Genomics and epigenomics

Genomics uses high-throughput sequencing techniques, including targeted next-generation sequencing (NGS) panels, whole-exome sequencing (WES) and whole-genome sequencing (WGS), to identify genetic variants associated with disease susceptibility and pathogenesis. Genome-wide association studies (GWAS) have further enabled the systematic identification of disease-associated loci, linking genotypes to phenotypes (Refs Reference Stanzick, Li, Schlosser, Gorski, Wuttke, Thomas, Rasheed, Rowan, Graham, Vanderweff, Patil, Robinson-Cohen, Gaziano, O’Donnell, Willer, Hallan, Åsvold, Gessner, Hung, Pattaro, Köttgen, Stark, Heid and Winkler16, Reference Hellwege, Velez Edwards, Giri, Qiu, Park, Torstenson, Keaton, Wilson, Robinson-Cohen, Chung, Roumie, Klarin, Damrauer, DuVall, Siew, Akwo, Wuttke, Gorski, Li, Li, Gaziano, Wilson, Tsao, O’Donnell, Kovesdy, Pattaro, Köttgen, Susztak, Edwards and Hung17). However, clinical translation remains limited, as most GWAS signals reside in noncoding regions with unclear regulatory functions, and individual variants exert modest effects. Polygenic risk scores (PRS) address this limitation by aggregating the effects of multiple variants, with genome-wide models improving predictive accuracy. PRS are now available for several kidney diseases, including CKD, DKD, IgA nephropathy and proteinuria (Ref. Reference Khan and Kiryluk18).

Epigenetics investigates how gene expression is regulated beyond the DNA sequence itself, encompassing mechanisms such as DNA methylation, histone modifications and noncoding RNA-mediated regulation (Ref. Reference Ding, Zhang, Yang, Zhang and Li19). These mechanisms are essential for regulating gene expression and may elucidate the elusive heritability observed in renal diseases. By mediating gene–environment interactions, epigenetic regulation substantially influences the progression of diverse kidney conditions, ultimately resulting in phenotypic modifications (Refs Reference Li, Li, Ledru, Xuanyuan, Wu, Asthana, Byers, Tullius, Orlando and Waikar20, Reference Guo, Dong, Liang and Dong21). Aberrant epigenetic modifications have been implicated in AKI, CKD, DKD, glomerulonephritis and renal cell carcinoma, in part through modulation of metabolic pathways and transcriptional programs (Ref. Reference Tan, Jia, Li, Wang and Kantawong22).

Transcriptomics and single-cell omics

Transcriptomics profiles RNA expression, including both coding and non-coding RNAs, linking genomic variation to gene regulation through expression quantitative trait loci. In nephrology, transcriptomics has become a vital tool for understanding the molecular basis of different kidney diseases. By comparing gene expression profiles between healthy and diseased renal tissues, researchers can identify differentially expressed genes that are either upregulated or downregulated throughout disease progression (Ref. Reference Craciun, Bijol, Ajay, Rao, Kumar, Hutchinson, Hofmann, Joshi, Luyendyk, Kusebauch, Moss, Srivastava, Himmelfarb, Waikar, Moritz and Vaidya23).

Advances in single-cell omics significantly deepen the understanding of kidney diseases. Since the advent of single-cell RNA sequencing (scRNA-seq) in 2009, single-cell methodologies have rapidly expanded to include genomics, epigenomics, proteomics and metabolomics. Single-cell transcriptomics, the most advanced method, has redefined kidney cell types and subtypes, resulting in comprehensive cell atlases and the discovery of specific markers. Both scRNA-seq and single-nucleus RNA sequencing (snRNA-seq) methodologies enhance understanding of renal cells in both healthy and diseased states (Ref. Reference Lake, Chen, Hoshi, Plongthongkum, Salamon, Knoten, Vijayan, Venkatesh, Kim, Gao, Gaut, Zhang and Jain24). Techniques such as single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq) strengthen single-cell epigenomics by enabling prediction of regulatory networks and transcription factor activity (Refs Reference Pliner, Packer, McFaline-Figueroa, Cusanovich, Daza, Aghamirzaie, Srivatsan, Qiu, Jackson, Minkina, Adey, Steemers, Shendure and Trapnell25, Reference Schep, Wu, Buenrostro and Greenleaf26). These advances provide a comprehensive view of dynamic cellular states and microenvironmental changes, particularly during disease progression.

Proteomics and metabolomics

As downstream functional layers, proteomics and metabolomics directly bridge static genetic predisposition and dynamic clinical phenotypes. Proteomics characterizes protein abundance, post-translational modifications and interaction networks, whereas metabolomics monitors small-molecule fluctuations reflecting active biochemical pathways. Because the kidney regulates filtration, tubular transport and metabolic homeostasis, circulating and urinary proteo-metabolomic signatures serve as sensitive indicators of both renal impairment and systemic dysregulation. Blood and urine are therefore invaluable, non-invasive matrices for biomarker discovery, disease stratification and longitudinal monitoring (Ref. Reference Mosterd, Verhaar, van den Born, Nieuwdorp and van Raalte27). Clinically, anti-PLA2R autoantibodies in membranous nephropathy represent a landmark biomarker associated with disease activity and treatment response (Ref. Reference Beck, Bonegio, Lambeau, Beck, Powell, Cummins, Klein and Salant28). Additional antigens, such as THSD7A and amyloid A1, further refine prognostic assessment (Ref. Reference Sethi29). Metabolomics complements these insights by capturing dynamic metabolic alterations, with changes in amino acids, lipids and other metabolites that distinguish CKD patients from healthy individuals and predict outcomes (Refs Reference Wang, Ma, Chen, Chen, Liu, Liu and Zhao30, Reference Zhu, Zhang, Shen, Sun, Xia, Chen, Tao and Yu31).

