Visual Abstract

Ab-secreting cells (ASC) or plasma cells are essential components of the humoral immune system. Although Abs of different isotypes have distinct functions, it is not known if the ASC that secrete each isotype are also distinct. ASC downregulate their surface BCR upon differentiation, hindering analyses that couple BCR information to other molecular characteristics. In this study, we developed a methodology using fixation, permeabilization, and intracellular staining coupled with cell sorting and reversal of the cross-links to allow RNA sequencing of isolated cell subsets. Using hemagglutinin and nucleoprotein Ag-specific B cell tetramers and intracellular staining for IgM, IgG, and IgA isotypes, we were able to derive and compare the gene expression programs of ASC subsets that were responding to the same Ags following influenza infection in mice. Intriguingly, whereas a shared ASC signature was identified, each ASC isotype-specific population expressed distinct transcriptional programs controlling cellular homing, metabolism, and potential effector functions. Additionally, we extracted and compared BCR clonotypes and found that each ASC isotype contained a unique, clonally related CDR3 repertoire. In summary, these data reveal specific complexities in the transcriptional programming of Ag-specific ASC populations.

This article is featured in In This Issue, p.2027

The immune system is composed of a diverse and complex mixture of specialized cells that protect the host from invading pathogens and facilitate organism homeostasis. Using tools that integrate expression of various surface markers, immune cells can be defined and subdivided, based on cell type and/or specialized functions. It is increasingly apparent that phenotyping based on intracellular markers can further specify functionally significant cell populations. For example, CD4 Th cell subsets are identified by lineage-defining transcription factor expression (13); innate lymphoid cells are defined by transcription factors and/or cytokine production patterns (4, 5); and Ab-secreting cells (ASC) or plasma cells are classified based on BCR isotypes (6). Additionally, fluorescent dyes can be used to track DNA content, organelle function and number, as well as metabolic status. The identification of intracellular targets for FACS is routinely accomplished by fixation, using a formaldehyde solution, followed by permeabilization (7, 8). An advantage to fixation-based phenotyping is the inactivation of pathogens that might be present in blood or tissue samples as well as the stabilization of cellular states over time. When fixation is not possible, surrogate surface markers for intracellular factors can be used. For example, CXCR3 surface expression can be used to identify cells that express the transcription factor Tbx21 (T-BET) (911). However, surrogate markers are imperfect, and, given the widespread use of intracellular assays, there is a need to improve and validate the limited techniques that facilitate the reversal of fixation for downstream readouts such as global gene expression (12, 13).

Upon differentiation of B cells to ASC, the BCR transitions from a membrane-bound to a secreted form (1416). For this reason, ASC are particularly challenging to phenotype and are largely identified based on surrogate markers, such as CD138 (Syndecan-1), for both mouse and human ASC (17, 18) as well as TACI or SCA-1 (19, 20). The genetic knock in of the GFP fluorescent reporter into the Prdm1 locus has also allowed for the identification of Blimp-1–expressing cells (i.e., ASC) without the need for intracellular staining (21). Using combinations of these markers, RNA sequencing (RNA-seq)–based studies have identified ASC-specific gene expression signatures (22). However, none of these markers provide information about the BCR antigenic target or whether the transcriptional signatures are shared among ASC of distinct BCR isotypes. We, therefore, developed a fixation and staining protocol to identify influenza-responding and -specific ASC of distinct BCR subtypes, followed by fixation reversal and isolation of RNA for deep sequencing. Isotype-specific ASC demonstrated unique expression patterns of key signature genes that regulate BCR class switching and homing to distinct tissues. Furthermore, we were able to extract BCR clonal frequencies and identify VDJ combinations and CDR3 sequences that were specific to each ASC isotype. These data define an approach for transcriptome profiling, based on intracellular phenotypes and identify unique features of ASC subsets that correlate with functional differences.

Raji human Burkitt lymphoma cell line was purchased from the American Type Culture Collection (CCL-86) and cultured in RPMI 1640 containing 10% FBS and 100 U/ml penicillin and streptomycin.

C57/BL6J mice were 8–12 wk of age and infected with 15,000 viral foci units influenza A/PR8/34 (23). Spleen and mediastinal lung draining lymph node (dLN) were analyzed 14 d postinfection. All animal protocols were approved by the Emory Institutional Animal Care and Use Committee.

