Diabetic kidney disease (DKD) is defined as kidney damage resulting from diabetes. In total, ~40% of patients with diabetes will eventually develop DKD (1,2). Moreover, with disease progression, patients with DKD experience a decline in renal function, ultimately culminating in kidney failure and end-stage kidney disease (3). Under the pathological condition of DKD, metabolic dysregulation can trigger oxidative stress (4-7), inflammatory cascades (8), tubulointerstitial injury (9,10) and glomerular endothelial dysfunction via fenestration alterations (11). Therefore, there is an urgent requirement to fully understand the pathological changes in different regions of DKD and explore effective diagnostic and therapeutic approaches.

Metabolomics is an omics technology that enables the comprehensive analysis of metabolites in organisms (12). Classic metabolomics methods focus on identifying and quantifying metabolites, providing a global metabolic pathway profile that captures systemic alterations under physiological or pathological conditions (13,14). This enables the identification of metabolic disparities across diverse sample cohorts (15,16). However, data obtained by these methods lack information on the spatial distribution of metabolites within tissues. This limitation has driven the development of spatial metabolomics.

Spatial metabolomics is a technology developed over the past two decades, and its application scope has been continuously expanded in the last 5 years with the advancement of technical methodologies (17,18). This technique integrates metabolomics with spatial biology, offering a novel approach to explore the heterogeneity of metabolites within tissue microenvironments under disease conditions. Recent studies on cancer types such as hepatocellular carcinoma have reported that the spatial distribution patterns of immune cells can more accurately predict diseases than gene expression levels alone (19-22). This highlights the importance of spatial location information in disease research. Currently, spatial metabolomics has demonstrated great potential in several fields, including neuroscience (23,24), microbiology (25), plant science (26), drug development (27) and clinical applications (28). Notably, the application of spatial metabolomics in kidney research is still in its developmental stage, with most existing studies concentrating on the heterogeneity between the renal cortex and medulla (29-31).

The present review discusses the fundamental principles and advantages of spatial metabolomics technology, summarizing recent advancements in its application to metabolic disorders in DKD. The focus is on characteristics associated with carbohydrate metabolism, lipid metabolism, amino acid metabolism and nucleic acid metabolism within cortical and medullary regions. Finally, the review outlines potential future research directions, aiming to provide valuable insights and contributions to researchers in the fields of DKD and related pathophysiology.

Spatial metabolomics represents a cutting-edge technology that integrates mass spectrometry imaging (MSI) with the principles of metabolomics. The fundamental working principle involves employing specific ionization methods to ionize metabolites present in tissue sections of samples (32). Currently, three primary ionization techniques are widely utilized in spatial metabolomics: Matrix-assisted laser desorption/ionization (MALDI)-MSI, desorption electrospray ionization (DESI)-MSI and secondary ion (SIMS)-MSI (33-38). These ionized metabolites are then sent to a mass spectrometer for mass analysis, where they are analyzed to obtain information on their mass-to-charge ratio. At the same time, the spatial distribution images of metabolites in the tissue sections are reconstructed by combining the mass spectrometry data with the spatial coordinate information of the tissue samples (32,39,40). To enable co-localization analysis, previous studies spatially aligned consecutive kidney sections stained with hematoxylin and eosin with MSI data (41,42) (Fig. 1).

The core advantage of spatial metabolomics is its capability to resolve spatial information, enabling in situ localization of metabolites within biological tissues. This is particularly valuable in kidney research, where the average cellular diameter is ~10 μm (43). MALDI-MSI boasts high sensitivity and the capability to detect low-abundance metabolites, thus occupying a dominant position in the field of spatial metabolomics. Using MALDI-MSI with a spatial resolution of 20 μm, the overall metabolic heterogeneity distribution from the cortex to the medulla in DKD kidney sections can be clearly visualized, revealing metabolic gradient changes in different functional regions under disease conditions (31). By contrast, employing a higher resolution of 5 μm with MALDI-MSI and isotope labeling enables in situ, cell-type-specific dynamic metabolic measurements, uncovering cellular metabolism within the tissue structure (44). A key advantage of DESI-MSI is its compatibility with fresh tissues, facilitating rapid detection of clinical samples. Operating at room temperature and atmospheric pressure, DESI-MSI requires no complex sample pretreatment (45,46). With a typical spatial resolution of 50-200 μm (47) and a moderate detectable molecular weight range, it is more suitable for imaging large tissue regions. This facilitates the investigation of the pervasive metabolic landscape in advanced DKD, a condition often characterized by extensive interstitial fibrosis. SIMS-MSI operates under high-vacuum conditions and has stringent temperature requirements. While SIMS-MSI achieves an ultra-high spatial resolution of <1 μm, it has the narrowest detectable molecular weight range among all the discussed techniques. Nevertheless, it provides the highest spatial resolution among the technologies addressed here, making it ideally suited for single-cell metabolomics research (35,37). Therefore, the selection of an appropriate spatial metabolomics technique for DKD research necessitates a trade-off between its spatial resolution and metabolite coverage. Future studies may integrate multiple technologies to obtain more comprehensive metabolic information, thereby enhancing our in-depth understanding of disease mechanisms.

