IL-10 is a potent immunomodulatory cytokine produced by multiple cell types to restrain immune activation. Many herpesviruses use the IL-10 pathway to facilitate infection, but how endogenous IL-10 is regulated during primary infection in vivo remains poorly characterized. In this study, we infected mice with murine gammaherpesvirus 68 (γHV68) and analyzed the production and genetic contribution of IL-10 by mass cytometry (cytometry by time-of-flight) analysis. γHV68 infection elicited a breadth of effector CD4 T cells in the lungs of acutely infected mice, including a highly activated effector subset that coexpressed IFN-γ, TNF-α, and IL-10. By using IL-10 GFP transcriptional reporter mice, we identified that IL-10 was primarily expressed within CD4 T cells during acute infection in the lungs. IL10gfp-expressing CD4 T cells were highly proliferative and characterized by the expression of multiple coinhibitory receptors, including PD-1 and LAG-3. When we analyzed acute γHV68 infection of IL-10–deficient mice, we found that IL-10 limits the frequency of both myeloid and effector CD4 T cell subsets in the infected lung, with minimal changes at a distant mucosal site. These data emphasize the unique insights that high-dimensional analysis can afford in investigating antiviral immunity and provide new insights into the breadth, phenotype, and function of IL-10–expressing effector CD4 T cells during acute virus infection.

The gammaherpesviruses (γHVs) are a group of large dsDNA lymphotropic viruses that includes the human pathogens EBV and Kaposi sarcoma associated herpesvirus and the small animal model murine γHV 68 (γHV68) (13). The γHVs establish a lifelong infection in their host, with most infections in immunocompetent hosts asymptomatic. In contrast, immunosuppressed individuals are at a significantly increased risk for the development of a variety of chronic pathologic conditions, including γHV-associated malignancies (4).

γHV infection is critically regulated by the immune system, and γHVs have evolved numerous strategies to either subvert or avoid immune destruction, thereby facilitating lifelong infection (57). Among these, IL-10 is a multifunctional cytokine that downregulates the expression of multiple proinflammatory cytokines and cell surface molecules and can influence a wide array of immune cell types (810). IL-10 is a frequent target of manipulation by the herpesviruses. Some γHVs, including EBV, encode their own IL-10 homolog (11, 12). In contrast, other viruses, including γHV68, induce cellular IL-10 (13). IL-10 has been reported to regulate multiple aspects of γHV68 infection. For instance, γHV68-infected IL-10–deficient (IL-10KO) mice fail to control leukocytosis and have greater splenomegaly and a reduced latent load (14), with these mice susceptible to an exacerbated inflammatory bowel disease (15). IL-10 has been reported to be induced in B cells by the γHV68 M2 gene product (13). IL-10–expressing CD8 T cells have also been observed during chronic γHV68 infection in mice depleted for CD4 T cells, an immunosuppressive phenotype associated with chronic viral pathogenesis (16).

Based on the aforementioned studies, IL-10 regulates γHV68 infection. In this study, we applied high-dimensional, single-cell analysis using mass cytometry (cytometry by time-of-flight [CyTOF]) (17) to define the breadth and cellular phenotype of IL-10–expressing cells elicited during acute γHV68 infection. We further analyzed the genetic impact of IL-10 in limiting γHV68-induced inflammation. These studies demonstrate the acute impact of IL-10 on primary γHV infection in vivo and define effector CD4 T cells as a major cell source of IL-10 during acute pulmonary infection.

Mice were obtained from The Jackson Laboratory and bred in-house at the University of Colorado, including the C57BL/6J (B6; stock no. 000664), IL-10KO (B6.129P2-Il10tm1Cgn/J, stock no. 002251), and IL10gfp (B6.129S6-Il10tm1Flv/J, stock no. 008379) genotypes. Mice were intranasally infected with 4 × 105 PFU of wild-type (WT) γHV68 and subjected to isoflurane-induced anesthesia. Mice were used between 8 and 15 wk of age, with experimental cohorts age- and sex-matched. Mice were infected with WT γHV68 (strain WUMS; ATCC VR-1465) (18) using either bacterial artificial chromosome–derived WT γHV68 (19) or WT γHV68.ORF73βla, which encodes a fusion between ORF73 and the β-lactamase gene (20). γHV68 was grown and titrated by plaque assay on 3T12 fibroblasts, as previously published (21). Mice subjected to anti-CD3 Ab injection were injected i.p. with 15 μg anti-CD3ε Ab (clone 145-2C11, Cat. No. BP0001-1; Bio X Cell) at 0 and 46 h, with spleens harvested at 50 h after primary injection (22). Lung data in Fig. 4 are from a previously published dataset (23). All procedures were performed under protocols approved by the Institutional Animal Care and Use Committee at the University of Colorado Anschutz Medical Campus.

In-depth processing and staining protocols can be found in (23). Briefly, lungs were perfused using 10–12 ml PBS, harvested, minced, and enzymatically digested with collagenase D for 1 h at 37°C. Lungs and spleens were further subjected to mechanical disruption to generate single-cell suspensions that were subjected to RBC lysis and resuspended for staining. Colons were surgically dissected, rinsed, and then vortexed in PBS to remove fecal material and incubated with PBS (Life Technologies) containing 15 mM HEPES (HyClone) and 1 mM EDTA (Thermo Fisher Scientific) at room temperature for 15 min while samples were vigorously vortexed to remove intraepithelial lymphocytes. Colonic tissue was then rinsed over a sieve, washed with ice-cold PBS, minced, and subjected to enzymatic digestion using collagenase VIII (100–200 U/ml final concentration, Sigma-Aldrich) diluted in RPMI 1640 (Life Technologies) and supplemented with 5% FBS, 15 mM HEPES, and 1% penicillin/streptomycin. Enzymatic digestion was done for 20 min at 37°C under continuous vortexing. Enzymatic digestion was quenched using ice-cold RPMI 1640 with 5% FBS. Colonic samples were washed with a 1:10,000 dilution of Benzonase Nuclease (≥250 U/μl; Sigma-Aldrich) in RPMI 1640 to minimize cell aggregation prior to staining with cisplatin as a live/dead discriminator. For samples analyzed for intracellular cytokine staining, cells were pharmacologically stimulated with PMA and ionomycin (Thermo Fisher Scientific) in the presence of the Golgi apparatus inhibitors brefeldin A and monensin (BioLegend) for 5 h. Cells were stained with cisplatin according to the manufacturer’s recommendations (Cell-ID Cisplatin; Fluidigm), incubated with Fc receptor blocking Ab (clone 2.4G2; Tonbo Biosciences) for 10–20 min, and stained with primary surface Abs for 30 min at 22°C or 15 min at 37°C and 15 min at 22°C. Secondary surface stains, to detect fluorophore-conjugated Abs, were incubated for 20–30 min and washed, with intracellular staining done using the FoxP3 Fix/Perm Buffer Kit (Thermo Fisher Scientific) for 2 h or overnight at 4°C. Following cell staining, cells were washed and resuspended in intercalator (Cell-ID Intercalator-Ir). A subset of experiments (Figs. 1, 3, 4B, 4D, 4F, 5) were subjected to isotopic barcoding using the Fluidigm barcoding kit (Cell-ID 20-Plex Pd Barcoding Kit) prior to staining with the primary surface stains. Abs used for these studies are listed in Tables IV. All Abs that were directly conjugated to isotopically purified elements were obtained from Fluidigm. In each of the CyTOF panels, a subset of Abs were detected using a secondary detection approach, with FITC-, PE-, allophycocyanin-, or biotin-conjugated Abs (clone and source identified in Tables IV) detected using metal-conjugated secondary Abs against FITC, PE, allophycocyanin, or biotin (Fluidigm).

Samples were collected on a Helios mass cytometer (Fluidigm), with samples resuspended with equilibration beads to allow for signal normalization and with the normalization software downloaded from the Nolan Laboratory GitHub page (https://github.com/nolanlab) (as in Ref. 23). For experiments in which the samples were subjected to isotopic barcoding (Figs. 1, 3, 4B, 4D, 4F, 5), the debarcoding software was used following normalization (https://github.com/nolanlab/single-cell-debarcoder). Normalized, debarcoded data were subjected to traditional Boolean gating in FlowJo, identifying singlets (191Ir+193Ir+) that were viable (195Pt). These events were then gated and exported for downstream analysis. Additional Boolean gating was later performed for either CD45+ events or CD4+ T cell events, with gating criteria identified within each figure.

Manually gated singlet (191Ir+193Ir+) viable (195Pt) events or further gated populations were imported into PhenoGraph, with relevant clustering markers selected (28–35 cellular markers depending on the experiment; note that markers used for gating imported populations were not used for clustering). All parameters used for clustering are indicated in the associated tables. PhenoGraph was run with the following settings: 1) files were merged using either the merge method “min” (Figs. 1, 2A, 3), “all” (Fig. 2B), or “ceiling” (Figs. 35); 2) files were transformed using the transformation method “cytofAsinh”; and 3) the “Rphenograph” clustering method was chosen, coupled with the t-distributed stochastic neighbor embedding (tSNE) visualization method. All other settings were automatically chosen using default PhenoGraph settings.

PhenoGraph-defined clusters were displayed on tSNE plots within the R package Shiny (23). Within the Shiny application, cluster color was altered or colored according to protein expression. Multiple .csv files were produced by PhenoGraph, including “cluster median data” and “cluster cell percentage,” which were used to determine cluster phenotype, distribution between conditions, and statistical significance between groups.

Software for data analysis included R studio (Version 1.1.453), downloaded from the official R Web site (http://www.r-project.org/); the Cytofkit package (Version 3.7), downloaded from Bioconductor (https://Bioconductor.org/packages/release/bioc/html/cytofkit.html); Excel 16.15; FlowJo 10.4.2; GraphPad Prism 7.0c; and Adobe Illustrator CC 22.1. Cytofkit was opened using R studio and XQuartz. Statistical significance was tested in GraphPad Prism using an unpaired t test with statistical significance as identified. For contexts in which we tested statistical significance across all of the identified nodes/clusters (Figs. 4, 5), statistical analysis was subjected to multiple comparison correction in GraphPad Prism.