Splenocytes were washed and resuspended at 25 × 107 cells/ml in PBS with 1% BSA and 2 mM EDTA (FACS). Cells were stained with anti–CD138–allophycocyanin (clone 281-2; BioLegend) at 4°C for 30 min. Cells were then washed with 10 ml MACS buffer (1× PBS, 0.5% BSA, and 2 mM EDTA) and incubated with 35 μl anti-allophycocyanin microbeads (130-090-855; Miltenyi Biotec) at 4°C for 15 min. Cells were washed with 10 ml PBS plus 0.5% BSA and 2 mM EDTA and run over a magnetic column, as per manufacturer’s instructions. Enriched cells were resuspended in 100 μl FACS plus surface Ab mixture for 30 min on ice. Surface stains were as follows: Zombie Yellow Viability dye (77168), CD11b allophycocyanin–Cy7 (clone M1/70), Thy1/2 allophycocyanin–Cy7 (clone 30-H12), F4/80 allophycocyanin–Cy7 (clone BM8), CD98 PE–Cy7 (clone RL388), and CD36 PE–Cy7 (clone HM36) were all from BioLegend; B220 PE–Cy7 (clone RA3-6B2) was from Tonbo Biosciences; LAG-3 PE–Cy7 (clone eBioC9B7W) was from Invitrogen. Cells were washed in 1 ml FACS before fixation and permeabilization with BD Cytofix/Cytoperm (554714) as per manufacturer’s instructions. The intracellular staining mix included 20 min of staining, first with a decoy reagent (streptavidin–PE–Alexa Fluor 647, S20992; Invitrogen) to eliminate intracellular streptavidin:biotin interactions with the tetramers. Next, cells were washed and incubated with hemagglutinin (HA)–PE and nucleoprotein (NP)–PE B cell tetramers (24); IgM v450 (clone eB121-15F9) and IgA FITC (clone mA-6E1) were from Invitrogen; IgG PerCp–Cy5.5 (clone Poly4053) and BCL-2 PE–Cy7 (clone Bcl/10C4) were from BioLegend. Cells were sorted, using a FACSAria II, directly into 300 μl RLT buffer (QIAGEN) with 1% 2-ME, snap frozen, and stored at −80°C. FCS files were analyzed using FlowJo (v10).

To reverse the formaldehyde cross-linking, the samples were heated to 65°C for the indicated amount of time in RLT buffer (Qiagen). Next, total RNA was purified using the Quick-RNA MicroPrep Kit (Zymo Research) and eluted in 10 μl. RNA-seq libraries were generated using the SMARTer Stranded Total RNA-Seq Kit v2 - Pico Input Mammalian (Takara Bio), according to the manufacturer’s instructions, and sequencing was done on a NextSeq500, using paired-end 75-base chemistry at the New York University Genome Technology Center and Emory Integrated Genomics Core.

Reads were mapped to the mm10 version of the mouse genome using STAR (25) and processed as previously described (26). Genes were filtered for detection, based on being expressed at ≥10 reads per million in all samples of one group (i.e., IgM, IgG, or IgA), resulting in 8766 genes. Differential expression between ASC subsets was determined using edgeR v3.18.1 (27) and an false discovery rate <0.05 and log2 fold change >1. Principal component analysis (PCA) was performed using the vegan package v2.4-3 (http://vegan.r-forge.r-project.org/). To determine Igh expression, the Ig gene segments were downloaded from the International ImMunoGeneTics (28), and expression was annotated using the GenomicRanges v1.30.3 (29). All RNA data are available from the National Center for Biotechnology Information's Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE124578.

Clonotype information was extracted for each sample with MiXCR v2.1.12 (30) using the “align -p rna-seq -s mmu -OallowPartialAlignments = true” options. Aligned clones were exported using the “exportClones -c IGH -o -t” options. Clone information was analyzed using VDJtools (31) and custom R/Bioconductor scripts.

To develop methods that allow the transcriptome to be interrogated from cells that have been fixed and permeabilized, we first determined the optimal conditions for reversing the cross-linking. Nucleic acid fixation with formaldehyde is reversible by incubation at 65°C (32). We first tested the size distribution and quantity of RNA obtained following incubation of 50,000 Raji B cells that were fixed and permeabilized using a formaldehyde-based protocol (See 2Materials and Methods). Incubation at 65°C for 0, 5, 15, and 30 min followed by RNA purification resulted in increasing yield of total RNA (Fig. 1A). Closer assessment of the RNA size distribution revealed an enrichment of small RNA fragments that likely represent fragmentation of RNA from the fixation and heating process (Fig. 1B). Low RNA yields were obtained from samples that were not subjected to incubation at 65°C, indicating the heating process was necessary to release the RNA from fixation. Thus, we demonstrated that RNA can be successfully isolated from cells that had undergone fixation and permeabilization.

