Summary
Epstein-Barr Virus infection of B lymphocytes elicits diverse host responses via well-adapted transcriptional control dynamics. Consequently, this host-pathogen interaction provides a powerful system to explore fundamental processes leading to consensus fate decisions. Here we use single-cell transcriptomics to construct a genome-wide multistate model of B cell fates upon EBV infection. Additional single-cell data from human tonsils revealed correspondence of model states to analogous in vivo phenotypes within secondary lymphoid tissue, including a previously uncharacterized EBV+ analog of multipotent activated precursors that can yield early memory B cells. These resources yield exquisitely detailed perspectives of the transforming cellular landscape during an oncogenic viral infection that simulates antigen-induced B cell activation and differentiation. Thus, they support investigations of state-specific EBV-host dynamics, effector B cell fates, and lymphomagenesis. To demonstrate this potential, we identify EBV infection dynamics in FCRL4+ / TBX21+ atypical memory B cells that are pathogenically associated with numerous immune disorders.
Introduction
Epstein-Barr Virus (EBV) is an oncogenic gammaherpesvirus present in >90% of adults (Rickinson and Kieff, 2007) and associated with up to 2% of human cancers (Cohen et al., 2011). Recent reports have also provided epidemiological and mechanistic evidence supporting an etiological role for EBV in multiple sclerosis (MS) (Bjornevik et al., 2022; Lanz et al., 2022). In its initial stages, EBV infection within primary B lymphocytes manifests an array of host and viral programs. Upon entry into the host cell, the linear dsDNA viral genome rapidly circularizes to form an episome that is retained within the nucleus (Lindahl et al., 1976; Nonoyama and Pagano, 1972). Within hours to days, host innate immune responses are generated to restrict viral progression (Lünemann et al., 2015; Martin et al., 2007; Smith et al., 2013; Tsai et al., 2011). Simultaneously, viral genes are expressed to counteract host defenses (Ressing et al., 2015), co-opt B cell-intrinsic activation and proliferation (Calender et al., 1987; Thorley-Lawson, 2001; Thorley-Lawson and Mann, 1985), and attenuate DNA damage and stress responses instigated by virus-induced growth (McFadden et al., 2016; Nikitin et al., 2010). A consequence of these intimately adapted host-pathogen dynamics is that EBV infection can precipitate diverse responses and outcomes for host B cells. These include unsuccessful infection routes resulting from effective antiviral restriction and DNA damage-induced growth arrest as well as successful infection leading to immortalization in vitro (Bird, 1981; Henle et al., 1967; Pope et al., 1968; Zhao et al., 2011) or lifelong latency in vivo within memory B cells (Babcock, 1998; Longnecker et al., 2013; Miyashita et al., 1997) that retain oncogenic potential (Raab-Traub, 2007; Thorley-Lawson and Gross, 2004).
Since its discovery in 1964 as the first human tumor virus (Epstein et al., 1964; Young and Rickinson, 2004), extensive research has revealed the molecular means by which EBV establishes infection and underlies various malignancies. The entire EBV genome is ~172 kilobases and contains at least 80 protein-coding sequences including six EBV nuclear antigens (EBNAs); several latent membrane proteins (LMPs); and loci that encode replicative and transcriptional machinery as well as structural proteins. The EBV genome also contains functional non-coding RNAs: the BHRF and BART microRNAs and the EBV-encoding regions (EBERs) (Rickinson and Kieff, 2007; Young et al., 2007).
EBNAs are especially important in establishing distinct forms of latency depending on their combinatorial expression (Price and Luftig, 2015). EBNA1 is a transcription factor (TF) that is essential for viral genome maintenance and B cell transformation and ubiquitously binds and epigenetically regulates host chromatin (in this context, EBNA-mediated epigenetic regulation includes DNA methylation and histone modification) (Altmann et al., 2006; Canaan et al., 2009; Dheekollu et al., 2021; Humme et al., 2003; Lamontagne et al., 2021; Lu et al., 2010; Lupton and Levine, 1985; Wood et al., 2007; Yates et al., 1985). EBNALP is another essential factor (Mannick et al., 1991; Szymula et al., 2018) that initiates host cell proliferation alongside its co-activated target, EBNA2 (Alfieri et al., 1991; Harada and Kieff, 1997; Sinclair et al., 1994), and interacts with several host proteins including TFs (Han et al., 2001; Ling et al., 2005; Matsuda et al., 2003). EBNA2 is likewise required for B cell immortalization (Cohen et al., 1989), notably through coordination with host TFs and their binding sites (Lu et al., 2016; Zhao et al., 2011) and with EBNALP to drive early cell proliferation and viral LMP1 expression (Peng et al., 2005). The EBNA3 proteins (EBNA3A, EBNA3B, and EBNA3C) mediate a delicate balance of anti- and pro-oncogenic processes (Allday et al., 2015; Banerjee et al., 2014; Parker et al., 1996; Tomkinson et al., 1993; White et al., 2010). These include epigenetic repression of host tumor suppressor genes (BIM, p14, p16) and viral promoters (Maruo et al., 2011; Paschos et al., 2012; Saha et al., 2015; Skalska et al., 2010; Styles et al., 2017), competitive binding of the EBNA2-interacting host factor RBPJ (Robertson et al., 1995; Wang et al., 2015), and inhibition of apoptosis (Price et al., 2017). Collectively, the EBNAs reshape the nuclear regulatory and transcriptional landscape of EBV+ B cells, effectively hijacking B cell-intrinsic activation, expansion, and differentiation programs. Thus, EBV co-opts antigen-responsive host immune mechanisms for the ulterior purposes of viral replication and propagation.
While the EBNAs engage cell proliferation machinery at the epigenetic and transcriptional level in the nucleus, the LMPs (LMP1, LMP2A, and LMP2B) do so at the cell membrane by simulating antigen-induced signal transduction pathways. The essential LMP1 promotes B cell activation through mimicry of a constitutively active CD40 receptor (Kilger et al., 1998; Uchida et al., 1999) and interacts with Tumor Necrosis Factor (TNF) receptor-associated factors (TRAFs) to activate NF-κB pathway signaling via IKK (Devergne et al., 1996; Eliopoulos et al., 2003; Greenfeld et al., 2015; Luftig et al., 2003). These interactions induce anti-apoptotic pathways, MHC-mediated immune recognition, pro-inflammatory responses, and cell migration. Downstream consequences include oncogenic proliferation and survival but also induction of pro-apoptotic responses (Devergne et al., 1998; Fries et al., 1999; Greenfeld et al., 2015; Henderson et al., 1991; Shair et al., 2008; Wang et al., 2017). Thus, as in antigen-induced B cell activation (and subsequent differentiation), adept regulatory control of NF-κB signaling (Hoffmann et al., 2002; Mitchell et al., 2018; O’Dea et al., 2007; Roy et al., 2019) is dispositive for the fate of a given EBV+ B cell. Although it is not essential for transformation, LMP2A promotes cell survival through mimicry of a stimulated B cell receptor (BCR), which activates signaling cascades complementary to those induced by LMP1 (Anderson and Longnecker, 2008; Fish et al., 2020; Guasparri et al., 2008; Portis and Longnecker, 2004). LMP2A expression further predisposes EBV+ B cells to survival by lowering antigen affinity selection thresholds in vivo (Minamitani et al., 2015). Thus, EBV latent membrane proteins play integral roles in B cell proliferation in the absence of antigen licensing and in avoiding replicative dead ends effected by antiviral sensing.
