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Mass spectrometry imaging tutorial: From cancer biomarker discovery to clinical applications.

作者信息

Mansouri Amirsalar, Varukattu Nipun Babu, Curole Brennan J, Moaven Omeed, Adamec Jiri

机构信息

Department of Interdisciplinary Oncology, LSU LCMC Health Cancer Center, LSU Health-New Orleans, New Orleans, USA.

Department of Interdisciplinary Oncology, LSU LCMC Health Cancer Center, LSU Health-New Orleans, New Orleans, USA; School of Medicine, Louisiana State University Health Sciences Center (LSUHSC), New Orleans, LA, 70112, USA; Division of Surgical Oncology, Department of Surgery, Louisiana State University Health Sciences Center, New Orleans, LA, USA.

出版信息

Anal Chim Acta. 2026 Feb 15;1387:344925. doi: 10.1016/j.aca.2025.344925. Epub 2025 Nov 24.

DOI:10.1016/j.aca.2025.344925
PMID:41577411
Abstract

Mass spectrometry (MS), particularly mass spectrometry imaging (MSI), is an important analytical technique that facilitates the detection and spatial visualization of biomolecules, and more specifically cancer biomarkers, in complex biological tissue samples. Over the past thirty years, innovations such as electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI) have significantly expanded MS's capabilities, enabling detailed molecular profiling of proteins, lipids, nucleic acids, and metabolites directly from clinical samples. It is here where MSI can uniquely contribute to cancer biomarker discovery by revealing the spatial distribution of these molecules in tissue sections, thereby providing crucial insights into tumor microenvironments. Despite its strengths, traditional matrix-based MSI faces limitations related to analyte specificity, reproducibility, and data interpretation. Matrix-free alternatives, such as desorption electrospray ionization (DESI) and rapid evaporative ionization mass spectrometry (REIMS), offer clinical promise but present challenges, including low ionization efficiency and complex data interpretation that require advanced processing, normalization, and machine learning to extract meaningful biological insights. While imaging techniques like the inclusion of heavy metal isotope (HMI) or photocleavable (PC) mass tags (MTs) can mitigate these factors by providing greater sensitivity and selectivity during MSI, powerful data processing and analysis is still needed to improve accuracy and reproducibility of datasets to allow for the use of MSI to permeate into routine clinical practice. In this tutorial, a variety of useful tools are provided to bolster each step of the data processing and analysis workflow. Furthermore, MSI has wide-reaching applications, not only in oncology but also in neurology, infectious disease, and drug development, offering molecular insights critical for diagnostics and personalized therapies. MS based surgical and diagnostic tools, such as iKnife, SpiderMass, and MasSpec Pen, may further enable intraoperative and point-of-care applications, positioning MSI at the forefront of next-generation clinical and translational research.

摘要

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