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March 5, 2026Journal of Pharmaceutical Analysis0 citationsOpen Access

Integrating mass spectrometry imaging and data-driven segmentation for spatial metabolic mapping of diabetic eye disease

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SCShuohan ChengSWShuo WangTLTian Lan

Key Points

  • This study aims to explore the molecular changes in diabetic eye disease (DED) using advanced mass spectrometry imaging techniques.
  • Used AFADESI-MSI and MALDI-MSI for detailed metabolic mapping.
  • Applied data-driven segmentation with SCiLS Lab software for retinal micro-region analysis.
  • Characterized metabolic alterations through physiological and biochemical assessments.
  • Conducted unsupervised k-means clustering on high-resolution data to identify functional micro-regions.
  • Identified 135 annotated metabolites in the rat eye across different retinal regions.
  • Detected significant dysregulation of 39 metabolites associated with diabetes, affecting amino acid, glucose, lipid, and redox metabolism.
  • Ferulic acid treatment positively influenced nine key metabolites and restored metabolic balance and structural integrity in the retina.

Abstract

Diabetic eye disease (DED) is a leading cause of vision impairment worldwide, yet the molecular mechanisms underlying its progression remain incompletely understood. In this study, we applied a dual-platform spatial metabolomics strategy integrating air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) and matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) to characterize spatial metabolic alterations in the eyes of diabetic rats. Data-driven segmentation of retinal micro-regions using SCiLS Lab software enabled fine-scale mapping of metabolic heterogeneity. Physiological, biochemical, and histopathological analyses were combined with spatial metabolite mapping to construct a metabolic atlas and evaluate the regulatory effects of ferulic acid. We established a comprehensive spatial metabolome atlas of the rat eye, identifying 135 annotated metabolites and revealing significant region-specific metabolic heterogeneity. Unsupervised k-means clustering was further applied to the high-resolution MALDI-MSI data, successfully delineating distinct functional micro-regions of the retina solely based on endogenous metabolic profiles, demonstrating the power of data-driven tissue segmentation. In diabetic eyes, 39 metabolites were significantly dysregulated, involving amino acid, glucose, lipid, and redox metabolism. Notably, lysine, arginine, carnitine, and GSH were depleted, while glucose-6-phosphate (G6P), glycerol-3-phosphate (G3P), and pro-inflammatory lipids were elevated, highlighting profound metabolic reprogramming across ocular compartments. Ferulic acid treatment restored nine key metabolites, alleviated oxidative stress, normalized lipid and glucose metabolism, and improved retinal structural integrity in a dose-dependent manner. This study shows that integrating mass spectrometry imaging with data-driven tissue segmentation reveals spatial metabolic reprogramming in DED and highlights ferulic acid as a promising therapeutic candidate. • AFADESI-MSI and MALDI-MSI were used for mapping rat eye metabolic atlas. • Regional metabolic heterogeneity in the rat eye was revealed. • Identified and spatially resolved 39 key metabolites in diabetic eye disease eyes. • Provided novel insights into diabetic eye disease pathology and therapeutic potential of ferulic acid.

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Cite This Study

Cheng et al. (2026) studied this question.

synapsesocial.com/papers/69a91d55d6127c7a504c003ahttps://doi.org/10.1016/j.jpha.2026.101596
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