Single-cell RNA sequencing (scRNA-seq), as a cutting-edge branch of transcriptomics, focuses on the types, quantities, structures and functions of all transcript products (mRNA and non-coding RNA) within an organism at the single-cell level under specific time points and conditions. Its core advantage lies in overcoming the 'averaging' limitations of traditional bulk transcriptomics, enabling precise analysis of expression patterns across diverse cell types within complex renal tissue. This makes scRNA-seq a foundational platform for cell identity identification and heterogeneity analysis in kidney research, providing essential molecular clues for subsequent exploration of cellular regulatory potential, tracking cell fate trajectories, and deciphering intercellular interactions (5,6).

The primary workflow of scRNA-seq includes sample collection and processing, single-cell suspension preparation and isolation, RNA extraction and reverse transcription, library construction, sequencing and data analysis (7). Among these steps, the preparation and isolation of single-cell suspensions are critical for enhancing efficiency and throughput. Iterations of this technology have significantly advanced kidney research: Early microscopy or laser capture methods allowed the analysis of only a few cells, failing to capture kidney cell diversity; subsequent FACS and MACS technologies enabled preliminary enrichment of kidney cell subpopulations through automated sorting; and in October 2016, the 10x Genomics Chromium platform emerged. Leveraging microfluidic chips, barcoding, and unique molecular identifier (UMI) technology has enabled high-throughput, efficient, and cost-effective sequencing of kidney cells. The 10x Genomics Chromium platform has been widely applied in constructing kidney single-cell atlases, mapping cellular developmental trajectories, studying rare cells, and exploring immune mechanisms (8) and has become the mainstream commercial platform for kidney single-cell research (9-11). In 2024, the scGRO-seq technology developed by Mahat et al (12) employed click chemistry to capture nascent RNA, enabling precise analysis of real-time gene expression regulation in kidney cells. This addresses the limitations of scRNA-seq in studying cellular transcriptional dynamics, providing a new tool for elucidating transient regulatory mechanisms in kidney cell function.

SCP, an innovative branch of proteomics, involves the systematic investigation of the expression levels, post-translational modifications, interaction networks, and functional mechanisms of proteins expressed in individual cells, tissues, or organisms under specific temporal and conditional circumstances. It serves as a pivotal tool for deciphering the molecular foundations of biological activities. Its core value lies in validating transcriptional regulatory features identified by scRNA-seq, revealing the functional implementation of cellular regulatory potential, bridging the 'transcription-translation disconnect' gap between transcriptomes and proteomes, and simultaneously enabling the discovery of kidney cell-specific protein biomarkers and the elucidation of protein interaction networks. This provides direct evidence for elucidating kidney cell functional outputs (13,14). Iterative advancements in SCP technology have further enhanced its applicability in renal research. In 2018, Budnik et al (15) developed SCP by mass spectrometry (MS), which integrates isotope labelling and carrier proteomics to achieve quantitative analysis of more than 1,000 proteins in mouse embryonic stem cells, laying the technical foundation for renal cell proteomics research. In 2025, Ye et al (16) introduced the innovative SCP technology Chip-Tip. By integrating a novel chip with a liquid chromatography system, it unified cell sample preparation, protein extraction and MS analysis. This approach increased the number of identifiable proteins in a single kidney cell from ~2,000 to 5,000-6,500, significantly enhancing the detection sensitivity for protein profiles in rare kidney cell subpopulations. Subsequently, the team developed single-cell pulsed stable isotope labelling, which for the first time enabled precise analysis of protein turnover rates in individual kidney cells. This breakthrough provides a novel tool for investigating dynamic protein regulatory mechanisms during renal cell damage repair, such as the synthesis and degradation of proteins involved in tubular epithelial cell repair, which represents a major advance in SCP.

Metabolomics, a vital branch of systems biology, involves the quantitative analysis of the dynamic changes in all small-molecule metabolites (such as amino acids, carbohydrates, lipids and nucleotides) within an organism. This approach reveals the comprehensive metabolic network landscape under specific physiological or pathological conditions, elucidating the regulatory mechanisms of life activities (17,18). SCMET, an innovative branch of metabolomics, primarily employs MS, nuclear magnetic resonance, combined with microfluidics, laser capture microdissection, and other techniques to isolate and extract small-molecule metabolites from individual cells. Its core advantage lies in capturing metabolic heterogeneity among renal cells, deciphering the metabolic phenotypes ultimately resulting from cellular regulatory potential, elucidating the functional output characteristics of renal cells at the metabolic level, and providing a microscopic perspective for studying renal physiological metabolic mechanisms and disease-related metabolic disorders (19,20).

