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A pipeline to unravel complexity of the human peripheral blood B cell compartment through multiomic cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) analyses

GSE283984 Homo sapiens Expression profiling by high throughput sequencing; Other 3 samples Submitted 2026/02/18 Platform GPL24676
Summary
Classifying heterogeneous human B cells into discrete subsets based on surface phenotype alone remains a major challenge. We discovered a mouse splenic IgM+IgDlow/- B cell subset (BDL) that induces proliferation of CD4+Foxp3+ T regulatory cells, making them a therapeutic target for autoimmunity. Because they are rare and lack sufficient cell surface markers, a multiomic strategy is required to identify and track them. The lack of correlation between mRNA expression levels and cell surface expression is a drawback to a transcriptome-only scRNA-seq approach because ‘novel’ cell populations identified by surface protein transcripts may prove impossible to validate. Thus, a pipeline was developed using human peripheral blood (PB) IgM+IgDlow/- B cells. First, a standardized flow cytometry gating scheme (CD27 versus IgD and CD24 versus CD38) that captures all major human PB B cell subsets was utilized. Second, CD19+IgM+IgDlow/- PB B cells were tagged with the four markers using cellular indexing of transcriptomes and epitopes by sequencing. Biaxial plots similar to flow cytometry were used to identify naïve, transitional, and memory B cells, and then confirmed with scRNA-seq. These studies using a multiomic workflow allowed the identification of IgM+IgDlow/- B cells by cell surface expression that could subsequently be analyzed for unique transcriptional profiles.
Published in
A method to unravel complexity in human peripheral blood B-cell subsets through multiomic cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) analyses
Meinhardt NJ, Veltri AJ, Gurski CJ et al. · ImmunoHorizons 2026 · PMID 41679729 · doi:10.1093/immhor/vlaf088
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Direct links to NCBI, no account and no request form: the whole study as GSE283984_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 3 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA1196477 and SRA study SRP550605. Searching any of these in the dataset finder brings you back here.

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