FIGURE 1.

Optimized RNA-seq libraries following fixation and intracellular staining. (A) Concentration of purified RNA from formaldehyde-fixed Raji B cells following incubation at 65°C for the indicated number of minutes. (B) Size distribution of purified RNA from (A). The location of the standard in each sample and size in base pair is indicated. (C) Scatterplot of normalized expression levels for 13,545 detected genes in each sample. The Pearson correlation r value is indicated for each comparison. FPKM, fragments per kb per million. FU, fluorescent units.

FIGURE 1.

Optimized RNA-seq libraries following fixation and intracellular staining. (A) Concentration of purified RNA from formaldehyde-fixed Raji B cells following incubation at 65°C for the indicated number of minutes. (B) Size distribution of purified RNA from (A). The location of the standard in each sample and size in base pair is indicated. (C) Scatterplot of normalized expression levels for 13,545 detected genes in each sample. The Pearson correlation r value is indicated for each comparison. FPKM, fragments per kb per million. FU, fluorescent units.

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To further determine the efficiency of decross-linking, RNA-seq libraries were generated from Raji cells from each of the three conditions tested above as well as unfixed cells as a control. Consistent with unfixed cells, we detected the expression of 13,545 genes (RPM > 3) in each of the conditions. Importantly, an almost perfect correlation was observed in the expression levels of genes between each of the fixed samples (Fig. 1C). Comparison of the 30 min fixed to unfixed cells revealed a high correlation of gene expression that was the same for all the other fixed samples (data not shown). These data demonstrate the feasibility of performing global transcriptome profiling by RNA-seq from cells that have undergone fixation and permeabilization protocols that are based on formaldehyde.

Upon differentiation, ASC use alternative mRNA processing to convert the same BCR to a secreted isoform (1416). Therefore, ASC have low to undetectable surface staining with anti-isotype Abs and B cell tetramers (3335). To directly phenotype isotype-specific ASC, we used a model of influenza in which mice were infected intranasally with influenza strain A/PR8/34 (PR8). At day 14 postinfection, all CD138+ cells from the spleen and lung dLN were magnetically enriched (Fig. 2A). Following enrichment, cells were first stained with extracellular surface markers for phenotyping followed by fixation and intracellular staining using B cell tetramers and Abs against distinct BCR isotypes. After intracellular staining, cells were washed and phenotyped by flow cytometry. Analysis of ASC by isotype revealed distinct populations representing IgM, IgG, and IgA. The intracellular staining provided a clear separation between isotypes (Fig. 2B–E). Although a B cell tetramer was used in the initial gating strategy, nonspecific intracellular interactions, which were not present in non–B cell lineage cells (Fig. 2C), precluded the analysis of Ag specificity without decoy tetramer reagents (see below). Therefore, Ag specificity was excluded from the analysis of these cell populations. The relative frequency of total cells expressing IgM, IgG, and IgA varied somewhat between mice (Fig. 2F). The early emergence of IgM ASC has been reported for model Ags, and IgG ASC tend to accumulate later in infection (36). However, the kinetics of IgA ASC are not as clearly defined. These results demonstrate that single-isotype ASC can be readily identified.

FIGURE 2.

Identification and phenotyping of isotype-specific ASC. (A) Schematic detailing the experimental procedure used to identify, fix, sort, reverse cross-link, and sequence the cells of interest. (B) Flow cytometry gating strategy for identification of ASCs from the spleen and dLN at day 14 postinfection. Cells were enriched on CD138–allophycocyanin, stained with surface markers, and fixed/permeabilized for intracellular staining. (C) Flow cytometry gating of PE–HA and PE–NP tetramers in non–B cell lineage cells from (B). (D) Sorted ASC populations from (B) (colored gates) were overlaid to show staining relative to each isotype. (E) Overlaid histograms of the gate drawn for each subset relative to other isotypes from (B). (F) Summary of the relative frequency of each isotype from (B) for three independent mice.

