Evidence map›Paper›PMID 40678547›Full record

ArticleiScience2025

Deep metabolic profiling of immune cells by spectral flow cytometry-A comprehensive validation approach.

Claire L Wishart, Alanna G Spiteri, Jian Tan, Claudio Counoupas, James A Triccas, Laurence Macia, Nicholas J C King

Abstract read
In one paragraph

Article in iScience, 2025. 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. Article
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

7 authors.

Claire L WishartInfection, Immunity, Inflammation Research Theme, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia.
Alanna G SpiteriInfection, Immunity, Inflammation Research Theme, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia.
Jian TanChronic Diseases Research Theme, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia.
Claudio CounoupasCharles Perkins Centre, The University of Sydney, Camperdown, NSW, Australia.
James A TriccasCharles Perkins Centre, The University of Sydney, Camperdown, NSW, Australia.
Laurence MaciaChronic Diseases Research Theme, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia.
Nicholas J C KingInfection, Immunity, Inflammation Research Theme, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The advancing field of immunometabolism requires tools that link single-cell metabolism with immune function. Metabolic flow cytometry provides this capability, but its broad adoption has been limited by costly custom reagents and a lack of standardized methods for validating metabolic targets. Here, we present a standardized and user-friendly spectral flow cytometry panel that profiles eight key metabolic pathways at single-cell resolution using only commercially available antibodies, enabling simultaneous analysis of immune phenotype and metabolic activity . Applying this approach to lung myeloid and T cells following intranasal adenoviral CD40L vaccination revealed distinct metabolic phenotypes between resident and infiltrating myeloid cells, as well as functionally divergent metabolic programs in naive, effector, and tissue-resident memory T cells. Additionally, leveraging NAD(P)H autofluorescence allowed label-free detection of glycolysis and expanded the panel's utility. This standardized approach reduces cost and experimental complexity, enabling researchers to elucidate how metabolism drives immune function across broader immunological and clinical contexts.

Indexed as

Biocomputational methodImmune responseImmunological methodsMetabolomics

Identifiers

PMID40678547
PMCPMC12268576

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.