Both fields employ complementary untargeted and targeted strategies. Untargeted discovery relies on high-resolution mass spectrometry (MS) for unbiased, large-scale screening to chart novel pathways. Conversely, targeted strategies focus on predefined molecular panels, leveraging selective or parallel reaction monitoring (SRM/PRM) and isotope internal standards to achieve the high sensitivity and absolute quantification required for clinical assay development. Liquid chromatography-MS (LC–MS) remains the gold standard due to its superior sensitivity and broad chemical coverage. Gas chromatography-MS (GC–MS) provides resolution for volatile molecules, while nuclear magnetic resonance (NMR) offers exceptional reproducibility (Ref. Reference Kalim and Rhee32). Recently, single-cell and spatial omics, alongside high-throughput platforms like Olink, SomaScan and MS imaging, have enabled the localized resolution of low-abundance molecules within specific renal tissue microenvironments (Refs Reference Sharma, Hansen, Susztak, Eberlin, Anderton, Alexandrov and Iyengar13, Reference Schmidt and Rhee33).

Nonetheless, analytical and biological bottlenecks hinder clinical translation. Data preprocessing, normalization and batch-effect correction remain non-standardized, limiting cross-study comparability (Ref. Reference Schmidt and Rhee33). This methodological challenge is compounded by confounding biological factors, as renal function trajectories, diet, medications and dialysis modalities substantially modulate proteo-metabolomic profiles. Furthermore, high-confidence metabolite annotation and protein modification mapping are often restricted by incomplete reference libraries and a scarcity of chemical standards. Overcoming these constraints requires rigorous quality control, standardized workflows and transparent data reporting (Ref. Reference Cabała and Herosimczyk34). Ultimately, integrating these functional layers with genomics, transcriptomics and experimental validation is essential for unravelling the multi-dimensional landscape of kidney diseases.

Spatial omics and multimodal integration

Spatial omics extends molecular profiling into tissue context by mapping genomic, transcriptomic and proteomic data within intact architecture (Ref. Reference Vandereyken, Sifrim, Thienpont and Voet35). This approach enables visualization of cell states, interactions and microenvironmental organization in three-dimensional space. Technologies such as spatial transcriptomics, multiplex fluorescence in situ hybridization and DNA-barcoded antibody labelling provide a more comprehensive understanding of molecular patterns in complex kidney tissues across multiple hierarchical dimensions (Ref. Reference Lee, Lee, Shin, Kim and Kim36). When integrated with single-cell data, spatial omics enables precise cell-type annotation, inference of ligand–receptor interactions and identification of spatial niches. These approaches have been applied to characterize fibrotic microenvironments, spatial metabolic alterations in DKD and region-specific injury and repair programs (Refs Reference Sharma, Hansen, Susztak, Eberlin, Anderton, Alexandrov and Iyengar13, Reference Gorman and Lukowski37). Emerging technologies such as spatial CITE-seq now enable simultaneous co-mapping of transcriptomes and hundreds of protein epitopes at cellular resolution, while spatial ATAC-RNA-seq allows parallel profiling of chromatin accessibility and gene expression within the same tissue section (Ref. Reference Liu, Peng, Xu, Lin, Hu, Chu, Liu, Xu, Ding, Li, Cao and Wu38).

Collectively, multimodal integration provides a powerful framework for linking molecular alterations to tissue architecture, enabling anatomically resolved biomarker discovery and therapeutic targeting. As resolution approaches the single-cell and subcellular level, these technologies will be central to constructing comprehensive kidney atlases and advancing precision nephrology (Ref. Reference Klomp, Mavrommatis, Arcolino, Lantermans, Levtchenko, Kuppe and Westland39).

From single-dimension to multi-dimensional integration

Although single-omic technologies have identified numerous disease-associated pathways and candidate biomarkers, each captures only a limited layer of biological regulation. Multi-omics integration overcomes this limitation by combining complementary molecular information and enabling the reconstruction of multidimensional regulatory networks. By jointly analysing genomic, transcriptomic, proteomic, metabolomic and epigenomic data, researchers can uncover shared pathogenic mechanisms that may not be apparent from single-omics analyses alone.

Accumulating evidence from integrated multi-omics studies indicates that kidney disease progression is driven by several conserved biological programs despite substantial clinical heterogeneity. Among the most consistently identified mechanisms is mitochondrial dysfunction accompanied by impaired fatty acid oxidation (FAO) in proximal tubular epithelial cells (Refs Reference Li and Susztak66, Reference Miguel and Kramann67). Suppression of mitochondrial energy metabolism reduces ATP production, promotes oxidative stress and impairs adaptive tissue repair, thereby facilitating fibrosis and progressive loss of renal function (Ref. Reference Cao, Zhao, Xia, Shi, Zhao, Li and Jiang68). In parallel, immune–metabolic reprogramming has emerged as another central feature, characterized by activation of inflammatory monocyte/macrophage populations, NF-κB/AP-1 signalling and extensive epithelial–immune cell communication (Ref. Reference Moon, Hong, Chung and Baek69).

