IgAN [144]. Collectively, this body of work strengthens diagnostic criteria, improves patient stratification, and supports development of targeted therapies for IgAN [146]. Table 2 summarizes the recent human studies on non-cancerous kidney disease.

9. Conclusions

Kidney disease is difficult to characterize with bulk assays because cellular heterogeneity obscures cell type specific programs. Single-cell sequencing overcomes this limitation by measuring individual renal and immune cells, resolving pathogenic states, intercellular signaling, and inflammation/fibrosis markers that support biomarker discovery and targeted therapy development. The core modalities include scRNA-seq for transcriptome wide cell-state mapping and atlas construction and scATAC-seq for chromatin accessibility based regulatory inference. Analytical advances are improving interpretation and multimodal integration, but broad adoption remains limited by cost, computational burden, reduced sensitivity for low-abundance transcripts, and dropout-driven sparsity. Commercial and academic platforms now enable higher throughput, multiplexing, automation compatibility, and more consistent cohort scale profiling. Multiomic single-cell assays build on these gains by jointly measuring RNA, chromatin, and proteins to capture regulatory mechanisms and disease-relevant state transitions that single-modality analyses can miss. Spatial approaches complement these methods by mapping molecular readouts onto tissue architecture, linking histology with transcriptomic and proteomic context to localize microenvironmental niches that shape progression and therapeutic response. WGS/WES and eQTL analyses further connect genetic variation to gene regulation and kidney phenotypes, while snRNA-seq enables retrospective disease-specific profiling of archived or hard-to-dissociate biopsies. Across LN, DN, AKI, and IgA nephropathy, integrated single-cell, spatial, and epigenomic analyses refine disease subtyping, reveal immune, epithelial injury, metabolic, and fibrotic programs, and nominate biomarkers and targets that advance precision nephrology.
Most importantly, single-cell sequencing is reshaping kidney disease characterization by resolving cell type specific programs that bulk assays obscure, strengthening biomarker discovery, therapeutic targeting, and precision classification across heterogeneous renal pathologies. The field’s highest-impact trajectory is integration: combining single-cell transcriptomics and epigenomics with multiomic layers and spatial or WSI context preserves tissue architecture, reveals transient cell states and cell-to-cell signaling, and maps molecular changes to microenvironmental context, improving patient stratification and target nomination. Complementary genomic frameworks (WGS/WES and eQTL) and snRNA-seq further expand interpretability and feasibility by linking genetic variation to cell-resolved expression and enabling analysis of archived or difficult-to-dissociate kidney tissue, supporting retrospective and cohort-scale studies. Despite rapid platform and computational advances, high cost, analytical complexity, and sensitivity limits remain the primary constraints on broad adoption, making continued methodological and bioinformatics innovation essential for translating these approaches into routine nephrology research and, ultimately, clinical translation.

10. Future

Four research directions could close the gap between high-resolution molecular profiling and clinically usable kidney disease classification and treatment. First, fragmentation should be reduced by building interoperable multi-center reference atlases that standardize sampling, preprocessing, and annotation while integrating scRNA-seq, scATAC-seq, snRNA-seq, spatial transcriptomics and WSI context, and clinical phenotypes to support reproducible patient stratification. Second, key bottlenecks, cost, low expression sensitivity, and computational burden should be addressed through improved chemistries and scalable algorithms that better model dropout, mitigate batch effects, and enable robust multimodal data fusion, supporting routine use beyond specialized centers. Third, spatial molecular integration should be deepened by developing methods that reliably link cell states and cell–cell signaling programs to tissue architecture and microenvironmental niches so histology and omics jointly identify early lesions and actionable pathways. Lastly, the genomics-to-mechanism pipeline should be strengthened by integrating WGS/WES and eQTL (and related QTL) signals with cell-type-specific regulatory maps to nominate and validate biomarkers and therapeutic targets across major renal diseases, supported by functional follow-up studies that test whether prioritized pathways can be modulated to alter disease trajectories.

