Evidence map›Paper›PMID 42827948›Full record

ArticleFrontiers in immunology2026

Immune signatures of common exposures through co-occurrence of T-cell receptors in tens of thousands of donors.

Damon H May, Steven Woodhouse, Matthew T Noakes, Kathryn Doroschak, Rebecca Elyanow, Amanda J Moore, Edward J Osborne, Brad Greenfield, Tim Hayes, Ruth Taniguchi and 10 more

Abstract read
In one paragraph

Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

20 authors.

Damon H May *Adaptive Biotechnologies, Seattle, WA, United States.
Steven Woodhouse *Adaptive Biotechnologies, Seattle, WA, United States.
Matthew T NoakesAdaptive Biotechnologies, Seattle, WA, United States.
Kathryn DoroschakAdaptive Biotechnologies, Seattle, WA, United States.
Rebecca ElyanowAdaptive Biotechnologies, Seattle, WA, United States.
Amanda J MooreAdaptive Biotechnologies, Seattle, WA, United States.
Edward J OsborneAdaptive Biotechnologies, Seattle, WA, United States.
Brad GreenfieldAdaptive Biotechnologies, Seattle, WA, United States.
Tim HayesAdaptive Biotechnologies, Seattle, WA, United States.
Ruth TaniguchiAdaptive Biotechnologies, Seattle, WA, United States.
Zheng YangAdaptive Biotechnologies, Seattle, WA, United States.
John R GrinoAdaptive Biotechnologies, Seattle, WA, United States.
Rachel ByronAdaptive Biotechnologies, Seattle, WA, United States.
Jamie OaksAdaptive Biotechnologies, Seattle, WA, United States.
Haiyin Chen-HarrisAdaptive Biotechnologies, Seattle, WA, United States.
Anna SherwoodAdaptive Biotechnologies, Seattle, WA, United States.
Julia GreisslMicrosoft Research, Redmond, WA, United States.
Bryan HowieAdaptive Biotechnologies, Seattle, WA, United States.
Mark KlingerAdaptive Biotechnologies, Seattle, WA, United States.
Harlan S RobinsAdaptive Biotechnologies, Seattle, WA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Memory T cells are records of clonal expansion from prior immune exposures such as infections, vaccines and chronic diseases. Some of the receptors of these expanded T cell clones in a typical immune repertoire are highly public (present in many individuals) because they respond to the same peptide from a prevalent immune exposure, presented by the same Human Leukocyte Antigen (HLA) allele. Only a tiny fraction of public T-cell receptor β sequences (TCRs) have known associations with exposures or specific peptides. Methods: We mined the TCR repertoires of tens of thousands of donors to define "ECOclusters" (Exposure Co-Occurrence clusters): clusters of public TCRs that tend to occur in the same donors. First, we built models to infer donor HLA type from the TCR repertoire, then associated public TCRs with HLA alleles. Next, we derived co-occurrence clusters of TCRs responding to antigens presented by the same HLA allele, then combined those clusters by co-occurrence across HLA alleles. Each such cross-HLA ECOcluster putatively represents a public TCR signature of a single exposure. Results: We constructed sensitive, specific models to predict the presence of 220 HLA alleles from TCR repertoires and clustered 8,618,285 HLA allele-associated TCRs to define 11,058 ECOclusters. Using serologically labeled repertoires, we identified ECOclusters associated with HSV-1, HSV-2, EBV, Parvovirus,

Indexed as

HLA AntigensMemory T CellsReceptors, Antigen, T-CellReceptors, Antigen, T-Cell, alpha-betaAllelesCytomegalovirusHumansSARS-CoV-2HLA AntigensReceptors, Antigen, T-CellReceptors, Antigen, T-Cell, alpha-betaCytomegalovirusmachine learningT cell receptorT cellsTCR repertoireTCR-seq

Identifiers

PMID42827948
PMCPMC13630998

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.