ReviewAnalytica chimica acta2026
Mass spectrometry imaging tutorial: From cancer biomarker discovery to clinical applications.
Review in Analytica chimica acta, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Towards spatial lipid profiling by using mass spectrometry: analytical challenges and applications.Analytical and bioanalytical chemistry · 2026Review
- MALDI-MSI Profiling of Effusion Cytology Cell Blocks Distinguishes High-Grade Serous Ovarian Carcinoma from Benign Effusions.Cancers · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.