Evidence map›Paper›PMID 42907629›Full record

ReviewAdvances in experimental medicine and biology2026

Advancing Spatial Lipidomics with MALDI-MS Imaging.

Estefania Garcia-Calvo, Gabriel A Peñalver, Jose Luis Luque-Garcia

Abstract readReview
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In one paragraph

Review in Advances in experimental medicine and biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Estefania Garcia-CalvoDepartment of Analytical Chemistry, Faculty of Chemical Sciences, Complutense University of Madrid, Madrid, Spain.
Gabriel A PeñalverDepartment of Analytical Chemistry, Faculty of Chemical Sciences, Complutense University of Madrid, Madrid, Spain.
Jose Luis Luque-GarciaDepartment of Analytical Chemistry, Faculty of Chemical Sciences, Complutense University of Madrid, Madrid, Spain. jlluque@ucm.es.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spatial lipidomics has emerged as a powerful approach for investigating the molecular organization of biological tissues by combining lipid analysis with spatially resolved information. Among the available techniques, matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) has become a key platform for mapping lipid distributions directly in tissue sections without the need for labeling. This chapter provides a comprehensive overview of recent advances in spatial lipidomics using MALDI-MSI, with particular emphasis on sample preparation strategies, matrix selection and deposition methods, and analytical approaches for lipid identification. Critical steps influencing data quality, including tissue preservation, sectioning, matrix application, and mitigation of ion suppression effects, are discussed in detail. Furthermore, the identification of major lipid classes, such as phospholipids, sphingolipids, neutral lipids, glycolipids, fatty acids, and cholesterol, is addressed through characteristic ionization behaviors and diagnostic fragmentation patterns. Finally, the integration of MALDI-MSI with thin-layer chromatography is presented as a complementary strategy to enhance lipid coverage and reduce ion suppression. Together, these approaches highlight the versatility of MALDI-MSI as a robust tool for advancing spatial lipidomics in biomedical research.

Indexed as

LipidomicsLipidsSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationAnimalsChromatography, Thin LayerHumansLipidsIon suppressionLipid imagingMALDI-MSIMatrix selectionSample preparationSpatial lipidomicsTandem mass spectrometryThin-layer chromatography

Identifiers

PMID42907629

What OpenQuestion holds

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Registered trials

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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 OpenQuestion graph.