Clearly, key EBV gene products manipulate diverse host programs at early stages to achieve sustained latency (Mrozek-Gorska et al., 2019; Pich et al., 2019). Many such perturbations involve extensive rewiring of epigenetic and transcriptional regulatory modules. EBV researchers have used methods such as RNA-, ATAC- (Assay for Transposase-Accessible Chromatin), and ChIP-seq (Chromatin Immunoprecipitation) to study these changes at various levels in the gene regulatory hierarchy within early infected cells and transformed lymphoblastoid cell lines (LCLs) (Arvey et al., 2012; Jiang et al., 2017; McClellan et al., 2013; Mrozek-Gorska et al., 2019; Wang et al., 2019; Zhou et al., 2015). Recently, the epigenetic and transcriptional roles of EBNA1 were interrogated through time-resolved multi-omics (Lamontagne et al., 2021). While these and other studies provide indispensable insights regarding virus-induced genome-wide expression and regulation, they have relied on bulk ensemble sequencing. Such assays yield population-averaged measurements that obscure variation arising from intrinsic stochasticity (Raj et al., 2006; Raj et al., 2010; Raj and Van Oudenaarden, 2008), asynchronous behaviors, and heterogeneous cell subsets. Specifically, ensemble averaging fails to capture cell-matched measurements across genes, which precludes identification of coordinated expression programs or epigenomic regulatory patterns in specific phenotypes. By contrast, single-cell sequencing provides refined genome-wide views of expression and regulation that preserve the ability for the identification of heterogenous cell states with low bias (Buenrostro et al., 2015; Junker and van Oudenaarden, 2014; Shalek et al., 2013; Shapiro et al., 2013; Wills et al., 2013). Given the complexity of host-virus relationships, single-cell approaches are essential to dissect the early stages of EBV infection and the distinct fate trajectories it comprises. We previously used single-cell RNA sequencing (scRNA-seq) to identify EBV-driven heterogeneity in LCLs (SoRelle et al., 2021). To study early infection, we leveraged single-cell multiomics (scRNA-seq + scATAC-seq) to capture and explore the distinct gene expression and regulatory signatures that determine the course of EBV infection in primary human B lymphocytes. Here we perform an in-depth analysis of the scRNA data, develop a refined germinal center (GC) model of EBV early infection, and detail previously uncharacterized EBV-infected B cell niches. Integration of the scATAC-seq data as well as development, validation, and implementation of a bioinformatic workflow to predict state-resolved regulatory relationships are reported in a companion piece (SoRelle et al., 2022).
Results
EBV asynchronously induces primary B cells into distinct phenotypic states early after infection
To interrogate gene regulatory changes that occur upon EBV infection, we isolated primary human B cells from the peripheral blood of two donors and infected them with the B95-8 strain of EBV. Infections were performed at a multiplicity of infection (MOI) of 5 to ensure latent gene expression in every cell (Nikitin et al., 2010). We cryopreserved samples of infected cells at 2, 5, and 8 days post-infection in addition to uninfected cells (Day 0) from each donor sample following B cell enrichment. Cell samples from each donor and timepoint were simultaneously thawed, prepared to >90% viability, and processed into single-cell multiome libraries. Single-cell matched transcript and accessibility data were obtained through standard NGS, alignment, counting, and quality control (QC) methods (Table S1).
EBV infection induced broad transcriptomic changes in B lymphocytes at high efficiency, as evidenced by the near-complete loss of resting phenotypes (Day 0) within two days of infection. New states emerged between Day 2 and Day 5, while subtle shifts in state proportions defined the period between Day 5 and Day 8 (Figure 1A). Total and unique transcripts per cell increased, particularly between Day 0 and Day 2, while mitochondrial gene expression increased gradually (Figure 1B). Total transcript and mitochondrial distributions at Day 2 exhibited two modes, which was consistent with the presence of both non-proliferative and mitotic cells identified by S-phase and G2M-phase marker scoring (Figure 1C).
Figure 1. Time-resolved single-cell gene expression during early EBV infection of B lymphocytes.
(A) Dimensionally reduced (UMAP) single-cell gene expression timecourse data from early EBV infection.
(B) General expression trends during early infection. Total mRNA refers to all transcripts captured per cell, while Feature mRNA refers to the number of unique transcripts per cell.
(C) Cell cycle phase scoring of expression data after cell cycle marker regression.
(D) UMAP visualization of unsupervised clustering of early infected cell expression in merged timepoint data.
(E) Pairwise correlation of identified clusters.
(F) Cluster membership by timepoint. Fit lines show coarse changes in phenotype frequency over time.
(G) Single-cell expression of the top 15 gene markers by cluster (n = 100 randomly sampled cells per cluster depicted; a.u.= arbitrary units).
See also Figures S1–S2
Unsupervised methods revealed subpopulations (clusters) in cell cycle-regressed aggregated scRNA-seq time courses. Two clusters corresponded to uninfected B cells (c3, c8); seven were post-infection B cell phenotypes (c0, c1, c2, c4, c5, c6, c7); and two were T cells (c9) and CD14+ monocytes (c10) carried over from PBMCs despite extensive B-cell enrichment (Figure 1D). Genome-wide expression correlation was higher among post-infection states relative to uninfected cells, and certain phenotypes were more strongly correlated (e.g., c0 with c1; c4 with c7, Figure 1E). Sorting cluster membership by day yielded coarse-grained dynamics of cell state transitions (Figure 1F). We determined top differential genes in each cluster (one-vs-all-others) to inform state identity annotations (Figure 1G). Identified clusters included many genes known to be modulated in EBV infection and were broadly consistent across both donors with respect to top marker genes, cell population frequencies, and temporal emergence (Figures S1–S2). The infection timecourse was globally defined by induction of interferon response and B cell signaling genes, modulation of chemokines and transcriptional regulators, and subset-specific expression of genes involved in regulation of apoptosis and B cell differentiation (Figure S2C–D).
Infected cell state heterogeneity is linked to antiviral and B cell-intrinsic responses
Cluster analysis deconvolved heterogeneous biological states within each sample and revealed phenotypes retained across multiple timepoints (Figures 2, S3–S4). At this resolution, we estimated time- and state-level trends in viral gene expression, variation in metabolic activity, and transcript diversity (Figure 2A). Overall, c0, c1, c2, c5, and c6 exhibited the highest levels of EBV transcripts and more unique transcripts than c3, c4, c7, and c8. Mitochondrial gene fraction and unique feature content were highest in c6 and lowest in c4 and c7, although c7 had a long-tailed distribution of mitochondrial expression (20–80%) prior to QC, indicative of (pre-) apoptotic cells. All clusters except uninfected B cells (c3, c8) displayed broad innate antiviral and interferon-stimulated gene (ISG and IFI member) expression. Antiviral gene expression was generally higher and exhibited greater variance in c7 than c4 and persisted at roughly uniform levels in c0, c1, c2, c5, and c6 (Figure 2B). Through joint consideration of cluster-resolved expression trends for viral, mitochondrial, and interferon-stimulated genes, we distinguished uninfected cells (c3, c8) and two classes of cells with the hallmarks of antiviral response: those with low proliferation and negligible viral expression (c4, c7) and those with viral and metabolic indicators of progressive EBV infection (c0, c1, c2, c5, c6).
Figure 2. High-resolution dissection of infected B cell phenotypes.
(A) Overview of global gene expression trends by phenotype.
(B) Induction of interferon response genes in all EBV+ clusters.
(C) Phenotype-resolved transcriptomic signatures in resting and EBV+ B cells. Select cluster-resolved comparisons of gene expression were evaluated via the Kolmogorov-Smirnov D statistic (KS D) and associated p value (* p < 1e-5; ** p < 1e-10; *** p < 1e-15) from 500 randomly sampled cells per cluster.
