ArticleEBioMedicine2025
Early lymph node T follicular helper cell signalling hub drives influenza vaccine response in an ancestrally diverse cohort.
Article in EBioMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Multimodal analysis definesbioRxiv : the preprint server for biology · 2025Article
- Multi-site Ultrasound-guided Fine Needle Aspiration to Study Cells and Soluble Factors From Human Lymph Nodes.Current protocols · 2024Article
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Authors and funding
31 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly 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.
methodsIn 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.
findingsHLA 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
interpretationEarly CD4
fundingThe 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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