FIGURE 2.

Identification and phenotyping of isotype-specific ASC. (A) Schematic detailing the experimental procedure used to identify, fix, sort, reverse cross-link, and sequence the cells of interest. (B) Flow cytometry gating strategy for identification of ASCs from the spleen and dLN at day 14 postinfection. Cells were enriched on CD138–allophycocyanin, stained with surface markers, and fixed/permeabilized for intracellular staining. (C) Flow cytometry gating of PE–HA and PE–NP tetramers in non–B cell lineage cells from (B). (D) Sorted ASC populations from (B) (colored gates) were overlaid to show staining relative to each isotype. (E) Overlaid histograms of the gate drawn for each subset relative to other isotypes from (B). (F) Summary of the relative frequency of each isotype from (B) for three independent mice.

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Using the staining and gating strategy detailed above, we FACS isolated ASC that intracellularly expressed either IgM, IgG, or IgA. Following sorting, formaldehyde cross-linking was reversed by treating for 15 min at 65°C. The 15-min time point was chosen as a balance of maintaining a short RNA extraction workflow with enough time to adequately reverse the cross-linking (Fig. 1). The resulting RNA was purified, and RNA-seq was performed to analyze the transcriptomes of the ASC. Overall, we detected the expression of 8766 genes in at least one ASC isotype. PCA of all detected genes demonstrated clear separation of ASC by isotype, suggesting that unique transcriptional differences were present (Fig. 3A). Pairwise differential analysis between isotypes identified 1438 significantly differentially expressed genes (DEG) (false discovery rate < 0.05 and log2 fold change > 1) (Fig. 3B, Supplemental Table I). Consistent with the PCA clustering, IgG and IgM ASC were the most distinct. Interestingly, IgA ASC expressed a set of genes that were shared with IgM or IgG. Therefore, ASC of distinct isotypes have both shared and unique gene expression programs.

FIGURE 3.

Distinct ASC isotypes contain unique transcriptomes. (A) PCA of 8766 detected genes in each population. Percentage of variation covered indicated on axes with circles denoting 99% confidence intervals. (B) Heatmap of 1438 DEGs. (C) Volcano plots for each pairwise comparison. Number of DEG in each comparison is indicated. (D) Bar plots showing expression of the indicated gene in each population. (E) Overlap of bone marrow ASC signature genes (22) and ASC isotype dataset described in this study. (F) Heatmap of ASC overlap genes from (E) that are differentially expressed between any two isotype subsets. (G) Example bar plots of DEG from the genes displayed in (F). Samples represent ASC subsets from three independent mice.

FIGURE 3.

Distinct ASC isotypes contain unique transcriptomes. (A) PCA of 8766 detected genes in each population. Percentage of variation covered indicated on axes with circles denoting 99% confidence intervals. (B) Heatmap of 1438 DEGs. (C) Volcano plots for each pairwise comparison. Number of DEG in each comparison is indicated. (D) Bar plots showing expression of the indicated gene in each population. (E) Overlap of bone marrow ASC signature genes (22) and ASC isotype dataset described in this study. (F) Heatmap of ASC overlap genes from (E) that are differentially expressed between any two isotype subsets. (G) Example bar plots of DEG from the genes displayed in (F). Samples represent ASC subsets from three independent mice.

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Further analysis of the genes that were expressed in an isotype-specific manner revealed important functional differences between the subsets. IgA ASC expressed high levels of Ccr9, Ccr10, and Itgb7 (Fig. 3D). Each of these three genes are important for homing to mucosal sites. CCR9 binds to CCL25, which is constitutively expressed by the cells of the intestinal epithelium (37, 38). ITGB7 can heterodimerize with integrin α-4 to make α4β7, which, together with CCR9/CCL25 interactions, strengthen gut homing signals (39, 40). CCR10 can bind to CCL27 and CCL28, which canonically directs skin homing but has also been shown to position IgA ASC in the gut lamina propria (41). IgM and IgG ASC expressed significantly higher levels of Cxcr4 (the chemokine receptor responsive to CXCL12) than IgA ASC. CXCL12 is prevalent in the bone marrow and is pivotal in establishing the bone marrow microniche for long-lived ASC (42, 43). IgG ASC expressed Cxcr3, which serves to direct Ag-specific cells to the lung during influenza infection (44).

