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Spatial metabolomics: A new tool for unravelling the metabolic disorders and heterogeneity in diabetic kidney disease (Review)

  • Authors:
    • Hanfei Li
    • Yuxi Li
    • Bo Zhang
    • Wenhao Cheng
    • Guowei Ma
    • Jin Rong
    • Shiru Duan
    • Di Feng
    • Tingting Zhao
  • View Affiliations / Copyright

    Affiliations: Key Laboratory of Geriatric Nutrition and Health (Beijing Technology and Business University), Ministry of Education, Beijing 100048, P.R. China, Institute of Clinical Medical Sciences, State Key Laboratory of Respiratory Health and Multimorbidity, Beijing Key Laboratory of Critical Bridging Technologies for Chronic Disease Drug Development, China‑Japan Friendship Hospital, Beijing 100029, P.R. China
    Copyright: © Li et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 76
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    Published online on: January 29, 2026
       https://doi.org/10.3892/ijmm.2026.5747
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Abstract

Diabetic kidney disease (DKD) is a microvascular complication of diabetes, characterized by region‑specific metabolic reprogramming that disrupts kidney function and markedly impairs patient prognosis. By enabling in situ visualization and analysis of metabolite distribution within kidney tissue, spatial metabolomics offers a unique advantage in detecting spatial heterogeneity in metabolic alterations, which is inaccessible through conventional metabolomics. This approach not only enhances the understanding of DKD pathophysiology but also provides a solid foundation for the development of precision nephrology strategies informed by spatial metabolite data. The present review discusses the fundamental workflows and spatial resolution capabilities of spatial metabolomics, summarizing the key metabolites involved in regional metabolic disruptions in multiple DKD animal models. Moreover, it highlights notable metabolites, including glucose, succinate, phosphatidylserine, lysophosphatidylglycerol, phosphatidylglycerol, sphingomyelin, phosphatidylcholine, phosphatidylethanolamine, taurine, glutamate, L‑carnitine, choline, adenosine monophosphate and guanosine monophosphate. The continued advancement of imaging technologies and data analysis methodologies is expected to further refine the spatial resolution and precision of spatial metabolomics, thereby facilitating its broader application in clinical practice.

View Figures

Figure 1

Schematic workflow of spatial
metabolomics analysis for kidney samples. After kidney tissue is
obtained, the workflow encompasses sample preparation,
high-resolution mass spectrometry imaging, multimodal data
collection, and integration of variance analysis and pathway
enrichment analysis. This results in the construction of a spatial
metabolic map that captures metabolic heterogeneity between the
renal cortex and medulla, offering a visual reference for
mechanistic investigations into metabolic diseases such as diabetic
kidney disease. H&E, hematoxylin and eosin; KEGG, Kyoto
Encyclopedia of Genes and Genomes.

Figure 2

Global metabolic disorder network of
DKD revealed by classical metabolomics. The key metabolic
alterations and their pathogenic relevance to DKD progression are
shown. Intracellular glucose influx is increased, leading to
cytoplasmic glucose accumulation that activates the polyol pathway
and disrupts glycolytic intermediate balance, further exacerbating
lactate buildup. These changes drive a metabolic shift toward
anaerobic glycolysis. FFAs are taken up through CD36 and FATP
receptors, resulting in the accumulation of oxidized low-density
lipoprotein. The functions of cholesterol reverse transport
proteins ABCA1/LDLR are impaired, leading to lipid droplet
accumulation and oxidative stress. PGC-1α and PPAR regulate FAO,
while SREBPs can affect cholesterol synthesis. Mitochondrial
oxidative phosphorylation dysfunction and peroxisome dysfunction
further worsen lipotoxicity. The reduction of various amino acids
is closely related to the decrease of intermediates in the TCA
cycle. Red arrows indicate an increase; blue arrows indicate a
decrease. DKD, diabetic kidney disease; GLUT, glucose transporter;
G6P, glucose-6-phosphate; F6P, fructose-6-phosphate; G3P,
glyceraldehyde-3-phosphate; (N)EAA's, (non)essential amino acids;
FFAs, free fatty acids; FATP, fatty acid transport protein; CD36,
cluster of differentiation 36; ox-LDL, oxidized low-density
lipoprotein; LDL, low-density lipoprotein; LOX-1, lectin-like
oxidized low-density lipoprotein receptor-1; LDLR, low-density
lipoprotein receptor; HDL, high-density lipoprotein; ABCA1,
ATP-binding cassette transporter A1; TCA cycle, tricarboxylic acid
cycle; ATP, adenosine triphosphate; ROS, reactive oxygen species;
OXPHOS, oxidative phosphorylation; FAO, fatty acid oxidation; ACSL,
acyl-CoA synthetase long-chain family; CPT1, carnitine
palmitoyltransferase 1; α-KG, α-ketoglutarate; NF-κB, nuclear
factor κ-light-chain-enhancer of activated B cells; AP-1, activator
protein-1; SREBP, sterol regulatory element-binding protein;
PGC-1α, peroxisome proliferator-activated receptor γ coactivator
1-α; PPARα, peroxisome proliferator-activated receptor α; PPARγ,
peroxisome proliferator-activated receptor γ.

