Early lymph node T follicular helper cell signalling hub drives influenza vaccine response in an ancestrally diverse cohort
Bibliographic record
Abstract
Background Early in vivo dynamics of human immune-cell activation across regionally activated lymphoid tissue sites upon immunisation are poorly characterised in ancestrally-diverse individuals with consequences for pandemic preparedness. Methods In this experimental medicine study, draining and non-draining lymph nodes (dLNs and ndLNs) were studied by ultrasound (US)-guided fine-needle aspiration (FNA) in 13 adults aged 18–55 years with African and Asian ancestry, before and after receiving adjuvanted seasonal influenza vaccine (aQIV). A multi-modal investigation of ultrasound data, genotyping, systems serology, and single-cell multi-omics was undertaken. Findings HLA subtypes reflected self-declared ethnicity and included understudied alleles. Draining but not ndLNs rapidly increased in size post-vaccination, by day 3, with distinct cellular dynamics culminating in a cross-protective serological response. Dissecting LN cellular diversity into 42 lymphoid and non-lymphoid cell states, early post-vaccination cell abundance changes were observed across all LNs, but dLNs were characterised by CD4 + T follicular helper (CD4 + Tfh) cell expansion. Gene expression analysis revealed a dLN post-vaccination hub defined by CD4 + Tfh signalling, cross-compartmental activation, translation, and enhanced antigen-presentation capacity. Interpretation Early CD4 + Tfh coordination in draining lymphoid tissue underpins robust responses to adjuvanted influenza vaccine that transcend ancestral inter-individual variation in young adults, with implications for vaccine design in ancestrally-diverse populations. Funding The study was funded by the Silicon Valley Community Foundation with a Chan Zuckerberg Initiative donation. The funder had no role in the study design, data analysis or decision to publish. The funder provided infrastructure support for the posting of the dataset with CELLxGENE.
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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 | 0.000 |
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.
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".