See also Figures S3–S4
Next, we extensively analyzed differentially expressed genes (DEGs) among clusters and groups, including pairwise comparisons of all post-infection phenotypes (Figures 2C, S3–S4). The two resting cell phenotypes differed in their expression of IGHD, IGHM, CD27, and other markers that distinguish naïve (c8) from memory (c3) B cells. In addition to interferon response signatures, non-proliferating infected cells exhibited an overall reduction in gene expression and upregulated stress response markers. These included the highest overall expression of actin sequestration genes (TMSB10, TMSB4X) and, particularly within c7, numerous ribosomal subunit genes (e.g., RPS27A). Cells in c4 were distinguished by elevated expression of MARCH1, which encodes an E3 ubiquitin ligase that regulates the type I interferon response (Wu et al., 2020). Unlike c4, c7 cells also contained high transcript levels for genes involved in oxidative stress (TXN, FTL, FTH1), cytochrome oxidase subunits (e.g., COX7C), ubiquitin genes (UBA52, UBL5) and highly variable mitochondrial fractions. Among EBV+ cells with hallmarks of elevated respiration, those in c6 were most clearly consistent with proliferating cells based on upregulated cell cycle markers. Cells in c0 were distinguished by upregulation of FCRL5 and LY86-AS1, an antisense RNA to a lymphocyte antigen (LY86) that mediates innate immune responses. Cells in this cluster also displayed markers consistent with the early stages of pre- germinal center activated B cells (e.g., CCR6, CD69, POU2AF1, TNFRSF13B, PIK3AP1). Notably, cells in c1 and c2 contained the highest levels of the EBV genome BHRF1 locus RNAs. Between these two phenotypes, c2 was enriched for genes involved in NF-κB signaling and known markers of EBV-mediated B cell activation (NFKBIA, TNFAIP3, EBI3) while c1 appeared to be derived from naïve cells (based on IGHD and other carryover genes) and exhibited near-unique expression of SH3RF3/POSH2 and FIRRE, a MYC-regulated long non-coding RNA (lncRNA). Finally, c5 displayed upregulation of immunoglobulin heavy and light chains (IGHA1, IGHG1, IGHM, IGKC, IGLC1-3) as well as genes involved in B cell differentiation (MZB1, PRDM1/BLIMP1, XBP1). Gene ontology (GO) networks were also generated for top DEGs from one-versus-all-other comparisons to facilitate phenotype annotations (Figures S5). Top cluster-resolved DEGs are also provided in Table S2.
A map of B cell phenotypes and fate trajectories in early EBV infection
Graph-based pseudotime (Qiu et al., 2017) approximated EBV-induced state transitions when anchored from resting cells (Figure 3A). Pseudotime scoring was used to track state dynamics of the top 25 marker genes for each phenotype and four example expression trajectories are highlighted (Figure 3B). Collectively, flow cytometry for the B cell marker CD19 and CD23 (upregulated in EBV infection) at each timepoint (Figure S6), cluster-specific DEGs, network ontologies, and pseudotime led us to propose a multi-phenotype model for heterogeneous cell fate trajectories (Figures 3C, S7) that manifest in early EBV infection in vitro. In this model, naïve (c8) and memory (c3) B cells infected with EBV either undergo antiviral response-mediated arrest (c4) or EBV-driven hyperproliferation (c6) within several days of infection. Hyperproliferating cells can subsequently enter one of several activated states (c0, c1, c2) or undergo growth arrest (c7). Further, differentiated B cells (c5) can develop following activation in a manner analogous to effector cell exit from the germinal center reaction.
Figure 3. A model of B cell fate trajectories in early EBV infection.
(A) Monocle3 pseudotime scoring of merged timecourse expression data relative to resting B cells (day 0). Unlike the 2D UMAP, 3D UMAPs depict closer proximity of c6 (first observed at day 2, blue dashed circle) to resting cells, consistent with the temporal emergence of the c6 phenotype prior to the c0, c1, c2, and c5 phenotypes.
(B) Pseudotime-resolved expression dynamics of top differentially expressed genes (DEGs) across phenotypes. Genes are hierarchically clustered by pseudo-temporal expression pattern similarity. Spline interpolant fits are shown for expression of select genes in pseudotime (insets i-iv). After sorting cells by pseudotime score in ascending order, the average pseudotime score of every 25-cell interval was computed for efficient visualization (i.e., pseudotime for 52,271 cells at 25 cell resolution).
(C) Annotated state model of EBV+ B cell phenotypes and fate trajectories. Empirically observed and putative directed state transitions are depicted in solid and dashed edges, respectively. Edges drawn to groups of phenotypes (dotted ovals) indicate transitions to/from each cluster within the group.
See also Figures S5–S7
Among activated phenotypes, c2 matched classical EBV-mediated activation of NF-κB pathway genes, apoptotic regulators, and other known biomarkers (Cahir-McFarland et al., 2004; Messinger et al., 2019). Cells in c1 were consistent with a related activation intermediate that originated from EBV+ naïve cells. Despite the relatively short timecourse, c2 and c5 began to reflect the continuum of activation and differentiation phenotypes we previously characterized in LCLs (SoRelle et al., 2021), which are considered to be immortalized at 21–28 days post-infection (Nilsson et al., 1971). We confirmed these similarities by merging Day 8 and the LCL GM12878, for which scRNA-seq data was previously reported and analyzed (Osorio et al., 2019; Osorio et al., 2020; SoRelle et al., 2021) (Figure S8). Conceivably, EBV+ cells could also transition to a plasmablast phenotype (c5) from memory cells (c3) through hyperproliferation (c6) via division-linked differentiation (Hodgkin et al., 1996), effectively bypassing intermediate states.
Conversely, cells in c7 highlighted diverse origins of EBV+ cell growth arrest, apoptosis, and senescence, which each provide host defenses against oncogenic malignancies (Bartkova et al., 2006; Nikitin et al., 2010). In addition to highly variable mitochondrial expression and the lowest transcript levels of any state, this phenotype was defined by broad upregulation of genes involved in ribosome biogenesis-mediated senescence (RPS14, RPL29, RPS11, RPL5) (Lessard et al., 2018; Nishimura et al., 2015) and stress-associated sequestration of actin monomers that favor G-actin formation (TMSB4X, TMSB10, PFN1) (Kwak et al., 2004). A subset of cells within c7 also contained elevated levels of cell cycle markers (MKI67, TOP2A, CCNB1, CENPF) carried over from pre-arrest hyperproliferation (Figure S9).
Evidence for EBV induction of an activated precursor to early memory B cells (AP-eMBC)
We next sought to compare early infected phenotypes from our multistate model with cells isolated from secondary lymphoid organs. We acquired single-cell RNA-seq data from human tonsil tissue and identified germinal center (GC) cell subsets (Figure 4A), which we analyzed alongside early infection phenotypes of interest. EBV+ NF-κB activated cells (c2) clearly mimicked GC light zone (LZ) B cells; MKI67hi cells (c6) matched actively cycling cells (including GC dark zone (DZ) B cells); and EBV+ differentiated cells (c5) matched plasmablasts and plasma cells (PB / PC). Cells in c0 were most like pre-GC naïve and memory B cell (MBC) subsets (Figure 4B–D). Further, numerous c0 markers were consistent with both pre-GC activated B cells (SELL, BANK1, CD69, GPR183 (EBI2)) and memory B cell phenotypes (SELL, BANK1, GPR183, PLAC8) recently identified from scRNA-seq of tonsils in response to antigen challenge (King et al., 2021) (Figure 4C–D). Cells in c0 further exhibited upregulation of genes with essential roles in B cell activation (TNFRSF13B/TACI) (Wu et al., 2000) and germinal center formation (POU2AF1/OCA-B) (Kim et al., 1996; Luo and Roeder, 1995; Schubart et al., 1996). Moreover, c0 displayed elevated CCR6, a marker of an activated precursor (AP) state that can generate early memory B cells (eMBCs) (Glaros et al., 2021; Suan et al., 2017) (Figure 5A).
Figure 4. A subset of early infected cells exhibits hallmarks of a multipotent activated precursor to early memory B cells (eMBCs).