Figure 3

Spatial metabolic heterogeneity is
observed in the cortex and medulla of DKD animal models. (A)
Schematic diagram of regional distribution of metabolites with
consistent change trends across two or more DKD animal models.
Through regional demarcation and arrow notation (↑ denotes
elevation, ↓ denotes reduction), the region-specific dysregulation
of metabolites implicated in glucose metabolism (e.g., glucose),
lipid metabolism (e.g., PS, SM), amino acid metabolism (e.g.,
taurine, glutamic acid), and nucleotide metabolism (e.g., AMP) are
explicitly illustrated. (B) Spatial distribution images of
representative metabolites obtained via spatial metabolomics.
Adapted from Zhang et al (66), https://doi.org/10.3390/metabo13030324, under the
terms of the CC BY 4.0 license: Glucose and glutamate in this
figure are adapted from Fig. 8 of this study, AMP from Fig. 9,
choline from Fig. 10, and SM(34:1), LysoPG(18:1), PG(32:0),
PS(36:1), PC(34:1) and PE(34:1) from Figs. 6 and 7. The images
compare the control CON and DKD groups across two models (HFD and
STZ treated rats, db/db mice), with the color scale (from 0 to
100%) indicating metabolite abundance. Scale bar, 3 mm. CON,
control; DKD, diabetic kidney disease; HFD, high-fat diet; STZ,
streptozotocin; PG, phosphatidylglycerol; LysoPG,
lysophosphatidylglycerol; PS, phosphatidylserine; PC/PE, the ratio
of phosphatidylcholine to phosphatidylethanolamine; SM(34:1),
SM(d18:1/16:0); SM, sphingomyelin; AMP, adenosine monophosphate;
GMP, guanosine monophosphate.

Figure 4

Spatial metabolomics-driven precision
medicine research for DKD. This framework illustrates the
multi-omics integration strategy of spatial metabolomics with
single-cell transcriptomics, epigenomics and pathological imaging.
Leveraging AI-powered data mining technologies, a
'metabolite-gene-cell' spatial interaction network is constructed
to systematically decipher the heterogeneous pathological
mechanisms of DKD. This network further generates three major
clinical translation outcomes: Novel spatial biomarkers, precise
molecular typing systems and efficient therapeutic targets.
Ultimately, these findings support the development of precision
treatment strategies and promote the advancement of precision
medicine in DKD diagnosis and treatment through therapeutic
efficacy monitoring. DKD, diabetic kidney disease; AI, artificial
intelligence.
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Copy and paste a formatted citation
Spandidos Publications style
Li H, Li Y, Zhang B, Cheng W, Ma G, Rong J, Duan S, Feng D and Zhao T: <p>Spatial metabolomics: A new tool for unravelling the metabolic disorders and heterogeneity in diabetic kidney disease (Review)</p>. Int J Mol Med 57: 76, 2026.
APA
Li, H., Li, Y., Zhang, B., Cheng, W., Ma, G., Rong, J. ... Zhao, T. (2026). <p>Spatial metabolomics: A new tool for unravelling the metabolic disorders and heterogeneity in diabetic kidney disease (Review)</p>. International Journal of Molecular Medicine, 57, 76. https://doi.org/10.3892/ijmm.2026.5747
MLA
Li, H., Li, Y., Zhang, B., Cheng, W., Ma, G., Rong, J., Duan, S., Feng, D., Zhao, T."<p>Spatial metabolomics: A new tool for unravelling the metabolic disorders and heterogeneity in diabetic kidney disease (Review)</p>". International Journal of Molecular Medicine 57.4 (2026): 76.
Chicago
Li, H., Li, Y., Zhang, B., Cheng, W., Ma, G., Rong, J., Duan, S., Feng, D., Zhao, T."<p>Spatial metabolomics: A new tool for unravelling the metabolic disorders and heterogeneity in diabetic kidney disease (Review)</p>". International Journal of Molecular Medicine 57, no. 4 (2026): 76. https://doi.org/10.3892/ijmm.2026.5747
Copy and paste a formatted citation
x
Spandidos Publications style
Li H, Li Y, Zhang B, Cheng W, Ma G, Rong J, Duan S, Feng D and Zhao T: <p>Spatial metabolomics: A new tool for unravelling the metabolic disorders and heterogeneity in diabetic kidney disease (Review)</p>. Int J Mol Med 57: 76, 2026.
APA
Li, H., Li, Y., Zhang, B., Cheng, W., Ma, G., Rong, J. ... Zhao, T. (2026). <p>Spatial metabolomics: A new tool for unravelling the metabolic disorders and heterogeneity in diabetic kidney disease (Review)</p>. International Journal of Molecular Medicine, 57, 76. https://doi.org/10.3892/ijmm.2026.5747
MLA
Li, H., Li, Y., Zhang, B., Cheng, W., Ma, G., Rong, J., Duan, S., Feng, D., Zhao, T."<p>Spatial metabolomics: A new tool for unravelling the metabolic disorders and heterogeneity in diabetic kidney disease (Review)</p>". International Journal of Molecular Medicine 57.4 (2026): 76.
Chicago
Li, H., Li, Y., Zhang, B., Cheng, W., Ma, G., Rong, J., Duan, S., Feng, D., Zhao, T."<p>Spatial metabolomics: A new tool for unravelling the metabolic disorders and heterogeneity in diabetic kidney disease (Review)</p>". International Journal of Molecular Medicine 57, no. 4 (2026): 76. https://doi.org/10.3892/ijmm.2026.5747
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