γHV68 infection induces diverse effector CD4 T cell subsets throughout the course of infection (24). To provide a high-dimensional analysis of effector CD4 T cell function during primary infection, the lungs from γHV68-infected mice were harvested at 9 d postinfection (dpi), subjected to pharmacological stimulation with PMA and ionomycin, and analyzed by mass cytometry using a panel of 35 isotopically purified, metal-conjugated Abs (Table I). CD4 T cells were initially analyzed for expression of IFN-γ and TNF-α expression, two hallmark effector cytokines elicited in antiviral CD4 T cells. Between 40 and 60% of CD4 T cells harvested from infected lungs expressed either IFN-γ or TNF-α or coexpressed IFN-γ and TNF-α (Fig. 1A). IFN-γ+ TNF-α+ effector CD4 T cells were more frequent than either IFN-γ+ or TNF-α+ single positive cells (Fig. 1A). To gain an unbiased perspective on the phenotypic diversity within these cytokine-defined effector CD4 T cells, we next subjected these cell populations to the PhenoGraph algorithm to define cell clusters present in each cytokine-defined subset (25). By clustering cells based on the expression of 30 proteins (including CD44, Tbet, IRF4, and multiple coinhibitory receptors but not CD3, CD4, MHC class II [MHC II], IFN-γ, or TNF-α) (Table I), PhenoGraph defined 16 CD4 T cell clusters present in the virally infected lung (Fig. 1A, 1B). The relative frequency of these cell clusters was notably affected by whether cells expressed cytokines, and if so, which cytokines. Some clusters, depicted by shades of gray in Fig. 1B, were present in relatively comparable frequencies across all subsets of CD4 T cells, regardless of whether they expressed TNF-α or IFN-γ (e.g., cluster 10, a population of CD4 T cells characterized by intermediate expression of GITR, PD-1, and ICOS; Fig. 1C). In contrast, cluster 9 (depicted in black; Fig. 1B, 1C) contained CD44high IRF4mid IL-2+ cells that were never found among the IFN-γ TNF-α subset of CD4 T cells. Beyond these distinctions, there were two classes of cell clusters that were inversely related: 1) clusters enriched among IFN-γ CD4 T cells (depicted in pastel colors), and 2) clusters enriched among IFN-γ+ CD4 T cells (depicted in saturated colors; Fig. 1B, 1C). Clusters enriched among IFN-γ cells were primarily CD44low with limited expression of notable phenotypic markers (Fig. 1C). Although we did not characterize CD4 T cell phenotypic diversity from uninfected lungs, we anticipate that CD4 T cells from uninfected lungs of specific pathogen–free mice would primarily overlap with these CD44low clusters that are dominant within IFN-γ TNF-α and IFN-γ TNF-α+ subsets. Conversely, clusters enriched among IFN-γ+ subsets (IFN-γ+ or IFN-γ+ TNF-α+; clusters 8, 12, and 16) included 1) cluster 16, a Tbethigh Lag3high IRF4high PD-1high GITR+ CD25+ CTLA4+ ICOS+ population, and 2) cluster 8, a Tbetintermediate IRF4high IL-10high GITR+ CTLA4+ PD-1+ ICOS+ population (Fig. 1C). These data demonstrate that γHV68 elicits a diverse set of CD4 T cells during primary infection, including the induction of an IFN-γ+ IL-10+ effector CD4 T cell subset.

Table I.
Ab conjugates used for the analysis in Figs. 1 and 5 
TagTargetAb CloneSurface/IntracellularClusteringa
89CD45 30-F11 Surface Yes 
141Pr Gr1 (Ly6C/Ly6G) RB6-8C5 Surface Yes 
142Nd CD11c N418 Surface Yes 
143Nd GITR (CD357) DTA1 Surface Yes 
144Nd IL-2 JES6-5H4 Intracellular Yes 
145Nd CD69 H1.2F3 Surface Yes 
146Nd CD8α 53-6.7 Surface Yes 
148Nd CD11b M1/70 Surface Yes 
149Sm CD19 6D5 Surface Yes 
150Nd CD25 3C7 Surface Yes 
151Eu CD64 X54-5/7.1 Surface Yes 
152Sm CD3ε 145-2C11 Surface No 
153Eu PD-L1 (CD274) 10F.9G2 Surface Yes 
154Sm CTLA4 (CD152) UC10-4B9 Intracellular Yes 
155Gd IRF4 3E4 Intracellular Yes 
156Gd Foxp3-PE/anti-PE FJK-16s (Thermo Fisher Scientific)/PE001 (anti-PE) Intracellular/secondary Yes 
158Gd IL-10 JES5-16E3 Intracellular Yes 
159Tb PD-1 (CD279) RMP1-30 Surface Yes 
160Gd GM-CSF–FITC/anti-FITC MP1-22E9 (Pharmingen)/FIT-22 (anti-FITC) Intracellular/secondary Yes 
161Dy Tbet 4B10 Intracellular Yes 
162Dy TNF-α MP6-XT22 Intracellular No (Fig. 1
    Yes (Fig. 5
163Dy Lag3-allophycocyanin/anti-allophycocyanin C9B7W (BioLegend)/APC003 (anti-allophycocyanin) Surface/secondary Yes 
164Dy IκBα L35A5 Intracellular Yes 
165Ho IFN-γ XMG1.2 Intracellular No (Fig. 1
    Yes (Fig. 5
166Er IL-4 11B11 Intracellular Yes 
167Er IL-6 MP5-20F3 Intracellular Yes 
168Er Ki-67 B56 Intracellular Yes 
169Tm Ly-6A/E (Sca-1) D7 Surface Yes 
170Er NK1.1 (CD161) PK136 Surface Yes 
171Yb CD44 IM7 Surface Yes 
172Yb CD4 RM4-5 Surface No 
173Yb CD117 (ckit) 2B8 Surface Yes 
174Yb MHC II (IA/IE) M5/114.15.2 Surface No 
175Lu CD127 A7R34 Surface Yes 
176Yb ICOS (CD278) 7E.17G9 Surface Yes 
195Pt Cisplatin Cell-ID Cisplatin  No 
191Ir, 193Ir Intercalator Cell-ID Intercalator-Ir  No 
140Ce 151Eu 153Eu 165Ho 175Lu Normalization beads   No 
TagTargetAb CloneSurface/IntracellularClusteringa
89CD45 30-F11 Surface Yes 
141Pr Gr1 (Ly6C/Ly6G) RB6-8C5 Surface Yes 
142Nd CD11c N418 Surface Yes 
143Nd GITR (CD357) DTA1 Surface Yes 
144Nd IL-2 JES6-5H4 Intracellular Yes 
145Nd CD69 H1.2F3 Surface Yes 
146Nd CD8α 53-6.7 Surface Yes 
148Nd CD11b M1/70 Surface Yes 
149Sm CD19 6D5 Surface Yes 
150Nd CD25 3C7 Surface Yes 
151Eu CD64 X54-5/7.1 Surface Yes 
152Sm CD3ε 145-2C11 Surface No 
153Eu PD-L1 (CD274) 10F.9G2 Surface Yes 
154Sm CTLA4 (CD152) UC10-4B9 Intracellular Yes 
155Gd IRF4 3E4 Intracellular Yes 
156Gd Foxp3-PE/anti-PE FJK-16s (Thermo Fisher Scientific)/PE001 (anti-PE) Intracellular/secondary Yes 
158Gd IL-10 JES5-16E3 Intracellular Yes 
159Tb PD-1 (CD279) RMP1-30 Surface Yes 
160Gd GM-CSF–FITC/anti-FITC MP1-22E9 (Pharmingen)/FIT-22 (anti-FITC) Intracellular/secondary Yes 
161Dy Tbet 4B10 Intracellular Yes 
162Dy TNF-α MP6-XT22 Intracellular No (Fig. 1
    Yes (Fig. 5
163Dy Lag3-allophycocyanin/anti-allophycocyanin C9B7W (BioLegend)/APC003 (anti-allophycocyanin) Surface/secondary Yes 
164Dy IκBα L35A5 Intracellular Yes 
165Ho IFN-γ XMG1.2 Intracellular No (Fig. 1
    Yes (Fig. 5
166Er IL-4 11B11 Intracellular Yes 
167Er IL-6 MP5-20F3 Intracellular Yes 
168Er Ki-67 B56 Intracellular Yes 
169Tm Ly-6A/E (Sca-1) D7 Surface Yes 
170Er NK1.1 (CD161) PK136 Surface Yes 
171Yb CD44 IM7 Surface Yes 
172Yb CD4 RM4-5 Surface No 
173Yb CD117 (ckit) 2B8 Surface Yes 
174Yb MHC II (IA/IE) M5/114.15.2 Surface No 
175Lu CD127 A7R34 Surface Yes 
176Yb ICOS (CD278) 7E.17G9 Surface Yes 
195Pt Cisplatin Cell-ID Cisplatin  No 
191Ir, 193Ir Intercalator Cell-ID Intercalator-Ir  No 
140Ce 151Eu 153Eu 165Ho 175Lu Normalization beads   No 
a

Clustering parameters used for Figs. 1 and 5 differed as indicated.

FIGURE 1.

High-dimensional analysis of CD4 T cells elicited during primary γHV68 infection.

