Single cell clonal analysis identifies an AID ‐dependent pathway of plasma cell differentiation
2022; Springer Nature; Volume: 23; Issue: 12 Linguagem: Inglês
10.15252/embr.202255000
ISSN1469-3178
AutoresCarmen Gómez‐Escolar, Álvaro Serrano, Alberto Benguría, Ana Dopazo, Fátima Sánchez‐Cabo, Almudena R. Ramiro,
Tópico(s)CAR-T cell therapy research
ResumoArticle7 October 2022Open Access Transparent process Single cell clonal analysis identifies an AID-dependent pathway of plasma cell differentiation Carmen Gómez-Escolar Carmen Gómez-Escolar orcid.org/0000-0002-9508-2752 B Lymphocyte Biology Lab, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain Contribution: Data curation, Formal analysis, Investigation, Visualization, Methodology Search for more papers by this author Alvaro Serrano-Navarro Alvaro Serrano-Navarro orcid.org/0000-0001-7799-1410 B Lymphocyte Biology Lab, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain Contribution: Data curation, Methodology Search for more papers by this author Alberto Benguria Alberto Benguria orcid.org/0000-0002-5536-566X Genomics Unit, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain Contribution: Methodology Search for more papers by this author Ana Dopazo Ana Dopazo orcid.org/0000-0002-4910-1684 Genomics Unit, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain CIBER de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain Contribution: Resources Search for more papers by this author Fátima Sánchez-Cabo Fátima Sánchez-Cabo orcid.org/0000-0003-1881-1664 Bioinformatics Unit, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain Search for more papers by this author Almudena R Ramiro Corresponding Author Almudena R Ramiro [email protected] orcid.org/0000-0002-7539-3844 B Lymphocyte Biology Lab, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain Contribution: Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Writing - original draft, Project administration, Writing - review & editing Search for more papers by this author Carmen Gómez-Escolar Carmen Gómez-Escolar orcid.org/0000-0002-9508-2752 B Lymphocyte Biology Lab, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain Contribution: Data curation, Formal analysis, Investigation, Visualization, Methodology Search for more papers by this author Alvaro Serrano-Navarro Alvaro Serrano-Navarro orcid.org/0000-0001-7799-1410 B Lymphocyte Biology Lab, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain Contribution: Data curation, Methodology Search for more papers by this author Alberto Benguria Alberto Benguria orcid.org/0000-0002-5536-566X Genomics Unit, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain Contribution: Methodology Search for more papers by this author Ana Dopazo Ana Dopazo orcid.org/0000-0002-4910-1684 Genomics Unit, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain CIBER de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain Contribution: Resources Search for more papers by this author Fátima Sánchez-Cabo Fátima Sánchez-Cabo orcid.org/0000-0003-1881-1664 Bioinformatics Unit, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain Search for more papers by this author Almudena R Ramiro Corresponding Author Almudena R Ramiro [email protected] orcid.org/0000-0002-7539-3844 B Lymphocyte Biology Lab, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain Contribution: Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Writing - original draft, Project administration, Writing - review & editing Search for more papers by this author Author Information Carmen Gómez-Escolar1, Alvaro Serrano-Navarro1, Alberto Benguria2, Ana Dopazo2,3, Fátima Sánchez-Cabo4 and Almudena R Ramiro *,1 1B Lymphocyte Biology Lab, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain 2Genomics Unit, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain 3CIBER de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain 4Bioinformatics Unit, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain *Corresponding author. Tel: +34914531200; E-mail: [email protected] EMBO Reports (2022)23:e55000https://doi.org/10.15252/embr.202255000 PDFDownload PDF of article text and main figures.PDF PLUSDownload PDF of article text, main figures, expanded view figures and appendix. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Germinal centers (GC) are microstructures where B cells that have been activated by antigen can improve the affinity of their B cell receptors and differentiate into memory B cells (MBCs) or antibody-secreting plasma cells. Here, we have addressed the role of activation-induced deaminase (AID), which initiates somatic hypermutation and class switch recombination, in the terminal differentiation of GC B cells. By combining single cell transcriptome and immunoglobulin clonal analysis in a mouse model that traces AID-experienced cells, we have identified a novel subset of late-prePB cells (L-prePB), which shares the strongest clonal relationships with plasmablasts (PBs). Mice lacking AID have various alterations in the size and expression profiles of transcriptional clusters. We find that AID deficiency leads to a reduced proportion of L-prePB cells and severely impairs transitions between the L-prePB and the PB subsets. Thus, AID shapes the differentiation fate of GC B cells by enabling PB generation from a prePB state. Synopsis A combination of single cell transcriptome and immunoglobulin gene sequencing identifies a novel pre-plasmablast/plasma cell (L-prePB) subset. Activation-induced deaminase (AID) deficiency reduces this L-prePB subset and the transition toward PB/PC cells. L-prePB cells are non-germinal center B cells with strong clonal relationships to PB/PC cells. AID deficiency compromises the generation and survival of L-prePB cells. AID deficiency severely impairs transitions between L-prePB and PB subsets. Introduction During the immune response, B cells that have been stimulated by antigen with T cell help can engage in the germinal center (GC) reaction, where they can differentiate into either memory B cells (MBC) or high-affinity plasma cells (PC). GCs are key to the efficiency of the immune response and underlie the mechanism of action of most vaccination strategies. In GCs, B cells proliferate, modify their immunoglobulin genes by somatic hypermutation (SHM), are selected by affinity maturation, and terminally differentiate into alternate fates (Allen et al, 2007; Victora & Nussenzweig, 2012; Mesin et al, 2016; Shlomchik et al, 2019; Laidlaw & Cyster, 2020). Activation-induced deaminase (AID) initiates SHM and CSR (Muramatsu et al, 2000; Revy et al, 2000) with the deamination of cytosines on the DNA of immunoglobulin genes, which can be subsequently processed by various molecular pathways (Methot & Di Noia, 2017). In the case of SHM, AID deaminates the antigen recognizing, variable region of immunoglobulin genes, generating mutations that can give rise to variants with altered affinity for antigen (Methot & Di Noia, 2017). In CSR, AID-induced deaminations promote a recombination reaction between switch regions—highly repetitive sequences that precede constant regions—thus promoting the exchange of IgM/IgD isotypes with IgG, IgE, or IgA isotypes encoded by downstream constant genes at the immunoglobulin heavy (IgH) locus (Methot & Di Noia, 2017). Therefore, immunoglobulin diversification by AID is central to the GC reaction. Germinal centers comprise two different compartments, the dark zone (DZ), where B cells proliferate and undergo SHM in their variable genes, and the light zone (LZ), where B cells are selected in the context of T follicular helper cells and follicular dendritic cells (Allen et al, 2007; Victora & Nussenzweig, 2012). CSR can take place in the GC but frequently occurs prior to its entry into the GC (Roco et al, 2019). B cells perfection affinity maturation with iterative cycles of mutation and proliferation in the DZ and positive selection in the LZ (Victora et al, 2010; Victora & Nussenzweig, 2012, 2022). Notably, differentiation into the MBC and the PC fates from the GC is not stochastic; instead, higher affinity B cells preferentially differentiate into PCs, while MBCs generally show lower affinity for antigen (Phan et al, 2006; Taylor et al, 2015; Mesin et al, 2016; Shinnakasu et al, 2016; Kräutler et al, 2017; Suan et al, 2017; Viant et al, 2020, 2021). Accordingly, the average frequency of SHM is higher in PCs than in MBCs (Shinnakasu