Development of a Laser Microdissection-Coupled Quantitative Shotgun Lipidomic Method to Uncover Spatial Heterogeneity

Cells. 2023 Jan 28;12(3):428. doi: 10.3390/cells12030428.

Abstract

Lipid metabolic disturbances are associated with several diseases, such as type 2 diabetes or malignancy. In the last two decades, high-performance mass spectrometry-based lipidomics has emerged as a valuable tool in various fields of biology. However, the evaluation of macroscopic tissue homogenates leaves often undiscovered the differences arising from micron-scale heterogeneity. Therefore, in this work, we developed a novel laser microdissection-coupled shotgun lipidomic platform, which combines quantitative and broad-range lipidome analysis with reasonable spatial resolution. The multistep approach involves the preparation of successive cryosections from tissue samples, cross-referencing of native and stained images, laser microdissection of regions of interest, in situ lipid extraction, and quantitative shotgun lipidomics. We used mouse liver and kidney as well as a 2D cell culture model to validate the novel workflow in terms of extraction efficiency, reproducibility, and linearity of quantification. We established that the limit of dissectible sample area corresponds to about ten cells while maintaining good lipidome coverage. We demonstrate the performance of the method in recognizing tissue heterogeneity on the example of a mouse hippocampus. By providing topological mapping of lipid metabolism, the novel platform might help to uncover region-specific lipidomic alterations in complex samples, including tumors.

Keywords: cryosection; in situ lipid extraction; laser microdissection; mass spectrometry; quantitative shotgun lipidomics; spatial resolution; tissue heterogeneity.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Diabetes Mellitus, Type 2*
  • Lasers
  • Lipidomics*
  • Lipids / analysis
  • Mice
  • Microdissection
  • Reproducibility of Results

Substances

  • Lipids

Grants and funding

This research was funded by the National Research, Development and Innovation Office, Hungary (OTKA ANN 139553 to G.B.). P.H. and E.M. acknowledge support from the LENDULET-BIOMAG Grant (2018-342), from OTKA SNN 139455, from TKP2021-EGA09, from CZI Deep Visual Proteomics, from H2020-Fair-CHARM, and from the ELKH-Excellence grant. HCEMM received funding from the EU’s Horizon 2020 research and innovation program under grant agreement No. 739593.