Mass cytometric analysis of cells recovered from the lungs of γHV68-infected B6 mice at 9 dpi and subjected to ICCS analysis using a 35-Ab panel (Table I). Data were gated on viable CD4 T cells, defined as 191Ir+193Ir+195Pt152CD3ε+172CD4+174MHC II events, where numbers indicate isotopic mass for each measured parameter. (A) Analysis of IFN-γ and TNF-α production from CD4 T cells subjected to pharmacologic stimulation with PMA and ionomycin. The mean ± SEM for each population is identified in each quadrant. Events in each quadrant were further analyzed using the PhenoGraph algorithm and plotted using the tSNE dimensionality reduction algorithm. Events were imported into PhenoGraph and clustered on 8320 events total and 30 markers (clustering parameters identified in Table I). In total, 16 clusters were identified, with clusters colored by cluster identifier (ID) and displayed on tSNE plots. (B) Distribution of PhenoGraph-defined clusters within CD4 T cells, stratified by expression of IFN-γ and TNF-α. Each pie chart is subdivided into clusters that are equally represented in IFN-γ and IFN-γ+ CD4 T cells (shades of gray), clusters that are enriched in IFN-γ CD4 T cells (pastel colors), clusters that are enriched only in cytokine-producing CD4 T cells (black), and clusters that are enriched in IFN-γ+ CD4 T cells (saturated colors), as defined in the key. Right panel shows events depicted using tSNE, in which each cluster is colored according to the cytokine profile subsets (see key). (C) Phenotypic marker expression (in columns) of PhenoGraph-defined clusters (in rows), stratified by their enrichment as a function of cytokine profile. Clusters are stratified as in (B). Data are from virally infected B6 lungs (n = 4 mice) harvested 9 dpi, with cells stimulated with PMA and ionomycin for 5 h prior to Ab staining.

FIGURE 1.

High-dimensional analysis of CD4 T cells elicited during primary γHV68 infection.

Mass cytometric analysis of cells recovered from the lungs of γHV68-infected B6 mice at 9 dpi and subjected to ICCS analysis using a 35-Ab panel (Table I). Data were gated on viable CD4 T cells, defined as 191Ir+193Ir+195Pt152CD3ε+172CD4+174MHC II events, where numbers indicate isotopic mass for each measured parameter. (A) Analysis of IFN-γ and TNF-α production from CD4 T cells subjected to pharmacologic stimulation with PMA and ionomycin. The mean ± SEM for each population is identified in each quadrant. Events in each quadrant were further analyzed using the PhenoGraph algorithm and plotted using the tSNE dimensionality reduction algorithm. Events were imported into PhenoGraph and clustered on 8320 events total and 30 markers (clustering parameters identified in Table I). In total, 16 clusters were identified, with clusters colored by cluster identifier (ID) and displayed on tSNE plots. (B) Distribution of PhenoGraph-defined clusters within CD4 T cells, stratified by expression of IFN-γ and TNF-α. Each pie chart is subdivided into clusters that are equally represented in IFN-γ and IFN-γ+ CD4 T cells (shades of gray), clusters that are enriched in IFN-γ CD4 T cells (pastel colors), clusters that are enriched only in cytokine-producing CD4 T cells (black), and clusters that are enriched in IFN-γ+ CD4 T cells (saturated colors), as defined in the key. Right panel shows events depicted using tSNE, in which each cluster is colored according to the cytokine profile subsets (see key). (C) Phenotypic marker expression (in columns) of PhenoGraph-defined clusters (in rows), stratified by their enrichment as a function of cytokine profile. Clusters are stratified as in (B). Data are from virally infected B6 lungs (n = 4 mice) harvested 9 dpi, with cells stimulated with PMA and ionomycin for 5 h prior to Ab staining.

Close modal

Our initial analysis focused on the phenotypic diversity of CD4 T cells elicited during virus infection, in which we identified a prominent IL-10–producing effector CD4 T cell subset (cluster 8; Fig. 1C). To gain a broader perspective on IL-10–expressing cells during primary virus infection, we next infected IL-10 transcriptional reporter mice [i.e., mice expressing the IL-10 tiger allele, in which the enhanced GFP gene is inserted 3′ of the endogenous Il10 gene (26)]. γHV68-infected lungs were harvested at 6 dpi and subjected to mass cytometric analysis, in which samples were stained with a metal-conjugated anti-GFP Ab to detect the IL-10 reporter (Table II). As anticipated, γHV68 infection resulted in prominent changes in the frequency and distribution of PhenoGraph-defined clusters relative to mock-infected lungs (Fig. 2A). IL-10 green fluorescent protein (IL10gfp) expression was detected in a fraction of CD45+ cells in infected lungs (Fig. 2A). The majority of IL10gfp+ events were CD4 T cells (58.8%), with additional contributions to the IL10gfp+ fraction from CD8 T cells (13.8%), CD11c+ cells (5.8%), CD11b+ Ly6Chigh cells (5.6%), and CD11b+ Ly6C+/− MHC II+ cells (4.4%).

Table II.
Ab conjugates used for the analysis in Fig. 2 
TagTargetAb CloneSurface/IntracellularClusteringa
89CD45 30-F11 Surface Yes 
141Pr Gr1 (Ly6C/Ly6G) RB6-8C5 Surface Yes 
142Nd CD11c N418 Surface Yes 
143Nd CD103-biotin/anti-biotin 2E7 (BioLegend)/1D4C3 (anti-biotin) Surface Yes 
144Nd MHC class I 28-14-8 Surface Yes 
145Nd CD69 H1.2F3 Surface Yes 
146Nd CD8α 53-6.7 Surface Yes 
150Nd CD27 LG.3A10 Surface Yes 
151Eu CD25 3C7 Surface Yes 
152Sm CD3ε 145-2C11 Surface Yes (Fig. 2A
    No (Fig. 2B–D
154Sm CD11b M1/70 Surface Yes 
156Gd CD49b-PE/anti-PE HMa2 (BioLegend)/PE001 (anti-PE) Surface/secondary Yes 
159Tb PD-1 (CD279) RMP1-30 Surface Yes 
160Gd KLRG1-FITC/anti-FITC 2FI (Thermo Fisher Scientific)/FIT-22 (anti-FITC) Surface/secondary Yes 
161Dy Tbet 4B10 Intracellular Yes 
162Dy Ly6C HK1.4 Surface Yes 
163Dy Lag3-allophycocyanin/anti-allophycocyanin C9B7W (BioLegend)/APC003 (anti-allophycocyanin) Surface/secondary Yes 
164Dy IkBα L35A5 Intracellular Yes 
166Er CD19 6D5 Surface Yes 
167Er CD150 TC15-12F12.2 Surface Yes 
168Er Ki-67 B56 Intracellular Yes 
169Tm IL10gfp 5F12.4 (anti-GFP) Intracellular Yes 
170Er CD40L MR1 Surface Yes 
171Yb CD44 IM7 Surface Yes 
172Yb CD4 RM4-5 Surface Yes (Fig. 2A
    No (Fig. 2B–D
173Yb CD117 (ckit) 2B8 Surface Yes 
174Yb MHC II (IA/IE) M5/114.15.2 Surface Yes (Fig. 2A
    No (Fig. 2B–D
175Lu CD127 A7R34 Surface Yes 
176Yb ICOS (CD278) 7E.17G9 Surface Yes 
195Pt Cisplatin Cell-ID Cisplatin  No 
191Ir, 193Ir Intercalator Cell-ID Intercalator-Ir  No 
140Ce 151Eu 153Eu 165Ho 175Lu Normalization beads   No 
TagTargetAb CloneSurface/IntracellularClusteringa
89CD45 30-F11 Surface Yes 
141Pr Gr1 (Ly6C/Ly6G) RB6-8C5 Surface Yes 
142Nd CD11c N418 Surface Yes 
143Nd CD103-biotin/anti-biotin 2E7 (BioLegend)/1D4C3 (anti-biotin) Surface Yes 
144Nd MHC class I 28-14-8 Surface Yes 
145Nd CD69 H1.2F3 Surface Yes 
146Nd CD8α 53-6.7 Surface Yes 
150Nd CD27 LG.3A10 Surface Yes 
151Eu CD25 3C7 Surface Yes 
152Sm CD3ε 145-2C11 Surface Yes (Fig. 2A
    No (Fig. 2B–D
154Sm CD11b M1/70 Surface Yes 
156Gd CD49b-PE/anti-PE HMa2 (BioLegend)/PE001 (anti-PE) Surface/secondary Yes 
159Tb PD-1 (CD279) RMP1-30 Surface Yes 
160Gd KLRG1-FITC/anti-FITC 2FI (Thermo Fisher Scientific)/FIT-22 (anti-FITC) Surface/secondary Yes 
161Dy Tbet 4B10 Intracellular Yes 
162Dy Ly6C HK1.4 Surface Yes 
163Dy Lag3-allophycocyanin/anti-allophycocyanin C9B7W (BioLegend)/APC003 (anti-allophycocyanin) Surface/secondary Yes 
164Dy IkBα L35A5 Intracellular Yes 
166Er CD19 6D5 Surface Yes 
167Er CD150 TC15-12F12.2 Surface Yes 
168Er Ki-67 B56 Intracellular Yes 
169Tm IL10gfp 5F12.4 (anti-GFP) Intracellular Yes 
170Er CD40L MR1 Surface Yes 
171Yb CD44 IM7 Surface Yes 
172Yb CD4 RM4-5 Surface Yes (Fig. 2A
    No (Fig. 2B–D
173Yb CD117 (ckit) 2B8 Surface Yes 
174Yb MHC II (IA/IE) M5/114.15.2 Surface Yes (Fig. 2A
    No (Fig. 2B–D
175Lu CD127 A7R34 Surface Yes 
176Yb ICOS (CD278) 7E.17G9 Surface Yes 
195Pt Cisplatin Cell-ID Cisplatin  No 
191Ir, 193Ir Intercalator Cell-ID Intercalator-Ir  No 
140Ce 151Eu 153Eu 165Ho 175Lu Normalization beads   No 
a

Clustering parameters used for Fig. 2B–D excluded CD3ε, CD4, and MHC II.

FIGURE 2.

High-dimensional analysis of IL10gfp expression during acute γHV68 infection in the lung.