The kidney is a vital excretory and regulatory organ, whose functions rely on the sophisticated structure and division of labor between the renal cortex and medulla (48,49). The renal cortex, composed primarily of abundant renal corpuscles and proximal tubules, is principally responsible for blood filtration, reabsorption and secretion of substances (50,51). By contrast, the renal medulla, through the loops of Henle and collecting ducts, maintains internal environmental homeostasis by regulating water-electrolyte balance and urine concentration (52,53). Owing to these functional distinctions between the two regions, there are notable differences in their demand for and utilization of metabolites (54).

In the onset and progression of DKD, the dysregulation of regional metabolic functions is particularly critical, and metabolomics research provides an essential technological approach for systematically elucidating its molecular mechanisms (55,56). Classical metabolomics, through the synergistic application of non-targeted metabolic profiling and targeted metabolite quantification strategies, has systematically constructed a comprehensive molecular map of the metabolic network of the kidney (Fig. 2), revealing ATP depletion driven by mitochondrial dysfunction, imbalance of tricarboxylic acid (TCA) cycle intermediates and inhibition of fatty acid oxidation (FAO), which ultimately leads to apoptosis, autophagy, oxidative stress and lipid droplet accumulation (57,58). Therefore, the integration of spatial metabolomics has enabled unprecedented precision in regional metabolite localization (59,60).

A hyperglycemic environment can induce an imbalance in energy metabolism homeostasis, characterized by mitochondrial oxidative phosphorylation dysfunction and metabolic reprogramming (61,62). Notably, the renal medulla mainly relies on glucose for energy (63,64). Previous research reported that the levels of hexoses in the cortical tissue increased when male C57BL/6J-Ins2Akita mice reached 17 weeks of age (65). When DKD rats were 24 and 28 weeks old, respectively, the kidney glucose levels exhibited a marked global increase, with particularly pronounced elevations observed in the cortex region (31,66). By contrast, rats subjected to prolonged feeding exhibited notably reduced glucose levels in the renal medulla region (66). However, it should be noted that these findings reflect the complexity of metabolic heterogeneity across different regions and time points. The discrepancies in glucose levels between the renal cortex and medulla have led to varied interpretations of the pathophysiological mechanisms of DKD, highlighting the necessity of considering tissue specificity and its potential impacts on overall disease progression when investigating metabolic alterations.

The distribution of TCA cycle intermediates also exhibits spatial heterogeneity. Clinical studies have reported that, compared with healthy controls, succinic acid and malic acid are markedly reduced in kidney biopsies of patients with type 1 diabetes across several kidney regions, suggesting subclinical mitochondrial dysfunction (67). These spatial distribution abnormalities are highly congruent with the global findings of classical metabolomics. In individuals with DKD, enzymes involved in glycolytic, sorbitol, methylglyoxal and mitochondrial pathways have been reported to exhibit reduced expression and activity. Moreover, pyruvate kinase M2 demonstrates marked decreases in both its transcriptional expression and enzymatic activity (68). From the perspective of metabolic pathways, the accumulation of TCA cycle intermediates induces a burst of mitochondrial reactive oxygen species, driving the progression of DKD (69,70). Table I consolidates information on spatially resolved metabolic alterations in carbohydrate and TCA cycle intermediates.