et al, 2016; Weisel et al, 2016; Laidlaw et al, 2020). Likewise, BCR isotype influences the outcome of GC differentiation toward the PC or MBC fates (Kometani et al, 2013; Gitlin et al, 2016; King et al, 2021). This skewed selection into the MBC versus PC fate ensures high-affinity protection by the PC effector compartment while preserving an MBC reservoir with a broader range of affinities, which could be critical to provide a rapid defense against closely-related pathogens, as previously proposed (Kaji et al, 2012; Viant et al, 2020; Victora & Nussenzweig, 2022). Activation-induced deaminase deficiency does not only promote a complete block in CSR and SHM, but also it causes lymphoid hyperplasia, both in mouse and man (Muramatsu et al, 2000; Revy et al, 2000), indicating a role of AID in B cell homeostasis. Indeed, AID−/− mice have an increased number of GC B cells upon immunization (Muramatsu et al, 2000). Interestingly, AID−/− GC B cells show a reduced apoptosis rate (Zaheen et al, 2009), accumulate in the LZ, and do not efficiently form PCs in mixed bone marrow chimeras (Boulianne et al, 2013). However, the contribution of AID to shaping B cell fate in GCs is not well understood. Here, we have approached this question by combining single cell transcriptome analysis with single cell V(D)J analysis of B cells from wild-type and AID-deficient mice. To that end, we have used a genetic mouse model that irreversibly labels cells that have expressed AID. We found that AID-experienced B cells clustered into 8 distinct transcriptional clusters, including a novel L-prePB cluster, which shares a strong clonal relationship with PB. The GC response in AID deficient mice showed alterations in cluster proportions and transcriptome differences in some of these clusters. Further, clonal relationships were profoundly altered in AID deficient mice, where the connection between L-prePB and PB clusters was severely impaired. Thus, our data reveal a critical role of AID in shaping the ultimate fate of B cell differentiation in GCs. Results Single cell analysis of the GC response identifies 8 transcriptional clusters To map GC differentiation at the single cell level, we made use of the AicdaCre+/ki; R26tdTom+/ki (hereafter AicdaCre/+) mouse model, which allows genetic tracing of cells that have expressed AID. In this model, the cDNA encoding the Tomato (Tom) fluorescent protein was inserted in the Rosa26 endogenous locus preceded by a transcriptional stop sequence flanked by loxP sites (R26tdTom allele). The Cre recombinase was inserted in the endogenous Aicda locus (AicdaCre allele; Robbiani et al, 2008; Rommel et al, 2013). In AicdaCre/+ mice, activation of the Aicda locus promotes Cre expression and excision of the transcriptional stop at the R26tdTom allele, unleashing the expression of the Tom protein. Thus, B cells that have been activated for AID expression, become irreversibly Tom+ (Fig EV1A). To trigger a GC immune response, we first adoptively transferred CD4+ T cells from OT-II mice, which harbor a TCR recognizing a peptide from the ovalbumin (OVA) protein, into AicdaCre/+ mice. Mice were immunized with OVA 1 and 15 days after the transfer of OT-II cells. Mice were sacrificed for analysis 15 days after the second immunization (Fig 1A). Flow cytometry analysis showed that OVA immunization expectedly resulted in the generation of Tom+ cells, which comprised GC B cells (Tom+ CD138− GL7+), plasmablasts (PB) and PCs (Tom+ CD138+) and putative memory B (pMem) cells (Tom+ CD138− GL7− CD38+), with various proportions of switched cells (complete gating strategy in Figs 1B and C, and EV1B and C). ELISA analysis showed an accumulation of anti-OVA IgG antibody titers (Fig EV1D). Figure 1. Single cell RNA sequencing of AID-labeled Tomato+ (Tom+) cells identifies eight cell clusters Immunization protocol. AicdaCre/+ mice were immunized intraperitoneally (i.p.) with OVA in alum (n = 8) 1 day after OT-II CD4+ T cell transfer. Two weeks later, mice were boosted with OVA i.p. Representative flow cytometry plots of spleen Tom+ cells, germinal center B cells (GC; Tom+ CD138− GL7+), plasma cells/plasmablasts (PB; Tom+ CD138+) and putative memory B cells (pMem; Tom+ CD138− GL7− CD38+). Quantification of flow cytometry analysis of immunized mice as shown in A and B (n = 8 mice per group). Percentages of the different subsets within total live cells are shown. Data information: Bars and error bars indicate mean ± standard deviation. Splenic Tom+ cells from two immunized AicdaCre/+ mice were analyzed by single cell RNA sequencing (scRNA-seq) using the 10x Genomics platform. Cells were clustered based on transcriptomic data and mapped to a UMAP plot. Clusters are labeled from 0 to 5 according to decreasing cell numbers. Cluster 0 was subclusterized in 0a, 0b and 0c (Fig EV2A). Heatmap showing expression of the top 20 upregulated genes within the clusters identified in D. Yellow indicates higher gene expression. Representative gene names are indicated on the right. UMAP plots showing expression of representative genes of the different B cells clusters, as shown in D. Blue color indicates higher gene expression. UMAP plots showing enrichment scores for previously published gene signatures (MBC, (Glaros et al, 2021); PC, (Heng et al, 2008); GC.DZ and GC.LZ (Victora et al, 2012)). UMAP plot showing the cell cycle phase of individual cells in the different clusters as shown in D. Heatmap of G1, S, and G2M subclusters of GC.DZ cells showing expression of the top 10 upregulated genes in S and G2M phases. Gene expression information was obtained for 4,061 AicdaCre/+ Tom+ cells. Cluster 0: 1425 [0a: 998, 0b:354, 0c:73], 1: 867, 2: 820, 3: 480, 4:345, 5:124. See Materials and Methods for details. Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Mouse model and experimental design Genetic model to study the GC reaction with a fluorescent tracer. AicdaCre+/ki; R26tdTom+/ki (AicdaCre/+), and AicdaCre−/ki; R26tdTom+/ki (AicdaCre/−) mice are shown. Complete gating strategy for B cell subsets analyzed in Fig 1B and C. FACS representative plots and quantification of IgG1, IgG2B, and IgG2C within B cell populations in Fig 1C (n = 6 AicdaCre/+ mice). Antibody titers specific for OVA were measured in the plasma of control AicdaCre/+ mice (PBS; n = 3) and immunized AicdaCre/+ mice (OVA; n = 8) by ELISA. Statistics were calculated with the paired t-test. ****P < 0.0001. n indicates biological replicates. Experimental approach followed for single cell RNA sequencing. Data information: Bars and error bars indicate mean ± standard deviation. Download figure Download PowerPoint To analyze the B cell immune response at the single cell level we performed 10× Genomics analysis in Tom+ spleen cells isolated from OT-II transferred AicdaCre/+ mice 15 days after the boost OVA immunization (Fig 1A). Two individual immunized mice were multiplexed by hashtag labeling (HTO, see Materials and Methods) and gene expression and V(D)J sequencing of individual cells was performed (Fig EV1E). Seurat clustering of gene expression sequencing of individual Tom+ cells initially identified 6 independent clusters, labeled from 0 to 5 according to cluster size (Fig EV2A). Further subclustering of cluster 0 resulted in 8 distinct transcriptional clusters, as explained below (Fig 1D). Click here to expand this figure. Figure EV2. Cluster analysis UMAP plot showing transcriptional clusters obtained before cluster 0 subclustering. UMAP plot showing three transcriptionally distinct subclusters (0a, 0b, and 0c). Dot plot depicting the expression levels of the top 10 upregulated genes in clusters 0a, 0b, and 0c. Anti-FcRγ antibody test. Representative flow cytometry plots of B3Z (NIH) parental cells (left) and FcRγ-CD2-transfected cell lines stained with anti-FcRγ and anti-CD2 antibodies. Gating strategy for L-prePB identification by flow cytometry and cell sorting. L-prePB backgating shown in black. Expression analysis of the indicated genes obtained by scRNA-seq shown in Fig 3A. aAverage log2 fold change between the two groups being compared. bProportion of cells expressing the indicated gene within FcRγ+ cells. cProportion of cells expressing the indicated gene within the non-FcRγ+ cells. FACS representative plot for L-prePB staining in the spleen of AicdaCre/+ mice 2 weeks after a single OVA immunization. Anti-FcRγ staining in GL7− and GL7+ cells within live, singlets, Tom+, CD138−, B220+ gated cells. AicdaCre/+ mice (n = 7) were immunized with OVA following the protocol in Fig 1A. Four mice were sacrificed 2 weeks after the first immunization. The proportion of prePB (Tom+ B220+ CD138− GL7− FcRγ+) and PB (Tom+ CD138+) cells was determined by flow cytometry within total live cells. Data information: Bars and error bars indicate mean ± standard deviation. Download figure Download PowerPoint Clusters 1 and 2 showed high levels of Aicda, S1pr2, or Mef2b and were both enriched in GC B cell signature (Fig 1E–G). Cluster 1 and cluster 2 were distinctly enriched for LZ and DZ signatures, as defined before (Victora et al, 2010, 2012; Fig 1G; Dataset EV1). We found that the vast majority of proliferating Tom+ cells were contained in cluster 2, and conversely, virtually all the cells (99%, 811/820 cells) in cluster 2 were in the S+G2M phases of the cell cycle (Fig 1H and I). UMAP projection precisely distinguished between cells in the S phase expressing high levels of replication genes (Mcm2, Mcm3, Mcm4, Mcm6, Cdc6, etc) and cells in the G2 and M phases, with high expression of mitotic genes (Cdc20, Ccnb2, Cdca8, etc; Fig 1H and I). Thus, cluster 1 was designated as LZ GC B cells (GC.LZ) and cluster 2 was designated as DZ GC B cells (GC.DZ). Cluster 5 displayed high levels of Xbp1, Jchain, and immunoglobulin genes (Fig 1E and F; Dataset EV1) and was enriched in cells expressing the PB/PC signature as defined in the Immunological Genome Project gene set (Heng et al, 2008; Fig 1G) and was thus designated as PB. Cluster 0 showed high expression of Klf2 and Ccr6 (Fig 1E and F; Dataset EV1), previously associated with the MBC transcriptional program (Suan et al, 2017; Laidlaw et al, 2020). Signature enrichment analysis of MBC TFs (Glaros et al, 2021) further supported the MBC identity of cluster 0 (Fig 1G). Subclusterization of cluster 0 identified three transcriptionally distinct populations: one major subset (0a) with highest expression levels of Klf2, Ccr6, and Hhex, one subset (0b) with highest levels of Zbtb32 and Vim, and a minor subset (0c) with highest levels of Irf7 and Isg15 (Figs 1D and E, and EV2B and C; Dataset EV1). We found that subcluster 0a was enriched in the gene signature of a recently identified subset of MBCs that derive from activated B cells (Viant et al, 2021); conversely, subcluster 0b was enriched in a distinct MBC signature of cells that originate from highly proliferative GC precursors (Viant et al, 2021; Fig 2A). Thus, we have identified two major subsets of MBCs: cluster 0a, hereafter, Mem.Act, and cluster 0b, hereafter, Mem.GC. Finally, 0c is a minor subcluster showing more association with activated B cell-derived MBC signature and will be labeled as Mem.Act2. Figure 2. Identification of two prePB clusters Enrichment scores of gene signatures derived from GC-independent and GC-dependent memory B cell populations described in (Viant et al, 2021). Heatmap showing expression levels of cluster 4 markers in different Immunological Genome Project (ImmGen) datasets (Heng et al, 2008). Pie charts depicting the proportion of shared markers between cluster 4 and PB cluster with the rest of the clusters, as analyzed by Quickmarkers. UMAP plot showing enrichment score for the top 100 upregulated genes in Fraction 1 prePB cells (Ise et al, 2018). Monocle Pseudotime analysis of the cells displayed in Fig 1D. The projection is colored by cluster identity and the cells are ordered by Pseudotime. Cumulative frequency of cluster 3 and cluster 4 cells across Pseudotime. UMAP plot of cells in Fig 1D with assigned cell identities. Download figure Download PowerPoint Identification of two prePB clusters Clusters 3 and 4 were identified as distinct clusters that could not obviously be assigned as MBC, GC, or PC cells (Fig 1G; Dataset EV1). However, the transcriptional profile of cluster 4 partially resembled PB and MBC Immunological Genome Project gene sets (Heng et al, 2008; Fig 2B). To get insights into the transcriptional inter-relationships of cluster 4 with other clusters, we performed an analysis of transcriptional transitions using QuickMarkers (Dataset EV2). This approach quantifies the proportion of markers in a given cluster