Additionally, we compared our ASC expression profiles to a previously defined bone marrow ASC gene expression signature (22) to determine the overlap of ASC from the periphery with those of the bone marrow. Of the reported ASC signature genes, 96% (289/301) were also expressed in IgM, IgA, and IgG ASC (Fig. 3E). These data show an overlapping gene expression signature for ASC in the periphery and in the bone marrow. We also determined whether ASC signature genes were similarly expressed between isotypes and found 15% (43/289) of the overlapping genes were DEG between at least two of the comparisons (Fig. 3F). Many of the gene expression differences were consistent with homing properties of cells of each isotype. As mentioned above, IgA ASC upregulated Ccr9, Ccr10, and Itgb7, which facilitate homing to mucosal sites. Additionally, IgA ASC expressed high levels of Tgfbr1, which can induce switching to IgA (45). Furthermore, Tnfrsf17, which encodes B cell maturation Ag, was more highly expressed in IgM and IgA ASC, and Ada, which encodes adenosine deaminase, an enzyme involved in purine metabolism, was more highly expressed in IgG ASC (Fig. 3G). Thus, each ASC isotype has a distinct transcriptional signature that reflects their developmental history, homing, and specialization potential and reinforces the importance of identifying the transcriptomes of more highly subdivided ASC.

The development of B cell tetramers facilitates the tracking of Ag-specific B cells during immune responses (24, 36). We sought to compare the gene expression changes observed in isotype-specific ASC to isotype- and Ag-specific ASC. Therefore, mice were infected with PR8, enriched, and stained as detailed above (Fig. 2A). In addition to the staining panel above, we intracellularly stained with a decoy streptavidin–PE–Alexa Fluor 647 reagent, as well as PE-labeled HA and NP B cell tetramers that are specific for PR8 (H1N1) (24). This allowed the exclusion of cells from the analysis that bound nonspecifically to the biotin, streptavidin, or the PE fluorophore present in the tetramers (Fig. 4A). There were fewer overall ASCs identified in naive mice than day 14 PR8-infected mice, and, importantly, no tetramer-positive cells were present in naive mice. As anticipated, naive mice have a large proportion of IgM ASC, and this frequency is decreased in PR8-infected mice, signifying the expansion of class-switched cells following infection. At day 14 postinfection, ∼15% of cells remained IgM+, up to 60% of ASC expressed IgG, and ∼5% were IgA+ from the spleen and dLN (Fig. 4B).

FIGURE 4.

Influenza-specific ASC show complementary transcriptomes to bulk ASC. (A) Flow cytometry gating strategy for identification of influenza-specific ASCs from the spleen and dLN at day 14 postinfection. Cells were enriched on CD138–allophycocyanin, stained with surface markers, and fixed/permeabilized for intracellular staining. Naive mice are used as a staining control and to draw gates for tetramer-binding cells. (B) Summary of ASC from naive (n = 5) and influenza-infected (n = 10) mice, displayed as the frequency of parent gate. ASC are from the total CD11b, F4/80, Thy1.2- (Fig. 2B, panel 1); tetramer+ (Tet) are of the total ASC; IgM, IgG, and IgA are of total Tet ASC. (C) PCA of 13,665 genes identified by RNA-seq in cells colored in (A) from two independent mice. (D) Mean fluorescence intensity (MFI) of CD36, BCL-2, LAG-3, and CD98 on total CD138+ cells and Tet isotype-specific cells as indicated. (E) Bar plot summarizing MFI data from (D). Data is representative of 10 mice from two independent groups. (F) Bar plots displaying mRNA expression in fragments per kb per million (FPKM) for each gene. Influenza-responding ASC (Fig. 3) are present in closed circles. TetDecoy ASC are included in open circles.

FIGURE 4.

Influenza-specific ASC show complementary transcriptomes to bulk ASC. (A) Flow cytometry gating strategy for identification of influenza-specific ASCs from the spleen and dLN at day 14 postinfection. Cells were enriched on CD138–allophycocyanin, stained with surface markers, and fixed/permeabilized for intracellular staining. Naive mice are used as a staining control and to draw gates for tetramer-binding cells. (B) Summary of ASC from naive (n = 5) and influenza-infected (n = 10) mice, displayed as the frequency of parent gate. ASC are from the total CD11b, F4/80, Thy1.2- (Fig. 2B, panel 1); tetramer+ (Tet) are of the total ASC; IgM, IgG, and IgA are of total Tet ASC. (C) PCA of 13,665 genes identified by RNA-seq in cells colored in (A) from two independent mice. (D) Mean fluorescence intensity (MFI) of CD36, BCL-2, LAG-3, and CD98 on total CD138+ cells and Tet isotype-specific cells as indicated. (E) Bar plot summarizing MFI data from (D). Data is representative of 10 mice from two independent groups. (F) Bar plots displaying mRNA expression in fragments per kb per million (FPKM) for each gene. Influenza-responding ASC (Fig. 3) are present in closed circles. TetDecoy ASC are included in open circles.

