Evidence map›Paper›PMID 41577411›Full record

ReviewAnalytica chimica acta2026

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

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

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Amirsalar MansouriDepartment of Interdisciplinary Oncology, LSU LCMC Health Cancer Center, LSU Health-New Orleans, New Orleans, USA.
Nipun Babu VarukattuDepartment of Interdisciplinary Oncology, LSU LCMC Health Cancer Center, LSU Health-New Orleans, New Orleans, USA.
Brennan J CuroleDepartment of Interdisciplinary Oncology, LSU LCMC Health Cancer Center, LSU Health-New Orleans, New Orleans, USA.
Omeed MoavenDepartment 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.
Jiri AdamecDepartment of Interdisciplinary Oncology, LSU LCMC Health Cancer Center, LSU Health-New Orleans, New Orleans, USA. Electronic address: jadame@lsuhsc.edu.

Funding

Translational Genomics Core (TGC)P20GM121288 · NIGMS · LSU HEALTH SCIENCES CENTER · PI AUGUSTO C. OCHOA · 2017 to 2026
$22.4M
NIGMS NIH HHS P20 GM121288
6 · The paper itself

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

Biomarkers, TumorMass SpectrometryNeoplasmsHumansSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationBiomarkers, TumorDesorption electrospray ionization (DESI)Mass spectrometry imaging (MSI)Matrix-assisted laser desorption/ionization (MALDI)Rapid evaporative ionization mass spectrometry (REIMS)Spatial multi-omics

Identifiers

PMID41577411
PMCPMC13496517

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.