E-MTAB-9544
Single-cell RNAseq of phenotypically defined human B cell subsets
Separation of B cells has been historically important in discovering their functional relevance, particularly in relation to infection, immune disorders and vaccination. Traditional use of phenotypic markers often poses problems in distinguishing heterogeneous populations such as the Double Negative (DN, CD19+CD27-IgD-) cells. B cells represent a small subset of PBMCs; this represents challenges to use bottom-up approaches such as single-cell transcriptomics in defining B cell subpopulations. In this study we therefore used the 10X single-cell RNAseq platform on B cell populations already defined by FACS sorting (Transitional, CD19+CD27-IgD+CD10+; Naïve, CD19+CD27-IgD+CD10-; Classical Memory, CD19+CD27+IgD-; IgM Memory, CD19+CD27+IgD+; and DN). These data match known phenotypes to transcriptionally defined B cell subpopulations, and provide a reference atlas for researchers interested in better defining B cell subsets in their data.
Separation of B cells has been historically important in discovering their functional relevance, particularly in relation to infection, immune disorders and vaccination. Traditional use of phenotypic markers often poses problems in distinguishing heterogeneous populations such as the Double Negative (DN, CD19+CD27-IgD-) cells. B cells represent a small subset of PBMCs; this represents challenges to use bottom-up approaches such as single-cell transcriptomics in defining B cell subpopulations. In this study we therefore used the 10X single-cell RNAseq platform on B cell populations already defined by FACS sorting (Transitional, CD19+CD27-IgD+CD10+; Naïve, CD19+CD27-IgD+CD10-; Classical Memory, CD19+CD27+IgD-; IgM Memory, CD19+CD27+IgD+; and DN). These data match known phenotypes to transcriptionally defined B cell subpopulations, and provide a reference atlas for researchers interested in better defining B cell subsets in their data.