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Record W4415944733 · doi:10.1101/2025.11.04.686558

Temporal Dynamics of Antigen-Specific T Cell Expansion in Primary SARS-CoV-2 Infection

2025· preprint· W4415944733 on OpenAlexaff
Andrew G. T. Pyo, Joshua Rosenheim, Clare Thakker, Lucy Bell, Gayathri Nageswaran, Suzanne Byrne, Ben Killingley, Scobie Dr, Mariya Kalinova, Alison Boyers, Andrew Catchpole, Alex Mann, Rik G.H. Lindeboom, Marko Nikolić, Sarah A. Teichmann, Helen R. Wagstaffe, Mahdad Noursadeghi, Christopher Chiu, Curtis G. Callan, Andreas Mayer, Ned S. Wingreen, Benny Chain

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Language
FieldBiochemistry, Genetics and Molecular Biology
Topicvaccines and immunoinformatics approaches
Canadian institutionsInstitute of Cancer Research
FundersWellcome Trust
KeywordsT-cell receptorT cellCD8Dynamics (music)Cytotoxic T cellCd4 t cellCellT lymphocyte

Abstract

fetched live from OpenAlex

Abstract Quantifying T cell response during primary infection in humans is crucial for understanding adaptive immunity. Leveraging a controlled human challenge to SARS-CoV-2, we characterized antigen-specific T cell response within and across individuals. Notably, individual clones reached similar maximum frequencies despite differences in the timing of their peak expansion. Mathematical modeling showed that this observation is consistent with precursor frequency, but not TCR signal strength, as the source of inter-clonal variability. Single-cell profiling revealed distinct temporal programs for CD4 + and CD8 + T cells, with CD4 + cells expanding earlier but contracting to a lower frequency. Clones with similar receptors, likely recognizing the same antigen, expanded at similar times. Together, these findings highlight how clone-intrinsic properties such as precursor frequency and lineage shape T cell clonal kinetics. These insights provide a quantitative framework for understanding T cell response in humans, with implications for vaccine design.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.216
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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