Author Contributions

Conceptualization, J.L.A. and A.V.; methodology, J.L.A. and A.V.; validation, J.L.A. and A.V.; investigation, J.L.A., A.V. and C.M.A.; resources, C.M.A.; writing original draft preparation, J.L.A.; writing review and editing, J.L.A.; visualization, A.V.; supervision, C.M.A.; project administration, J.L.A.; funding acquisition, C.M.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research is supported in part by the National Artificial Intelligence Research Resource (NAIRR) Pilot and Microsoft Azure through the CloudBank project, which is supported by National Science Foundation grant #1925001.

Data Availability Statement

No new data were created or analyzed in this study.

Acknowledgments

I would like to thank Robert Pozos, for his time proofreading this publication. I also thank Tyler Graden for his friendly advice, which allowed me the time to complete this manuscript. During the preparation of this manuscript, the authors used AI for the purposes of further proofreading this text. The authors have reviewed and edited the output and take full responsibility for the content of this publication.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Overview of the 10x Genomics single-cell ATAC + gene expression (multiome) sequencing workflow applied to kidney tissue. Fresh kidney tissue is enzymatically dissociated, and nuclei are isolated to enable parallel profiling of chromatin accessibility and transcriptomes from the same single cells. Nuclei are loaded onto the Chromium X platform with barcoded gel beads and oil to generate gel beads in emulsion (GEMs). Within each GEM, accessible chromatin regions are indexed using transposase mediated tagmentation, while mRNA is reverse transcribed and labeled with unique cell specific barcodes and molecular identifiers (UMIs). Following GEM recovery, ATAC and cDNA libraries are separately amplified, fragmented, end-repaired, adaptor ligated, and index PCR-amplified to generate sequencing ready libraries. Sequencing reads are processed through automated pipelines for barcode demultiplexing, chromatin accessibility peak calling, transcript alignment, UMI-based quantification, and construction of paired gene cell and peak cell matrices for integrated epigenomic transcriptomic analysis.
Figure 1. Overview of the 10x Genomics single-cell ATAC + gene expression (multiome) sequencing workflow applied to kidney tissue. Fresh kidney tissue is enzymatically dissociated, and nuclei are isolated to enable parallel profiling of chromatin accessibility and transcriptomes from the same single cells. Nuclei are loaded onto the Chromium X platform with barcoded gel beads and oil to generate gel beads in emulsion (GEMs). Within each GEM, accessible chromatin regions are indexed using transposase mediated tagmentation, while mRNA is reverse transcribed and labeled with unique cell specific barcodes and molecular identifiers (UMIs). Following GEM recovery, ATAC and cDNA libraries are separately amplified, fragmented, end-repaired, adaptor ligated, and index PCR-amplified to generate sequencing ready libraries. Sequencing reads are processed through automated pipelines for barcode demultiplexing, chromatin accessibility peak calling, transcript alignment, UMI-based quantification, and construction of paired gene cell and peak cell matrices for integrated epigenomic transcriptomic analysis.
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Figure 2. Spatial transcriptomics method in CKD. Key steps in using spatial transcriptomics to study CKD, including tissue sectioning, spatial barcoding, sequencing, and computational analysis to reveal spatial gene expression and cellular interactions in the diseased kidney.
Figure 2. Spatial transcriptomics method in CKD. Key steps in using spatial transcriptomics to study CKD, including tissue sectioning, spatial barcoding, sequencing, and computational analysis to reveal spatial gene expression and cellular interactions in the diseased kidney.
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Table 1. Single-cell sequencing platforms characteristics.
Table 1. Single-cell sequencing platforms characteristics.
PlatformCore Technology & FeaturesMain StrengthsMain LimitationsRelevance to Kidney Research
10x GenomicsChromium Flex Assay, GEM-X Flex Gene Expression, Universal Multiplex:Automation-compatible multiplexing system.High-throughput platform that profiles up to 384 samples and 100 M cells/week, reducing costs for large-scale studies (e.g., CRISPR screens).Potential cost barrier as the platform may remain too expensive for labs not conducting large-scale researchHigh capacity and flexibility support large renal studies involving diverse samples.