MS, with its high sensitivity, rapid speed and label-free nature, serves as the core technology for SCMET. Among these methods, matrix-assisted laser desorption/ionization MS (MALDI-MS) is the most effective tissue imaging technique and has been widely applied in renal tissue metabolic imaging studies (21,22). Previous innovations in SCMET technology have further expanded its applications in renal research: Wang et al (23) innovatively combined isotope labelling with MALDI-MS, overcoming the limitations of traditional techniques in capturing dynamic metabolic processes. They successfully traced the metabolic transition from glycolysis to fatty acid β-oxidation during the differentiation of renal vesicles into proximal tubules, revealing for the first time the functional transformation mechanism during renal cell development at the single-cell metabolic level. In 2024, Qin et al (24) developed the in-depth organic MS flow cytometry technique. By optimizing lysis strategies and extending the analysis time, more than 600 metabolites were successfully identified within individual renal cells. This achieved precise single-cell metabolite identification and isomer quantification based on secondary MS, enabling accurate differentiation of metabolic differences between distinct renal cell subpopulations (for example, proximal vs. distal convoluted tubule cells). In 2025, the team integrated organic MS flow cytometry with stable isotope tracing to establish a high-throughput, label-free, dynamic SCMET platform. This platform was successfully applied to identify the polarization subtypes of tumour-associated macrophages and is equally applicable for studying the metabolic phenotypes of renal immune cells, providing a novel tool for deciphering the metabolic regulatory mechanisms of immune cells during renal inflammation (2).

In 2016, the collaborative efforts of Ståhl et al (25) first introduced the concept of 'Spatial Transcriptomics (ST)'. The core breakthrough of this technology lies in preserving the spatial structural integrity of kidney tissue and addressing the limitations of scRNA-seq, SCP and SCMET by preserving the spatial integrity of kidney tissue. This enables simultaneous analysis of 'gene expression and spatial localization', providing a novel tool for investigating intercellular interactions, spatial organization patterns, and the spatial regulatory mechanisms underlying cellular functional outputs in the kidney (26,27). It serves as a critical bridge connecting single-cell molecular characteristics with kidney tissue function.

ST technology integrates high-throughput sequencing with spatial imaging, simultaneously capturing gene expression data while preserving the spatial positioning of renal cells. This precisely reveals the spatial distribution patterns of genes within renal tissue, providing robust support for investigating dynamic changes in the cellular microenvironment associated with kidney diseases and elucidating disease mechanisms (12,28-30). Currently employed approaches primarily fall into three categories: Microarray-based techniques, bead-based techniques and microfluidic-based techniques. In microarray-based techniques, spatially barcoded oligonucleotide primers are fixed onto slides. After kidney tissue sections are combined with the array, mRNA is captured via poly(A) tails or probe hybridization, reverse-transcribed into spatially tagged cDNA, and sequenced to obtain spatial expression data (31,32). In bead-based techniques, including slide-seq (26) and HDST (27), beads with unique DNA barcodes are randomly distributed across kidney tissue sections. By combining mRNA sequencing with bead localization, these methods can be used to reconstruct spatial coordinates of gene expression, enabling higher-resolution spatial mapping of renal cells. In microfluidic-based techniques, microfluidic chips are utilized to generate microchannels on the surface of kidney tissue. Barcoded reagents are injected into specific regions, enabling precise capture and sequencing of localized mRNA to increase spatial resolution. In 2020, Liu et al (33) developed DBiT-seq technology, which uses microfluidic channels to precisely position spatially deterministic barcodes onto RNA and proteins. This technique successfully mapped the fine structures of the retinal pigment epithelium and microvascular endothelium in mouse embryos, validating its high-resolution advantage (34). This technology can be further applied to perform detailed spatial transcriptomics analysis of distinct regions within kidney tissue, including glomeruli, tubules and interstitial spaces, thereby deciphering the molecular characteristics of kidney cell spatial organization.

In summary, single-cell omics represents a frontier branch of systems biology. scRNA-seq can be employed to identify kidney cell identities and reveal regulatory potential at the transcriptional level; SCP can be used to validate regulatory features at the protein level, bridging transcription and function; SCMET can be used to decipher metabolic phenotypes, clarifying the metabolic basis of functional outputs; and ST can be used to supplement spatial information, elucidating cellular spatial organization patterns and spatially specific interactions. These four interconnected and complementary technical platforms integrate multidimensional molecular information from individual cells, collectively advancing a comprehensive and in-depth understanding of renal cell identity, regulatory potential, spatial organization and functional outputs. This propels kidney research from the 'cell population level' to the 'single-cell precision level,' aiding in the elucidation of renal physiological functions, disease mechanism studies, and the identification of therapeutic targets. This drives the paradigm shift in kidney disease diagnosis and treatment from 'empirical medicine' to 'molecular medicine'. In recent years, these approaches have demonstrated significant application value in studies of kidney cell proliferation, development, ageing and disease states (Fig. 2).