Mass cytometric analysis of cells recovered from the lungs of γHV68-infected IL10gfp mice at 6 dpi. Files were normalized, with events gated on (A) total viable single cells (defined as 191Ir+193Ir+195Pt) or (BD) viable CD4+ T cells (defined as 191Ir+193Ir+195Pt152CD3ε+172CD4+174MHC II) prior to analysis, where numbers indicate isotopic mass for each measured parameter. (A) PhenoGraph analysis of cellular phenotypes in mock- and virally infected lung (28,924 events total), clustered based on 29 markers (Table II), identified 34 unique clusters, with cluster phenotype defined according to the indicated lineage markers. The CD45+ cluster was defined by its expression of CD45+ and absence of other lineage-defining markers. The right panel of (A) shows all viable singlet events from mock- and virally infected lungs, with events colored according to IL10gfp expression. The green boundary line defines CD4+ T cells. (B) Mass cytometric analysis and PhenoGraph-based cell clustering of CD4+ T cells (5464 events total, clustered based on 26 markers, excluding CD3, CD4, and MHC II; Table II). In total, 16 unique clusters were identified and visualized on a tSNE plot (left). The number and frequency of CD4 T cell clusters are shown in the right panel, with pie charts sized proportionally to the relative number of CD4 T cells in mock- or virus-infected lung. (C) Phenotypic analysis of CD4 T cells, with events predominantly in mock infection denoted with a gray boundary and events predominantly in virus infection denoted with a red boundary. Data depict CD4 T cells plotted according to tSNE1 and tSNE2 as in (B), with individual plots depicting relative protein expression for the identified marker portrayed by color intensity, with range of expression indicated on the bottom of each panel. Panels are ordered based on the frequency of positive events. (D) Summary of CD4 T cell clusters (in columns) that differ between mock- and γHV68-infected lungs, with protein expression denoted in rows (gray shading scaled relative to expression level). Data are from the lungs of a mock- or γHV68-infected mouse harvested at 6 dpi, with (D) quantifying the frequency of events in each cluster as a percentage of CD4+ T cells.

FIGURE 2.

High-dimensional analysis of IL10gfp expression during acute γHV68 infection in the lung.

Mass cytometric analysis of cells recovered from the lungs of γHV68-infected IL10gfp mice at 6 dpi. Files were normalized, with events gated on (A) total viable single cells (defined as 191Ir+193Ir+195Pt) or (BD) viable CD4+ T cells (defined as 191Ir+193Ir+195Pt152CD3ε+172CD4+174MHC II) prior to analysis, where numbers indicate isotopic mass for each measured parameter. (A) PhenoGraph analysis of cellular phenotypes in mock- and virally infected lung (28,924 events total), clustered based on 29 markers (Table II), identified 34 unique clusters, with cluster phenotype defined according to the indicated lineage markers. The CD45+ cluster was defined by its expression of CD45+ and absence of other lineage-defining markers. The right panel of (A) shows all viable singlet events from mock- and virally infected lungs, with events colored according to IL10gfp expression. The green boundary line defines CD4+ T cells. (B) Mass cytometric analysis and PhenoGraph-based cell clustering of CD4+ T cells (5464 events total, clustered based on 26 markers, excluding CD3, CD4, and MHC II; Table II). In total, 16 unique clusters were identified and visualized on a tSNE plot (left). The number and frequency of CD4 T cell clusters are shown in the right panel, with pie charts sized proportionally to the relative number of CD4 T cells in mock- or virus-infected lung. (C) Phenotypic analysis of CD4 T cells, with events predominantly in mock infection denoted with a gray boundary and events predominantly in virus infection denoted with a red boundary. Data depict CD4 T cells plotted according to tSNE1 and tSNE2 as in (B), with individual plots depicting relative protein expression for the identified marker portrayed by color intensity, with range of expression indicated on the bottom of each panel. Panels are ordered based on the frequency of positive events. (D) Summary of CD4 T cell clusters (in columns) that differ between mock- and γHV68-infected lungs, with protein expression denoted in rows (gray shading scaled relative to expression level). Data are from the lungs of a mock- or γHV68-infected mouse harvested at 6 dpi, with (D) quantifying the frequency of events in each cluster as a percentage of CD4+ T cells.

Close modal

Infection of IL-10 transcriptional reporter mice afforded a major advantage over intracellular cytokine stain (ICCS), as it measured IL-10 mRNA expression without the need for additional pharmacologic stimuli, which can alter cellular phenotype. We therefore focused on CD4 T cell phenotypes in either mock- or virally infected lungs using the PhenoGraph clustering algorithm, which identified 16 CD4 T cell clusters across mock- and γHV68-infected lungs. Virally infected lungs had more CD4 T cells relative to mock infection, with a pronounced shift in CD4 T cell clusters (Fig. 2B) toward a prominent CD44high Ki67high ICOShigh PD-1high population (Fig. 2C). Within these virally elicited effector CD4 T cells, Lag3, Ly6C, and CD49b were expressed in partially overlapping cell subsets (Fig. 2C). IL10gfp expression was detected in a subset of virally elicited effector CD4 T cells (Fig. 2C), specifically clusters 1 and 5 (Fig. 2D). IL10gfp+ CD4 T cells between these two clusters shared a conserved CD44high Ki67high ICOS+ PD-1high Lag3high CD49bmid phenotype, with Ly6Chigh and Ly6Clow subsets. These studies using an IL-10 transcriptional reporter demonstrate that CD4 T cells are a frequent source of IL-10 expression during acute γHV68 infection and further identify a core phenotype associated with IL-10 expression within effector CD4 T cells.

Multiple CD4 T cell subsets can produce IL-10, including type 1 regulatory CD4 T cells, a Foxp3 IL-10+ subset of CD4 T cells reported to coexpress the cell surface proteins Lag3 and CD49b (22). Based on the expression of Lag3 and CD49b in IL-10–expressing CD4 T cells (Fig. 2C, 2D), we sought to compare these cells with Tr1 cells generated by an established method. One published method to elicit Tr1 cells is the repeated injection of anti-CD3 Ab into mice, a method associated with both polyclonal T cell activation and the generation of Tr1 cells (22). In this context, Tr1 cells are found especially in the small intestine, with a lower induction of these cells in other tissues (26). To understand how virally induced IL-10+ CD4 T cells compare with IL-10+ CD4 T cells elicited following anti-CD3 Ab injection, we compared CD4 T cells from the lungs of γHV68-infected mice with CD4 T cells from the spleens of mice repeatedly injected with anti-CD3 Ab. Cells were harvested and subjected to mass cytometric analysis using a panel of 34 Abs (Table III). When CD4 T cells from these two conditions were subjected to the PhenoGraph algorithm, we identified 20 clusters of CD4 T cells (Fig. 3A). tSNE plots of CD4 T cells for each condition revealed largely nonoverlapping cell clusters, suggesting phenotypic divergence (Fig. 3A). Across both conditions, 5 of 20 CD4 T cell clusters had above-average expression of IL10gfp (Fig. 3B). There were large differences in the frequency of IL10gfp+ cells among CD4 T cells between conditions, with ∼10% of CD4 T cells that were IL10gfp+ in the spleens of anti-CD3 Ab–injected mice and ∼50% of CD4 T cells that were IL10gfp+ in γHV68-infected lungs (Fig. 3C). When we analyzed the phenotype of IL10gfp+ clusters between these two conditions, we found a significant phenotypic divergence. Within anti-CD3 Ab–injected mice, the highest frequency of IL10gfp+ cells was found within a Foxp3+ regulatory T cell (Treg) cluster (Fig. 3D). In contrast, IL10gfp+ clusters that were dominant in γHV68 infection were CD44high Lag3high PD-1high Tbet+ and Foxp3, suggesting they may be either Tr1 or Th1 cells (Fig. 3D). Although overall clustering patterns between spleen and lung may be influenced by tissue-specific phenotypes such as Cxcr3, which was much higher in the lung than the spleen (Supplemental Fig. 1), IL-10–expressing clusters in spleen and lung had relatively comparable Cxcr3 expression (Fig. 3D). These data emphasize the multiple potential cellular sources of IL-10 that can occur with diverse stimuli and across tissues and reveal that virus infection elicits a distinct effector T cell phenotype when compared with anti-CD3 Ab injection.