(A) Top phenotype markers of healthy human tonsil subsets identified from scRNA-seq.
(B) UMAP merging of tonsil and key early infection cluster scRNA-seq assays. Tonsil clusters are colored to match the closest corresponding cells from early infection.
(C) UMAP Correspondence of key gene expression across tonsillar subsets and early infection phenotypes. Select markers of multipotent progenitors and eMBCs were informed by data from (Suan et al., 2017) and (Glaros et al., 2021).
(D) Dot plot visualization of key genes across early infection (ei) c0, c2, c5, and c6 and their analogs within tonsils (t).
Figure 5. FACS validation of CCR6+ AP-eMBCs derived from naïve and memory B cells.
(A) Relative expression of CCR6 and FCER2/CD23 by model phenotype determined by scRNA-seq.
(B) CCR6 surface expression on uninfected and EBV-infected B cells by number of cell divisions (CellTrace Violet). CCR6hi cells (blue gate) exhibits reduced proliferation relative to CCR6lo (magenta gate) cells.
(C) CCR6 and CD23 (FCER2) surface expression over the early infection timecourse.
(D) Cell divisions and IgD status of cells at Day 8 gated by CCR6 and CD23 expression. Gated fractions are colored by approximate correspondence to scRNA-seq model phenotypes.
(E) A fate model for EBV-induced AP-eMBC-like cells. P0→…n signifies the probability of the eventual transition of a cell from cluster 0 (AP-eMBC analog) to a given cluster n. Relative probability relationships for naïve and memory cells are proposed based on empirical findings from FACS.
See also Figures S10–S11
We subsequently validated the generation of CCR6+ AP-eMBC B cells in response to EBV infection through time-resolved FACS (Figures 5B–D, S10). Resting B cells were CCR6lo and remained so until between 2 and 5 days after infection. Further, we observed that the most proliferative cell fraction at day 8 was CCR6lo and a moderately proliferative cell population was CCR6hi. While the most proliferative cells were CCR6lo/CD23lo, the proliferative CCR6hi cells displayed variable CD23 levels (Figure 5B). Consistent with our scRNA time course, CCR6hi/CD23hi and CCR6hi/CD23lo populations respectively corresponded to c1/c2 and c0 and emerged within 5 days (Figure 5C). Based on CD27 and IgD status, these populations predominantly originated from naïve or non-switched memory versus switched memory cells, respectively; notably, cells from these different resting phenotypes were present in each population gated by CCR6 and CD23 status (Figure 5D, S10C–E). Indeed, we found that purified naïve B cells induced the AP-eMBC characteristic CCR6hi/CD23hi state upon EBV infection at a markedly greater rate than non-switched and switched memory B cells (Figure S10F–G). Rapidly proliferative CCR6lo/CD23lo/CD27hi/IgDlo cells were consistent with infected memory B cells transitioning to plasmablasts (c3→c6→c5 model trajectory; ~72% of CCR6lo/CD23lo cells). Marginally less proliferative CCR6hi/CD23lo/CD27hi/IgDlo cells were consistent with stimulated AP-eMBCs (c3→c6→c0 model trajectory; ~74% of CCR6hi/CD23lo cells). We also observed an IgDhi naïve population that matched the pre-GC AP-eMBC phenotype (c8→c6→c0; (~25% of CCR6hi/CD23lo cells). Finally, an even less proliferative CCR6hi/CD23hi/IgDhi population matched activated naïve (or non-switched memory) cells destined for GC BCs (c8→c6/c0→c1/c2; ~80% of CCR6hi/CD23hi cells) and the minor subset (~17%) of CCR6hi/CD23hi cells that was IgDlo was consistent with MBCs induced by EBV to undergo a pseudo-GC reaction (c3→c6/c0→c2) (Figure 5C–E). Intriguingly, a subset of CCR6+ cells displaying the AP phenotype apparently persists long after the early stages of infection based on scRNA-seq data from LCLs (Figure S11). Thus, c0 in our model matches a virus-induced common progenitor state from which PBs, GC BCs, and early MBCs have been shown to originate in response to antigen stimulation (Taylor et al., 2015). Our results further indicate that both naïve and memory B cells can achieve this multipotent state at different frequencies upon in vitro infection and that the AP-eMBC phenotype is perpetuated in EBV-immortalized B cells.
Single-cell imputation reveals transcriptomic dynamics of EBV infection of FCRL4+/TBX21+ B cells
The EBV early infection model presented herein captures robust B cell phenotypes, each of which are exhibited by >1000 cells in the assay. However, we did not preclude the possibility that distinct rare populations of B cells may also be represented within the dataset. To explore this possibility with sufficient sensitivity, we implemented a recently reported method that minimizes technical read noise from transcript dropout in scRNA data (Linderman et al., 2022). Briefly, this method (ALRA) adaptively thresholds a low-rank approximation of the single-cell expression matrix in order to preserve biological zeros (true negatives) for gene expression and impute probable values in place of technical zeros (false negatives from dropout). Unsupervised clustering of ALRA-imputed data from Day 5 of the early infection timecourse revealed a small (1.5% of cells) yet distinctive population of B cells (Figure 6A). Notably, this subset expressed FCRL4, which encodes an inhibitory receptor that blocks BCR signaling (Davis, 2007; Sohn et al., 2011); TBX21 / T-bet, a gene for a canonical Th1 subset homeobox TF (Szabo et al., 2000) that is also essential to an unconventional memory B cell subset (Johnson et al., 2020; Rubtsova et al., 2013; Wang et al., 2012); and CXCR3, which encodes a chemokine receptor that mediates migration in a subset of MBCs (Muehlinghaus et al., 2005). While these FCRL4+ / TBX21+ / CXCR3+ cells displayed robust expression of lineage markers (e.g., CD19, MS4A1/CD20) and genes involved in the early stages of B cell activation (e.g., CCR6, CD69), they notably lacked expression of GC reaction gene signatures or plasmablast differentiation (Figure 6B). Rather, the most prominent feature of this population was broad upregulation of FCRL family genes including FCRL5 and FCRL6, which was originally found to be expressed on NK and T cells (Wilson et al., 2007) but has recently been identified in B cell progenitors (Honjo et al., 2020). While these cells expressed markers of double-negative (DN & DN2) B cell populations (TBX21, ITGAX, CXCR3), such cells are typically FCRL4− (Jenks et al., 2018; Scharer et al., 2019). This population was most consistent with tissue-like or atypical memory B cells (atMBCs) based on the expression of the markers discussed above in addition to being CD21− / FCRL5+ / SOX5+ / RTNR4+, although differential FCRL4 expression in atMBC subsets has been reported (Li et al., 2016; Rakhmanov et al., 2009). Top markers of the FCRL4+ atMBC-like phenotype in our dataset were also enriched for genes with roles mediating innate immunity and inflammatory responses; proto-oncogenes including FGR, which has been shown to be induced by EBNA2 during EBV infection (Cheah et al., 1986; Knutson, 1990); and an array of genes whose overexpression contributes to cell migration, metastasis, plasticity, and the potential for self-renewal in a variety of cell types. After confirming the presence of a Day 0 (EBV−) precursor to the Day 5 atMBC phenotype, we isolated these cells to assess pre- and post-infection differential gene expression (Figure 6C). Although atMBCs were FCRL4+ at Day 0, the expression of FCRL4 increased following infection, consistent with our lab’s prior finding that FCRL4 is a host biomarker of the EBV Latency IIb program (Messinger et al., 2019). While CCR6 expression in these cells increased following infection, key NF-κB pathway genes associated with subsequent GC B cell activation events were downregulated after infection. Although upregulation of genes in this canonical pro-survival pathway was not observed, we found evidence for a PAX5 / FOXP4-AS1 / FOXP4 axis of proliferation and anti-apoptotic signaling within EBV-infected atMBCs (Figure 6D, violin plots) (Wu et al., 2019). Critically, we observed EBV latent transcripts within atMBCs at Day 5 and Day 8 in addition to upregulation of numerous genes with roles in cellular plasticity, tumorigenesis, migration, and self-renewal (Figure 6D, dot plot). In addition to FCRL4, TBX21, CXCR3, ITGAX/CD11c, and FCRL5, an unexpected set of genes known to mediate neural development, adhesion, and signaling were conserved and/or upregulated markers of this cell subset during the early infection timecourse (Figures 6E, S12). These genes included NRCAM, DPF3, PPP1R17, PCDH1, GABRA4, and GRID1, which were identified through GO Process enrichment within atMBCs (GO:0007399, FDR = 0.0075, 62 genes and GO:0035239, FDR = 0.035, 23 genes). Notably, several of these were also observable in subsets within the GM12878 LCL. Many of these and other genes upregulated within FCRL4+ atMBCs following EBV infection were also found in GM12878, which was derived from an independent biological donor (Figure S12).