For instance, one study used multi-omics integration to systematically analyse immune-metabolic reprogramming in AKI induced by ischemia–reperfusion injury (Ref. Reference Zhang, Zhang, Wang, Xiong, Ji and Wang70). The results showed significant dysregulation of nucleotide metabolism in ischaemia-reperfusion injury (IRI), characterized by monocyte-specific activation. Using machine-learning analysis, the study identified Cmpk1 as a key hub gene, with its upregulation closely associated with mitochondrial dysfunction, pyroptosis and inflammatory responses. Spatial transcriptomics confirmed abundant Cmpk1 expression in renal tubulointerstitial monocytes, and in vivo and in vitro functional experiments also demonstrated that inhibiting Cmpk1 could significantly alleviate kidney injury and inflammation. This study revealed the central role of monocyte-driven nucleotide metabolism reprogramming in AKI and provided a potential therapeutic strategy targeting metabolic pathways.

Overall, multi-omics technologies are transforming kidney disease research from descriptive molecular profiling toward an integrated understanding of metabolic dysfunction, immune activation, cellular stress responses and fibrotic remodelling. Such systems-level insights are increasingly revealing common pathogenic networks that transcend individual disease etiologies and may provide a foundation for future precision nephrology.

From single-cell identification to spatial localization

Multi-omics has advanced from cataloguing differential gene expression to building detailed kidney cell atlases and understanding disease-specific cell states. Single-cell technologies allow detailed analysis of renal cell types and their regulatory networks. Recent studies identified major kidney cell populations, including previously unrecognized states and transitional phenotypes (Ref. Reference Lake, Menon, Winfree, Hu, Melo Ferreira, Kalhor, Barwinska, Otto, Ferkowicz and Diep71). Notably, proximal tubular cells show high plasticity in response to injury, underscoring their role in disease progression (Ref. Reference Legouis, Rinaldi, Malpetti, Arnoux, Verissimo, Faivre, Mangili, Rinaldi, Ruinelli, Pugin, Moll, Clivio, Bolis, de Seigneux, Azzimonti and Cippà72). These insights offer a basis for developing cell-specific therapies. Identifying injury-related and maladaptive tubular states suggests that interventions could target harmful cells while sparing repair mechanisms (Ref. Reference Kirita, Wu, Uchimura, Wilson and Humphreys73).

However, dissociation-based single-cell methods lack spatial context, crucial for understanding cell interactions and injury spread. Spatial multi-omics technologies address this by mapping molecular profiles within tissue, revealing region-specific niches and signalling networks. Combined single-cell and spatial analyses have found inflammatory proximal tubular subpopulations in fibrotic areas in CKD, driven by HNF4α inactivation and AP-1/NF-κB activation, promoting disease via immune recruitment and fibroblast activation (Ref. Reference Reck, Baird, Veizades, Sutherland, Bell, Hur, Cairns, Janas, Campbell, Nam, Yang, Schurman, Williams, O’Sullivan, Beniazza, Corsinotti, Bellamy, Hughes, Laird, Denby, Chandra, Ferenbach and Conway74). These insights inform therapies, such as targeting these signalling pathways, modulating epithelial–immune crosstalk or removing senescent, inflammatory cells to reduce fibrosis.

Ultimately, integrating these approaches shifts the focus from descriptive atlases to mechanism-based, microenvironment-targeted therapies, bridging molecular discovery with clinical application.

From correlation-based description to causal inference

Traditional omics research is limited by reverse causality and confounding bias, making it difficult to determine whether biomarkers are causes or effects of disease. Multi-omics approaches are expected to overcome these limitations. For instance, integrative analyses of genotype, transcriptome and metabolome data have enabled the construction of causal metabolic networks in CKD, identifying metabolites such as N-acetylisoleucine and decenedioic acid as potential drivers of disease via metabolic reprogramming. Mechanistic investigations suggest that these metabolites drive renal injury by modulating energy metabolism and oxidative stress via NAT8- and ACOT1/4-mediated metabolic reprogramming. Similarly, multi-omics studies have identified TNFRSF1A as a causal mediator of immune remodelling in diabetic nephropathy, supported by Mendelian randomization and experimental validation (Ref. Reference Martinez Leon and Susztak75). These approaches provide a stronger basis for therapeutic target prioritization and mark a shift toward mechanism-driven research.

In summary, the paradigm shifts described above are profoundly changing the research landscape in kidney diseases. Across studies, a more integrative picture is emerging: CKD and AKI are repeatedly associated with mitochondrial dysfunction, impaired fatty acid oxidation, purine and nucleotide metabolic remodelling, amino-acid and uremic-solute accumulation, immune-metabolic activation and extracellular-matrix remodelling. These convergent pathways are more informative than isolated biomarkers because they connect molecular alterations to tubular stress, inflammatory niches, fibrosis and clinical progression. A structured comparison of representative multi-omics studies is provided in Table 1. However, key challenges remain, including variability in sample quality, lack of standardization, limited longitudinal datasets and the need for robust cross-cohort validation. Addressing these issues will be essential to translate multi-omics insights into clinically actionable tools and advance precision nephrology.

Table 1.

