Elaboration Pipeline for the Management of MALDI-MS Imaging Datasets

A Guide to MALDI Imaging Mass Spectrometry for Tissues

Imaging mass spectrometry is a well-established technology that can easily and succinctly communicate the spatial localization of molecules within samples. This review communicates the recent advances in the field, with a specific focus on matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) applied on tissues. The general sample preparation strategies for different analyte classes are explored, including special considerations for sample types (fresh frozen or formalin-fixed,) strategies for various analytes (lipids, metabolites, proteins, peptides, and glycans) and how multimodal imaging strategies can leverage the strengths of each approach is mentioned. This work explores appropriate experimental design approaches and standardization of processes needed for successful studies, as well as the various data analysis platforms available to analyze data and their strengths. The review concludes with applications of imaging mass spectrometry in various fields, with a focus on medical research, and some examples from plant biology and microbe metabolism are mentioned, to illustrate the breadth and depth of MALDI IMS.

Cytological Cytospin Preparation for the Spatial Proteomics Analysis of Thyroid Nodules Using MALDI-MSI

The application of innovative spatial omics approaches in the context of cytological specimens may open new frontiers for their diagnostic assessment. In particular, spatial proteomics using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) represents one of the most promising avenues, owing to its capability to map the distribution of hundreds of proteins within a complex cytological background in a multiplexed and relatively high-throughput manner. This approach may be particularly beneficial in the heterogeneous context of thyroid tumors where certain cells may not present clear-cut malignant morphology upon fine-needle aspiration biopsy, highlighting the necessity for additional molecular tools which are able to improve their diagnostic performance.This chapter aims to provide a detailed overview of a cytospin-based preparation workflow that has been optimized to facilitate the reliable spatial proteomics analysis of cytological thyroid specimens using MALDI-MSI, indicating the key aspects which should be considered when handling such samples.

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

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.

Data parsing in mass spectrometry imaging using R Studio and Cardinal: A tutorial

Mass spectrometry imaging (MSI) has emerged as a rapidly expanding field in the MS community. The analysis of large spectral data is further complicated by the added spatial dimension of MSI. A plethora of resources exist for expert users to begin parsing MSI data in R, but there is a critical lack of guidance for absolute beginners. This tutorial is designed to serve as a one-stop guide to start using R with MSI data and describe the possibilities that data science can bring to MSI analysis.