Table III.
Ab conjugates used for the analysis in Fig. 3 
TagTargetAb CloneSurface/IntracellularClustering
89CD45 30-F11 Surface Yes 
141Pr p-SHP2 [Y580] D66F10 Intracellular Yes 
142Nd CD11c N418 Surface Yes 
143Nd GITR (CD357) DTA1 Surface Yes 
144Nd CXCR3-FITC/anti-FITC CXCR3-173 (BioLegend)/FIT-22 (anti-FITC) Surface/secondary Yes 
145Nd CD69 H1.2F3 Surface Yes 
146Nd CD8α 53-6.7 Surface Yes 
147Sm p-Histone H2AX (SER139) JBW301 Intracellular Yes 
148Nd CD11b M1/70 Surface Yes 
149Sm CD19 6D5 Surface Yes 
150Nd CD27 LG.3A10 Surface Yes 
151Eu CD25 3C7 Surface Yes 
152Sm CD3ε 145-2C11 Surface No 
153Eu PD-L1 (CD274) 10F.9G2 Surface Yes 
154Sm CTLA4 (CD152) UC10-4B9 Intracellular Yes 
155Gd IRF4 3E4 Intracellular Yes 
156Gd 41BB-PE/anti-PE 17B5 (Thermo Fisher Scientific)/PE001 (anti-PE) Surface/secondary Yes 
158Gd Foxp3 FJK-16s Intracellular Yes 
159Tb PD-1 (CD279) RMP1-30 Surface Yes 
160Gd CD5 53-7.3 Surface Yes 
161Dy Tbet 4B10 Intracellular Yes 
162Dy Tim3 (CD366) RMT3–23 Surface Yes 
163Dy BCL6 K112-91 Intracellular Yes 
164Dy IkBα L35A5 Intracellular Yes 
165Ho β-catenin (active) D13A1 Intracellular Yes 
166Er Arginase-1 Polyclonal Intracellular Yes 
167Er Gata3 TWAJ Intracellular Yes 
168Er Ki-67 B56 Intracellular Yes 
169Tm IL10gfp 5F12.4 (anti-GFP) Intracellular Yes 
170Er CD49b HMa2 Surface Yes 
171Yb CD44 IM7 Surface Yes 
172Yb CD4 RM4–5 Surface No 
173Yb CD117 (ckit) 2B8 Surface Yes 
174Yb Lag3 (CD223) C9B7W Surface Yes 
175Lu CD127 A7R34 Surface Yes 
176Yb ICOS (CD278) 7E.17G9 Surface Yes 
209Bi MHC II (IA/IE) M5/114.15.2 Surface No 
195Pt Cisplatin Cell-ID Cisplatin  No 
191Ir, 193Ir Intercalator Cell-ID Intercalator-Ir  No 
140Ce 151Eu 153Eu 165Ho 175Lu Normalization beads   No 
TagTargetAb CloneSurface/IntracellularClustering
89CD45 30-F11 Surface Yes 
141Pr p-SHP2 [Y580] D66F10 Intracellular Yes 
142Nd CD11c N418 Surface Yes 
143Nd GITR (CD357) DTA1 Surface Yes 
144Nd CXCR3-FITC/anti-FITC CXCR3-173 (BioLegend)/FIT-22 (anti-FITC) Surface/secondary Yes 
145Nd CD69 H1.2F3 Surface Yes 
146Nd CD8α 53-6.7 Surface Yes 
147Sm p-Histone H2AX (SER139) JBW301 Intracellular Yes 
148Nd CD11b M1/70 Surface Yes 
149Sm CD19 6D5 Surface Yes 
150Nd CD27 LG.3A10 Surface Yes 
151Eu CD25 3C7 Surface Yes 
152Sm CD3ε 145-2C11 Surface No 
153Eu PD-L1 (CD274) 10F.9G2 Surface Yes 
154Sm CTLA4 (CD152) UC10-4B9 Intracellular Yes 
155Gd IRF4 3E4 Intracellular Yes 
156Gd 41BB-PE/anti-PE 17B5 (Thermo Fisher Scientific)/PE001 (anti-PE) Surface/secondary Yes 
158Gd Foxp3 FJK-16s Intracellular Yes 
159Tb PD-1 (CD279) RMP1-30 Surface Yes 
160Gd CD5 53-7.3 Surface Yes 
161Dy Tbet 4B10 Intracellular Yes 
162Dy Tim3 (CD366) RMT3–23 Surface Yes 
163Dy BCL6 K112-91 Intracellular Yes 
164Dy IkBα L35A5 Intracellular Yes 
165Ho β-catenin (active) D13A1 Intracellular Yes 
166Er Arginase-1 Polyclonal Intracellular Yes 
167Er Gata3 TWAJ Intracellular Yes 
168Er Ki-67 B56 Intracellular Yes 
169Tm IL10gfp 5F12.4 (anti-GFP) Intracellular Yes 
170Er CD49b HMa2 Surface Yes 
171Yb CD44 IM7 Surface Yes 
172Yb CD4 RM4–5 Surface No 
173Yb CD117 (ckit) 2B8 Surface Yes 
174Yb Lag3 (CD223) C9B7W Surface Yes 
175Lu CD127 A7R34 Surface Yes 
176Yb ICOS (CD278) 7E.17G9 Surface Yes 
209Bi MHC II (IA/IE) M5/114.15.2 Surface No 
195Pt Cisplatin Cell-ID Cisplatin  No 
191Ir, 193Ir Intercalator Cell-ID Intercalator-Ir  No 
140Ce 151Eu 153Eu 165Ho 175Lu Normalization beads   No 
FIGURE 3.

γHV68 infection elicits a dominant IL10gfp-expressing CD4 T cell population that differs relative to IL10gfp-expressing CD4 T cells in anti-CD3 Ab–injected mice.

Mass cytometric analysis of cells recovered from IL10gfp mice comparing CD4 T cell phenotypes between the spleens of anti-CD3 Ab–injected mice and the lungs of γHV68-infected mice harvested at 6 dpi. Files were normalized and gated on viable CD4+ T cells (defined as 191Ir+193Ir+195Pt152CD3ε+172CD4+209MHC II) prior to analysis, where numbers indicate isotopic mass for each measured parameter. (A) PhenoGraph analysis of CD4 T cells comparing anti-CD3 Ab–injected versus γHV68-infected samples (75,483 events total, clustered on 33 markers, excluding CD3, CD4, and MHC II; Table III) identified 20 unique clusters, each denoted with a distinct color. (B) PhenoGraph-defined CD4 T cell clusters were ranked based on IL10gfp expression, with five clusters having higher than average IL10gfp expression identified in red text. Figure insets depict all events from (A), colored according to (left panel) IL10gfp expression or (right panel) cluster identifier (ID) for five IL10gfp+ clusters. (C) The frequencies of CD4 T cell clusters are shown for both conditions, with focused analysis on the distribution of IL10gfp+ clusters identified by shaded gray extensions and pie charts on either side. Pie charts denote the frequencies of IL10gfp+ events in each condition, with pie charts sized according to the relative number of IL10gfp+ events present in each condition. (D) Comparison of median protein expression within IL10gfp+ CD4 T cell clusters in anti-CD3 Ab–injected spleens and/or γHV68-infected lungs. Cellular markers are ordered by range in median expression between clusters from greatest to least in value. Data are from IL10gfp mice using either spleens from mice that were injected with an anti-CD3 Ab (injected with Ab at 0 and 46 h with harvest at 50 h, n = 4 mice) or lungs from γHV68-infected mice (n = 5) harvested at 6 dpi.

FIGURE 3.

γHV68 infection elicits a dominant IL10gfp-expressing CD4 T cell population that differs relative to IL10gfp-expressing CD4 T cells in anti-CD3 Ab–injected mice.

Mass cytometric analysis of cells recovered from IL10gfp mice comparing CD4 T cell phenotypes between the spleens of anti-CD3 Ab–injected mice and the lungs of γHV68-infected mice harvested at 6 dpi. Files were normalized and gated on viable CD4+ T cells (defined as 191Ir+193Ir+195Pt152CD3ε+172CD4+209MHC II) prior to analysis, where numbers indicate isotopic mass for each measured parameter. (A) PhenoGraph analysis of CD4 T cells comparing anti-CD3 Ab–injected versus γHV68-infected samples (75,483 events total, clustered on 33 markers, excluding CD3, CD4, and MHC II; Table III) identified 20 unique clusters, each denoted with a distinct color. (B) PhenoGraph-defined CD4 T cell clusters were ranked based on IL10gfp expression, with five clusters having higher than average IL10gfp expression identified in red text. Figure insets depict all events from (A), colored according to (left panel) IL10gfp expression or (right panel) cluster identifier (ID) for five IL10gfp+ clusters. (C) The frequencies of CD4 T cell clusters are shown for both conditions, with focused analysis on the distribution of IL10gfp+ clusters identified by shaded gray extensions and pie charts on either side. Pie charts denote the frequencies of IL10gfp+ events in each condition, with pie charts sized according to the relative number of IL10gfp+ events present in each condition. (D) Comparison of median protein expression within IL10gfp+ CD4 T cell clusters in anti-CD3 Ab–injected spleens and/or γHV68-infected lungs. Cellular markers are ordered by range in median expression between clusters from greatest to least in value. Data are from IL10gfp mice using either spleens from mice that were injected with an anti-CD3 Ab (injected with Ab at 0 and 46 h with harvest at 50 h, n = 4 mice) or lungs from γHV68-infected mice (n = 5) harvested at 6 dpi.

Close modal

To define how IL-10 regulates the distribution and phenotype of immune cell subsets during γHV68 infection, we used mass cytometry to compare cellular composition and phenotype between γHV68-infected WT (B6) and IL-10KO mice. This analysis focused on cellular diversity among hematopoietic (CD45+) cells within the lungs, a primary site of virus replication, and colon, a distal mucosal site in which γHV68 has been reported to induce chronic pathologic conditions in IL-10KO mice (15). Tissues were harvested from separate experimental cohorts, with cells harvested from these tissues stained, subjected to mass cytometric analysis, and analyzed using the PhenoGraph algorithm for clustering analysis (Tables IV, V). This analysis identified 29 cell clusters in lungs harvested from γHV68-infected mice, with 20 cell clusters identified in the colons of γHV68-infected mice (Fig. 4A, 4B). Cell clusters were further defined based on canonical lineage markers to quantify the frequencies of distinct leukocyte populations (Fig. 4C, 4D). When we compared cluster distribution between B6 and IL-10KO mice, we found pronounced shifts in cellular distribution between B6 and IL-10KO infected lungs, particularly among CD4 T cells (Fig. 4C). In contrast, colons from virally infected mice had a very limited number of changes in cell clusters at this time postinfection (Fig. 4D). Among the cell clusters present in infected lungs, IL-10KO mice had a significantly increased frequency in two cell clusters: 1) PD-1+ Lag3+ Ki67+ CD4 T cells (cluster 14) and 2) a small but significant increase in CD64+ cells (cluster 25) (Fig. 4E). Colons from infected IL-10KO mice had a selective increase in the frequency of CD64+ cells characterized by a CD11b+ CD11c+ phenotype with variable CD103 expression (cluster 13) (Fig. 4F). Cluster 13 was unique among CD64+ clusters in CD11c and CD103 expression relative to other CD64+ clusters in the colon (Fig. 4F). These findings suggest that during acute γHV68 infection, IL-10 limits the expansion of effector CD4 T cells in the lung and further constrains the frequency of CD64+ mononuclear phagocytic cells in both the lung and the colon.