Figure 6. Discovery and characterization of EBV-driven transcriptomic dynamics in FCRL4+ / TBX21+ atypical memory B cells (atMBCs).
(A) Unbiased clustering of Day 5 scRNA assay processed using biological zero-preserving imputation by adaptive thresholding of low-rank approximation (ALRA; see (Linderman et al., 2022)). Early infection model cluster numbering (top panel) and key markers of the EBV-induced pseudo-GC reaction are depicted (bottom-left column). A non-GC cluster of FCRL4+ / TBX21+ / CXCR3+ B cells, consistent with atMBCs, was also identified (bottom-right column).
(B) Hierarchically ordered phenotypes from imputed Day 5 data based on expression representative model state genes and top markers of the atMBC phenotype. With respect to the depicted genes, the atMBC state shares greater similarity to non-B cell lineages than other infected B cell states. Annotations are provided for atMBC markers genes, which include previously identified lineage markers (e.g., ITGAX/CD11c).
(C) Identification of the atMBC phenotype in ALRA-imputed Day 0 scRNA assay. Cells matching this phenotype from Day 0 and Day 5 were extracted and merged to evaluate differential gene expression pre- and post-EBV infection.
(D) Differential gene expression in atMBCs in response to EBV infection.
(E) Expression of genes involved in nervous system development (GO:0007399, FDR = 0.0075) and tube morphogenesis (GO:0035239, FDR = 0.035) within atMBCs before (d0) and after (d5, d8) EBV infection.
See also Figure S12
Discussion
Our data reveal heterogeneous gene expression dynamics within individual cells during the critical early stages of a viral infection. By capturing the initial phases of EBV infection with high resolution methods, we discern the genome-wide expression changes associated with diverse infected cell fates and their respective developmental trajectories. These include effective host antiviral responses, virus-triggered oncogene-induced senescence, and the path to sustained EBV latency and host cell immortalization, which is accessed via simulated B cell activation. Thus, the resource established herein yields a vividly detailed representation of the genome-wide interplay of host and virus.
The identification of an EBV-infected analog of an AP-eMBC phenotype is consistent with results from in vitro and in vivo antigen stimulation experiments (Suan et al., 2017; Taylor et al., 2015), as well as previous work from our lab that identified CCR6 as an EBV Latency IIb program biomarker that becomes downregulated in the transition to Latency III in LCLs (Messinger et al., 2019).The development of this state in vitro implies that EBV may gain access to the memory pool in vivo via progenitors that have limited involvement in the GC reaction. In the context of normal antigen stimulation, this subset of eMBCs undergoes early exit from the cell cycle and GC reaction as a consequence of restricted access to or engagement with cognate antigen (Glaros et al., 2021). It is thus conceivable that EBV-infected B cells may differentially develop into GC BCs, PBs, or eMBCs from the AP state in a manner dependent on the extent to which the LMPs, EBNAs and other viral gene products induce mimicry of BCR activation and signaling. This model accommodates a surprising possibility – that EBV may access long-term persistence and survival not only within post-GC high-affinity MBCs but also via GC-independent eMBCs that avoid extensive proliferation.
The activated B cell and plasmablast phenotypes that developed within 5 days are generally consistent with our findings in LCLs (SoRelle et al., 2021) and resemble in vivo tonsil cell subset transcriptomes. Notably, prior studies found that only 50% of EBV-infected cells that secrete immunoglobulin go on to become immortalized (Tosato et al., 1985). Additional work within LCLs demonstrated that EBV+ cells with upregulated Ig production exhibited reduced DNA synthesis and EBNA downregulation (Wendel-Hansen et al., 1987). Collectively, these findings support a model for continuous EBV-driven B cell differentiation in vitro, wherein plasmablasts are continually generated through activation-induced maturation yet selected against by their reduced proliferation. While this disadvantage limits viral replication via cell division, the reduced MYC levels, increased XBP1, and endoplasmic reticulum stress in these cells may support EBV lytic reactivation (Guo et al., 2020; Laichalk and Thorley-Lawson, 2005; Sun and Thorley-Lawson, 2007; Taylor et al., 2011).
The identification and characterization of genome-wide EBV infection dynamics within FCRL4+ atMBCs (as well as other B subsets including FCRL4+ / TBX21− and FCRL4− / TBX21+ B cells evident within our data) provides an intriguing road map for subsequent research into EBV pathogenesis in atypical B cell niches and their potential role(s) in human disease. Specifically, the tissue-homing capacity, innate immune mediation roles, and progenitor-like features of atMBCs make them a high-interest target cell type in a range of cancers and autoimmune conditions. Notably, TBX21 and CXCR3 have separately been found at high frequencies in specific B cell lymphoma subtypes including Chronic Lymphocytic Leukemia (CLL), splenic marginal zone lymphoma (SMZL), extranodal marginal zone lymphoma (EMZL), precursor B-cell Lymphoblastic Leukemia, and Hairy Cell Leukemia (Dorfman et al., 2004; Jones et al., 2000).
Generally, it has been observed that expansion of B cells with a base expression profile of low CD21 (CR2) and inhibitory receptors including FCRL4 is a fundamental feature of chronic infections and (auto) immune disorders (Freudenhammer et al., 2020). Expanded atypical MBC populations expressing FCRL4 and ITGAX have been identified in patients with chronic Plasmodium falciparum infections living in regions with endemic malaria (Weiss et al., 2009) and in exhausted B cell populations (also CXCR3+) described in the context of HIV viremia (Moir et al., 2008). FCRL4+ / TBX21+ / ITGAX+ B cells have also recently been identified as a pathogenic subset in primary Sjögren’s Syndrome (pSS) with lymphomagenic potential (Verstappen et al., 2020). FCRL4+ / CD20hi B cells expressing RANKL/TNFSF11 have been reported as a subset that contributes to inflammation in rheumatoid arthritis (RA) (Yeo et al., 2015) – this matches an additional phenotype that we observed in ALRA-imputed data from Day 8 and in the GM12878 LCL. Notably, RANKL/TNFSF11 was not expressed in these cells until after EBV infection (data not shown). A variety of B cells phenotypes have been found to be clonally expanded in systemic lupus erythematosus (SLE). These include CXCR3+/CD19hi B cells (Nicholas et al., 2008), FCRL4− DN2 cells (Jenks et al., 2018) and notably, a recent large cohort study identified IL-21 stimulation drove expansion of tissue-homing CD11c+ / T-bet+ (ITGAX+ / TBX21+) with significantly elevated levels of FCRL3, FCRL4, and FCRL5 (Wang et al., 2018).
