Mass spectrometry imaging protocol for spatial mapping of lipids, N‐glycans and peptides in murine lung tissue.

Rationale: The application of matrix‐assisted laser desorption/ionization mass spectrometry imaging (MALDI‐MSI) to murine lungs is challenging due to the spongy nature of the tissue. Lungs consist of interconnected air sacs (alveoli) lined by a single layer of flattened epithelial cells, which requires inflation to maintain its natural structure. Therefore, a protocol that is compatible with both lung instillation and high spatial resolution is essential to enable multi‐omic studies on murine lung disease models using MALDI‐MSI. Methods and results: To maintain the structural integrity of the tissue, murine lungs were inflated with 8% (w/v) gelatin for lipid MSI of fresh frozen tissues or 4% (v/v) paraformaldehyde neutral buffer for N‐glycan and peptide MSI of FFPE tissues. Tissues were sectioned and prepared for enzymatic digestion and/or matrix deposition. Glycerol‐free PNGase F was applied for N‐glycan MSI, while Trypsin Gold was applied for peptide MSI using the iMatrixSpray and ImagePrep Station, respectively. For lipid, N‐glycan and peptide MSI, α‐cyano‐4‐hydroxycinnamic acid matrix was deposited using the iMatrixSpray. MS data were acquired with 20 μm spatial resolution using a timsTOF fleX MS instrument followed by MS fragmentation of lipids, N‐glycans and peptides. For lipid MSI, trapped ion mobility spectrometry was used to separate isomeric/isobaric lipid species. SCiLS™ Lab was used to visualize all MSI data. For analyte identification, MetaboScape®, GlycoMod and Mascot were used to annotate MS fragmentation spectra of lipids, N‐glycans and tryptic peptides, respectively. Conclusions: Our protocol provides instructions on sample preparation for high spatial resolution MALDI‐MSI, MS/MS data acquisition and lipid, N‐glycan and peptide annotation and identification from murine lungs. This protocol will allow non‐biased analyses of diseased lungs from preclinical murine models and provide further insight into disease models. [ABSTRACT FROM AUTHOR]