Table IV.
Ab conjugates used for the analysis in the lung (Fig. 4A, 4C, 4E)
TagTargetAb CloneSurface/IntracellularClustering
89CD45 30-F11 Surface No 
141Pr Gr-1 (Ly6C/Ly6G) RB6-8C5 Surface Yes 
142Nd CD11c N418 Surface Yes 
143Nd GITR (CD357) DTA1 Surface Yes 
144Nd MHC class I 28-14-8 Surface Yes 
145Nd CD69 H1.2F3 Surface Yes 
146Nd CD8α 53-6.7 Surface Yes 
148Nd CD11b M1/70 Surface Yes 
149Sm p-4E-BP1 [T37/T46] 236B4 Intracellular Yes 
150Nd CD25 3C7 Surface Yes 
151Eu CD64 X54-5/7.1 Surface Yes 
152Sm CD3ε 145-2C11 Surface Yes 
153Eu PD-L1 (CD274) 10F.9G2 Surface Yes 
154Sm CTLA4 (CD152) UC10-4B9 Intracellular Yes 
155Gd IRF4 3E4 Intracellular Yes 
156Gd Siglec-F-PE/anti-PE E50-2440 (BD Pharmingen)/PE001 (anti-PE) Surface/secondary Yes 
158Gd Foxp3 FJK-16s Intracellular Yes 
159Tb PD-1 (CD279) RMP1-30 Surface Yes 
160Gd KLRG1-FITC/anti-FITC 2FI (Thermo Fisher Scientific)/FIT-22 (anti-FITC) Surface/secondary Yes 
161Dy Tbet 4B10 Intracellular Yes 
162Dy Tim3 (CD366) RMT3-23 Surface Yes 
163Dy Lag3-allophycocyanin/anti-allophycocyanin C9B7W (BioLegend)/APC003 (anti-allophycocyanin) Surface/secondary Yes 
164Dy IkBα L35A5 Intracellular Yes 
165Ho β-catenin (active) D13A1 Intracellular Yes 
166Er CD19 6D5 Surface Yes 
167Er NKp46 29A1.4 Surface Yes 
168Er Ki-67 B56 Intracellular Yes 
169Tm Ly-6A/E (Sca-1) D7 Surface Yes 
170Er PD-L2-biotin/anti-biotin TY25 (BioLegend)/1D4-C5 (anti-biotin) Surface/secondary Yes 
171Yb CD44 IM7 Surface Yes 
172Yb CD4 RM4–5 Surface Yes 
173Yb CD117 (ckit) 2B8 Surface Yes 
174Yb MHC II (IA/IE) M5/114.15.2 Surface Yes 
175Lu CD127 A7R34 Surface Yes 
176Yb ICOS 7E.17G9 Surface Yes 
195Pt Cisplatin Cell-ID Cisplatin  No 
191Ir, 193Ir Intercalator Cell-ID Intercalator-Ir  No 
140Ce 151Eu 153Eu 165Ho 175Lu Normalization beads   No 
TagTargetAb CloneSurface/IntracellularClustering
89CD45 30-F11 Surface No 
141Pr Gr-1 (Ly6C/Ly6G) RB6-8C5 Surface Yes 
142Nd CD11c N418 Surface Yes 
143Nd GITR (CD357) DTA1 Surface Yes 
144Nd MHC class I 28-14-8 Surface Yes 
145Nd CD69 H1.2F3 Surface Yes 
146Nd CD8α 53-6.7 Surface Yes 
148Nd CD11b M1/70 Surface Yes 
149Sm p-4E-BP1 [T37/T46] 236B4 Intracellular Yes 
150Nd CD25 3C7 Surface Yes 
151Eu CD64 X54-5/7.1 Surface Yes 
152Sm CD3ε 145-2C11 Surface Yes 
153Eu PD-L1 (CD274) 10F.9G2 Surface Yes 
154Sm CTLA4 (CD152) UC10-4B9 Intracellular Yes 
155Gd IRF4 3E4 Intracellular Yes 
156Gd Siglec-F-PE/anti-PE E50-2440 (BD Pharmingen)/PE001 (anti-PE) Surface/secondary Yes 
158Gd Foxp3 FJK-16s Intracellular Yes 
159Tb PD-1 (CD279) RMP1-30 Surface Yes 
160Gd KLRG1-FITC/anti-FITC 2FI (Thermo Fisher Scientific)/FIT-22 (anti-FITC) Surface/secondary Yes 
161Dy Tbet 4B10 Intracellular Yes 
162Dy Tim3 (CD366) RMT3-23 Surface Yes 
163Dy Lag3-allophycocyanin/anti-allophycocyanin C9B7W (BioLegend)/APC003 (anti-allophycocyanin) Surface/secondary Yes 
164Dy IkBα L35A5 Intracellular Yes 
165Ho β-catenin (active) D13A1 Intracellular Yes 
166Er CD19 6D5 Surface Yes 
167Er NKp46 29A1.4 Surface Yes 
168Er Ki-67 B56 Intracellular Yes 
169Tm Ly-6A/E (Sca-1) D7 Surface Yes 
170Er PD-L2-biotin/anti-biotin TY25 (BioLegend)/1D4-C5 (anti-biotin) Surface/secondary Yes 
171Yb CD44 IM7 Surface Yes 
172Yb CD4 RM4–5 Surface Yes 
173Yb CD117 (ckit) 2B8 Surface Yes 
174Yb MHC II (IA/IE) M5/114.15.2 Surface Yes 
175Lu CD127 A7R34 Surface Yes 
176Yb ICOS 7E.17G9 Surface Yes 
195Pt Cisplatin Cell-ID Cisplatin  No 
191Ir, 193Ir Intercalator Cell-ID Intercalator-Ir  No 
140Ce 151Eu 153Eu 165Ho 175Lu Normalization beads   No 
Table V.
Ab conjugates used for the analysis in the colon (Fig. 4B, 4D, 4F)
TagTargetAb CloneSurface/IntracellularClustering
89CD45 30-F11 Surface No 
141Pr Ly6G 1A8 Surface Yes 
142Nd CD11c N418 Surface Yes 
143Nd CD103-biotin/anti-biotin 2E7 (BioLegend)/1D4C3 (anti-biotin) Surface/secondary Yes 
144Nd MHC class I 28-14-8 Surface Yes 
145Nd Siglec-F-PE/anti-PE E50-2440 (BD Pharmingen)/PE001  (anti-PE) Surface/secondary Yes 
146Nd CD8α 53-6.7 Surface Yes 
148Nd CD11b M1/70 Surface Yes 
149Sm CD19 6D5 Surface Yes 
150Nd CD25 3C7 Surface Yes 
151Eu CD64 X54-5/7.1 Surface Yes 
152Sm CD3ε 145-2C11 Surface Yes 
153Eu PD-L1 (CD274) 10F.9G2 Surface Yes 
154Sm CTLA4 (CD152) UC10-4B9 Intracellular Yes 
155Gd IRF4 3E4 Intracellular Yes 
156Gd CD90.2 30-H12 Surface Yes 
158Gd Foxp3 FJK-16s Intracellular Yes 
159Tb RORgt B2D Intracellular Yes 
160Gd CXCR3-FITC/anti-FITC CXCR3-173 (BioLegend)/FIT-22 (anti-FITC) Surface/secondary Yes 
161Dy Tbet 4B10 Intracellular Yes 
162Dy Ly6C HK1.4 Surface Yes 
163Dy F4/80-allophycocyanin/anti-allophycocyanin MB8 (Thermo Fisher Scientific)/APC003 (anti-allophycocyanin) Surface/secondary Yes 
164Dy IkBα L35A5 Intracellular Yes 
165Ho β-catenin (active) D13A1 Intracellular Yes 
166Er Arginase-1 Polyclonal Intracellular Yes 
167Er NKp46 29A1.4 Surface Yes 
168Er Ki-67 B56 Intracellular Yes 
169Tm Ly-6A/E (Sca-1) D7 Surface Yes 
170Er CD49b HMa2 Surface Yes 
171Yb CD44 IM7 Surface Yes 
172Yb CD4 RM4–5 Surface Yes 
173Yb CD117 (ckit) 2B8 Surface Yes 
174Yb Lag3 (CD223) C9B7W Surface Yes 
175Lu CD127 A7R34 Surface Yes 
176Yb ICOS (CD278) 7E.17G9 Surface Yes 
209Bi MHC II (IA/IE) M5/114.15.2 Surface Yes 
195Pt Cisplatin Cell-ID Cisplatin  No 
191Ir, 193Ir Intercalator Cell-ID Intercalator-Ir  No 
140Ce 151Eu 153Eu 165Ho 175Lu Normalization beads   No 
TagTargetAb CloneSurface/IntracellularClustering
89CD45 30-F11 Surface No 
141Pr Ly6G 1A8 Surface Yes 
142Nd CD11c N418 Surface Yes 
143Nd CD103-biotin/anti-biotin 2E7 (BioLegend)/1D4C3 (anti-biotin) Surface/secondary Yes 
144Nd MHC class I 28-14-8 Surface Yes 
145Nd Siglec-F-PE/anti-PE E50-2440 (BD Pharmingen)/PE001  (anti-PE) Surface/secondary Yes 
146Nd CD8α 53-6.7 Surface Yes 
148Nd CD11b M1/70 Surface Yes 
149Sm CD19 6D5 Surface Yes 
150Nd CD25 3C7 Surface Yes 
151Eu CD64 X54-5/7.1 Surface Yes 
152Sm CD3ε 145-2C11 Surface Yes 
153Eu PD-L1 (CD274) 10F.9G2 Surface Yes 
154Sm CTLA4 (CD152) UC10-4B9 Intracellular Yes 
155Gd IRF4 3E4 Intracellular Yes 
156Gd CD90.2 30-H12 Surface Yes 
158Gd Foxp3 FJK-16s Intracellular Yes 
159Tb RORgt B2D Intracellular Yes 
160Gd CXCR3-FITC/anti-FITC CXCR3-173 (BioLegend)/FIT-22 (anti-FITC) Surface/secondary Yes 
161Dy Tbet 4B10 Intracellular Yes 
162Dy Ly6C HK1.4 Surface Yes 
163Dy F4/80-allophycocyanin/anti-allophycocyanin MB8 (Thermo Fisher Scientific)/APC003 (anti-allophycocyanin) Surface/secondary Yes 
164Dy IkBα L35A5 Intracellular Yes 
165Ho β-catenin (active) D13A1 Intracellular Yes 
166Er Arginase-1 Polyclonal Intracellular Yes 
167Er NKp46 29A1.4 Surface Yes 
168Er Ki-67 B56 Intracellular Yes 
169Tm Ly-6A/E (Sca-1) D7 Surface Yes 
170Er CD49b HMa2 Surface Yes 
171Yb CD44 IM7 Surface Yes 
172Yb CD4 RM4–5 Surface Yes 
173Yb CD117 (ckit) 2B8 Surface Yes 
174Yb Lag3 (CD223) C9B7W Surface Yes 
175Lu CD127 A7R34 Surface Yes 
176Yb ICOS (CD278) 7E.17G9 Surface Yes 
209Bi MHC II (IA/IE) M5/114.15.2 Surface Yes 
195Pt Cisplatin Cell-ID Cisplatin  No 
191Ir, 193Ir Intercalator Cell-ID Intercalator-Ir  No 
140Ce 151Eu 153Eu 165Ho 175Lu Normalization beads   No 
FIGURE 4.

