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Distinct stage-specific transcriptional states of B cells derived from human tonsillar tissue
Diego A. Espinoza, Carole Le Coz, Emylette Cruz Cabrera, Neil Romberg, Amit Bar-Or, Rui Li
Diego A. Espinoza, Carole Le Coz, Emylette Cruz Cabrera, Neil Romberg, Amit Bar-Or, Rui Li
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Resource and Technical Advance Cell biology Immunology

Distinct stage-specific transcriptional states of B cells derived from human tonsillar tissue

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Abstract

B cells within secondary lymphoid tissues encompass a diversity of activation states and multiple maturation processes that reflect antigen recognition and transition through the germinal center (GC) reaction, in which mature B cells differentiate into memory and antibody-secreting cells (ASCs). Here, utilizing single-cell RNA-seq, we identify a range of distinct activation and maturation states of tonsil-derived B cells. In particular, we identify what we believe is a previously uncharacterized CCL4/CCL3 chemokine–expressing B cell population with an expression pattern consistent with B cell receptor/CD40 activation. Furthermore, we present a computational method that leverages regulatory network inference and pseudotemporal modeling to identify upstream transcription factor modulation along a GC-to-ASC axis of transcriptional maturation. Our data set provides valuable insight into diverse B cell functional profiles and will be a useful resource for further studies into the B cell immune compartment.

Authors

Diego A. Espinoza, Carole Le Coz, Emylette Cruz Cabrera, Neil Romberg, Amit Bar-Or, Rui Li

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Figure 1

scRNA-seq of human tonsillar B cells identifies distinct B cell maturation and activation states.

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scRNA-seq of human tonsillar B cells identifies distinct B cell maturati...
(A) Experimental design for tonsillar B cell isolation, culture, and scRNA-seq. (B) UMAP plot of 45,376 single B cell transcriptomes from 3 human donors. (C) Visualization of CCR7, CD38, and PRDM1 expression patterns in UMAP space using the kernel density estimation method in the R package Nebulosa. (D) Selected marker gene average expression (scaled log-normalized counts) for each cluster and proportion of cells with transcript detected. Genes were determined to be marker genes if their average log2(fold change) was greater than 0.3 for the cluster of interest and adjusted P value was less than 0.01. Marker-gene expression is denoted by square boxes on gene-cluster pairs. (E) Selected scaled average AUCell scores for 5 gene sets across each cluster. Heatmap cells were labeled with an asterisk (*) if the gene set was enriched within that cluster (Wilcoxon’s rank-sum test, AUC > 0.85).

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