Evidence map›Paper›PMID 40034696›Full record

ArticleFrontiers in immunology2025

A large-scale database of T-cell receptor beta sequences and binding associations from natural and synthetic exposure to SARS-CoV-2.

Sean Nolan, Marissa Vignali, Mark Klinger, Jennifer N Dines, Ian M Kaplan, Emily Svejnoha, Tracy Craft, Katie Boland, Mitchell W Pesesky, Rachel M Gittelman and 19 more

Abstract read
In one paragraph

Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 170 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
170citing papers in PubMed, 1 pooled it
–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

170 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Resolving SARS-CoV-2 CD4Cell reports. Medicine · 2022
    Pooled it
  2. T-cell receptor repertoires as potential diagnostic markers for patients with COVID-19.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2021
    Trial
  3. Review
  4. Article
  5. Article
  6. Article
  7. T Cell Thoughts.Immunological reviews · 2026
    Review
  8. Technical review of artificial intelligence in TCR-T therapy.Journal of the National Cancer Center · 2026
    Review
  9. Article
  10. Ensembles ofbioRxiv : the preprint server for biology · 2026
    Article
  11. Article
  12. Review
  13. Article
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  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. AI-Driven BCR Modeling for Precision Immunology.International journal of molecular sciences · 2026
    Review

110 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

29 authors.

Sean NolanAdaptive Biotechnologies, Seattle, WA, United States.
Marissa VignaliAdaptive Biotechnologies, Seattle, WA, United States.
Mark KlingerAdaptive Biotechnologies, Seattle, WA, United States.
Jennifer N DinesAdaptive Biotechnologies, Seattle, WA, United States.
Ian M KaplanAdaptive Biotechnologies, Seattle, WA, United States.
Emily SvejnohaAdaptive Biotechnologies, Seattle, WA, United States.
Tracy CraftAdaptive Biotechnologies, Seattle, WA, United States.
Katie BolandAdaptive Biotechnologies, Seattle, WA, United States.
Mitchell W PeseskyAdaptive Biotechnologies, Seattle, WA, United States.
Rachel M GittelmanAdaptive Biotechnologies, Seattle, WA, United States.
Thomas M SnyderAdaptive Biotechnologies, Seattle, WA, United States.
Christopher J GooleyMicrosoft Research, Redmond, WA, United States.
Simona SempriniUnit of Microbiology - The Great Romagna Hub Laboratory, Pievesestina ITALY and DIMES, University of Bologna, Bologna, Italy.
Claudio CerchioneIRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy.
Fabio NicoliniImmunotherapy, Cell Therapy and Biobank (ITCB), IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy.
Massimiliano MazzaImmunotherapy, Cell Therapy and Biobank (ITCB), IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy.
Ottavia M DelmonteImmune Deficiency Genetics Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States.
Kerry DobbsImmune Deficiency Genetics Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States.
Gonzalo Carreño-TarragonaHematology Department, Hospital 12 de Octubre, i+12, CNIO, Complutense University, Madrid, Spain.
Santiago BarrioHematology Department, Hospital 12 de Octubre, i+12, CNIO, Complutense University, Madrid, Spain.
Vittorio SambriUnit of Microbiology - The Great Romagna Hub Laboratory, Pievesestina ITALY and DIMES, University of Bologna, Bologna, Italy.
Giovanni MartinelliIRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy.
Jason D GoldmanSwedish Medical Center, Seattle, WA, United States.
James R HeathInstitute for Systems Biology, Seattle, WA, United States.
Luigi D NotarangeloImmune Deficiency Genetics Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States.
Joaquin Martinez-LopezHematology Department, Hospital 12 de Octubre, i+12, CNIO, Complutense University, Madrid, Spain.
Bryan HowieAdaptive Biotechnologies, Seattle, WA, United States.
Jonathan M CarlsonMicrosoft Research, Redmond, 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

We describe the establishment and current content of the ImmuneCODE™ database, which includes hundreds of millions of T-cell Receptor (TCR) sequences from over 1,400 subjects exposed to or infected with the SARS-CoV-2 virus, as well as over 160,000 high-confidence SARS-CoV-2-associated TCRs. This database is made freely available, and the data contained in it can be used to assist with global efforts to understand the immune response to the SARS-CoV-2 virus and develop new interventions.

Indexed as

COVID-19Receptors, Antigen, T-Cell, alpha-betaSARS-CoV-2Databases, GeneticHumansReceptors, Antigen, T-Cell, alpha-betacellular immunityCOVID-19immune responseSARS-CoV-2T cellTCR repertoire

Identifiers

PMID40034696
PMCPMC11873104

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.