Evidence mapPaperPMID 42443595Full record

ReviewNature reviews. Immunology2026

The challenge and promise of studying human antigen-specific T cells.

Sam Farrar, Elie Antoun, Julian C Knight, Yanchun Peng, Tao Dong

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Sam FarrarChinese Academy of Medical Science Oxford Institute, University of Oxford, Oxford, UK.
Elie AntounChinese Academy of Medical Science Oxford Institute, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-9477-1564
Julian C KnightChinese Academy of Medical Science Oxford Institute, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-0377-5536
Yanchun PengChinese Academy of Medical Science Oxford Institute, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0003-2340-0499
Tao DongChinese Academy of Medical Science Oxford Institute, University of Oxford, Oxford, UK. tao.dong@ndm.ox.ac.uk.ORCID http://orcid.org/0000-0003-3545-3758

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Each antigen-specific T cell population represents only a small fraction of the total T cell repertoire, yet these populations play a disproportionately large role in immune responses to viruses and cancer and act as key effectors in autoimmune disease. Understanding their behaviour and phenotypes is therefore crucial for elucidating disease mechanisms. The rarity of these cells and the difficulty of isolating them meant that most prior studies examined bulk, unselected T cell populations. Such approaches capture substantial heterogeneity arising from diverse antigen targets, HLA alleles and potential bystander cells, yet have shaped much of our understanding of T cell responses and phenotypes. In this Review, we discuss how a combination of fundamental and emerging technologies now enables the detailed study of antigen-specific T cells. We highlight how insights linking epitope specificity, T cell receptor (TCR) usage and functional profiles across blood and tissues are transforming our understanding of T cell immunity. Furthermore, we emphasize that the widespread adoption of paired TCR sequencing is generating antigen-specific TCR datasets that can serve as durable reference libraries, enabling future studies and immune atlases to annotate and characterize antigen-specific T cell responses within broader datasets.

Identifiers

PMID42443595

What Socratic holds

Textmetadata
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 Socratic graph.