Published January 22, 2025 | Version v1
Dataset Open

Comprehensive Multimodal Immune Response Dataset for LAIV Vaccination in Pediatric Cohorts

  • 1. ROR icon Boston University
  • 2. ROR icon University of Sheffield

Description

This dataset is part of the publication "Integrative Mapping of Pre-existing Immune Landscapes for Vaccine Response Prediction". It provides detailed immune profiling of 244 children from The Gambia who received the trivalent live attenuated influenza vaccine (LAIV, Nasovac-S) during the 2016–2017 and 2017–2018 influenza seasons. It integrates multi-dimensional immune parameters across baseline and post-vaccination timepoints (day 0, day 2, day 7, and day 21) to capture individual immunogenicity profiles.

The dataset includes:

  • Humoral immune responses: Haemagglutination inhibition (HAI) assays, influenza virus protein microarray (IVPM), antibody-dependent cellular cytotoxicity (ADCC) activity, and enzyme-linked immunosorbent assay (ELISA) data for IgA and IgG levels. These measurements assess responses to vaccine-matched and cross-reactive influenza strains, including H1N1, H3N2, and B/Victoria.
  • Cellular immune responses: T-cell cytokine production (e.g., interferon-gamma (IFN-γ) and interleukin-2 (IL-2)) measured via intracellular cytokine staining (ICS) following stimulation with hemagglutinin (HA), neuraminidase (NA), nucleoprotein (NP), and matrix (M) protein peptides.
  • Viral shedding and microbiome analysis: Viral load quantification by reverse-transcription PCR (RT-PCR) for vaccine strains (H1N1, H3N2, and Flu B) and nasopharyngeal microbiome profiling focusing on Streptococcus pneumoniae density.
  • Immunophenotyping: Flow cytometry data on innate and adaptive immune cell subsets, including myeloid dendritic cells (mDCs), plasmacytoid dendritic cells (pDCs), monocyte subsets (classical, intermediate, and non-classical), and T follicular helper (Tfh) cells.
  • Transcriptomics: RNA-sequencing (RNA-seq) data from nasal and blood samples, providing transcriptomic profiles analyzed through gene set enrichment analysis (GSEA).

This dataset offers a unique resource for studying LAIV-induced immune responses and developing predictive models for vaccine immunogenicity, with applications in precision-guided vaccination strategies.

Technical info

Description of Variables in the Dataset

This dataset includes a comprehensive set of variables to evaluate immune responses to the live attenuated influenza vaccine (LAIV). Below is a detailed description of each variable, grouped into relevant categories for clarity:

1. Participant Information

  • subject_ID: Unique identifier for each participant.
  • year: Year of study enrollment.
  • sex: Participant’s sex (M for male, F for female).
  • z_score_continuous: Weight-for-height Z-score, a continuous variable assessing nutritional status. Higher values indicate better nutrition, while lower or negative values may indicate undernutrition. This metric provides critical insights into the participant's baseline health, which can influence immune responses to vaccination.

2. Responder Status

  • max_HAI_responder: Indicates whether the participant had a fourfold or greater increase in haemagglutination inhibition (HAI) titers.
  • max_iga_responder: Indicates a twofold or greater increase in mucosal IgA response.
  • max_mnp_cd4_responder: A twofold or greater increase in CD4+ T-cell response to vaccine strains.
  • max_mnp_cd8_responder: A twofold or greater increase in CD8+ T-cell response to vaccine strains.

3. Haemagglutination Inhibition (HAI) Assays

  • h1_hai_v0_gmt, h1_hai_v21_gmt: Baseline (v0) and post-vaccination (v21) HAI titers for H1N1 virus strain.
  • H1_HAI_FC: Fold change in HAI titers for H1N1 virus.
  • Similar variables exist for H3N2 (h3_hai_*) and influenza B virus strains (b_hai_*).

4. IgA Responses

  • ph1n1_na_IgA_V0, ph1n1_na_IgA_V21: Baseline and post-vaccination IgA levels for H1N1 neuraminidase (NA).
  • H1N1_NA_FC: Fold change in IgA levels for H1N1 neuraminidase.
  • Similar variables exist for H3N2 hemagglutinin (h3n2_ha_IgA_*) and NA (h3n2_na_IgA_*), and influenza B virus hemagglutinin (HA) (bvic_ha_IgA_*) and NA (bvic_na_IgA_*).

5. Cellular Immune Responses

  • h1_v0_cd8_ifng, h1_v21_cd8_ifng: Baseline and post-vaccination interferon-gamma (IFNg) levels in CD8+ T cells specific to H1N1 virus.
  • H1_CD8_IFNg_FC: Fold change in CD8+ IFNg levels for H1N1 virus.
  • Similar variables for CD8+ IL-2 (*_cd8_il2) and other strains (H3N2, B).