Representative multi-omics studies in kidney disease research

Table 1. Long description

The table consists of five columns: Study (year), Disease type, Omics layers, Key findings, and Clinical significance.

* Dumoulin et al. (Ref. 76): Focuses on D K D using single-cell spatial transcriptomics and sn R N A-seq. It identified a B-cell-enriched subgroup and developed a 14-protein plasma biomarker panel for non-invasive detection.

* Liu et al. (Ref. 77): Focuses on C K D using G W A S, bulk R N A-seq, and single-nucleus multiome. It identified 1026 independent loci and provides a Kidney Disease Genetic Scorecard.

* Zhang et al. (Ref. 70): Focuses on A K I using bulk transcriptomics, metabolomics, sc R N A-seq, and spatial transcriptomics. It identified C m p k 1 as a central hub gene and therapeutic target.

* Abedini, A. et al. (Ref. 78): Focuses on C K D using sn R N A-seq, sc R N A-seq, sn A T A C-seq, and spatial transcriptomics. It revealed four distinct microenvironments including the fibrotic microenvironment (F M E).

* Lake et al. (Ref. 71): Focuses on healthy and injured kidneys (A K I or C K D) using sn R N A-seq, spatial transcriptomics, 3 D imaging, and epigenomics. It defined 51 main cell types and 28 injury-altered states.

* Li D et al. (Ref. 79): Focuses on D K D using proteomics and single-cell R N A-seq. It identified A K R 1 A 1 as a molecular hub for cellular dysfunction.

* Li et al. (Ref. 20): Focuses on kidney anatomy using simultaneous single-cell A T A C or R N A sequencing and spatially resolved metabolomics. It identified P L E K H A 1 as a disease marker.

* Wang et al. (Ref. 80): Focuses on Lupus nephritis (L N) using single-cell multiome, spatial transcriptomics, and G W A S. It identified V C A M 1-expressing proximal tubule cells as part of an L N-specific inflammatory niche.

* Castro et al. (Ref. 81): Focuses on C K D using transcriptomics, proteomics, metabolomics, and sn R N A-seq. It discovered two molecularly distinct patient groups with different survival curves.

* Si et al. (Ref. 82): Focuses on C K D using G W A S, T W A S, and proteomics. It identified 32 causal proteins for C K D via Mendelian randomization.

* Huang et al. (Ref. 83): Focuses on A K I using proteomics and metabolomics. It proposed the I C 3 diagnostic model for early screening.

AKI, acute kidney injury; ATAC-seq, assay for transposase-accessible chromatin using sequencing; CKD, chronic kidney disease; DKD, diabetic kidney disease; GWAS, genome-wide association study; LN, lupus nephritis; scRNA-seq, single-cell RNA sequencing; snATAC-seq, single-nucleus ATAC sequencing; snRNA-seq, single-nucleus RNA sequencing; TWAS, transcriptome-wide association study.

Multi-omics enhances CKD mechanistic research

Integrative multi-omics approaches offer substantial advantages over single-omics or conventional methods by enabling the construction of dynamic, multidimensional biochemical interaction networks. These frameworks allow the tracking of temporal molecular interactions, thereby moving beyond static molecular connectivity maps.

Mitochondrial dysfunction and impaired fatty acid oxidation

Proximal tubular epithelial cells (PTECs) are highly energy-dependent and rely primarily on mitochondrial FAO for ATP production (Refs Reference Miguel and Kramann67, Reference Doke and Susztak84). Across diverse CKD etiologies, including DKD, hypertensive nephropathy and glomerular disorders, multi-omics studies consistently identify impaired mitochondrial FAO as a central feature of disease progression. In CKD, persistent mitochondrial dysfunction drives oxidative stress, fibrosis and metabolic reprogramming, with epigenetic mechanisms regulating genes critical for mitochondrial homeostasis, such as PMPCB and TFAM (Reference Pavlović, Križanac, Kumrić, Vukojević and Božić85).

Metabolic reprogramming and lipotoxicity are direct consequences of FAO failure. Metabolomic studies reveal accumulation of short- and middle-chain acylcarnitines and disrupted tricarboxylic acid (TCA) cycle intermediates, reflecting compromised mitochondrial energy metabolism (Refs Reference Cuevas-Delgado, Miguel, Rupérez, Lamas and Barbas86, Reference Miguel, Tituaña, Herrero, Herrero, Serra, Cuevas, Barbas, Puyol, Márquez-Expósito, Ruiz-Ortega, Castillo, Sheng, Susztak, Ruiz-Canela, Salas-Salvadó, González, Ortega, Ramos and Lamas87). Concurrently, defective FAO drives PTECs toward inefficient glycolysis, a shift mediated by HIF-1α and accompanied by upregulation of glycolytic enzymes such as HK2. This compensatory switch, however, occurs at the cost of ATP depletion, lactate accumulation and intracellular acidosis. Moreover, aberrantly elevated expression of fatty acid transporters CD36 and FATP2 promotes excessive free fatty acid uptake, while reduced ABCA1-mediated cholesterol efflux fosters lipid droplet accumulation and lipotoxicity (Refs Reference Mitrofanova, Merscher and Fornoni88, Reference Liu, Peng, Zhang, Tang, Zhao, Yan, Zhao, Huang, Lan and Li89). The resulting lipotoxicity exacerbates mitochondrial dysfunction and oxidative stress, establishing a self-reinforcing pathogenic cycle. Activation of the FXR pathway has been shown to mitigate these effects by downregulating SREBP-1c, stearoyl-CoA desaturase-1 and acetyl-CoA carboxylase, while upregulating PPARα, CPT1a, PGC-1α, uncoupling protein-2 (UCP-2) and lipoprotein lipase (LPL), providing renal protection (Refs Reference Mitrofanova, Merscher and Fornoni88, Reference Wang, Jiang, Shen, Adorini, Pruzanski, Gonzalez, Scherzer, Lewis, Miyazaki-Anzai and Levi90).

