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Abstract: SA-OR91

Spatial Metabolomics Identifies Novel Glomerular Metabolite Signatures of Normal and Diabetic Kidneys

Session Information

Category: Glomerular Diseases

  • 1401 Glomerular Diseases: From Inflammation to Fibrosis

Authors

  • Hejazi, Leila, SygnaMap, San Antonio, Texas, United States
  • Sharma, Shoba, SygnaMap, San Antonio, Texas, United States
  • Ruiz, Aaron, SygnaMap, San Antonio, Texas, United States
  • Zhang, Guanshi, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States
  • Tucci, Fabio C., Epigen Biosciences Inc, San Diego, California, United States
  • Sharma, Kumar, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States
Background

The Zucker Diabetic Fatty (ZDF) rat is a model of type 2 diabetes as it develops pathologic changes in the glomerulus similar to human disease. Untargeted matrix-assisted laser desorption/ionization (MALDI) - mass spectrometry imaging (MSI) can be a powerful platform to identify metabolic pathways linked to pathologic features. However, quantification of metabolites from specific regions of interest in an unbiased manner via MALDI-MSI is challenging. MSI-DeepPath is a new computational platform that enables spatial quantitation of metabolites.

Methods

Control and ZDF male rats (age 6 mo, n=3/group) were used for the current study. Spatial metabolomic analysis of glomeruli in Control and ZDF rat kidneys was performed using MALDI-MSI at 20 µm spatial resolution from replicate samples per animal. Metabolites were detected at mass accuracy <2 ppm using Q-Exactive orbitrap MS followed by annotations using METASPACE. SygnaMap’s MSI-DeepPath computational platform was used to register each glomerulus in a section to the corresponding MSI pixels and then quantify the annotated metabolites per glomerular pixel.

Results

Of 1600 metabolites annotated at m/z range 70-500 Da the top features were selected by ranking individual metabolite intensity per pixel in the normal and the diabetic groups. The top metabolite in normal glomeruli was L-glutamic acid and in diabetic glomeruli was D-glucose. Based on the top 20 enriched metabolites, the most prominent pathway in the normal glomeruli was Arginine biosynthesis whereas the most prominent pathway in the diabetic glomeruli was Glyoxylate and dicarboxylate metabolism.

Conclusion

For the first time, MSI-DeepPath enables quantification of spatial metabolomics of glomeruli using untargeted MALDI-MSI data. With MSI-DeepPath-based analysis of kidney sections from the ZDF rat, we identified that Glyoxylate-dicarboxylate metabolism was the most prominent pathway in the diabetic glomeruli. As glyoxylate is a highly reactive aldehyde and can be nephrotoxic via conversion to oxalate, further studies on glomerular glyoxylate-dicarboxylate metabolism will shed new light on its potential role in diabetic glomerular disease.

Funding

  • NIDDK Support