High-dimensional analysis of IL-10–dependent regulation of the antiviral response in the lung and the colon.

Mass cytometric analysis of cells recovered from lungs (A, C, and E) or colons (B, D, and F) of γHV68-infected B6 or IL-10KO mice harvested at 9 dpi. Files were normalized and gated on viable, single CD45+ cells (defined as 191Ir+193Ir+195Pt89CD45+) prior to analysis, where numbers indicate isotopic mass for each measured parameter. (A and B) PhenoGraph analysis of CD45+ cells from (A) lungs or (B) colons of γHV68-infected B6 or IL-10KO mice. Clustering was done on 4344 events per file (34,752 total), with clustering in the lung based on 34 markers (29 clusters identified; Table IV) and clustering in the colon based on 36 markers (20 clusters identified; Table V); lungs and colons were harvested from separate cohorts. PhenoGraph-defined clusters are colored according to cluster identifier. (C and D) Definition of cellular phenotypes across PhenoGraph-defined clusters according to the indicated lineage markers, with distinct cell types given unique colors. CD45+ MHC II+ and CD45+ MHC II clusters were defined by exclusion from other phenotypes. (E and F) Identification of clusters with statistically significant differences in frequency between B6 and IL-10KO infected (E) lungs and (F) colons, showing cluster frequencies (left panel) and expression for the identified parameters within the identified cluster (right panel). In the right panel of (E), plotted events are CD4+ T cells identified in (C). In the right panel of (F), plotted events are the dominant CD64+ cell clusters identified in (D). Parameters with a different maximum scale value between B6 and IL-10KO are identified by italicized text and an asterisk. Data are from γHV68-infected B6 and IL-10KO lungs (n = 4 mice per genotype) and colons (n = 4 mice per genotype) harvested 9 dpi. Data depict mean ± SEM, with individual symbols indicating values from independent samples. All samples were analyzed for statistical significance using unpaired t tests, corrected for multiple comparisons using the Holm-Sidak method. **p < 0.01, ***p < 0.001.

FIGURE 4.

High-dimensional analysis of IL-10–dependent regulation of the antiviral response in the lung and the colon.

Mass cytometric analysis of cells recovered from lungs (A, C, and E) or colons (B, D, and F) of γHV68-infected B6 or IL-10KO mice harvested at 9 dpi. Files were normalized and gated on viable, single CD45+ cells (defined as 191Ir+193Ir+195Pt89CD45+) prior to analysis, where numbers indicate isotopic mass for each measured parameter. (A and B) PhenoGraph analysis of CD45+ cells from (A) lungs or (B) colons of γHV68-infected B6 or IL-10KO mice. Clustering was done on 4344 events per file (34,752 total), with clustering in the lung based on 34 markers (29 clusters identified; Table IV) and clustering in the colon based on 36 markers (20 clusters identified; Table V); lungs and colons were harvested from separate cohorts. PhenoGraph-defined clusters are colored according to cluster identifier. (C and D) Definition of cellular phenotypes across PhenoGraph-defined clusters according to the indicated lineage markers, with distinct cell types given unique colors. CD45+ MHC II+ and CD45+ MHC II clusters were defined by exclusion from other phenotypes. (E and F) Identification of clusters with statistically significant differences in frequency between B6 and IL-10KO infected (E) lungs and (F) colons, showing cluster frequencies (left panel) and expression for the identified parameters within the identified cluster (right panel). In the right panel of (E), plotted events are CD4+ T cells identified in (C). In the right panel of (F), plotted events are the dominant CD64+ cell clusters identified in (D). Parameters with a different maximum scale value between B6 and IL-10KO are identified by italicized text and an asterisk. Data are from γHV68-infected B6 and IL-10KO lungs (n = 4 mice per genotype) and colons (n = 4 mice per genotype) harvested 9 dpi. Data depict mean ± SEM, with individual symbols indicating values from independent samples. All samples were analyzed for statistical significance using unpaired t tests, corrected for multiple comparisons using the Holm-Sidak method. **p < 0.01, ***p < 0.001.

Close modal

Next, we sought to define how IL-10 regulates CD4 T cell effector function during acute γHV68 infection as revealed by ICCS analysis. We compared CD4 T cell function between γHV68-infected B6 and IL-10KO mice by ICCS using mass cytometry. In contrast to the clustering analysis done in Fig. 1, CD4 T cells for both genotypes were subjected to PhenoGraph-defined clustering using 32 markers including IFN-γ, TNF-α, and IL-10 (Table I), identifying 16 unique clusters (Fig. 5A). CD4 T cells were predominantly CD44high, consistent with a large effector CD4 T cell population in virally infected lungs at this time (Fig. 5B). Seven of the sixteen PhenoGraph-defined clusters showed expression of IFN-γ, TNF-α, and/or IL-10 (Fig. 5B), with phenotypes ranging from single expression of IFN-γ+ or TNF-α+ to coexpression of IFN-γ+ and TNF-α+ and triple expression of IFN-γ+, TNF-α+, and IL-10+ (Fig. 5B, 5C). When we analyzed the frequency of CD4 T cells stratified by cytokine expression, we found that B6 and IL-10KO mice had comparable frequencies of TNF-α+ single positive and IFN-γ+ single positive CD4 T cells (Fig. 5C, 5D). As anticipated, IL-10KO mice had no detectable IL-10+ CD4 T cells (Fig. 5C, 5D). B6 mice had a significantly increased frequency of cytokine-negative CD4 T cells (IFN-γ TNF-α IL-10) relative to IL-10KO mice (Fig. 5C, 5D). In contrast, IL-10KO mice had a significantly increased frequency of IFN-γ+ TNF-α+ effector CD4 T cells (Fig. 5C, 5D). Although IL-10KO mice had an increased frequency of IFN-γ+ TNF-α+ CD4 T cells relative to B6 mice, both genotypes had phenotypic diversity within this cytokine-producing subset, including cell subsets with partially overlapping expression of CTLA-4, GITR, ICOS, Lag3, and PD-1 (Fig. 5E). These data demonstrate that IL-10 constrains the magnitude of highly activated IFN-γ+ TNF-α+ effector CD4 T cells during acute γHV68 infection, a population characterized by heterogeneous expression of CTLA-4, GITR, ICOS, Lag3, and PD-1.

FIGURE 5.

High-dimensional analysis of effector CD4 T cell function following γHV68 infection in B6 and IL-10KO mice.

Mass cytometric analysis of cells recovered from lungs of γHV68-infected B6 or IL-10KO mice harvested at 9 dpi, with cells subjected to pharmacologic stimulation for ICCS analysis using a 35-Ab panel (Table I). Files were normalized, with data gated on viable CD4 T cells, defined as 191Ir+193Ir+195Pt152CD3ε+172CD4+174MHC II events, where numbers indicate isotopic mass for each measured parameter. (A) CD4 T cells were imported into PhenoGraph and clustered on 33,022 total events (3669 events from each file) and 32 markers (excluding CD3, CD4, and MHC II; Table I), identifying 16 unique clusters portrayed on a tSNE plot. (B) All events from (A) are colored by CD44, IFN-γ, TNF-α, and IL-10 expression. (C) Events from (A) are colored based on their cytokine profile, stratified based on IFN-γ, TNF-α, and IL-10 expression. (D) Frequency of CD4 T cells in virally infected B6 and IL-10KO mice. (E) Comparison of CTLA-4, GITR, ICOS, Lag3, and PD-1 expression across CD4 T cells from B6 (top row) or IL-10KO (bottom row) mice, in which IFN-γ+ TNF-α+ CD4 T cells were identified by a green boundary line. Parameters with a different maximum scale value between B6 and IL-10KO are identified by italicized text and an asterisk. Data from virally infected lungs of B6 (n = 4) and IL-10KO (n = 5) mice harvested 9 dpi, with cells stimulated with PMA and ionomycin for 5 h prior to ICCS. Data for B6 mice were also included in Fig. 1, subjected to different clustering parameters (as outlined in Table I). Data show mean ± SEM with individual symbols denoting individual mice, with statistical analysis done by unpaired t test, corrected for multiple comparisons using the Holm-Sidak method. **p < 0.01, ***p < 0.001, ****p ≤ 0.0001. ns, not significant.

FIGURE 5.

High-dimensional analysis of effector CD4 T cell function following γHV68 infection in B6 and IL-10KO mice.