Mitochondrial injury also links metabolic collapse to inflammation and fibrosis. Excess reactive oxygen species (ROS) trigger regulated cell death pathways, including necroptosis via RIPK1/RIPK3/MLKL, pyroptosis via NLRP3 inflammasome and Gasdermin D and ferroptosis via lipid peroxidation (Refs Reference Doke and Susztak84, Reference Wu, Lan, Jiao, Wang, Deng, Chen, Zeng and Li91). Severe damage leads to mitochondrial DNA (mtDNA) release, activating innate immune signalling through cGAS–STING and promoting pro-fibrotic cytokine production (Ref. Reference Chung, Dhillon, Huang, Sheng, Shrestha, Qiu, Kaufman, Park, Pei and Baur92).

In summary, integrative multi-omics evidence positions mitochondrial FAO failure as an upstream metabolic switch in CKD progression. By depleting energy supply, generating ROS and mtDNA signals and activating inflammatory cell death pathways, this defect transforms PTECs into central pro-inflammatory and pro-fibrotic effector cells, thereby linking metabolic injury to downstream pathological events.

Epigenetic memory and persistent pathological transcriptional programs

A defining feature of CKD is its continued progression despite effective control of initiating insults such as hyperglycaemia or hypertension. Recent evidence shows that epigenetic regulation plays a crucial role in creating and maintaining this “disease memory” (Ref. Reference Liang and Wan93). ATP-dependent chromatin remodelling complexes have been linked to essential pathogenic processes such as fibroblast activation and inflammation (Ref. Reference Eustermann, Patel, Hopfner, He and Korber94). Significantly, changes in chromatin state not only affect immediate transcriptional responses but also create long-lasting transcriptional memory, allowing disease-related genes to remain active (Refs Reference Gisch, Brennan, Lake, Basta, Keller, Melo Ferreira, Akilesh, Ghag, Lu and Cheng95, Reference Mimura, Chen and Natarajan96). Furthermore, chromatin remodelling interacts extensively with DNA methylation and histone modifications, forming a complex regulatory network that drives CKD progression (Ref. Reference Sasaki and Masaki97). These insights offer an epigenetic explanation for CKD’s irreversibility and point to potential therapeutic targets involving epigenetic modifications.

Extracellular matrix remodelling and fibrotic niches

Despite substantial etiological heterogeneity, progressive fibrosis remains the final common pathway leading to kidney failure. Recent integration of single-cell transcriptomics, spatial transcriptomics, proteomics and chromatin accessibility profiling has revealed that extracellular matrix (ECM) remodelling arises from coordinated interactions among injured tubular epithelial cells, activated fibroblasts and pericytes, endothelial cells and infiltrating immune populations (Refs Reference Ledru, Wilson, Muto, Yoshimura, Wu, Li, Asthana, Tullius, Waikar, Orlando and Humphreys98, Reference Xuanyuan, Wu, Sundaramoorthi, Isnard, Chen, Rahmani and Humphreys99).

Among the pathways implicated, TGF-β signalling consistently emerges as the master regulator of fibrogenesis. Single-cell epigenomic and spatial proteomic analyses demonstrate that ECM stiffening promotes formation of YAP/TAZ–Smad3 complexes, which activate profibrotic enhancers and drive expression of collagen isoforms, fibronectin and α-smooth muscle actin (Ref. Reference Jiang, Zhang, Li, Miao and Zhao100).

Notably, spatial atlases of human kidney diseases have identified four major tissue microenvironments – glomerular, tubular, immune and fibrotic compartments. Among these, fibrotic niches provide prognostic information beyond conventional histopathology and more accurately reflect disease progression. Integration of spatial transcriptomics with ligand–receptor interaction analyses further highlights extensive intercellular communication networks that coordinate inflammatory and fibrotic responses, offering new mechanistic insights and potential therapeutic targets in CKD (Ref. Reference Abedini, Levinsohn, Klötzer, Dumoulin, Ma, Frederick, Dhillon, Balzer, Shrestha, Liu, Vitale, Bergeson, Devalaraja-Narashimha, Grandi, Bhattacharyya, Hu, Pullen, Boustany-Kari, Guarnieri, Karihaloo, Traum, Yan, Coleman, Palmer, Sarov-Blat, Morton, Hunter, Kaestner, Li and Susztak78).

Multi-omics facilitates biomarker discovery in CKD

Ideal biomarkers should be non-invasive, easy to detect and reproducible. Currently, clinical biomarkers for kidney injury are limited. Traditional indicators like serum creatinine and proteinuria are not sensitive enough for early detection. Estimated glomerular filtration rate (eGFR) based on cystatin C is not affected by muscle mass or race, is more sensitive in detecting early decline in kidney function and is closely related to microvascular complications and long-term mortality (Refs Reference Gottlieb, Estiverne, Tolan, Melanson and Mendu101, Reference Lees, Rutherford, Stevens, Chen, Scherzer, Estrella, Sullivan, Ebert, Mark and Shlipak102). However, it is more costly than creatinine and is easily affected by inflammation, making it less commonly used in clinical practice.