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We performed RNA-seq on IgA, IgG, and IgM populations of influenza-specific ASC. Similar to bulk ASC, PCA of all detected genes showed influenza-specific IgM and IgA to have transcriptomes more closely related to each other than to IgG (Fig. 4C). The expression of specific DEGs identified between the three subsets were validated by flow cytometry (Fig. 4D, 4E) and compared with expression levels derived by RNA-seq (Fig. 4F). CD98, which is a protein composed of the two subunits Slc3a2 and Slc7a5, was highly expressed on IgM and IgA ASC but was reduced on IgG expressing ASC. CD98 is a neutral amino acid transporter which has been shown to correlate with Ki67 expression in multiple myeloma plasma cells (46). Cd36 was upregulated in IgM ASC and has been reported previously to be highly induced upon stimulation but downregulated in class-switched ASC in vivo (47). Antiapoptotic protein BCL-2 was more highly expressed on IgM and IgA ASC. BCL-2 had previously been reported to be expressed on normal and malignant plasma cells with various intensity (48). Its variable expression between the three isotypes suggests that the variations in intensity seen previously may be reflective a mixture of various isotype classes. Lag3 expression was identified specifically on IgM ASC in the periphery at day 14; however, Il10 expression, which has been shown to correlate with LAG-3 protein expression was not detected at this time point (49).

To analyze the purity of our isolation strategy we used two complementary approaches. First, using the International ImMunoGeneTics database of mouse BCR gene segment locations (28), we computed the expression levels of all Igh constant regions and summarized the percentage of total reads that mapped to each region for the samples in each isotype. This approach demonstrated that 80, 95.5, and 90% of the Igh mRNA for IgM, IgG, and IgA ASC mapped to the Ighm, Ighg, and Igha segments, respectively (Fig. 5A). This demonstrates that each isolated ASC isotype predominantly expressed Igh mRNA specific to the enriched population. As a complementary approach, we used the MiXCR software tool to extract enriched BCR clones from the RNA-seq reads (30). MiXCR successfully extracted the CDR3 sequences containing the VDJ junctions and, for a subset of the clones, identified the associated C region. The Igh C region usage for all clones in each ASC isotype was determined and summarized as above for each ASC isotype. Analyzing rearranged clones showed an even higher specificity, with 90% of IgM, 99% of IgG, and 96.1% of IgA clones representing the target isotype (Fig. 5B). These data confirm the specificity of the target ASC populations.

FIGURE 5.

Flow cytometry accurately captures specific ASC populations. Pie charts showing the percentage of fragments per kb per million–normalized RNA-seq mapped reads (A) or MiXCR extracted clones (B) that map to each Igh C region for the indicated ASC isotype. Data represent the total from each ASC isotype group. For IgM and IgA ASC groups, all Ighg isotypes are summarized for simplicity.

FIGURE 5.

Flow cytometry accurately captures specific ASC populations. Pie charts showing the percentage of fragments per kb per million–normalized RNA-seq mapped reads (A) or MiXCR extracted clones (B) that map to each Igh C region for the indicated ASC isotype. Data represent the total from each ASC isotype group. For IgM and IgA ASC groups, all Ighg isotypes are summarized for simplicity.

