Early lymph node T follicular helper cell signalling hub drives influenza vaccine response in an ancestrally diverse cohort

早期淋巴结滤泡辅助性T细胞信号枢纽驱动祖先多样性人群的流感疫苗反应

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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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