Evidence mapPaperPMID 41584502Full record

ReviewClinical & translational immunology2026

Integrating mass spectrometry-based multi-omic signatures of extracellular vesicles: from discovery to clinical translation.

Akila Wijerathna-Yapa, Dimitri Aubert, Arutha Kulasinghe

Abstract readReview
In one paragraph

Review in Clinical & translational immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. 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

3 authors.

Akila Wijerathna-YapaFaculty of Medicine, Frazer Institute The University of Queensland Woolloongabba QLD Australia.ORCID https://orcid.org/0000-0003-1935-8590
Dimitri AubertVesiculab Ltd Nottingham UK.ORCID https://orcid.org/0000-0001-5206-4442
Arutha KulasingheFaculty of Medicine, Frazer Institute The University of Queensland Woolloongabba QLD Australia.ORCID https://orcid.org/0000-0003-3224-7350

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Extracellular vesicles (EVs) are increasingly recognised as key mediators of intercellular communication and disease progression. Their capacity to carry bioactive molecules, namely proteins, lipids and metabolites, reflects the physiological and pathological states of their cells of origin, making them surrogates for diagnostic, prognostic and therapeutic endpoints. Recent advances in mass spectrometry have enabled comprehensive, high-resolution profiling of EVs across multiple omics layers. Proteomics has uncovered both conserved and disease-specific protein markers; lipidomics has revealed structurally distinct membrane compositions influencing EV stability and function; and metabolomics has captured dynamic snapshots of cellular metabolism. However, significant challenges persist for standardisation and interpretation of EVs, which include variation in EV isolation purity, scalability, EV heterogeneity and cross-study comparability. This perspective critically synthesises findings from recent EV multi-omics studies and proposes a conceptual framework for integrating these omics layers to better define EV identity and functionality. We highlight emerging clinical applications and outline future directions involving single-vesicle omics and the rational engineering of therapeutic EVs. The integration of multi-omics approaches with translational aims holds promise for advancing EVs from experimental tools to new pillars of precision medicine.

Indexed as

exosomesextracellular vesiclesliquid biopsymass spectrometrymulti‐omics

Identifiers

PMID41584502
PMCPMC12830070

What Socratic holds

Textmetadata
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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.