Single-Cell Transcriptomic Analyses Define Distinct Peripheral B Cell Subsets and Discrete Development Pathways.

Area of Science:

• Immunology
• Single-cell genomics
• B cell biology

Background:

• B cell subsets are crucial for understanding immune responses, but some populations, like Double Negative (DN) B cells, are functionally heterogeneous.
• Phenotypic methods for B cell subsetting have limitations, necessitating integration with single-cell transcriptomic data for deeper insights.
• Characterizing B cell subsets within peripheral blood mononuclear cells (PBMCs) is challenging due to the low frequency of B cells.

Purpose of the Study:

• To create a reference single-cell dataset of phenotypically sorted B cells.
• To develop an unbiased method for classifying functional B cell subsets from single-cell data.
• To identify dynamic transitions and developmental pathways within B cell populations.

Main Methods:

• Phenotypic sorting of B cells followed by single-cell RNA sequencing.
• Unbiased clustering of single-cell transcriptomic data to identify B cell populations.
• Application of a geometry-inspired RNA velocity method to infer cell differentiation dynamics.

Main Results:

• Identification of 10 distinct B cell clusters.
• Discovery of two major developmental branches of memory B cells: a T-independent branch and a T-dependent branch.
• Characterization of novel Double Negative (DN) subpopulations, including an IgE-rich DN4 population linked to T-dependent memory B cells.

Conclusions:

• The study provides a robust framework for classifying B cell subsets using single-cell transcriptomics.
• The identified developmental branches offer new insights into memory B cell differentiation.
• The novel DN4 population suggests a distinct IgE-mediated T-dependent B cell response pathway.