Classical metabolomics studies have systematically characterized the systemic lipid metabolic dysregulation in DKD, elucidating imbalances in cholesterol esterification, phospholipid remodeling and triglyceride (TG) accumulation (71-73). Moreover, spatial metabolomics has revealed notable regional variations in phospholipids and TGs in DKD kidneys (66,74). Phospholipids are key components of cell membranes and are associated with the pathogenesis of DKD (75). Lysophosphatidic acid (LPA), lysophosphatidylcholine (LPC), lysophosphatidylethanolamine (LPE) and lycerylphosphorylethanolamine (GPE) are all lipid metabolites derived from phospholipids. In DKD model mice, the levels of LPA, LPC, LPE and GPE in the cortex were reported to be markedly elevated (65,76,77). Moreover, changes in the phosphatidylcholine (PC)/phosphatidylethanolamine (PE) ratio have been reported to have an impact on cellular processes associated with health and disease (77). In cardiovascular disease patients at risk of insulin resistance, changes in the PC/PE ratio affect cellular processes related to cardiovascular health and disease progression by regulating inflammatory responses and the physiological state of cardiomyocytes (78). Notably, in db/db mice, the levels of PC/PE ratio in the cortex and medulla showed opposite changes, indicating that metabolic responses in different kidney regions during disease progression are both complex and region-specific (66). TGs are subject to rapid turnover and re-arrangement of fatty acids (FAs) (79). A recent spatial multi-omics study of long-term DKD reported a marked accumulation of TG in the medullary region (74). Furthermore, in DKD rats at 24 weeks of age, renal cortical oleic acid levels were elevated, whilst polyunsaturated FAs, including linoleic acid, were markedly reduced (31).

Spatial metabolomics has advanced the understanding of lipid changes in the kidneys of DKD to the level of proximal tubular cells. Under physiological conditions, the kidney tubules predominantly utilize FAs (64,80,81), and this utilization requires carnitine as a carrier, facilitating the transport of long-chain fatty acids into the mitochondria via the carnitine shuttle system (82). Spatial metabolomics and multi-omics analyses of human kidney samples reported that the detection of acylcarnitines in cortical samples was insufficiently sensitive (83). This observation is consistent with the known downregulation of FAO in chronic kidney disease, indicating impaired FAO in proximal tubular cells of patients with kidney disease (84). Detailed lipid changes are presented in Table II.

It should be noted that the inherent differences in sample size, animal model selection and experimental methodologies among the aforementioned studies may impose significant constraints on the reproducibility and generalizability of the results. Although spatial metabolomics has successfully revealed the regional heterogeneity of lipid distribution in the kidney, existing research is mostly confined to single-species models (e.g., db/db mice and DKD rats). Moreover, the sample size used for spatial metabolomics analysis is only up to 6 animals per group, coupled with the lack of dynamic tracking data across different disease stages. This may not only induce biases in interpreting the causal relationship between lipid metabolic disorders and the pathological progression of DKD, but may also hinder the accurate reflection of the clinical metabolic characteristics of human DKD. Therefore, future studies should prioritize optimizing experimental designs by expanding sample sizes, diversifying animal models and conducting dynamic monitoring across multiple disease stages, so as to more comprehensively elucidate the metabolic response mechanisms of distinct renal regions during DKD progression.

The dysregulation of amino acid metabolism in DKD is not merely a consequence of renal dysfunction, but an active driver of pathogenesis (85,86). Spatial metabolomics techniques have revealed changes in amino acids in specific regions of the kidney. In db/db mice, glutamate, spermine, taurine and spermidine were notably reduced in both the cortex and medulla, whilst histamine, putrescine and indoxyl sulfate markedly accumulated in the cortex (66,87,88). Notably, spermidine has been reported to exhibit multiple pharmacological effects, including anti-aging, anti-oxidation, anti-inflammation and cardiovascular protection (89-92). Sulfate is a protein-binding gut-derived uremic toxin that has been reported to be closely associated with podocyte loss in DKD (93). Our previous research demonstrated that Tangshen formula can effectively decrease the concentration of sulfate during the treatment of DKD (94). Furthermore, compared with that in db/db mice, glutamate was reported to be similarly reduced in the cortex and medulla of high-fat diet and STZ-induced DKD rats (31,66). Additionally, glutamine, aspartate, threonine and leucine/isoleucine were reported to be markedly reduced (31,66,95). The heterogeneity of amino acid and related metabolism in the cortex and medulla of DKD revealed by spatial metabolomics is summarized in Table III.