6. Influenza Virus Protein Microarray (IVPM)

  • NC99_IVPM_H1_V0, NC99_IVPM_H1_V21: Baseline and post-vaccination IVPM titers for NC99 H1N1 strain.
  • NC99_H1_FC: Fold change in IVPM titers for NC99 H1N1.
  • Additional variables include other H1N1 (e.g., MICH15, Cal09, GD) and H3N2 strains (e.g., SWISS, HK14, KAN), and B/Yamagata lineage (B_PHU_YAM_*).

This HA-based microarray includes HA proteins from a diverse panel of influenza A and B viruses, enabling the simultaneous assessment of antibody reactivity to various subtypes and lineages. In our analysis, we incorporated HA antigens from influenza A H1N1 virus strains such as A/California/07/2009 (CAL09 H1 IVPM) and A/Michigan/45/2015 (MICH15 H1 IVPM), as well as H1 isolates A/New Caledonia/20/1999 (NC99 H1 IVPM) and A/Guangdong Maonan/SWL1536/2019 (GD H1 IVPM). For influenza A virus H3N2, we included HA proteins from A/Switzerland/9715293/2013 (SWISS H3 IVPM), A/Hong Kong/4801/2014 (HK14 H3 IVPM), and A/Kansas/14/2017 (KAN H3 IVPM). Additionally, the microarray featured HA antigens from influenza B viruses, specifically B/Phuket/3073/2013 (B/Yamagata/16/88-like lineage) and B/Washington/02/2019 (B/Victoria/2/87-like lineage).

7. Stalk-Specific Antibody Responses

  • cH6_ADCC_AUC_V0, cH6_ADCC_AUC_V21: Antibody-dependent cellular cytotoxicity (ADCC) activity for stalk-specific chimeric HA (cH6/1) at baseline and post-vaccination.
  • cH6_ADCC_FC: Fold change in ADCC activity for cH6/1.
  • Similar stalk-specific IgA responses (cH6_sIgA, cH7_sIgA) and titers (cH6_Titer). cH7/3

8. Viral Shedding

  • h1_v2_shed, h3_v2_shed, b_v2_shed: Viral shedding status on day 2 post-vaccination for H1N1, H3N2, and B.
  • h1_v7_shed, h3_v7_shed, b_v7_shed: Viral shedding status on day 7 post-vaccination.

9. Seropositive Status

  • h1_v0_seropositive, h3_v0_seropositive, b_v0_seropositive: Indicates whether participants were seropositive for H1N1, H3N2, and B virus strains at baseline. 

10. Nasopharyngeal Microbial Load

  • v0_pneumo_ng_log10copies_ul: Baseline nasopharyngeal load of Streptococcus pneumoniae (log10 copies per μL).

11. T Follicular Helper (TFH) Cells

  • TFH_CXCR3_ICOS_PD1_V0, TFH_CXCR3_ICOS_PD1_V7: Frequency of activated TFH cells at baseline and day 7.

12. Dendritic and Monocyte Subsets

  • v0_mDCs, v2_mDCs, v7_mDCs: Myeloid dendritic cell frequencies at baseline and post-vaccination.
  • v0_pDCs, v2_pDCs, v7_pDCs: Plasmacytoid dendritic cell frequencies.
  • v0_classical_monocytes, v2_classical_monocytes, v7_classical_monocytes: Classical monocyte frequencies.
  • v0_intermediate_monocytes, v0_nonclassical_monocytes: Intermediate and non-classical monocyte frequencies.

13. Pathway Scores

  • blood_baseline_go.*: Pathway enrichment scores in blood samples.
  • nasal_baseline_go.*: Pathway enrichment scores in nasal samples.
    • Examples include blood_baseline_go.0006415 (translation termination) and nasal_baseline_go.0045088 (innate immune defense).

14. Clustering Information

  • cluster: Immunophenotyic groupings assignment based on immune response phenotype derived from immunaut.

This dataset is structured to enable comprehensive analysis of LAIV-induced immune responses, capturing systemic, mucosal, and cellular immunity across multiple parameters, facilitating predictions of immunogenicity and responder classification.

Files

LAIV_Immune_Response_Integrated_Dataset.csv

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Additional details

Funding

National Institutes of Health
NIAID CENTERS OF EXCELLENCE FOR INFLUENZA RESEARCH AND RESPONSE (CEIRR) 75N93021C00015-P00008-9999-1

Software

Repository URL
https://github.com/atomiclaboratory/immunaut
Programming language
R
Development Status
Active