The rapid growth of single-cell omics technology has propelled the identification, localization and functional characterization of renal cell types, leading to the successful construction of a renal cell atlas. This technology holds profound significance for deepening the understanding of renal physiology and disease mechanisms and advancing precision medicine. In 2018, Park et al (35) performed single-cell sequencing on kidney cells from healthy mice, identifying 16 cell types and establishing the first mouse kidney cell atlas. They discovered a novel transitional cell type in the collecting duct expressing both principal cell and intercalated cell marker genes. Abedini et al (36) employed single-cell multi-omics analysis of 81 human kidney tissue samples from 58 participants. They precisely defined 44 major cell types and their subpopulations, including glomeruli, tubules at various segments, collecting ducts, endothelial cells and stromal cells, at the molecular level, generating a comprehensive, spatially resolved single-cell atlas of the human kidney. Li et al (37) combined single-cell transcriptomics with spatial metabolomics to reveal distinct transcriptional, epigenetic and metabolic signatures of the same tubular cell type across cortical, medullary and papillary regions, establishing a foundation for kidney disease research.

The vicious cycle of chronic inflammation triggered by the accumulation of cellular senescence and immune cell ageing represents a core mechanism exacerbating renal ageing and inflammatory injury (38,39). Single-cell omics technologies (centred on scRNA-Seq) serve as critical research tools in this field, enabling the precise identification of abnormal cell subtypes during renal ageing and the identification of key regulatory factors. These technologies provide essential technical support for elucidating the mechanisms underlying renal ageing.

scRNA-Seq plays an irreplaceable role in studying the vicious 'inflammation-ageing' cycle in the kidney. Lymphocyte proliferation and tertiary lymphoid tissue (TLT) expansion are closely linked to senescence-associated T cells and age-associated B cells (40). By analysing renal cells from aged mice with unilateral ischaemic-reperfusion injury, scRNA-seq revealed that senescence-associated T cells secrete factors that induce age-associated B-cell generation. This process, coupled with CD153/CD30 signalling, drives TLT expansion and exacerbates renal inflammation (41). Furthermore, scRNA-seq aided in identifying GZMK as a key biomarker of renal ageing (42) and elucidated the enrichment pattern of GZMK⁺CD8⁺ T cells in aged mouse kidneys, along with their mechanism of accelerating renal cell ageing through IFN-γ secretion (43). In macrophage-related studies, scRNA-seq further revealed bidirectional regulation between macrophages and renal ageing (44). Single-cell data from the kidneys of mice of different ages and aged mice with nephrotoxic serum nephritis (NTN) revealed that Stat1 upregulation in aged macrophages suppressed Pcbp1 transcription, activated ferroptosis, and promoted proinflammatory factor secretion, thereby accelerating renal interstitial fibrosis (45). Moreover, abnormal expression of CCR1, CCR2 and Fcγ receptor family members in the kidneys of aged NTN mice led to renal immune dysregulation and exacerbated renal injury (46,47). Additionally, scRNA-seq analysis of clinical data revealed that p21high macrophages mitigate transplant rejection in aged mice by upregulating Zfp36 expression and downregulating IL-27 expression, offering novel insights for kidney transplantation research (48).

In summary, using single-cell omics technologies to construct renal cell atlases, precisely identify abnormal cell subpopulations, and reveal key regulatory factors and molecular biomarkers not only provides core technical support for elucidating the pathogenesis of renal ageing and related diseases but also lays the foundation for early disease diagnosis, target screening and therapeutic optimization.

AKI is a clinically complex syndrome with high mortality rates. Its intricate pathogenesis and cellular heterogeneity significantly impede therapeutic progress (49,50). Single-cell omics technologies precisely capture novel cellular states and regulatory features during AKI progression and are pivotal for resolving diagnostic and therapeutic challenges. They offer novel approaches for early diagnosis and targeted treatment, establishing themselves as core technical support in AKI research.

DKD is a form of glomerulosclerosis triggered by metabolic abnormalities in patients with diabetes and is a major cause of end-stage renal disease. Previous studies indicated that 30-40% of diabetic patients worldwide develop DKD, with more than 50% of patients with DKD ultimately requiring end-stage renal replacement therapy, posing a significant threat to human health (60,61). Single-cell omics technology overcomes traditional research limitations by providing single-cell resolution. It revolutionizes DKD research by elucidating the dynamic states of cell types, core regulatory programs, and intercellular interaction patterns during DKD progression, offering core technical support for exploring its pathogenesis and identifying therapeutic targets (62).

IgAN is the most common primary glomerular disease (72,73). Single-cell omics technology has revealed core cellular interactions and regulatory programs, driving breakthroughs in mechanistic research. Key findings include the following:

Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease, and LN represents one of its most severe complications (79). Studies indicate that ~50% of SLE patients develop LN, with 10-30% of patients progressing to end-stage kidney disease within 10 years of onset (80-83). The core pathogenesis of LN remains unclear, and its high degree of heterogeneity not only complicates disease classification but also poses significant challenges for clinical diagnosis, treatment selection and prognosis assessment (80). Single-cell omics technology, leveraging its core advantage of single-cell resolution, has emerged as a pivotal tool for the thorough exploration of LN heterogeneity, the elucidation of its pathogenesis, and the development of novel diagnostic and therapeutic strategies.