Classic metabolomic studies have demonstrated that after inducing diabetes in rats via injection of alloxan, the levels of purine metabolites in their bodies are significantly elevated, including uric acid (the end product of purine metabolism) and metabolic intermediates such as xanthine, hypoxanthine, adenosine monophosphate (AMP) and inosine (96-98). Notably, a large-scale clinical metabolomics study, involving 4,503 healthy controls and 1,875 patients with DKD, reported that the levels of uracil in patients with DKD were markedly reduced (99). These phenomena suggest unique nucleic acid metabolic disturbances. However, to clarify their local in situ distribution and pathological significance within the kidney, spatial metabolomics is required.

Spatially resolved analyses have elucidated compartment-specific nucleotide dysregulation, offering mechanistic insights into DKD pathology (31,100). In db/db mice, the level of AMP, a key intermediate in purine nucleotide metabolism, was reported to be notably elevated across all renal regions (66). Moreover, in DKD rats, the levels of AMP and guanosine monophosphate (GMP) were reported to be markedly increased in the cortex, whilst they showed a decreasing trend in the medulla (31). Mammalian target of rapamycin (mTOR) integrates growth factor and insulin signaling pathways to orchestrate critical cellular processes, including cell proliferation, motility, survival, protein synthesis, autophagy and transcriptional regulation (101). In DKD, abnormal activation of the mTOR pathway can mediate metabolic reprogramming, with adenine reported to drive kidney pathological progression through this pathway. Spatial metabolomics studies reveal that in normal kidneys, adenine is primarily localized in the glomeruli and vascular regions (102,103). However, in the pathology of DKD, adenine diffusely increases throughout the entire tissue section, particularly in the regions of sclerotic vessels, mildly sclerotic glomeruli, atrophic renal tubules and areas of interstitial inflammation (102). The importance of adenine in rescue and targeted therapies has been elucidated using multimodal omics approaches (104). As the most abundant modified nucleoside in several RNA species, pseudouridine has been recognized as a new biomarker demonstrating better performance than creatinine in chronic kidney disease stratification (105). Previous studies have reported that diabetic mice exhibit relatively high levels of pseudouridine in the cortex tissue (65). Table IV summarizes spatial metabolomics research revealing dysregulation of nucleotide and purine metabolism in kidneys of organisms with DKD.

In-depth analyses of metabolic remodeling and pathological mechanisms in the renal microenvironment is the theoretical cornerstone for developing new targeted intervention strategies for DKD. The present review systematically summarizes the cutting-edge advancements in spatial metabolomics in this field. Its core contribution lies in overcoming the limitations of traditional global tissue analysis, revealing the fundamental spatial heterogeneity of metabolic regulation along the cortical-medullary axis and within specific pathological structures. Synthesizing existing research findings, the metabolite profiles in the renal cortex and medulla exhibit marked region-specific dysregulation across multiple DKD animal models. In the cortex region, the levels of glucose, AMP, GMP, PS(36:2), PS(36:1), LysoPG(18:1) and SM(d18:1/16:0) are significantly upregulated; concurrently, the levels of taurine, glutamate, L-carnitine, PG(32:0), PC/PE and choline are notably downregulated. In the medulla region, the levels of succinate, PS(36:1), SM(d18:1/16:0) and PC/PE show significant elevation; meanwhile, the levels of taurine, glutamate, L-carnitine and choline also display a decreasing trend. Fig. 3 offers an intuitive visual overview and acts as a reference for identifying regional or global metabolic targets in subsequent targeted interventions. Metabolites exhibit similar or opposite distribution and regional accumulation in different functional areas of the kidney. These are key spatial details that traditional tissue homogenate-based metabolomics cannot capture. Conventional methods provide overall averages, masking contrasting trends across anatomical regions. This obscures the true picture of metabolic disorders.