Standard BioTools (Fluidigm)Microfluidic-based Technology, KREX Precision Antibody Profiling: Enhances single-cell analysis workflows.High sensitivity is critical for detecting low-abundance transcripts, and scalability enables efficient expansion of genomics researchOperational complexity may require specialized expertise to operate microfluidic systems effectivelyHigh sensitivity for detecting low-abundance transcripts makes this approach well suited for detailed kidney studies.
IlluminaPIPseq Integration, NovaSeq X Plus Platform: Streamlined workflows for scRNA-seq.Comprehensive solutions support broader adoption. These cost savings are most impactful for large-scale projectsScale-dependent cost efficiency, with potentially high costs absent large-scale usageConsistent results provide the stability needed for precise renal biopsy analysis
Table 2. Kidney disease studies summary.
Table 2. Kidney disease studies summary.
YearStudy TypeDiseaseMain FindingClinical Implications
2019HumanDNIncreased potassium secretion signature and strong angiogenic signatures [154]Biomarker identification and therapeutic targets for DN intervention.
2019HumanCKDKidney organoids transplant a therapeutic option; maturation of nephron needed [155]Potential in CKD therapy after nephron differentiation improvements
2020Human and MiceCKDTissue and cell types inform understanding of function traits and gene expression at GWAS loci. [156]Basis for mechanistic studies to understand kidney function traits
2019Humankidney diseaseIdentified kidney cell types and subtypes using scRNA-seq [17]Reference for studying renal cell biology and kidney disease
2020HumanCKDIdentified glomerular cell types and injury responses using scRNA-seq [157]Novel disease-related genes and pathways for CKD
2021Humankidney diseasesnATAC-seq and snRNA-seq methods data integration [158]Insights into kidney-disease-injured cell populations
2021HumanCKDCellular origins and differentiation of human kidney myofibroblasts and precursors [159]Identified NKD2 as therapeutic target for kidney fibrosis
2022HumanDNDN leads to reduced accessibility of glucocorticoid receptor binding sites. [160]Glucocorticoid receptor inhibition could mitigate DN effects
2022HumanDNSingle-cell transcriptomics reveals therapeutic effects in DN [34]Guide personalized therapies for DN management
2024Human and MiceKidney DiseaseCell, genomic organization, and gene activity development [161]Understanding kidney development may inform regenerative disease therapies
2024HumanCKDMosaic loss of Y chromosome with age [133]Monitoring Y loss for CKD progressing quantification.
2024HumanCKDComprehensive spatially resolved molecular roadmap of human kidney [43]Targets fibrosis in strategies for managing kidney diseases.
2024HumanDNEnriched genetic variants in the PT and injured PT cells open chromatin regions [162]Insights into genetic pathways influencing DN management
2025HumanDNThree genes critical modulators of macrophage efferocytosis, indicating potential as biomarkers and therapeutic targets [163]Targets macrophage efferocytosis for potential DN treatment.
2025HumanDNTwo biomarkers found in endothelial cells, sirtuin2 (SIRT2) and caspase1 (CASP1) [164]Targets identified pathways and may guide therapy optimizations
2025HumanDNFive genes demonstrated causal relationships with DKD [165]Potential targets identified for DKD diagnostic and therapeutic strategies
2025HumanCKDScorecard for identifying relevant genes, cell types, and therapeutic targets [124]Enhances identification of drug targets for CKD treatments
2025HumanCKDNucleotide variants on chromosome 15 regulate the expression of Wasp homolog associated with actin, membranes, and microtubules in CKD [149]Targeting WHAMM disrupts CKD progression experimental models.
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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

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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

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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

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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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