Evidence map›Paper›PMID 40873476›Full record

ArticleBio-protocol2025

Assessing Human Treg Suppression at Single-Cell Resolution Using Mass Cytometry.

Jonas Nørskov Søndergaard, Janyerkye Tulyeu, David Priest, Shimon Sakaguchi, James B Wing

Abstract read
In one paragraph

Article in Bio-protocol, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Jonas Nørskov SøndergaardHuman Immunology Team, Center for Infectious Disease Education and Research (CiDER), The University of Osaka, Suita, Japan.
Janyerkye TulyeuHuman Immunology Team, Center for Infectious Disease Education and Research (CiDER), The University of Osaka, Suita, Japan.
David PriestHuman Immunology Team, Center for Infectious Disease Education and Research (CiDER), The University of Osaka, Suita, Japan.
Shimon SakaguchiLaboratory of Experimental Immunology, WPI-IFReC, The University of Osaka, Suita, Japan.
James B WingHuman Immunology Team, Center for Infectious Disease Education and Research (CiDER), The University of Osaka, Suita, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Regulatory T cells (Tregs) are essential for maintaining immune balance by controlling the activation and expansion of other immune cells. Conventional suppression assays often rely on co-culturing purified cell populations, which limits multiplexed phenotyping and physiological relevance. This protocol describes a high-dimensional, single-cell assay for profiling Treg-mediated suppression within a peripheral blood mononuclear cell (PBMC) system. Tregs are first isolated by cell sorting and then reintroduced into autologous PBMCs at defined ratios. A 52-marker mass cytometry (CyTOF) panel is used to quantify cell division and phenotypic responses across multiple immune subsets. This approach allows for integrated analysis of Treg function with broad compatibility for patient profiling and drug evaluation. Key features • Quantifies Treg-mediated suppression in autologous PBMCs at single-cell resolution. • Enables high-dimensional phenotyping and proliferation tracking across multiple immune subsets using a 52-marker CyTOF panel. • Maintains physiological relevance by assessing suppression in a complex PBMC environment. • Compatible with patient-derived samples and drug perturbation experiments for translational immunology applications.

Indexed as

CyTOFImmune profilingMass cytometryPBMCRegulatory T cellsSingle-cell analysisSuppression assay

Identifiers

PMID40873476
PMCPMC12378424

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.