Evidence map›Paper›PMID 41463343›Full record

ReviewBiomolecules2025

Beyond Bulk Metabolomics: Emerging Technologies for Defining Cell-Type Specific Metabolic Pathways in Health and Disease.

Yichen Gong, Samuel Weinberg

Abstract readReview
In one paragraph

Review in Biomolecules, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Insights into the maternal-fetal interface from spatial multi-omics.Frontiers in cell and developmental biology · 2026
    Review
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.

Yichen GongDepartment of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.
Samuel WeinbergDepartment of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.ORCID 0000-0002-9532-7051

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While metabolomics has emerged as a powerful tool for discovering disease biomarkers, the clinical utility of plasma or tissue metabolite profiles remains limited due to metabolic heterogeneity and flexibility across cell types. Traditional bulk metabolomics fails to capture the distinct metabolic programs operating within rare cell populations that often drive disease pathogenesis. This review examines cutting-edge approaches that overcome these limitations by characterizing metabolism at single-cell and cell-type-specific resolution, with particular emphasis on rare immune cell populations as a proof of concept. We discuss how the integration of flow cytometric metabolic profiling, molecular techniques, advanced metabolomics platforms, and computational modeling enables unprecedented insight into cell-intrinsic metabolic states within physiological contexts. We critically evaluate how these technologies reveal metabolic plasticity that confounds bulk measurements while identifying cell-type-specific metabolic vulnerabilities. Finally, we address the crucial challenge of establishing causality in metabolic pathways, a prerequisite for translating metabolomic discoveries into clinically actionable interventions. By moving beyond descriptive metabolomics toward a mechanistic understanding of cell-type-specific metabolism, these approaches promise to deliver the precision required for effective metabolic targeting in disease.

Indexed as

Metabolic Networks and PathwaysMetabolomicsAnimalsBiomarkersFlow CytometryHumansMetabolomeBiomarkersCRISPR screeningimmunometabolismLC-MS HILICrare immune cell metabolomicssingle-cell metabolic profiling

Identifiers

PMID41463343
PMCPMC12730452

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.