Cross-tissue single-cell analysis reveals transcriptional and functional diversity of T follicular helper cells from infancy to adulthood 3741
Bibliographic record
Abstract
Abstract Description T follicular helper (Tfh) cells play a crucial role in humoral immunity by fine-tuning the quality of neutralizing antibodies (nAbs). However, they are mostly restricted to tissue sites, making them challenging to study in the context of vaccination. In this study, we used spleens, lymph nodes, and tonsils collected from humans (n = 30, ages 2-58 years) to comprehensively profile the diversity of Tfh ex vivo using flow cytometry and single cell transcriptomics. Multimodal analyses revealed that Tfh were enriched and were more clonal in young children, but their cytokines responses to ex vivo stimulation and polarized phenotypes (Tfh1, Tfh2, and Tfh17 associated) increased with age. Next, we used the tonsil immune organoid system to dissect Tfh responses to ex vivo vaccination with influenza antigens. Single cell analyses revealed early proliferation and heightened Tfh activation in young children compared to adults. Frequencies of activated Tfh1 strongly correlated with nAbs in both groups. Depleting Tfh in organoids reduced germinal center (GC) B cell frequencies but did not alter the expansion of antigen-specific B cells. While overall antibody magnitude remained unchanged, nAbs were attenuated with Tfh depletion in adults but remained unchanged in children. These findings suggest that Tfh play distinct functional roles in GCs in children versus adults. Ongoing investigations are focused on understanding how Tfh regulate affinity maturation in GCs in different age groups. Funding Sources Supported by NIH/NIAID 1R01AI173023; NIH/NIAID 1U01AI180164; Wellcome LEAP (Wellcome Trust, UK); Bill and Melinda Gates Foundation Topic Categories Computational and Systems Immunology (COMP)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".