shared with the highest frequency by the rest of the clusters. We found that cluster 4 showed the highest transition probability with the PB cluster, and conversely, the PB cluster showed a high transcriptional transition probability with cluster 4, only second to GC cells (Fig 2C). These analyses suggested that cluster 4 could represent a subset related with PB differentiation. This prompted us to ask whether this subset was similar to a previously described subset of prePB cells (Fraction 1; Ise et al, 2018). Interestingly, gene signature analysis using the 100 most highly expressed genes by Fraction 1 prePB cells (Ise et al, 2018) did not bear a high enrichment in cluster 4 (Fig 2D). Instead, we found that the highest enrichment of the Fraction 1 prePB signature was split into two transcriptional clusters. Expectedly, one of them was a subset of the LZ cluster, in agreement with the initial definition of Fraction 1 prePB cells (Fig 2D). Notably, the other high enrichment hit of Fraction 1 prePB cells was with our cluster 3. Together, these analyses suggested that clusters 3 and 4 could represent 2 distinct subsets of prePB cells. To further assess the differentiation trajectories of the identified transcriptional clusters, we performed Monocle pseudotime analysis (Fig 2E). We expectedly found that GC.DZ and GC.LZ clusters represented the earliest differentiation states in the pseudotime analysis, while the PB cluster was the end differentiation state of the analysis. MBC clusters were interspread between GC and PB clusters. Interestingly, cluster 3 showed as an earlier state than cluster 4 (Fig 2E and F). Thus, cluster 3 was tentatively labeled as early-prePB (E-prePB) while cluster 4 was tentatively labeled as late-prePB (L-prePB). We conclude that single cell transcriptome analysis identified 8 distinct GC-related populations, including GC.DZ, GC.LZ, PB, 3 clusters of MBC, and 2 clusters of putative prePB cells, namely, E-prePB, which are closely related to previously described fraction 1 prePB cells (Ise et al, 2018), and a novel prePB cluster that we have named L-prePB (Fig 2G). Characterization of the L-prePB cluster To gain insights into the identity of cells in the L-prePB cluster, we first identified genes in our transcriptome data that could be potentially used as markers for L-prePB cells. We found that FcRγ, Actn1, Dnm3, and Ptpn22 are expressed by cells in the L-prePB cluster but very rarely expressed in other clusters (Fig 3A). Interestingly, all FcRγ, Actn1, Dnm3, and Ptpn22 are more highly expressed in PC/PB than in GC or MBC subsets, as defined in the Immunological Genome Project (Heng et al, 2008; Fig 3B), supporting the transcriptional link between L-prePB and PB cells. Further analysis of our transcriptome data with the Combinatorial Marker Detection from Single Cell Transcriptomic Data (COMET tool; Delaney et al, 2019) identified FcRγ as the best putative marker for the L-prePB cluster. FcRγ (high-affinity immunoglobulin epsilon receptor subunit gamma) is a component of various membrane receptors, including high-affinity IgE receptor, endowed with a signal transducing ITAM motif and a short extracellular segment. To assess the identity of L-prePB cells by flow cytometry, we first performed intracellular staining of spleen cells from mice immunized as shown in Fig 1A using an anti-FcRγ antibody. This antibody was tested in B3Z cells transfected with an FcRγ-CD2 fusion protein following the same fixation/permeabilization protocol (Fig EV2D). We found that a fraction of non-PC, non-GC, Tom+ B cells (Tom+ B220+ CD138− GL7− CD19+) expressed FcRγ (Fig 3C), consistent with the presence of L-prePB cells (complete gating strategy in Fig EV2E). We also confirmed the absence of FcRγ+ cells within the GC compartment (Fig EV2F). We next isolated FcRγ+ cells by cell sorting and performed quantitative RT–PCR of the L-prePB markers identified in our transcriptome data. We found that all FcRγ, Actn1, Dnm3, and Ptpn22 genes were more highly expressed in FcRγ+ cells than in total Tom+ cells, confirming that FcRγ+ cells are part of the L-prePB cluster (Figs 3D and EV2G). Finally, we verified that FcRγ+ cells were also detectable in mice who received