The core value of spatial metabolomics lies in addressing the 'where' and 'how' questions that conventional techniques cannot resolve, thereby revealing the molecular mechanisms and causal relationships driving pathological processes. It is known that metabolic disorders disrupt cellular energy homeostasis; for example, in the renal hyperglycemic environment, the pentose phosphate, polyol and hexosamine pathways are activated, leading to the local accumulation of toxic metabolites, which in turn activate key signaling pathways and ultimately trigger core pathological processes such as inflammation, oxidative stress and fibrosis (106,107). However, classical metabolomics cannot clearly illustrate in which region of the kidney these metabolic disorders and signaling pathway activations occur, nor how the spatial distribution of metabolites drives pathological processes. This is where spatial metabolomics demonstrates its unique value, and the research on endogenous adenine metabolism is a prime example. The accumulation of adenine in specific regions inhibits 5'-AMP-activated protein kinase whilst activating the mTORC1-S6K signaling axis, thereby translating metabolic dysregulation into typical pathological phenotypes such as cellular hypertrophy and tissue fibrosis (108-111). By integrating spatial metabolomics and spatial proteomics, research has reported that metformin acts in the cortical-medullary outer zone, upregulating nephrosis 2 and inhibiting IL-17 signaling (112). This specifically reverses purine metabolic imbalance, thereby blocking glomerulosclerosis and interstitial fibrosis (112). This intuitively shows how the three-dimensional spatial coupling of metabolism, transcription and proteins precisely remodels the pathological microenvironment. Nevertheless, notably, knowledge gaps remain regarding the regional dynamics of metabolites and the precise mechanisms by which they promote renal pathology at the molecular and cellular levels. The key direction for future research should shift from current descriptive associations to systematic functional validation and mechanistic exploration of these spatially anchored metabolites.

As a pivotal methodology for investigating the spatial distribution of metabolites in vivo, spatial metabolomics is an indispensable component of spatial multi-omics research. This technology not only provides a novel spatial perspective for understanding DKD but also deepens the comprehension of disease-specific metabolic processes. However, its application in the field of DKD still faces several pressing challenges that need to be addressed, for example: i) There are limitations in technical sensitivity and resolution. Low-abundance metabolites are prone to loss in imaging (113). Most existing studies only localize metabolites to large anatomical regions, such as the cortex or medulla, and they struggle to accurately depict the true distribution of these metabolites within fine functional units, such as glomeruli and specific tubular segments. If the resolution is enhanced, the acquisition speed may consequently be reduced (114). ii) The identification and annotation of metabolites often lack sufficient precision. When annotating metabolites, certain metabolites, such as isomers, cannot be annotated with absolute certainty (17,115,116). iii) Data analysis and interpretation present challenges. Crucial challenges exist in data and signal processing, data comparability and the need for optimization tailored to each tissue type (117). These challenges indicate that engineers and researchers need to address the shortcomings to provide more accurate spatial metabolic maps for the mechanistic analysis of complex diseases such as DKD.

Moreover, spatial metabolomics can directly present actual biochemical activities and functional metabolic outcomes, offering new opportunities for DKD research: Firstly, applying integrated spatial multi-omics to establish causal mechanisms. As life science research progresses, single-omics approaches are increasingly inadequate for comprehensively deciphering complex biological processes. The integration of spatial metabolomics with transcriptomics and proteomics has become more prevalent (118). By integrating data from spatial metabolomics and spatial transcriptomics, researchers can associate changes in metabolite levels with alterations in gene expression, enabling a deeper exploration of the molecular mechanisms underlying metabolic regulation (119). Secondly, targeted strategies can address regional metabolic heterogeneity. This moves beyond conventional treatment, enabling precision medicine. Spatial metabolomics technology can assess changes in the distribution of metabolites after drug intervention, effectively evaluate DKD drugs, clarify the mechanism of drug treatment and monitor treatment responses, thereby helping to further optimize treatment regimens (31). By combining nanotechnology with drug targeted delivery systems, precise treatment can be implemented for specific regions of metabolic heterogeneity (120). The combined application of these technologies could provide new possibilities for formulating personalized DKD treatment plans and achieving accurate prognostic assessment for patients with DKD (Fig. 4).

The present review systematically examines the regional metabolic characteristics of DKD, and it highlights the unique value of spatial metabolomics in elucidating the pathological mechanisms of DKD. With continuous improvements in spatial resolution and quantitative analytical techniques, this technology is expected to serve a critical role in the precision diagnosis and treatment of DKD. Future research may delve deeper into the cellular level, focusing on elucidating the more refined spatial localization and functional associations of specific metabolites. With the continuous advancement of multi-center data accumulation and clinical trial validation, region-specific metabolites or key enzymes are expected to be translated into reliable diagnostic biomarkers and intervenable therapeutic targets. Such initiatives will enable the tailored development of individualized therapies, ultimately boosting early diagnostic efficiency and optimizing long-term outcomes for patients with DKD.