Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Mar 18:12:602539.
doi: 10.3389/fimmu.2021.602539. eCollection 2021.

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

Affiliations

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

Alexander Stewart et al. Front Immunol. .

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.

Keywords: B cell development; B cell subsets; B cells; cell atlas; memory B cells; single-cellRNAseq.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Single-cell atlas of peripheral B cell subsets for one individual (HB6). (A) Two dimensional UMAP projection of scRNA-seq data of peripheral B cell subsets from sample HB6. (B) Breakdown of each cell cluster defined using scRNA-seq data, in terms of the FACS-defined B cell identities. (C) Expression of top markers for each cell cluster. Here, only markers with average log fold difference > 0.75 for at least one cluster are included. (D) Expression of CD19, MS4A1 (CD20), CD27, IGHD, and IGHM across cell clusters.
Figure 2
Figure 2
Trajectory and RNA velocity analyses of B cell clusters. (A) Trajectory inferred using monocle3, overlaid onto cell clusters in a three-dimensional UMAP space. (B) Pseudotime order of cells inferred using monocle3, in (top) Trans, Naïve and C-mem1, and (bottom) M-mem1, DN3, DN2 clusters. (C) Change in expression level of selected genes across the pseudotime axis for Trans, Naïve and C-mem1. (D, E) RNA velocity stream overlaid on three-dimensional UMAP space. Notice the different UMAP dimensions depicted in each panel. In (D), the inset represents a close-up view into the space between Naive, M-mem2, and the class-switched memory clusters. (F) Composite transition score between cell clusters, calculated based on examining geometrically the alignment of velocity streams to cluster positions. (G) Summary of trajectory and RNA velocity analyses.
Figure 3
Figure 3
Heterogeneous single-cell expression landscape of double-negative memory B cells. (A) Dimensionality projection of DN1-4 clusters on a two-dimensional UMAP space. (B) Expression of CD27 and IGH constant region genes across the four DN clusters. (C) Expression level of RHOB across the four DN clusters. (D) Expression of typical markers of precursor antibody secreting cells (DN2) in our DN clusters. (E) Breakdown of cells positive for TBX21 transcripts (which encodes Tbet) by the cell clusters defined in this dataset. (F) Differential expression analysis of Tbet+ and Tbet- DN cells. (G) Change in expression levels of selected genes through the pseudotime order of M-mem1, DN3 and DN2 cells.
Figure 4
Figure 4
DN1 and DN4 cells. (A) Expression level of DN1 markers illustrated with a dot plot. (B) Differential expression analysis of DN1 compared with other DN clusters. (C) Gene ontologies of DN1 markers. The top 5 pathways in this GO overrepresentation analysis were illustrated. Top markers overlapping these pathways are noted on the plot. (D) Expression levels of DN4 markers illustrated with a dot plot. (E) Differential expression analysis of DN4 compared with other DN clusters. (F) Inferred protein-protein interaction network of IL4R and its direct neighbors. The expression levels of these genes in the DN4 cluster were mapped onto the nodes of the network.
Figure 5
Figure 5
Comparison between classical memory (C-mem) and double-negative (DN) memory B cells. (A) Differential expression analysis of C-mem and DN cells. (B) Expression of differentially expressed genes between C-mem1/2 and DN2/3 visualized in a dot plot.
Figure 6
Figure 6
IgM memory cells. (A) Transcriptional markers for IgM Memory cells in comparison to other FACS-defined populations. (B) Numbers of genes and transcripts expressed per cell in M-mem1 and M-mem2 clusters. (C) Expression of CD1C and AP3B1 in M-mem1 and M-mem2 clusters. (D) Expression of differentially expressed genes between M-mem1 and M-mem2 cells. (E) Gene set enrichment analysis of the inferred PPINs for the M-mem1 and M-mem2 cells. The top 10 most significant pathways are shown here.
Figure 7
Figure 7
Validation in additional donor samples. (A) scRNAseq data of three individuals (HB6 [on whose cells all analyses above were based], HB34, and HB78) were projected onto the same dimensionality-reduced space using UMAP. Cells were clustered separately in each sample and colored accordingly. (B) Breakdown of each cell cluster in the HB34 and HB78 data in terms of their phenotypic identities.

Similar articles

Cited by

References

    1. Fischer M, Klein U, Küppers R. Molecular single-cell analysis reveals that CD5-positive peripheral blood B cells in healthy humans are characterized by rearranged Vkappa genes lacking somatic mutation. J Clin Invest (1997) 100:1667–76. 10.1172/JCI119691 - DOI - PMC - PubMed
    1. Klein U, Rajewsky K, Küppers R. Human immunoglobulin (Ig)M+IgD+ peripheral blood B cells expressing the CD27 cell surface antigen carry somatically mutated variable region genes: CD27 as a general marker for somatically mutated (memory) B cells. J Exp Med (1998) 188:1679–89. 10.1084/jem.188.9.1679 - DOI - PMC - PubMed
    1. Shi Y, Yamazaki T, Okubo Y, Uehara Y, Sugane K, Agematsu K. Regulation of Aged Humoral Immune Defense against Pneumococcal Bacteria by IgM Memory B Cell. J Immunol (2005) 175:3262–7. 10.4049/jimmunol.175.5.3262 - DOI - PubMed
    1. Griffin DO, Holodick NE, Rothstein TL. Human B1 cells in umbilical cord and adult peripheral blood express the novel phenotype CD20+CD27+CD43+CD70-. J Exp Med (2011) 208:67–80. 10.1084/jem.20101499 - DOI - PMC - PubMed
    1. Martin V, Wu YC, Kipling D, Dunn-Walters D. Ageing of the B-cell repertoire. Philos Trans R Soc B Biol Sci (2015) 370:e20140237. 10.1098/rstb.2014.0237 - DOI - PMC - PubMed

Publication types

-