mRNA-based influenza vaccine expands the breadth of the B cell response in humans

These are the processed BCR repertoire and transcriptomics data described in Matz et al., Nature Immunology, 2026. The raw sequencing data are available on SRA under BioProject PRJNA1049392. Code Code along with Docker containers for reproducing the NGS data-based figures and analyses in the published paper can be found on GitHub. Metadata File: WU397_matz_et_al_nat_imm_2026_meta.tsv Abbreviations: LN = lymph node PB = plasmablast NS = no sorting Processed BCR data - heavy chains File: WU397_matz_et_al_nat_imm_2026_bcr_heavy.tsv.gz Notes on columns: The columns largely follow the AIRR-C Rearrangement format. The main deviation is that CDR3s were used, as opposed to IMGT-defined "junctions". Nonetheless, junction-related columns are included here as some repositories use these. Non-standard columns are noted below. `cell_id`: single-cell sequences follow the format `[donor]_[sample]@[id]`. `sequence_id`: Sequence IDs follow the format `[donor]_[sample]@[id]`. `v_call_genotyped`: V gene annotation reassigned after individualized genotyping by TIgGER. `germline_[vdj]_call`: Clonal consensus germline calls after corresponding clonal consensus sequences were reconstructed via `CreateGermlines.py --cloned` from Change-O. `c_call`: constant gene annotation extracted from output of `cellranger vdj` for 10x sequences. Unlike `isotype`, `c_call` is down to the resolution of isotype subclass. `gex_anno`: Cell type identity annotation based on transcriptomic profiles. Mapped from `anno_leiden_0.95` from WU397_matz_et_al_nat_imm_2026_gex_b_cells.h5ad. `compartment`: B cell compartment. `timepoint` and `timepoint_2`: Timepoints in days. The latter simplifies `d180` and `d181` both as `d180`. `clone_id`: B cell clonal lineage IDs follow the format `[donor]@[id]`. `expressed`: `TRUE` if expressed as mAb; otherwise `FALSE`. `expressed_id`: IDs of expressed mAbs; `""` (blank) if not expressed. `ha_binding_binary`: Binary ELISA results for binding of recombinant mAbs to HA proteins. For expressed mAbs, `pos` and `neg` for positive and negative binding results respectively. `NA` if not expressed. `ha_binding_specs`: HA binding specificities if expressed and binding was positive. Multiple HA proteins are separated by semi-colon. `""` (blank) if expressed but binding was negative. `NA` if not expressed. `nuc_RS_1_312`: number of replacement and silent mutations between IMGT-numbered nucleotide positions 1-312 along IGHV sequences, calculated by `shazam::calcObservedMutations`. `nuc_denom_1_312`: number of informative nucleotide positions for counting mutations, excluding non-A/T/G/C positions (such as "N", "-", "."). `nuc_RS_freq_1_312`: nucleotide-level mutation frequency (= nuc_RS_1_312 / nuc_denom_1_312). Processed BCR data - light chains File: WU397_matz_et_al_nat_imm_2026_bcr_light.tsv.gz Light chains can be linked to their corresponding heavy chains via the `cell_id` column. Processed transcriptomics data Files: WU397_matz_et_al_nat_imm_2026_gex_all_cells.h5ad WU397_matz_et_al_nat_imm_2026_gex_b_cells.h5ad WU397_matz_et_al_nat_imm_2026_gex_b_cell_umap.tsv.gz Notes on the `h5ad` files: These files can be imported into Scanpy as an AnnData object. Each `AnnData` object has 3 `.layers`, each representing a version of the count matrix. `raw_counts`: Imported from `cellranger aggr` output by `scanpy.read_10x_mtx`. `log_norm`: Log-noramlized expression values outputted by `scanpy.pp.normalize_total` followed by `scanpy.pp.log1p`. `scaled`: The `log_norm` layer scaled to unit variance and zero mean by `scanpy.pp.scale`. The `gene_name` and `biotype` columns in `.var` were extracted from GENCODE v32 GTF. Columns in `.obs` (each row corresponds to a cell) `n_feature`: The `n_genes_by_counts` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The number of genes expressed. This is before subsetting the genes. `n_umi`: The `total_counts` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The total UMI counts in a cell. `pct_mt`: The `pct_counts_mt` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The percentage of counts in mitochondrial genes. `n_hkg`: The number of housekeeping genes for which expression was detected. `n_gene_expressed`: The total number of genes for which expression was detected. This is after subsetting the genes. `pre_qc_bcr`: `TRUE` if a cell also had paired BCR data available. Produced by cross-referencing the cellular barcodes in `cell_barcodes.json` outputted by `cellranger vdj`. At this point the BCR data had not gone through the QC process in the BCR processing pipeline (hence `pre_qc`). `leiden_[resolution]`: Cluster assignment by `scanpy.tl.leiden`. `anno_leiden_[resolution]`: Cell type identity annotations based on transcriptomic profiles. This was mapped onto the `gex_anno` column in the processed heavy chain BCR data. UMAP coordinates can be found in `.obsm["X_umap"]`. `.X` has been set to `None` in order to reduce file size. Note on the `tsv.gz` file: This file was derived from WU397_matz_et_al_nat_imm_2026_gex_b_cells.h5ad. It contains UMAP coordinates of the cells used for visualization in conjunction with BCR data. In addition, the preprocessed count matrix outputted by `cellranger aggr` is available from GEO under BioProject PRJNA1049392.