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Abstract
Separation of B cells into different subsets has been useful to understand their different functions in various immune scenarios. In some instances, the subsets defined by phenotypic FACS separation are relatively homogeneous and so establishing the functions associated with them is straightforward. Other subsets, such as the “Double negative” (DN, CD19+CD27-IgD-) population, are more complex with reports of differing functionality which could indicate a heterogeneous population. Recent advances in single-cell techniques enable an alternative route to characterize cells based on their transcriptome. To maximize immunological insight, we need to match prior data from phenotype-based studies with the finer granularity of the single-cell transcriptomic signatures. We also need to be able to define meaningful B cell subsets from single cell analyses performed on PBMCs, where the relative paucity of a B cell signature means that defining B cell subsets within the whole is challenging. Here we provide a reference single-cell dataset based on phenotypically sorted B cells and an unbiased procedure to better classify functional B cell subsets in the peripheral blood, particularly useful in establishing a baseline cellular landscape and in extracting significant changes with respect to this baseline from single-cell datasets. We find 10 different clusters of B cells and applied a novel, geometry-inspired, method to RNA velocity estimates in order to evaluate the dynamic transitions between B cell clusters. This indicated the presence of two main developmental branches of memory B cells. A T-independent branch that involves IgM memory cells and two DN subpopulations, culminating in a population thought to be associated with Age related B cells and the extrafollicular response. The other, T-dependent, branch involves a third DN cluster which appears to be a precursor of classical memory cells. In addition, we identify a novel DN4 population, which is IgE rich and closely linked to the classical/precursor memory branch suggesting an IgE specific T-dependent cell population.
Highlights
B cells can differentiate into plasma cells and secrete large amounts of antibody
Additional HB34 and HB78 B cell samples were isolated using StemCell (Kit), FACS sorted in the same manner and each population tagged with Biolegend TotalSeq-C Hashtag antibodies, washed on the Curiox laminar flow system (9 washes at 5 ul/sec) before being recombined in equal ratios to run on the 10X at 4,000 cells per lane; all other methods were identical
B cell sorted single-cell transcriptome libraries from five FACS sorted populations based on IgD/CD27/CD10 (Transitional ‘Trans’, Naive, IgM Memory ‘M-mem’, Classical Memory ‘Cmem’ and Double Negative ‘DN’) clustered into 10 unique populations using Uniform Manifold Approximation and Projection (UMAP) (Figure 1A)
Summary
B cells can differentiate into plasma cells and secrete large amounts of antibody. There are, many more B cell functions which contribute to an effective immune response. B cells can be activated via TLRs, producing proinflammatory cytokines such as IL6, TNFa and IFNg in the process and resulting in differentiation into short-lived plasmablasts. The former, T-dependent, response will involve formation of germinal centers over time and, since it is dependent on T cells for maturation which have been through tolerance checkpoints, it would normally have low risk of producing autoantibodies. The latter, extrafollicular, B cell response has the advantage of being more rapid, and runs some risk of producing lower specificity antibodies. B cells can be regulatory, producing IL10 and ensuring that autoreactive responses are not perpetuated
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