KIM-1 and NGAL are important markers for AKI. Currently, [TIMP2]*[IGFBP7] (NephroCheck) and NGAL have both been approved by the FDA for AKI (Ref. Reference Martin-Cleary, Sanz, Avello, Sanchez-Niño and Ortiz103). In CKD, these markers also show increased levels in kidney gene expression and in urine (Ref. Reference Castillo-Rodriguez, Fernandez-Prado, Martin-Cleary, Pizarro-Sánchez, Sanchez-Niño, Sanz, Fernandez-Fernandez and Ortiz104). For example, NGAL levels in serum and urine are related to eGFR and can independently predict CKD progression and cardiovascular complications (Ref. Reference Romejko, Markowska and Niemczyk105); KIM-1 is an important predictor of CKD progression and adverse renal outcomes. Studies have shown that high plasma KIM-1 levels are associated with a rapid decline in eGFR over 16 years and an increased risk of CKD (Ref. Reference Schulz, Engström, Nilsson, Almgren, Petkovic, Christensson, Nilsson, Melander and Orho-Melander106). Although many new biomarkers have emerged in recent years, the urinary peptidomics biomarker CKD273 remains one of the most extensively studied CKD biomarkers and has received FDA support (Ref. Reference Pontillo and Mischak107). The CKD273 classifier is a panel of 273 urinary peptides that enables early detection of CKD and prognosis of progression (Ref. Reference Good, Zürbig, Argilés, Bauer, Behrens, Coon, Dakna, Decramer, Delles and Dominiczak108). Multiple studies have shown that in individuals with normal eGFR, CKD273 can effectively assess and predict CKD progression, independent of confounding factors such as age and gender (Ref. Reference Zürbig, Mischak, Menne and Haller109). This conclusion has been validated in prospective clinical trials, demonstrating that CKD273 can identify patients at higher risk of progression with A2 proteinuria (Ref. Reference Tofte, Lindhardt, Adamova, Bakker, Beige, Beulens, Birkenfeld, Currie, Delles and Dimos110).

Despite the excellent performance of these biomarkers in research, except for cystatin C, they have not yet been widely applied in clinical practice. The main barriers include the lack of standardized cutoff values, the absence of large-scale prospective validation and limited evidence of cost-effectiveness. Precise risk stratification is difficult to achieve using a single biomarker, but combinations of multiple markers or systems biology approaches are expected to provide more comprehensive and less biased risk identification methods. For example, AKR1A1 is a biomarker and regulator in DKD, with evidence across omics layers (Ref. Reference Li, Hsu, Palmer, Liu, Choi, Murea, Parks, Bowden, Freedman and Ma79). Transcriptomics shows decreased AKR1A1 in the renal tubular epithelial cells of DKD patients, confirmed at the protein level. Metabolomic profiling indicated that a deficiency in AKR1A1 leads to the buildup of succinic semialdehyde and glucuronic acid. GWAS identified a promoter variant (rs2229540) that reduces AKR1A1 transcription. Mechanistic evidence further supports a role for AKR1A1 in carcinogenesis, antioxidant activity and glucose metabolism, supporting its causal role in DKD. Though direct genotype-metabolite links need confirmation, findings collectively identify AKR1A1 as a biomarker and causal regulator in DKD.

Another representative example is endogenous adenine, which has emerged as both a predictive biomarker and a potential mediator of CKD progression. Metabolomic analysis of the CRIC cohort found that adenine is significantly correlated with progressive renal failure (Ref. Reference Zhang, Fuhrer, Ye, Kwan, Montemayor, Tumova, Darshi, Afshinnia, Scialla, Anderson, Porter, Taliercio, Rincon-Choles, Rao, Xie, Feldman, Sauer, Sharma and Natarajan111). Spatial metabolomics further showed that adenine is present at low levels in healthy kidney tissue but is markedly enriched in DKD patients, particularly in glomerular matrix, atrophic tubules and sclerotic vessels, suggesting its potential involvement in fibrosis-related pathological processes (Ref. Reference Sharma, Zhang, Hansen, Bjornstad, Lee, Menon, Hejazi, Liu, Franzone, Looker, Choi, Fernandez, Venkatachalam, Kugathasan, Sridhar, Natarajan, Zhang, Sharma, Kwan, Waikar, Himmelfarb, Tuttle, Kestenbaum, Fuhrer, Feldman, de Boer, Tucci, Sedor, Heerspink, Schaub, Otto, Hodgin, Kretzler, Anderton, Alexandrov, Cherney, Lim, Nelson, Gelfond and Iyengar112). Mechanistically, integrated multi-omics analyses indicate that abnormal accumulation of adenine mainly results from an imbalance in purine salvage and polyamine metabolism in proximal tubule cells, rather than simply increased synthesis. This metabolic reprogramming not only reflects cellular metabolic disorder but may also drive downstream pathological processes. Functionally, adenine is thought to promote ribosome biogenesis by activating the mTORC1–S6 kinase signalling pathway, thereby inducing cell hypertrophy and fibrosis (Ref. Reference Liu and Sabatini113). In clinical translation, urinary adenine levels (or adenine/creatinine ratio) have been proposed as a non-invasive biomarker for predicting renal function decline and adverse outcomes, potentially independent of traditional indicators (Ref. Reference Sharma, Zhang, Hansen, Bjornstad, Lee, Menon, Hejazi, Liu, Franzone, Looker, Choi, Fernandez, Venkatachalam, Kugathasan, Sridhar, Natarajan, Zhang, Sharma, Kwan, Waikar, Himmelfarb, Tuttle, Kestenbaum, Fuhrer, Feldman, de Boer, Tucci, Sedor, Heerspink, Schaub, Otto, Hodgin, Kretzler, Anderton, Alexandrov, Cherney, Lim, Nelson, Gelfond and Iyengar112).