In the context of autoimmunity, comprehensive analysis of distinct B cell subsets – including how each of these may be affected by EBV infection – is especially relevant to resolving the etiology of multiple sclerosis (MS). While one report found clonally expanded B cells from the cerebrospinal fluid (CSF) of MS patients with upregulated TBX21, CXCR3, and SOX5, EBV reads were not identified in the samples (Ramesh et al., 2020). However, two recent reports that provide epidemiological (Bjornevik et al., 2022) and serological (Lanz et al., 2022) data supporting a causal role for EBV in MS have ignited efforts to explore the mechanistic foundations of this causality. That effort, along with research into other diseases associated with EBV-induced B cell dysregulation, will benefit from understanding the nuances of viral pathogenesis in distinct B cell niches. The data developed and analyzed herein provide a comprehensive portrait of de novo EBV infection within the canonical GC model and offer a tantalizing initial glimpse into non-canonical infection dynamics in at least one atypical yet critical B cell phenotype. These non-canonical responses to EBV infection are exemplified by the apparent lineage-inappropriate expression of genes that mediate neural cell development, adhesion, and signaling within a lymphoid compartment. While a mechanistic understanding is lacking, it is conceivable that elevated T-bet expression (and possibly increased chromatin accessibility at T-bet binding sites) mediated by EBV may contribute to this this aberrant expression signature. To this end, ChIP-seq for T-bet in T cells (Kanhere et al., 2012) detected binding sites associated with several genes with neural ontology found in our work to be upregulated in EBV+ TBX21+ B cells. These genes included TOX (7 T-bet binding sites) (Artegiani et al., 2015), ZEB2 (12 sites) (Hegarty et al., 2015), ST8SIA6 (4 sites) (Angata et al., 2000), SOX5 (3 sites) (Lai et al., 2008), RTN4 (1 site) (Wang et al., 2021), and PLXNC1 (9 sites) (Van Battum et al., 2015).
In summary, these time-resolved single-cell data elucidate aspects of cellular heterogeneity and responses that underlie previously obscured complexities of host-EBV dynamics. In addition to refining the GC model of EBV infection, this approach has highlighted the importance of future studies of viral dynamics within distinct B cell subsets. We expect this resource will facilitate advances in our understanding of the gene expression diversity intrinsic to peripheral B cells, their responses to EBV, and the potential roles of EBV-infected B cell niches across a spectrum of virus-associated diseases.
Limitations of the study
The reported single-cell data have several limitations. They do no capture aspects of host-virus dynamics acting at other molecular levels. Examples include epigenetic modifications (e.g., DNA methylation status), three-dimensional chromatin architecture changes, modulation of translation and protein abundance, post-translational modifications, protein-protein interactions, and signaling cascades (e.g., phosphorylation status).
STAR ★ Methods
Resource availability
Lead contact
Further information and requests for reagents or resources should be directed to and will be fulfilled by the Lead Contact, Micah A. Luftig (micah.luftig@duke.edu).
Materials availability
This study did not generate new unique reagents.
Data and Code Availability
Single cell sequencing data have been deposited in the NIH Sequence Read Archive (SRA) and Gene Expression Omnibus (GEO) and are publicly available as of the date of publication. Accession numbers are listed in the key resources table.
We performed analysis in R using both built-in functions and the software packages described in the key resources table. R code used for analysis and visualization is available on GitHub (https://github.com/esorelle/early_EBV_infection_scRNA) as well as Zenodo (doi: 10.5281/zenodo.6821702). Other information required to reanalyze the data herein is available upon request.
Additional information and requests for described data resources should be directed to and will be fulfilled by the lead contact author (micah.luftig@duke.edu).
Key Resources Table
REAGENT or RESOURCE | SOURCE | IDENTIFIER |
---|---|---|
Antibodies | ||
Mouse anti-human CD19-PE, clone HIB19 | BioLegend | Cat. #302208; RRID: AB_314238 |
Mouse anti-human CD23-PAC, clone EBVCS-5 | BioLegend | Cat. #338513; RRID: AB_1501123 |
Mouse anti-human CD27-FITC, clone O323 | BioLegend | Cat. #302806; RRID: AB_314298 |
Mouse anti-human CD27-APC, clone M-T271 | BioLegend | Cat. #356410; RRID: AB_2561957 |
Mouse anti-human IgD-FITC, clone IA6-2 | BioLegend | Cat. #348206; RRID: AB_10612567 |
Mouse anti-human CCR6-PE, clone R6H1 | Invitrogen | Cat. #12-1969-42; RRID: AB_1272087 |
Bacterial and virus strains | ||
Epstein-Barr Virus (EBV), B95-8 strain | Luftig Lab (original publication: Johannsen et al., 2004). | doi.org/10.1073/pnas.0407320101 |
Biological samples | ||
Whole blood samples, de-identified donors | Gulf Coast Regional Blood Center | N/A |
Discarded tonsil tissue, anonymized | Duke University Medical Center | N/A |
Chemicals, peptides, and recombinant proteins | ||
CellTrace Violet (proliferation tracking dye) | ThermoFisher / Invitrogen | Cat. #34571 |
Histopaque-1077 density gradient medium | Sigma | Cat. #H8889 |
Critical commercial assays | ||
B cell Negative Isolation Kit | BD Biosciences | Cat. #558007 |
Chromium Next GEM Single Cell Multiome ATAC + Gene Expression Reagent Bundle, 16 rxns | 10x Genomics | Cat. #1000283 |
Chromium Next GEM Chip J Single Cell, 16 rxns | 10x Genomics | Cat. #1000230 |
Dual Index Kit TT Set A, 96 rxns | 10x Genomics | Cat. #1000215 |
Chromium Next GEM Automated Single Cell 3’ Library and Gel bead Kit v3.1, 4 rxns | 10x Genomics | Cat. #1000147 |
Chromium Next GEM Chip G Single Cell Kit, 16 rxns | 10x Genomics | Cat. #1000128 |
Deposited data | ||
Early infection scRNA + scATAC data (two biological donors, four timepoints per donor) | Luftig Lab | GSE189141; SRA #SRP346796 |
Tonsil lymphocyte scRNA data | Luftig Lab | GSE159674; SRA #SRP287808 |
Software and algorithms | ||
cellranger-arc pipeline | 10x Genomics | v1.0.0 |
cellranger pipeline | 10x Genomics | v3.0 & v2.0 |
R | R Foundation | v4.0.5 |
R Studio | RStudio, PBC | v1.4.1106 |
Seurat | https://github.com/satijalab/seurat | v4.1 |
SeuratWrappers | https://github.com/satijalab/seurat | v0.3.0 |
Monocle3 | https://github.com/cole-trapnell-lab/monocle3/ | v1.0.0 |
ggplot2 | Hadley Wickham | v3.3.6 |
RColorBrewer | Erich Neuwirth | v1.1.3 |
viridis | https://sjmgarnier.github.io/viridis/ | v0.6.2 |
FlowJo | BD Biosciences | v10.8.1 |
Cytoscape | Institute for Systems Biology | v3.9.1 |
early_infection_analysis_code.R | Luftig Lab | DOI: 10.5281/zenodo.6821702 |
Experimental models and subject details
Ethics statement
All experiments were performed under the approval of institutional review boards (Duke University IRBs; eIRB# Pro00006262 and eIRB# Pro00061264). All blood samples were obtained through the Gulf Coast Regional Blood Center (Houston, TX) from de-identified donors. Anonymized, discarded human tonsil tissue samples were acquired through Duke University Medical Center. As these samples were not accompanied with any PHI or HIPAA identifiers, the described experiments were classified as non-human subjects research. Thus, no gender or age data were available for the analyzed samples.
PBMCs and Primary B cells
Human PBMCs and primary B cells were isolated from de-identified donor blood samples as described in the Method details section below.
Tonsils
Anonymized, discarded human tonsil tissue samples were prepared as described in the Method detail section below.