a single immunization challenge (Fig EV2H and I). Thus, we conclude that the L-prePB cluster represents a common B cell subset associated with T cell-dependent responses that can be identified by the expression of FcRγ. Figure 3. L-prePB cells can be identified by the expression of FcRγ UMAP plots showing expression of representative genes of the L-prePB cluster. Blue color indicates higher gene expression. Barplot showing expression levels of representative L-prePB markers in different Immgen datasets (Heng et al, 2008). Gating strategy for L-prePB identification by flow cytometry. Validation of the markers shown in A using RT–qPCR. Relative quantification comparing expression levels in prePB and total Tom+ cells by 2−ΔΔCt method. Download figure Download PowerPoint Single cell clonal analysis of the GC response To further characterize the identified transcriptional clusters, we first analyzed SHM in V(D)J transcripts of individual cells. We found that the mutation load at the IgH variable region widely varied across cells from different populations, with the highest mutation frequency found in both GC.DZ and GC.LZ B cells (Figs 4A–C and EV3A). Within MBC clusters, Mem.Act and Mem.Act2 harbored lower mutation frequencies than Mem.GC, in agreement with their labeling as MBCs from activated B cell and GC origin, respectively (Viant et al, 2021; Figs 4A–C and EV3A). Finally, we found that cells in the L-prePB and PB clusters had similar mutation frequencies (Figs 4A–C and EV3A), and that mutation load was significantly higher in L-prePB than in the E-prePB compartment. CSR analysis on V(D)J transcripts generally mirrored the SHM results, with the highest proportion of isotype switched cells within the GC.DZ and GC.LZ clusters (Figs 4D and E, and EV3B–D). Figure 4. Single cell analysis of SHM, CSR, and clonal relationships during the GC reaction Total number of somatic mutations at the IgH variable region (VH) in individual cells from the different B cell clusters. Symbols represent individual cells. Statistics were calculated with the Kruskal–Wallis test using the Dunn's multiple comparison test (P-values shown in Fig EV3A). UMAP plot showing mutational load in single cells of the clusters defined in Fig 1D. Quantification of data shown in B. UMAP plot showing CSR of individual cells (Unsw, unswitched; Sw, switched). Proportion of CSR in individual clusters (Unsw, unswitched; Sw, switched). UMAP plot showing the distribution of clonally expanded cells among clusters (Exp, expanded; Not exp, not expanded). Circos plot displaying pairwise clonal overlap between B cell clusters. Only clones shared between 2 clusters are shown. For the sake of clarity, Mem.Act, Mem.GC and Mem.Act2 clusters are shown together as Mem. Transition probabilities among the different clusters, based on the frequency of clonal sharing between 2 or more clusters. Representative phylogenetic trees of the three categories analyzed in panels J-L. Type 1 trees represent clonal families containing both E- and L-prePB cells. Types 2 and 3 contain GC B cells and either E- or L-prePB cells, respectively. Numbers in branches depict the mutation load acquired in each diversification event. Each branch represents a diversification step, as quantified in panel L. Quantification of the number of mutations in E- (n = 5 cells) and L-prePB (n = 23 cells) belonging to clones of type 1 trees (n = 3). n refers in all cases to biological replicates. Quantification of the number of mutations in GC B cells in type 2 trees (n = 12 trees; GC2, n = 98 cells) and type 3 trees (n = 9 trees; GC3, n = 39 cells). n refers in all cases to biological replicates. Quantification of the diversification steps of E-prePB cells (n = 13) and L-prePB cells (n = 17) belonging to clones from type 2 trees (n = 12) and type 3 trees (n = 9). n refers in all cases to biological replicates. Data information: Bars and error bars indicate mean ± standard deviation. Statistics were calculated with an unpaired t-test. *P ≤ 0.05, **P < 0.01. V(D)J information was obtained for 2,677 AicdaCre/+ Tom+ cells (see Materials and Methods for details). Download figure Download PowerPoint
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