Complex subsets but redundant clonality after B cells egress from spontaneous germinal centers
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eLife assessment
Understanding the heterogeneity of the B cell response induced in autoimmune individuals is important for the development of therapies designed to target the cells underlying disease progression. Here the authors use a new mouse model of autoimmunity to assess the heterogeneity of the B cell response using single-cell RNA-sequencing and BCR-sequencing and found that these B cell responses are similar to those by exogenous protein immunization.
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Abstract
Affinity matured self-reactive antibodies are found in autoimmune diseases like systemic lupus erythematous. Here, we used fate-mapping reporter mice and single-cell transcriptomics coupled to antibody repertoire analysis to characterize the post-germinal center (GC) B cell compartment in a new mouse model of autoimmunity. Antibody-secreting cells (ASCs) and memory B cells (MemBs) from spontaneous GCs grouped into multiple subclusters. ASCs matured into two terminal clusters, with distinct secretion, antibody repertoire and metabolic profiles. MemBs contained FCRL5+ and CD23+ subsets, with different in vivo localization in the spleen. GC-derived FCRL5+ MemBs share transcriptomic and repertoire properties with atypical B cells found in aging and infection and localize to the marginal zone, suggesting a similar contribution to recall responses. While transcriptomically diverse, ASC and MemB subsets maintained an underlying clonal redundancy. Therefore, self-reactive clones could escape subset-targeting therapy by perpetuation of self-reactivity in distinct subsets.
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eLife assessment
Understanding the heterogeneity of the B cell response induced in autoimmune individuals is important for the development of therapies designed to target the cells underlying disease progression. Here the authors use a new mouse model of autoimmunity to assess the heterogeneity of the B cell response using single-cell RNA-sequencing and BCR-sequencing and found that these B cell responses are similar to those by exogenous protein immunization.
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Reviewer #1 (Public Review):
In this manuscript, Castrillon et al. analyze the heterogeneity of B cells exiting spontaneous germinal center reactions by scRNA-seq in a new mouse model of autoimmunity. In this model, they track the fate of wild-type Aid-Cre ERT2-EYFP B cells in the presence of 564 lgi B cells harboring a BCR specific for RNP. Throughout the manuscript, the authors compared the results obtained in the autoimmune model with those obtained after acute immunization with NP/OVA in Alum. They found extensive clonal overlap among dark/light zone germinal centers, memory B cells, and antibody-secreting cells (ASC). Within the ASC compartment, they found seven clusters. Through pseudotime analysis, they conclude the presence of two early ASC clusters, three intermediate ASC clusters, and two terminal ASC clusters. The two late …
Reviewer #1 (Public Review):
In this manuscript, Castrillon et al. analyze the heterogeneity of B cells exiting spontaneous germinal center reactions by scRNA-seq in a new mouse model of autoimmunity. In this model, they track the fate of wild-type Aid-Cre ERT2-EYFP B cells in the presence of 564 lgi B cells harboring a BCR specific for RNP. Throughout the manuscript, the authors compared the results obtained in the autoimmune model with those obtained after acute immunization with NP/OVA in Alum. They found extensive clonal overlap among dark/light zone germinal centers, memory B cells, and antibody-secreting cells (ASC). Within the ASC compartment, they found seven clusters. Through pseudotime analysis, they conclude the presence of two early ASC clusters, three intermediate ASC clusters, and two terminal ASC clusters. The two late ASCs have different patterns of gene expression (CD28, Itga4 among them), isotype expression (ASC_Late_1 mostly class-switched while ASC_Late_2 mostly IgM), and potentially different antibody-secreting capacity and metabolic program based on Ig counts and OXPHOS signature. Regarding memory B cells, they found four clusters of memory B cells with similar isotype expression (except for MemB2 which expresses more IgM) but different gene expression patterns (CD83, Fcrl5, Vim, Fcer2a). Finally, the authors found that FCRL5+ and CD23+ memory B cells are located in different areas of the spleen based on confocal microscopy analysis and their accessibility to blood after anti-CD45 iv administration. The data provided by the authors are very attractive and interesting. Yet, I found that the manuscript over relies on scRNA-seq. It will be important that authors back up some of their conclusions made from the scRNA-seq analysis with functional experiments, like measuring the differential antibody-secreting capacity of both terminal ASC subsets or profiling their metabolic status through one of the many metabolic techniques available.
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Reviewer #2 (Public Review):
This paper uses single-cell RNA sequencing to assess the B cell response in a mouse model of autoimmunity. The authors find that the B cell response is transcriptionally similar to the response induced by protein immunization. They further determine that the memory B cell response is composed of transcriptionally distinct subsets that may have distinct spatial distributions.
A major strength of this manuscript is the author's use of an elegant model of autoimmunity in which self-reactive B cells can escape negative selection to become activated and participate in the germinal center response. This system allows the author's a system to study the development of B cells in an autoimmune setting without restricting the repertoire of those cells though the use of BCR transgenes. This single-cell data generated …
Reviewer #2 (Public Review):
This paper uses single-cell RNA sequencing to assess the B cell response in a mouse model of autoimmunity. The authors find that the B cell response is transcriptionally similar to the response induced by protein immunization. They further determine that the memory B cell response is composed of transcriptionally distinct subsets that may have distinct spatial distributions.
A major strength of this manuscript is the author's use of an elegant model of autoimmunity in which self-reactive B cells can escape negative selection to become activated and participate in the germinal center response. This system allows the author's a system to study the development of B cells in an autoimmune setting without restricting the repertoire of those cells though the use of BCR transgenes. This single-cell data generated in this study is also likely to be useful to individuals interested in understanding the differences in the B cell response between autoimmune and protein immunization settings.
One weakness of this study is that its main findings do not seem to represent a major conceptual advancement. There are already many published single-cell RNA-seq data sets that show that heterogeneity exists within B cell subsets. Therefore, the author's data primarily extends these findings to indicate that heterogeneity also exists in their model of autoimmunity.
Another major weakness of this study is that the authors only analyze about 13K cells in their single cell RNA-seq experiment with only 3.3K coming from the immunized mice. This low number of cells likely prevents the authors from identifying differences between specific B cell subsets between the two disease settings because there are likely very few cells in many of the clusters in the immunized group.
Finally, the author's data in which they seek to validate their use of Fcrl5 and CD23 to identify memory B cell subsets is not convincing. The flow cytometry gating used to distinguish the memory B cell subsets seem somewhat arbitrary with there not being a clear separation between the four populations shown using the author's gating strategy. This strategy also causes many CD23+ cells to not be analyzed in Fig. 6G.
The imaging data is also not clear as it is not apparent whether the S1pr2-expressing cells indicated by the authors express Fcrl5 since Fcrl5 does not encircle the indicated cell. The authors also do not quantify their images. While the authors do see a difference between the populations following in vivo labeling, it is not clear why the CD45+ population among the Fcrl5+ cells have a higher staining intensity than the Cd23+ cells. It is expected that cells that are exposed to circulation would have a similar staining intensity. Therefore, it is possible that there may be a technical issue with this data. Finally, it is not clear whether the results in figure 6 were repeated with several of the plots only having three mice per group limiting the conclusions that can be drawn from this data.
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