Overall, adenine exemplifies a class of metabolic molecules with both mechanistic and biomarker potential. Its study highlights the critical role of multi-omics integration in connecting metabolic reprogramming, tissue microenvironment and disease progression, providing important directions for precise stratification and targeted intervention in CKD. Table 2 outlines the main functions, clinical uses and limitations of these biomarkers, highlighting their potential for integration into clinical practice.

Table 2.

Potential biomarkers in kidney diseases – mechanisms, clinical significance, limitations and validation status

Table 2. Long description

The table contains 17 rows of biomarkers with the following data:

* C K D dash 273: For C K D or D K D. A 273-peptide urinary classifier for collagen degradation and inflammation. Identifies high-risk individuals with normal albumin. Limitations include high cost and mass spectrometry requirements. Validation: F D A letter of support.

* Cystatin C: For A K I or C K D. Produced by nucleated cells, filtered by glomerulus. Better than creatinine for early A K I. Affected by thyroid and steroids. Validation: In clinical use.

* N G A L: For A K I. Secreted by tubular cells. Warns 24 to 48 hours before creatinine rise. Elevated in systemic infection. Validation: F D A approved.

* K I M dash 1: For A K I or C K D. Transmembrane glycoprotein in proximal tubules. Predicts C K D progression. Also upregulated in R C C. Validation: Clinical validation phase.

* su P A R: For A K I or C K D. Soluble receptor released during inflammation. Predicts A K I onset and C K D progression. Poor specificity. Validation: Clinical validation phase.

* F G F dash 23: For C K D. Phosphate-regulating hormone. Early marker of C K D dash M B D. Influenced by iron deficiency and inflammation. Validation: Clinical validation phase.

* D K K 3: For C K D, A K I, or Alport syndrome. Tubule-derived Wnt antagonist. Marker of fibrosis. High E L I S A cost. Validation: Clinical validation phase.

* U M O D (Uromodulin): For C K D or A D T K D. Reflects tubular mass. Low levels indicate A K I risk. Affected by hydration status. Validation: Widely recognized for genetic diagnosis.

* T I M P dash 2 dot I G F B P 7: For A K I. Reflects G 1 cell cycle arrest. High specificity for severe A K I. Applicable to I C U populations. Validation: F D A approved (NephroCheck).

* alpha 1 dash Microglobulin: For C K D or D K D. Low-molecular-weight protein. Early marker of tubular injury. Affected by liver function. Validation: In clinical use.

* L dash F A B P: For A K I, C K D, or D K D. Released during oxidative stress. Predicts A K I after cardiac surgery. Not kidney-specific. Validation: Approved in Japan.

* Urinary E G F: For C K D, A K I, or D K D. Reflects functional tubular mass. Low levels associated with function decline. Requires standardized detection. Validation: Clinical validation phase.

* W F D C 2: For C K D or Fibrosis. Serine protease inhibitor. Potential therapeutic target. Limited clinical data. Validation: F D A authorized for KidneyIntelX.

* G D F 15: For A K I or C K D. T G F dash beta superfamily member. Associated with mortality risk. Not kidney-specific. Validation: Clinical research phase.

* Urinary C K 20: For A K I to C K D transition. Cytokeratin 20 upregulated after injury. Predicts major adverse kidney events. Needs generalizability validation. Validation: Clinical research phase.

* A K R 1 A 1: For D K D. Aldo-keto reductase family member. Protective role in African American patients. Limited ethnic group validation. Validation: Clinical research phase.

* A N G P T 2: For A K I and C K D. Endothelial activation marker. Correlates with sepsis-associated A K I severity. Not kidney-specific. Validation: Clinical research phase.

Note: The table summarizes 15 potential biomarkers for kidney disease, with each row listing the associated disease, mechanism, clinical significance, limitations, validation status and references.

ADTKD, Autosomal Dominant Tubulointerstitial Kidney Disease.

The role of multi-omics in advancing AKI mechanistic studies

Heterogeneous injury phenotypes of proximal tubular cells. PTECs are the most vulnerable cell type in AKI, relying heavily on fatty acid oxidation, containing many mitochondria and being highly susceptible to ischemia, nephrotoxins and inflammation (Ref. Reference Sun, Weng, Zhou, Chen, Zhang and Nie136). However, not all PTECs respond the same way to injury. Single-cell studies have shown that PTECs display a range of injury types after AKI. An integrated multi-omics analysis of human transcriptomic data identified three key genes: EGF, VCAM1 and SUGCT. Among these, SUGCT expression was associated with immune cell infiltration and mitochondrial dysfunction and was specifically enriched in PTECs (Ref. Reference Sun, Weng, Zhou, Chen, Zhang and Nie136). This connects mitochondrial metabolic issues with changes in the immune environment, suggesting PTECs are not just passive victims but active players in immune modulation.