Method details
PBMC isolation and B lymphocyte enrichment
Whole blood (50 mL each from two anonymous donors; TX1241/Donor 1 & TX1242/Donor 2) was obtained from the Gulf Coast Regional Blood Center (Houston, TX). Upon receipt, peripheral blood mononuclear cells (PBMCs) from each donor sample were isolated via Ficoll gradient separation (Histopaque®-1077, Sigma #H8889), resuspended at 10×106 cells/mL in RPMI 1640 + 15% heat-inactivated fetal bovine serum (FBS, v/v, Corning) (R15 media), and incubated overnight at 37°C and 5% CO2. The next day, CD19+ B cells were enriched from donor PBMCs via negative isolation (BD iMag Negative Isolation Kit, BD Biosciences #558007) and resuspended at 2×106 cells/mL in R15 supplemented with 2 mM L-glutamine, 100 units/mL penicillin, 100 μg/mL streptomycin, and 0.5 μg/mL cyclosporine A (R15+ media). Roughly 45×106 B cells were recovered per donor post-enrichment. Following CD19+ validation (see below: Flow cytometry), enriched B cell aliquots (1–2 mL at 3×106 cells/mL) were viably frozen in 90% FBS + 10% DMSO and stored in liquid N2.
EBV infection and cell culture
The type 1 EBV strain B95-8 was obtained in-house as viral supernatant from the inducible B95-8 Z-HT cell line as reported previously (Johannsen et al., 2004). Briefly, EBV virion production from B95-8 Z-HT cells was stimulated by the addition of 4-HT to promote lytic reactivation. B95-8 virions were harvested from culture supernatant after five days and purified via gradient centrifugation. Immediately after withholding and cryopreserving uninfected enriched B cells for each donor (day 0 samples), the remaining cells in culture were infected with B95-8 via resuspension in viral supernatant (100 μL per 1×106 cells) for 1 h at 37°C and 5% CO2. Infected B cells from each donor were rinsed with 1x PBS, resuspended in R15+ media, and incubated at the conditions described above throughout the course of infection. Aliquots were taken from each infected donor culture at 2-, 5-, and 8-days post-infection and viably frozen as described for uninfected day 0 samples.
Flow cytometry
The extent of B cell enrichment was quantified for each donor using flow cytometry. Following negative isolation, cell samples (2×105 per donor) were rinsed with FACS buffer (1x PBS + 2% FBS), stained with phycoerythrin (PE)-conjugated mouse anti-human CD19 (BioLegend, clone HIB19; catalog #302208; lot #B273508) in the dark for 30 min at room temperature, then rinsed again prior to analysis. Cell samples at each timepoint were prepared as described and co-stained with PE-anti-CD19 and allophycocyanin-conjugated mouse anti-human CD23 (APC-anti-CD23, BioLegend, clone EBVCS-5; catalog #338513; lot #B273489) to validate successful EBV infection. To validate the AP-eMBC phenotype (c0), enriched resting B cells from two additional donors (TX1253 and TX1254) were labeled with CellTrace Violet (ThermoFisher / Invitrogen, Cat #34571) and stained with one of the following combinations at days 0, 2, 5, and 8: CCR6/Memory panel (FITC-anti-CD27, PE-anti-CCR6, and APC-anti-CD23); Naïve/Memory panel (FITC-anti-IgD, PE-anti-CD19, and APC-anti-CD27); or CCR6/Naïve panel (FITC-anti-IgD, PE-anti-CCR6, and APC-anti-CD23). FITC-anti-CD27, FITC-anti-IgD, and APC-anti-CD27 were purchased from BioLegend (Cat #302806, #348206, and #356410, respectively) and PE-anti-CCR6 was purchased from Invitrogen (Cat #12-1969-42). Compensation matrices were calculated from single-stain controls for FITC and PE and applied to all samples for analysis. All cytometry measurements were acquired with a BD FACSCanto II (BD Biosciences) and analyzed using FlowJo version 10.8.1 (Ashland / BD Biosciences).
Human tonsil sample preparation
Tonsillar B cells were isolated from discarded, anonymized tonsillectomies from the Duke Biospecimen Repository and Processing Core (BRPC; Durham, NC). Tonsil tissue samples were manually disaggregated, filtered through a cell strainer, and isolated by layering over a cushion made from Histopaque-1077 (H8889; Sigma-Aldrich). Harvested lymphocytes were washed three times with FACS buffer (5% FBS in PBS) prior to scRNA library preparation.
Preparation of scRNA and scATAC libraries
Cryopreserved samples from each early infection timepoint of interest were simultaneously thawed by donor and purified to > 90% viable cells by Ficoll gradient separation. Viable cells from each timepoint and donor were then prepared as single-cell matched gene expression (scRNA) and chromatin accessibility (scATAC) libraries by the Duke Molecular Genomics Core staff with the 10x Chromium Next GEM Single Cell Multiome ATAC + Gene Expression Kit (10x Genomics, Pleasanton, CA) (Satpathy et al., 2019; Zheng et al., 2017). Briefly, nuclei were isolated from each sample and subjected to transposition at accessible chromatin sites. Next, transposed nuclei, barcoding master mix, and gel beads containing unique barcode sequences were prepared into single-cell GEMs (Gel bead emulsions) using the Chromium Controller and Chip J. Within each GEM, poly-adenylated (poly-A) mRNA transcripts from individual nuclei are captured by barcoded, indexed poly-T primers and reverse transcribed into cDNA. Simultaneously, a separate barcoded sequence containing a spacer and Illumina P5 adaptor sequence is added to transposed regions within the captured nucleus. Barcoded multiomes were then purified, pooled, and pre-amplified by PCR prior to library construction. The scRNA library for each sample is then constructed using PCR to incorporate the P5 and P7 sequencing adaptors. Two biological replicates of tonsillar lymphocytes were prepared as scRNA libraries using the 10x Genomics Next GEM 3’ Gene Expression kit with v3 chemistry (10x Genomics, Pleasanton, CA), and sequenced, processed, aligned, and analyzed as described above for early infection scRNA samples. The scATAC library sequencing, analysis, results, and informatic integration are described in a companion manuscript.
Sequencing, alignment, and count matrix generation
The 8 paired-end scRNA libraries were similarly pooled and sequenced at a target depth of 50,000 reads per cell. Tonsil scRNA libraries were likewise pooled and sequenced at 50,000 reads per cell. All sequencing runs were performed by staff at the Duke Center for Genomic and Computational Biology. Raw base calls for each assay were prepared as sample-demultiplexed FASTQ files using cellranger-arc mkfastq (Cellranger, 10x Genomics), a wrapper of the Illumina bcl2fastq function. Next, sample-matched scRNA reads were aligned against genomic references to produce multiome count matrices using cellranger-arc count. One set of count matrices was generated by mapping reads to a concatenated genomic reference constructed from the human genome (GRCh38) with the ~172 kB type 1 EBV genome (NC_007605) included as an extra chromosome. These outputs were used for downstream RNA-only analyses to capture host and viral gene expression. Compatible reference packages were assembled from the relevant genome (.fa) and annotation (.gtf) files using cellranger-arc mkref.