Mass cytometric analysis of cells recovered from lungs of γHV68-infected B6 or IL-10KO mice harvested at 9 dpi, with cells subjected to pharmacologic stimulation for ICCS analysis using a 35-Ab panel (Table I). Files were normalized, with data gated on viable CD4 T cells, defined as 191Ir+193Ir+195Pt152CD3ε+172CD4+174MHC II events, where numbers indicate isotopic mass for each measured parameter. (A) CD4 T cells were imported into PhenoGraph and clustered on 33,022 total events (3669 events from each file) and 32 markers (excluding CD3, CD4, and MHC II; Table I), identifying 16 unique clusters portrayed on a tSNE plot. (B) All events from (A) are colored by CD44, IFN-γ, TNF-α, and IL-10 expression. (C) Events from (A) are colored based on their cytokine profile, stratified based on IFN-γ, TNF-α, and IL-10 expression. (D) Frequency of CD4 T cells in virally infected B6 and IL-10KO mice. (E) Comparison of CTLA-4, GITR, ICOS, Lag3, and PD-1 expression across CD4 T cells from B6 (top row) or IL-10KO (bottom row) mice, in which IFN-γ+ TNF-α+ CD4 T cells were identified by a green boundary line. Parameters with a different maximum scale value between B6 and IL-10KO are identified by italicized text and an asterisk. Data from virally infected lungs of B6 (n = 4) and IL-10KO (n = 5) mice harvested 9 dpi, with cells stimulated with PMA and ionomycin for 5 h prior to ICCS. Data for B6 mice were also included in Fig. 1, subjected to different clustering parameters (as outlined in Table I). Data show mean ± SEM with individual symbols denoting individual mice, with statistical analysis done by unpaired t test, corrected for multiple comparisons using the Holm-Sidak method. **p < 0.01, ***p < 0.001, ****p ≤ 0.0001. ns, not significant.

Close modal

IL-10 is a multifunctional cytokine that critically shapes the magnitude and activation status of the immune system in response to infection. In addition to its host immunomodulatory functions, IL-10 is a frequent target of viral manipulation by the herpesviruses (12, 13). In this study, we sought to investigate how γHV68, a small animal model of γHV infection, intersects with IL-10, both in terms of what cells produce IL-10 and how the overall immune response is influenced by IL-10 during acute infection. For these studies, we have focused on acute, primary infection with γHV68, seeking new insights through the use of high-dimensional mass cytometry (CyTOF) analyses.

IL-10 is known to be produced by a large number of cell types, including CD4 and CD8 T cell subsets and B cells (8). In this study, we make use of IL-10eGFP transcriptional reporter mice and direct intracellular staining for IL-10 protein to identify CD4 T cells as a primary source of IL-10 production during acute γHV68 infection in the lung. IL-10+ CD4 T cells elicited during viral infection were associated with a highly activated effector phenotype characterized by high expression of CD44 with coexpression of the cytokines IFN-γ and TNF-α. IL10gfp+ CD4 T cells were proliferating and characterized by expression of PD-1, Lag3, ICOS, and CD49b, with variable expression of Ly6C. By querying these cellular phenotypes using mass cytometry, we have further analyzed IL10gfp expression across a wide range of leukocyte subsets. These studies demonstrated IL-10 expression in the infected lung across multiple cell types, with CD4 T cells representing the most frequent IL-10–expressing cell type at 6 dpi. Whether the cellular distribution of IL-10 transcript-positive cells during γHV68 infection changes with time remains unknown, although the cellular sources of IL-10 production can evolve over time in other viral infections (27).

The expression of IL-10 within these highly activated effector CD4 T cells raises the question of which effector subsets express IL-10 in this context. Our data demonstrate that Foxp3+ Tregs are not a prominent source of IL-10 during acute γHV68 infection in the lung. Instead, IL-10+ CD4 T cells appear to be either 1) type 1 Tregs, an IL-10–expressing, Foxp3 subset of CD4 T cells frequently characterized by coexpression of CD49b and Lag3 (22), or 2) an IL-10–expressing Th1 subset (2729). Although the IL-10–expressing effector CD4 T cells that we identified in this study coexpress CD49b and Lag3, a proposed marker of Tr1 cells (22), recent studies have emphasized that coexpression of CD49b and Lag3 is not a definitive marker of Tr1 cells (30, 31). Conversely, IFN-γ+ Th1 cells have been reported to express IL-10 in a variety of settings (32), and γHV68-induced IL-10+ effectors express Tbet, the canonical Th1 transcription factor. Despite these observations, at this time there remains no definitive marker that discriminates between Tr1 and Th1 cells. Alternatively, these IFN-γ+ IL-10–expressing cells may represent a distinct effector CD4 T cell subset (33). Recently, eomesodermin was identified as a transcriptional regulator for IL-10+ effector CD4 T cells in both Tr1 cells (30) and a potentially distinct IFN-γ+ IL-10+ effector CD4 T cell (34). Whether IL-10+ CD4 T cells elicited during γHV68 infection express, and require, eomesodermin remains to be tested. Given that IFN-γ+ IL-10+ effector CD4 T cells have been observed in multiple viral systems, including influenza, respiratory syncytial virus, and lymphocytic choriomeningitis virus (LCMV) (2729), an important unresolved question is whether IFN-γ+ IL-10+ effector CD4 T cells elicited during viral infection have a core conserved effector program or if this effector cytokine phenotype represents a convergent phenotype elicited by diverse stimuli. We anticipate that application of high-dimensional analysis approaches such as CyTOF will be particularly useful to gain new insights into the underpinnings of these cells.

IL-10 regulates its effects through signals transduced by the heterodimeric IL-10R targeting multiple cell types (9). High-dimensional analysis of the immune response after γHV68 infection identified a wide spectrum of cells in infected lung and colon, a phenotypic diversity readily characterized through use of the PhenoGraph clustering algorithm and visualized by the tSNE data dimensionality reduction method. When we compared cellular and phenotypic diversity between B6 and IL-10KO mice, we found a pronounced increase in the frequency of a PD-1+ Lag3+ CD4 T cell subset and an exaggerated induction of IFN-γ+ TNF-α+ effector CD4 T cells in infected lungs. We further found evidence for changes in CD64+ populations in both the infected lung and colon. The increased frequency of CD64+ cells within the colons of infected mice, without significant changes in CD4 T cells, suggests that IL-10 may directly regulate the frequency of CD64+ cells, with changes in CD4 T cells only occurring in tissues with active viral replication or a strong inflammatory milieu. We anticipate that colonic tissue will show time-dependent changes in leukocyte populations, as γHV68 has been reported to induce colitis in IL-10KO mice by 2 mo postinfection (15), a phenotype we have also independently observed during chronic infection (A.S. Baessler, L.M. Oko, L.F. van Dyk, and E.T. Clambey, manuscript in preparation).

Our studies have identified that infected IL-10KO mice have multiple changes in the frequency and function of leukocytes. How IL-10 regulates these processes is likely to be multifactorial. Although an early study suggested that γHV68-infected IL-10KO mice had a reduced latent load (14), a subsequent study found no discernable role for IL-10 in regulating viral load (15). We have also found that IL-10 appears to have a minimal impact on viral loads during acute or latent infection (A.S. Baessler, L.M. Oko, L.F. van Dyk, and E.T. Clambey, manuscript in preparation). Based on this, we postulate that the differences we observe in IL-10KO hosts reflect a combination of cell-intrinsic changes in function and an exaggerated inflammatory milieu. Whether IL-10 directly (35, 36) or indirectly (8, 10, 37, 38) regulates effector CD4 T cell function during γHV68 infection remains unresolved at this time. In the LCMV system, IL-10 has been reported to directly restrain effector and memory CD4 T cell differentiation, with an increased frequency of IFN-γ+ TNF-α+ IL-2+ CD4 T cells in IL-10KO conditions after either infection with LCMV Armstrong or the clone 13 variant (39, 40). Further studies have shown that IL-10 restricts Th1 differentiation with little impact on CD4 T follicular helper differentiation (41). Our data are consistent with IL-10–dependent regulation of Th1 differentiation during γHV68 infection, although our studies further suggest IL-10 limits expression of coinhibitory receptors, including PD-1 and Lag3. The increased frequency of PD-1+ Lag3+ effector CD4 T cells in IL-10KO mice suggests that increased coinhibitory receptor expression may function as one compensatory mechanism to constrain pathogenic CD4 T cell function in the absence of IL-10.

Beyond insights on IL-10–dependent regulation during γHV68 infection, these studies demonstrate the power of high-dimensional approaches, such as mass cytometry, to investigate the regulation of the immune response at a global level. By applying mass cytometry to CD4 T cells, our studies provide direct evidence for extensive phenotypic diversity of antiviral effector CD4 T cells elicited during primary viral infection. We anticipate that combining mass cytometry–based studies with host and viral genetics will afford new insights into the underpinnings of antiviral CD4 T cell function.

The authors acknowledge Melissa Ledezma for technical assistance; Kristina Terrell, Christine Childs, and Karen Helm for technical support for CyTOF studies, including machine operation and management of the University of Colorado CyTOF Ab bank; and the CyTOF User Group at the University of Colorado Anschutz Medical Campus for ongoing collaboration and insights.

This work was supported by National Institutes of Health Grants R01 AI121300 and R01 CA168558 (to L.F.v.D.), American Heart Association National Scientist Development Grant 13SDG14510023, the Crohn’s and Colitis Foundation of America (311295), a pilot grant from the Lung, Head and Neck Cancer Program within the University of Colorado Cancer Center, and a Career Enhancement Award from the University of Colorado Lung Cancer Specialized Program of Research Excellence (P50CA58187) (all to E.T.C.). The Lung, Head and Neck Cancer Program within the University of Colorado Cancer Center and the Flow Cytometry Shared Resource are directly funded through support from National Cancer Institute Cancer Center Support Grant P30CA046934.

The online version of this article contains supplemental material.

Abbreviations used in this article:

B6

C57BL/6J

CyTOF

cytometry by time-of-flight

dpi

day postinfection

γHV

gammaherpesvirus

γHV68

murine γHV 68

ICCS

intracellular cytokine stain(ing)

IL-10KO

IL-10–deficient

LCMV

lymphocytic choriomeningitis virus

MHC II

MHC class II

Treg

regulatory T cell

tSNE

t-distributed stochastic neighbor embedding

WT

wild-type.

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The authors have no financial conflicts of interest.

This article is distributed under the terms of the CC BY 4.0 Unported license.

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