In sepsis-related AKI, a combined bulk RNA-seq and scRNA-seq analysis identified Hmox1 as a marker of PTEC injury. Its increased expression in specific PTEC subpopulations supports its potential as a diagnostic marker (Ref. Reference He, Zhang, Li, Liu and Peng137). Single-cell analysis has also shown high levels of PECR and TP53I3 in PTECs and loop of Henle cells, along with associations with lactylation-related pathways, indicating that metabolic changes may have previously unrecognized roles in AKI development (Ref. Reference Jiang, Mai, Li, Zhou, Chen, Zou and Yu138).

Fine-scale resolution of immune cell subsets. AKI pathogenesis and progression heavily rely on immune system activity, but immune cell diversity has long been difficult to characterize due to technical limitations. Single-cell technologies are revolutionizing this area. A recent study combined scRNA-seq datasets from five etiological models across multiple time points to create the first single-cell atlas of neutrophils in AKI. It identified nine distinct subsets – six of which were new to AKI and three not previously reported in the literature (Ref. Reference Liu, Li, Mao, Lu, Cheng, Xin, Jiang, Wang, Sun, Xu, Zheng, Xiang, Chen, Wang, Liu and Yang139). These subsets showed significant functional differences in phagocytosis, antigen presentation, inflammatory control, vascular regulation and fibrosis. Spatial neighbourhood analysis revealed a neutrophil-centred microenvironment characterized by specific interactions between neutrophils and the thick ascending limb of the loop of Henle, thereby uncovering a new form of intercellular communication.

Zhang et al. combined scRNA-seq and spatial transcriptomics to map the dynamic and spatial distribution of renal macrophages in an AKI-to-CKD progression model (Ref. Reference Zhang, Tang, Wang, Wen, Feng, Yin, Jiang, Zhang, Li, Wu, Wu, Song, Crowley, Lan, Lv and Liu140). They identified functional differences between resident and monocyte-derived macrophages and discovered a new extracellular matrix-remodelling macrophage subset that interacts with fibroblasts through IGF signalling pathways. Single-cell omics have significantly transformed our understanding of macrophage roles in AKI, highlighting new therapeutic targets to prevent AKI and its chronic progression.

Molecular mechanisms of AKI-to-CKD transition. Post-AKI renal repair is highly heterogeneous: some patients achieve complete recovery, whereas others develop maladaptive repair characterized by inflammation, fibrosis and progressive decline in kidney function leading to CKD. Understanding the molecular factors behind these pathways is crucial to preventing the AKI-to-CKD transition.

A landmark study integrating lineage tracing with single-nucleus multi-omics provides key insights into this process. By labelling early-proliferating cells following AKI with Ki67 and performing joint snRNA-seq and snATAC-seq on the same nuclei, the authors generated a high-resolution dataset comprising over 80,000 nuclei (Ref. Reference Gerhardt, Koppitch, van Gestel, Guo, Cho, Wu, Kirita, Humphreys and McMahon141). This analysis revealed that AKI induces a widespread cell cycle response across both epithelial and non-epithelial compartments; however, the subsequent repair trajectories diverge markedly. Notably, a subset of pro-inflammatory tubular cells persists for months, indicating ongoing pathology. Differences in gene regulation and transcription factors distinguish adaptive from maladaptive repair, with maladaptive cells showing sustained inflammatory signals.

Importantly, these findings indicate that even morphologically recovered kidneys retain persistent transcriptional and epigenetic alterations, which may predispose to chronic disease. This supports early intervention to reprogram maladaptive cell states before fibrosis becomes irreversible. Epigenetic regulation further contributes to this transition. DNA methylation analyses reveal key genes, including Atp1a3, Ncf1, Lpl and Slc27a2, associated with renal outcomes, with pathway enrichment analyses highlighting PPAR signalling and metabolic regulation (Ref. Reference Zheng, Guo, Chen, Zhuang, Gong, Fu, Liang, Xu, Wang, Wang, Chen and Cai142). Pharmacological inhibition of DNA methyltransferases reduces fibrosis and restores lipid metabolism in models, showing that epigenetic changes are modifiable drivers of disease (Ref. Reference Zheng, Guo, Chen, Zhuang, Gong, Fu, Liang, Xu, Wang, Wang, Chen and Cai142). Complementing these observations, cross-species multimodal atlas studies have further demonstrated that distinct injury-related cell states are organized within specific niches and shift during disease. Gene networks identify critical transcription factors (e.g., SOX4, SOX9, NFKB1) that drive these transitions, linking them to outcomes (Ref. Reference Lake, Melo Ferreira, Hansen, Menon, Basta, Thiessen Philbrook, Reinert, Fallegger, Lagwankar and Chen143). These approaches also uncover biomarkers predictive of progression, aiding risk assessment and treatment timing.

Overall, sustained, spatially organized transcriptional and epigenetic reprogramming underpins the AKI-to-CKD transition. Multi-omics enhances understanding, identifies mechanisms and highlights opportunities for intervention.