Evidence map›Paper›PMID 40268295›Full record

ArticleImmunology and cell biology2025

A new method to measure cell metabolism of rare cells in vivo reveals a high oxidative phosphorylation dependence of lung T cells.

Aristeidis Roubanis, Morgane Hilaire, Morgane Le Teuff, Odile Devergne, Tim Sparwasser, Luciana Berod, Benoît L Salomon

Abstract read
In one paragraph

Article in Immunology and cell biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Aristeidis RoubanisCentre d'Immunologie et des Maladies Infectieuses (CIMI-Paris), INSERM, CNRS, Sorbonne Université, Paris, France.
Morgane HilaireCentre d'Immunologie et des Maladies Infectieuses (CIMI-Paris), INSERM, CNRS, Sorbonne Université, Paris, France.
Morgane Le TeuffToulouse Institute for Infectious and Inflammatory Diseases (Infinity), INSERM - CNRS - University Toulouse III, Toulouse, France.
Odile DevergneCentre d'Immunologie et des Maladies Infectieuses (CIMI-Paris), INSERM, CNRS, Sorbonne Université, Paris, France.
Tim SparwasserInstitute of Medical Microbiology and Hygiene and Research Center for Immunotherapy (FZI), University Medical Center, Johannes Gutenberg-University Mainz, Mainz, Germany.
Luciana BerodInstitute of Medical Microbiology and Hygiene and Research Center for Immunotherapy (FZI), University Medical Center, Johannes Gutenberg-University Mainz, Mainz, Germany.
Benoît L SalomonCentre d'Immunologie et des Maladies Infectieuses (CIMI-Paris), INSERM, CNRS, Sorbonne Université, Paris, France.ORCID https://orcid.org/0000-0001-9673-5578

Funding

Agence Nationale de la Recherche ANR-17-CE15-0030Agence Nationale de la Recherche ANR-21-CE15-0049Agence Nationale de la Recherche ANR-22-CE18-0045Deutsche Forschungsgemeinschaft 318346496H2020 Marie Skłodowska-Curie Actions 955321
6 · The paper itself

Abstract

Regulation of cellular metabolism is a central element governing the fate and function of T cells. However, the in vivo metabolic characteristics of rare cells, such as nonlymphoid tissue T cells, are poorly understood because of experimental limitations. Most techniques measuring cell metabolism require large cell numbers. The recent SCENITH method allows for studying the metabolism of rare cells by flow cytometry. However, this technique requires cells to be isolated and cultured ex vivo, which may alter their metabolism. Here, we propose a new experimental approach, called in vivo SCENITH, to investigate the cellular metabolism of T cells in vivo at a steady state in the spleen and lungs. For this purpose, we administered the metabolic modulators directly in mice, instead of applying these reagents ex vivo, as in the classical SCENITH method. Whereas ex vivo manipulation impacted the viability and phenotype of T cells, this toxic effect was not observed in the in vivo SCENITH. We observed that conventional and regulatory T cells shared similar metabolic profiles. Importantly, whereas spleen T cells used both oxidative phosphorylation and glycolysis, the metabolism of T cells in the lungs was mainly based on oxidative phosphorylation. Finally, metabolic inhibitors that interfere with protein translation and energy availability downregulated Foxp3 expression in regulatory T cells. These results describe an expansion of SCENITH that allows to measure the metabolic profile of rare cells in vivo, revealing a high dependence on oxidative phosphorylation of lung T cells.

Indexed as

LungOxidative PhosphorylationT-LymphocytesT-Lymphocytes, RegulatoryAnimalsForkhead Transcription FactorsGlycolysisMiceMice, Inbred C57BLSpleenForkhead Transcription FactorsCell metabolismFoxp3in vivo SCENITHlung T cellsTreg

Identifiers

PMID40268295
PMCPMC12392706

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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.