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Using the VDJ and CDR3 sequences extracted from each ASC population, we analyzed the repertoire of each ASC isotype to determine clonality within and between isotypes. The preference for specific V–J combinations was determined and revealed that, overall, the ASC clones profiled predominantly used the J-2 segment (Fig. 6A). However, the second most common J region differed. IgG and IgA ASC were enriched for clones that used J-4 segment, and unswitched IgM ASC displayed a preference for the J-1 segment. Therefore, usage of specific J regions can be shared or unique to isotype-specific cell populations. Overall, clones for each ASC isotype employed a diverse range of V segments (Fig. 6B). Analysis of the V–J linkages for the ASC isotypes between one example mouse and the influenza-specific ASC revealed that J regions connected with different V region gene segments in each of the three populations of isotype-specific ASCs, contributing to the diversity of the responding repertoire. Focusing on the IgG ASC, the v9-4 segment was enriched as one of the top three V segments in each sample. Additionally, the v9-2 segment was enriched in both influenza-specific IgA and IgG samples. For the influenza-specific IgM, only v1-75 was shared in the top enriched V segments. As anticipated, the influenza-specific ASC were more clonally enriched than either bulk ASC or LPS-generated ASCs, (26, 50, 51) which have many clones that each make up only a small proportion of the total (Fig. 6C). We next assessed the relationships between CDR3 amino acid sequences of the ASC. Using a Jensen–Shannon divergence metric (52) with Euclidean distance and multidimensional scaling, we found a surprising consistency between CDR3 sequences within ASC isotypes that was shared between different mice (Fig. 6D). Clustering the top 50 clones from each sample revealed that, despite coming from different mice, clones containing similar CDR3 amino acid sequences were shared in the bulk ASC repertoire (Fig. 6E).

FIGURE 6.

Repertoire of influenza-specific and bulk ASC of distinct isotypes. (A) Heatmap of J-segment usage for each ASC cell type. Data are Z-score normalized by column and hierarchically clustered. Bulk ASC are denoted by closed boxes, and influenza-specific ASC are denoted by open boxes. (B) Circos plots showing the V–J combinations for each ASC isotype from one representative mouse (top) for bulk ASC and each mouse for influenza-specific ASC. The top three to four V segments are indicated. (C) Clonality plot for the top 40% of clones from each ASC population. For each clone, the percentage contribution to the total clonal population is plotted versus the normalized clone size for each sample. LPS ASC data are from Haines et al. (50), Guo et al. (26), and Scharer et al. (51). (D) Multidimensional scaling plot of the pairwise Euclidean distance of the Jensen–Shannon divergence. Bulk ASC are denoted by closed circles, and influenza-specific ASC are denoted by open circles. (E) Heatmap depicting the frequency of CDR3 amino acid sequences for the top 50 clones of each bulk ASC population. Data are Z-score normalized by column and hierarchically clustered. For select clones, the CDR3 amino acid sequence is indicated. n.d., not detected.

FIGURE 6.

Repertoire of influenza-specific and bulk ASC of distinct isotypes. (A) Heatmap of J-segment usage for each ASC cell type. Data are Z-score normalized by column and hierarchically clustered. Bulk ASC are denoted by closed boxes, and influenza-specific ASC are denoted by open boxes. (B) Circos plots showing the V–J combinations for each ASC isotype from one representative mouse (top) for bulk ASC and each mouse for influenza-specific ASC. The top three to four V segments are indicated. (C) Clonality plot for the top 40% of clones from each ASC population. For each clone, the percentage contribution to the total clonal population is plotted versus the normalized clone size for each sample. LPS ASC data are from Haines et al. (50), Guo et al. (26), and Scharer et al. (51). (D) Multidimensional scaling plot of the pairwise Euclidean distance of the Jensen–Shannon divergence. Bulk ASC are denoted by closed circles, and influenza-specific ASC are denoted by open circles. (E) Heatmap depicting the frequency of CDR3 amino acid sequences for the top 50 clones of each bulk ASC population. Data are Z-score normalized by column and hierarchically clustered. For select clones, the CDR3 amino acid sequence is indicated. n.d., not detected.

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In this study, we took advantage of an optimized intracellular staining panel that used B cell tetramers and isotype-specific Abs to identify influenza-specific ASC that express IgM, IgG, or IgA BCR constant regions. This approach was combined with protocols that allowed the extraction of cellular RNA for deep sequencing, facilitating the molecular characterization of ASC subsets. This is a critical advancement in the understanding of B cell biology, as we can begin to correlate transcriptional programs of isotype-specific ASC with known developmental and functional differences. The isotypes of ASC responding to NP and HA Ags at day 14 postinfluenza infection was dominated by IgM and IgG subsets in circulation. The early emergence of IgM ASC has been reported for other model Ags (36), and IgG, specifically IgG1, IgG2b, and IgG2c, are characteristic of influenza ASC responses in C57BL/6 mice (53), but the kinetics of IgA ASC are not as clearly defined. These data highlight the diversified ASC response at an early immune time point.

