Evidence map›Paper›PMID 40849349›Full record

ReviewMikrochimica acta2025

Nanoparticle-assisted strategies in mass spectrometry-based metabolite analysis: recent advances across analytical workflow.

Roberto Gamboa-Becerra, Ernesto Beltrán-Partida, Benjamín Valdez-Salas, Jhonathan Castillo-Saenz, Jorge Salvador-Carlos, Mario Curiel-Álvarez

Abstract readReview
In one paragraph

Review in Mikrochimica acta, 2025. 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

6 authors.

Roberto Gamboa-Becerra *Core Facilities of Chemistry and Advanced Materials, Instituto de Ingeniería, Universidad Autónoma de Baja California, Calle de La Normal S/N and Boulevard Benito Juárez, Mexicali, 21100, Baja California, Mexico.
Ernesto Beltrán-Partida *Core Facilities of Chemistry and Advanced Materials, Instituto de Ingeniería, Universidad Autónoma de Baja California, Calle de La Normal S/N and Boulevard Benito Juárez, Mexicali, 21100, Baja California, Mexico.
Benjamín Valdez-SalasCore Facilities of Chemistry and Advanced Materials, Instituto de Ingeniería, Universidad Autónoma de Baja California, Calle de La Normal S/N and Boulevard Benito Juárez, Mexicali, 21100, Baja California, Mexico. benval@uabc.edu.mx.
Jhonathan Castillo-SaenzCore Facilities of Chemistry and Advanced Materials, Instituto de Ingeniería, Universidad Autónoma de Baja California, Calle de La Normal S/N and Boulevard Benito Juárez, Mexicali, 21100, Baja California, Mexico.
Jorge Salvador-CarlosCore Facilities of Chemistry and Advanced Materials, Instituto de Ingeniería, Universidad Autónoma de Baja California, Calle de La Normal S/N and Boulevard Benito Juárez, Mexicali, 21100, Baja California, Mexico.
Mario Curiel-ÁlvarezCore Facilities of Chemistry and Advanced Materials, Instituto de Ingeniería, Universidad Autónoma de Baja California, Calle de La Normal S/N and Boulevard Benito Juárez, Mexicali, 21100, Baja California, Mexico.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolite analysis plays a critical role in understanding phenotypic variations, biochemical processes, and physiological responses in biological systems. Whether through untargeted metabolomic profiling or targeted approaches aimed at quantifying specific or even individual metabolites, accurate detection presents significant analytical challenges due to their vast chemical diversity, low abundance, and complexity of biological matrices. This chemical analytical process encompasses a dynamic workflow that includes sample collection, extraction, enrichment, separation, and detection. Recent advances in nanotechnology offer promising alternatives to support and enhance each stage of this workflow, particularly within mass spectrometry (MS)-based applications. Nanoparticles, due to their high surface area, tunable surface chemistry, and ability to improve sensitivity, have been widely applied to improve sample pretreatment, selective enrichment, separation efficiency, and ionization, ultimately enhancing MS-based metabolites detection. This review provides an updated overview of nanoparticle-assisted strategies throughout the MS-based metabolite analysis workflow. It discusses the different classes of those nanomaterials and their applications across various phases stages, from sample preparation to ionization and detection, supporting analyses that range from untargeted and targeted metabolomics to the detection of individual metabolites. Although the primary focus is on MS-based workflows, we also reviewed nanoparticle-assisted separation strategies coupled with alternative detection platforms, such as optical or electrochemical methods, when these approaches show potential for integration with MS workflows. This inclusion reflects the current gap in literature addressing nanoparticle-assisted separation directly coupled with MS detection systems. These cases highlight underexplored opportunities where nanomaterials could enhance separation prior to MS detection, although further work is needed to ensure compatibility with MS platforms for suitable metabolite analysis. Furthermore, we highlight emerging trends and future perspectives in this evolving field, emphasizing the potential of nanotechnology to overcome current analytical limitations and expand the scope of both metabolomic profiling and focused metabolite analysis.

Indexed as

Mass SpectrometryMetabolomicsNanoparticlesHumansWorkflowAnalytical techniquesAnalytical workflow optimizationDetection sensitivity enhancementNanoparticle-assisted metabolomicsSample pretreatmentSelective metabolite enrichmentTargeted metabolite derivatization

Identifiers

PMID40849349
PMCPMC12374875

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.