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Abstract: FR-OR35

Identification of Early Diabetes-Induced Renal Changes Using Spatial Metabolomics

Session Information

Category: Diabetic Kidney Disease

  • 601 Diabetic Kidney Disease: Basic

Authors

  • Rietjens, Rosalie, Leids Universitair Medisch Centrum Niercentrum, Leiden, Zuid-Holland, Netherlands
  • Wang, Gangqi, Leids Universitair Medisch Centrum Niercentrum, Leiden, Zuid-Holland, Netherlands
  • Heijs, Bram, Leids Universitair Medisch Centrum Center for Proteomics and Metabolomics, Leiden, Netherlands
  • van den Berg, Bernard, Leids Universitair Medisch Centrum Niercentrum, Leiden, Zuid-Holland, Netherlands
  • Rabelink, Ton J., Leids Universitair Medisch Centrum Niercentrum, Leiden, Zuid-Holland, Netherlands
Background

Diabetic nephropathy usually presents itself with irreversible kidney damage. Clearly, there is a need to better understand early renal cell type specific diabetes-induced changes before occurrence of overt renal histopathology. The metabolism is a very dynamic process which is expected to change upon diabetes, preceding morphological changes. Mass spectrometry imaging (MSI) offers a label-free method to study metabolism in the context of tissue histology, giving us the opportunity to investigate renal cell type specific metabolic changes.

Methods

Apolipoprotein E-knockout mice were treated with streptozotocin (STZ) and put on an enriched cholesterol diet to induce diabetes. After 12 weeks, both control (n=4) and diabetic (n=4) mice were sacrificed and kidneys were harvested for immunohistochemistry and MSI. Post-MSI immunofluorescent microscopy (IF)-assisted annotation was used to identify various renal cell types. Metabolome-driven segmentation and subsequent multivariate analysis allowed us to find cell type specific metabolic changes in the diabetic kidney.

Results

Two weeks after STZ induction, blood glucose levels of diabetic mice were significantly elevated compared to control. Besides small glomerular changes, we could not find further histological signs of diabetic tubular injury. Metabolome-driven spatial segmentation analysis of the MSI data revealed that in both groups different renal cell types could be distinguished based on their metabolic profile. Using IF-assisted cell type annotation, we found various metabolites and lipids being reduced in glomeruli of the diabetic kidney. Furthermore, we found considerable changes in the metabolic profile of the proximal tubular cells in the S3 segment (PT-S3). Using a multivariate ROC analysis, we established metabolites and lipids that could distinguish PT-S3 between healthy and diabetic kidneys. Membrane lipid metabolism and protein homeostasis in these PT-S3 cells already appeared to be affected by diabetes, before showing signs of histological changes.

Conclusion

Using a spatial metabolomics approach, we were able to identify renal cell type specific diabetes-induced early changes in the diabetic kidney compared to the healthy control. Already finding such changes in molecular phenotype would allow to assess therapeutics for treatment before irreversible damage occurs in the kidney.