Analysis of the transcriptional programs of isotype-specific ASC revealed that genes expressed in bone marrow plasma cells (22) were also expressed in circulating (spleen and dLN) ASC at d14 following influenza infection. However, it was surprising to find 15% (43/289) of genes were differentially expressed between at least two ASC isotypes. This suggests functional diversification of ASC such as has been indicated for LAG3-expressing regulatory plasma cells (49). In fact, consistent with reported findings (49), LAG3 protein expression was highest in IgM ASCs and correlated with mRNA levels in this study. Further, many of the gene expression differences observed were consistent with homing properties for cells of each isotype. IgA ASC upregulated Ccr9, Ccr10, and Itgb7, which facilitates homing to mucosal sites. Additionally, IgA ASC expressed high levels of Tgfbr1, which induces switching to IgA (45). CD98, a neutral amino acid transporter, was expressed more highly on IgM and IgA ASC than IgG ASC. mRNA expression of the Slc3a2 subunit of CD98 correlates with protein expression; it is highest on IgM ASCs and lowest on IgG ASCs. CD98 expression is associated with dividing cells (46). In this scenario, CD98 expression may be reflective of the proliferative potential of early IgM plasmablasts. Cd36 was also expressed exclusively in IgM ASC, albeit at low levels. CD36 is a scavenger receptor that can bind to a variety of ligands, including lipoproteins and collagen (5456). In concordance with a reported role in marginal zone B cells, CD36 protein expression was found in this study to be expressed on some IgM ASCs. Finally, we validated BCL-2 expression by flow cytometry as well. BCL-2 was found more highly expressed on IgM and IgA ASC. Given its antiapoptotic function, divergent expression of BCL-2 may reflect the isotype-switching potential of IgM-expressing ASC and may be expressed on IgA ASC to preserve those cells in harsh or nutrient-deplete environments, such as the gut or the lung, as in the case of influenza infection. Thus, each ASC isotype has a distinct transcriptional signature that reflects their developmental history, homing potential, and potentially specialized functions. These data reinforce the importance of identifying the transcriptomes of more highly subdivided ASC.

We were able to extract information about the BCR V-J segment combinations from the sequencing data. This was used to confirm the purity of our ASC subsets but also to evaluate the clonal similarities and differences in each ASC population. IgG and IgA class-switched ASC were enriched in J-4 segment usage, whereas IgM were enriched in J-1 segments. Clustering of CDR3 amino acid sequences reveals similarity within isotype subsets, even among different mice. This suggests that distinct BCR clones may be selected into different ASC lineages, potentially from a common microbiota. This finding is in line with previous work in which there is evidence for a specific V region preference in the early response to PR8 in BALB/c mice (57, 58). It is important to note that our staining strategy pooled NP and HA tetramers. Therefore, the clonal differences between subsets could be caused by a skewing of one isotype toward HA or NP clones. Nevertheless, these data are generated from a snapshot of the repertoire, and these results do suggest that specific VDJ combinations may be preferred for constant region functional pairing or Ag specificities.

The unique transcriptional features of the ASC with distinct isotypes emphasizes the roles of cytokines and germinal center reactions that shape class switch recombination and indicates that these signals may impart broader molecular differences on ASC populations. Further investigation into the transcriptional networks that shape these phenotypic differences will be critical in vaccine design in which one isotypic subset is desired over another.

We thank members of the Boss laboratory for comments and critique, as well as R. Butler for animal husbandry and care, the Emory Flow Cytometry Core for cell sorting, and the Emory Integrated Genomics Core and the New York University Genome Technology Center for Illumina sequencing and library quality control.

This work was supported by National Institutes of Health (NIH) Grants 5R01 CA095318 (to C.D.S.), 1R01 AI123733 and P01 AI125180 (to J.M.B.), and NIH F31 AI138391 (to M.J.P.).

The RNA sequence data presented in this article have been submitted to the National Center for Biotechnology Information's Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE124578.

The online version of this article contains supplemental material.

Abbreviations used in this article:

ASC

Ab-secreting cell

DEG

differentially expressed gene

dLN

draining lymph node

HA

hemagglutinin

NP

nucleoprotein

PCA

principal component analysis

RNA-seq

RNA sequencing.

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The authors have no financial conflicts of interest.

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