Data QC and scRNA analysis
All direct analysis of scRNA data was conducted in R using Signac (Stuart et al., 2021), an extension of Seurat (Macosko et al., 2015; Satija et al., 2015; Stuart et al., 2019). Following read mapping and counting, scRNA data were obtained from between 8,934–20,000 cells per sample. After QC filtering by mitochondrial expression (n < 20%), total transcripts (n < 25,000), unique transcripts (n > 1,000), and minimum cells expressing a given feature (n > 3), data from between 8,376–19,310 cells per sample were analyzed (see Table S1). The mitochondrial gene expression threshold was selected based on the high metabolic activity of early-infected B cells and the high cell viability observed in each sample (> 90%) immediately prior to library preparation to preserve biologically relevant phenotypes (Osorio and Cai, 2021). After QC filtering, a total of 52,271 and 44,920 cells were analyzed across the infection timecourse for donors TX1241 and TX1242, respectively. Gene expression data (host and viral) across all timepoints for a given donor were merged into a single object, log normalized, scored for cell cycle markers, and scaled with cell cycle scoring regressed out. The top 2,000 differentially expressed features over the early infection timecourse data were identified and used for principal component analysis (PCA). The top 30 principal components were further dimensionally reduced via uniform manifold approximation projection (UMAP, (McInnes et al., 2018)), and clustering was performed to identify biologically distinct cell subpopulations. Merged scRNA dataset pseudotime trajectories were calculated using Monocle3 (Qiu et al., 2017), and were mapped along with cluster identities to 3D UMAP coordinates for visualization (Qadir, 2019; Qadir et al., 2020).
Quantification and statistical analysis
Statistical analysis
Genes with statistically significant differences in expression between clusters were identified using the Seurat FindMarkers() function. Raw and Bonferroni-adjusted p values calculated for these genes using built in two-sample Wilcoxon signed-rank tests (one cluster versus all others) are provided in a supplementary file containing top cluster marker data. For statistical comparisons of specific clusters (e.g., one versus one and one versus group, as in Figure 2C), significance was determined by Kolmogorov-Smirnov (KS) test distance statistic (D) and the associated p value as described in the text. Fit lines for pseudotime scatterplots (as in Figure 3B) were generated using the loess() fit function in R. Significant GO process enrichment was identified through false discovery rates (FDR) via built-in calculation in Cytoscape. Statistical details can be found in the figures and figure legends.
Software and visualization
The cellranger (v2.0.0 and v3.0.0) and cellranger-arc (v1.0.0) software packages were used to prepare and align FASTQ reads as described above. All scRNA data visualization was generated in R (v4.0.5) using ggplot2 (v3.3.6) and built-in functions of Seurat (v4.1). Additional R software packages used to control plot aesthetics are described in the key resources table. Detailed descriptions of single-cell analyses are described in the Method details section. Gene ontology networks for cell clusters of interest were prepared using Cytoscape (v3.9.1). Flow cytometry data analysis and visualization were performed using FlowJo (v10.8.1).
Supplementary Material
Table S2. Spreadsheet of the top differentially expressed genes for identified clusters. Data are output from the Seurat FindAllMarkers() function and include raw and Bonferroni-adjusted Wilcoxon p values for gene expression between clusters in one-versus-all-others comparisons.
Acknowledgments
The authors wish to thank members of the Duke University Molecular Genomics Core (MGC) and the Duke Center for Genomic and Computational Biology (GCB), especially Emily Hocke, Karen Abramson, Dr. Simon Gregory, and Dr. Nicolas Devos. Special thanks are also due to the anonymous donors, without whose blood donations this work would not have been possible. E.D.S. wishes to acknowledge funding support from the Department of Molecular Genetics and Microbiology Viral Oncology Training Grant (NIH T32 #T32CA009111) and a postdoctoral fellowship from the American Cancer Society (ACS, Award ID: PF-21-084-01-DMC). J.D. wishes to acknowledge funding support from a Ruth L. Kirschstein National Research Service Award (NIH NRSA F31 #F31DE027875). This work was supported by funding from the National Institute of Dental and Craniofacial Research (NIDCR award #R01DE025994).
Footnotes
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S2. Spreadsheet of the top differentially expressed genes for identified clusters. Data are output from the Seurat FindAllMarkers() function and include raw and Bonferroni-adjusted Wilcoxon p values for gene expression between clusters in one-versus-all-others comparisons.
Data Availability Statement
Single cell sequencing data have been deposited in the NIH Sequence Read Archive (SRA) and Gene Expression Omnibus (GEO) and are publicly available as of the date of publication. Accession numbers are listed in the key resources table.
We performed analysis in R using both built-in functions and the software packages described in the key resources table. R code used for analysis and visualization is available on GitHub (https://github.com/esorelle/early_EBV_infection_scRNA) as well as Zenodo (doi: 10.5281/zenodo.6821702). Other information required to reanalyze the data herein is available upon request.
Additional information and requests for described data resources should be directed to and will be fulfilled by the lead contact author (micah.luftig@duke.edu).
Key Resources Table
REAGENT or RESOURCE | SOURCE | IDENTIFIER |
---|---|---|
Antibodies | ||
Mouse anti-human CD19-PE, clone HIB19 | BioLegend | Cat. #302208; RRID: AB_314238 |
Mouse anti-human CD23-PAC, clone EBVCS-5 | BioLegend | Cat. #338513; RRID: AB_1501123 |
Mouse anti-human CD27-FITC, clone O323 | BioLegend | Cat. #302806; RRID: AB_314298 |
Mouse anti-human CD27-APC, clone M-T271 | BioLegend | Cat. #356410; RRID: AB_2561957 |
Mouse anti-human IgD-FITC, clone IA6-2 | BioLegend | Cat. #348206; RRID: AB_10612567 |
Mouse anti-human CCR6-PE, clone R6H1 | Invitrogen | Cat. #12-1969-42; RRID: AB_1272087 |
Bacterial and virus strains | ||
Epstein-Barr Virus (EBV), B95-8 strain | Luftig Lab (original publication: Johannsen et al., 2004). | doi.org/10.1073/pnas.0407320101 |
Biological samples | ||
Whole blood samples, de-identified donors | Gulf Coast Regional Blood Center | N/A |
Discarded tonsil tissue, anonymized | Duke University Medical Center | N/A |
Chemicals, peptides, and recombinant proteins | ||
CellTrace Violet (proliferation tracking dye) | ThermoFisher / Invitrogen | Cat. #34571 |
Histopaque-1077 density gradient medium | Sigma | Cat. #H8889 |
Critical commercial assays | ||
B cell Negative Isolation Kit | BD Biosciences | Cat. #558007 |
Chromium Next GEM Single Cell Multiome ATAC + Gene Expression Reagent Bundle, 16 rxns | 10x Genomics | Cat. #1000283 |
Chromium Next GEM Chip J Single Cell, 16 rxns | 10x Genomics | Cat. #1000230 |
Dual Index Kit TT Set A, 96 rxns | 10x Genomics | Cat. #1000215 |
Chromium Next GEM Automated Single Cell 3’ Library and Gel bead Kit v3.1, 4 rxns | 10x Genomics | Cat. #1000147 |
Chromium Next GEM Chip G Single Cell Kit, 16 rxns | 10x Genomics | Cat. #1000128 |
Deposited data | ||
Early infection scRNA + scATAC data (two biological donors, four timepoints per donor) | Luftig Lab | GSE189141; SRA #SRP346796 |
Tonsil lymphocyte scRNA data | Luftig Lab | GSE159674; SRA #SRP287808 |
Software and algorithms | ||
cellranger-arc pipeline | 10x Genomics | v1.0.0 |
cellranger pipeline | 10x Genomics | v3.0 & v2.0 |
R | R Foundation | v4.0.5 |
R Studio | RStudio, PBC | v1.4.1106 |
Seurat | https://github.com/satijalab/seurat | v4.1 |
SeuratWrappers | https://github.com/satijalab/seurat | v0.3.0 |
Monocle3 | https://github.com/cole-trapnell-lab/monocle3/ | v1.0.0 |
ggplot2 | Hadley Wickham | v3.3.6 |
RColorBrewer | Erich Neuwirth | v1.1.3 |
viridis | https://sjmgarnier.github.io/viridis/ | v0.6.2 |
FlowJo | BD Biosciences | v10.8.1 |
Cytoscape | Institute for Systems Biology | v3.9.1 |
early_infection_analysis_code.R | Luftig Lab | DOI: 10.5281/zenodo.6821702 |