Spatial Multiomics of Lipids, N-Glycans, and Tryptic Peptides on a Single FFPE Tissue Section

Abstract

Mass spectrometry imaging (MSI) is an emerging technology that is capable of mapping various biomolecules within their native spatial context, and performing spatial multiomics on formalin-fixed paraffin-embedded (FFPE) tissues may further increase the molecular characterization of pathological states. Here we present a novel workflow which enables the sequential MSI of lipids, N-glycans, and tryptic peptides on a single FFPE tissue section and highlight the enhanced molecular characterization that is offered by combining the multiple spatial omics data sets. In murine brain and clear cell renal cell carcinoma (ccRCC) tissue, the three molecular levels provided complementary information and characterized different histological regions. Moreover, when the spatial omics data was integrated, the different histopathological regions of the ccRCC tissue could be better discriminated with respect to the imaging data set of any single omics class. Taken together, these promising findings demonstrate the capability to more comprehensively map the molecular complexity within pathological tissue.

The journey towards clinical adoption of MALDI‐MS‐based imaging proteomics: from current challenges to future expectations

“…Moreover, trends towards the integration of spatial proteomics data with those obtained from complementary molecular levels, such as the lipidome and N-glycome, have also been reported and serve to strengthen the discriminatory power obtained by spatial proteomics. In particular, Denti et al [26] have recently reported the possibility to perform spatial multi-omics of these three molecular classes on a single FFPE tissue section, which can be particularly relevant in instances where clinical tissue may be scarce, such as for low incident diseases or those which require multiple histological and immunohistochemical assessments [27]. This approach represents a further promising avenue considering that it has already been highlighted that using classifiers which span multiple omics levels can improve the correlation of molecular features with clinical endpoints and improve patient stratification [28].…”

Mass spectrometry imaging advances and application in pharmaceutical research

“…The new MSI data computation method can be promptly translated into real practice when implemented in environments, such as RStudio, Python, MATLAB, and Delphi. Multivariate analytic methods, machine learning, and deep learning models are introduced into the MSI data processing tasks, such as automatic spatial segmentation [38,39], discriminative ion picking and latent feature extraction [40][41][42], region-specific matrix effect normalization [43], 3D image construction [44,45], high-quality image recovery by over-or sparse-sampling [46,47], co-localization (co-registration) among multi-modal images [48,49], and spatial multi-omics imaging data integration [50,51].…”

Chemical Decrosslinking-Based Peptide Characterization of Formaldehyde-Fixed Rat Pancreas Using Fluorescence-Guided Single-Cell Mass Spectrometry

“…Many successful cases using heat treatment were shown in the spatial characterization of protein assisted by various recent techniques including a nanodroplet in a pot system. 29,30 Heating the fixed sample in acidic or basic buffer partially removes the crosslinks and often facilitates subsequent trypsinization needed for MS-based proteomics/ peptidomics investigation of tryptic fragments. 31−35 However, these traditional approaches have not been effective in dealing with the single-cell analysis of FF tissue.…”

“…HIAR has been widely used in immunohistochemistry to improve the ability to detect antigens via restoring access to binding sites and their proper spatial confirmations. Many successful cases using heat treatment were shown in the spatial characterization of protein assisted by various recent techniques including a nanodroplet in a pot system. , Heating the fixed sample in acidic or basic buffer partially removes the crosslinks and often facilitates subsequent trypsinization needed for MS-based proteomics/peptidomics investigation of tryptic fragments. − …”