Evidence map›Paper›PMID 36065601›Full record

ReviewMass spectrometry reviews

Advances in mass spectrometry imaging for spatial cancer metabolomics.

Xin Ma, Facundo M Fernández

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Mass spectrometry reviews. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
94citing papers in PubMed, 1 pooled it
–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

94 citing papers in PubMed, 1 synthesis or guideline pooled it.

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34 more citing papers are in PubMed but not listed here.

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

2 authors.

Xin MaSchool of Chemistry and Biochemistry and Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, USA.ORCID 0000-0002-7058-0165
Facundo M FernándezSchool of Chemistry and Biochemistry and Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, USA.ORCID 0000-0002-0302-2534

Funding

Deep Ovarian Cancer MetabolomicsR01CA218664 · NCI · GEORGIA INSTITUTE OF TECHNOLOGY · PI Facundo Martin Fernandez, Jaeyeon Kim · 2018 to 2026
$4.3M
National Science Foundation CHE‐1726528NCI NIH HHS R01 CA218664
6 · The paper itself

Abstract

Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.

Indexed as

Artificial IntelligenceNeoplasmsHumansLipidomicsMetabolomicsSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationcancerDESIglycanslipidsMALDImass spectrometry imagingSIMSspatial metabolomics

Identifiers

PMID36065601
PMCPMC9986357

What OpenQuestion holds

Textmetadata
LicenceTDM
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 OpenQuestion graph.