Identity and Diversity of Human Peripheral Th and T Regulatory Cells Deﬁned by Single-Cell Mass Cytometry

Published November 27, 2017, doi:10.4049/jimmunol.1701025 The Journal of Immunology

Matthew A. Kunicki,*,1 Laura C. Amaya Hernandez,*,†,1 Kara L. Davis,‡ Rosa Bacchetta,*,2 and Maria-Grazia Roncarolo*,†,2

Human CD3+CD4+ Th cells, FOXP3+ T regulatory (Treg) cells, and T regulatory type 1 (Tr1) cells are essential for ensuring peripheral immune response and tolerance, but the diversity of Th, Treg, and Tr1 cell subsets has not been fully characterized. Independent functional characterization of human Th1, Th2, Th17, T follicular helper (Tfh), Treg, and Tr1 cells has helped to define unique surface molecules, transcription factors, and signaling profiles for each subset. However, the adequacy of these markers to recapitulate the whole CD3+CD4+ T cell compartment remains questionable. In this study, we examined CD3+CD4+ T cell populations by single-cell mass cytometry. We characterize the CD3+CD4+ Th, Treg, and Tr1 cell populations simultaneously across 23 memory T cell–associated surface and intracellular molecules. High-dimensional analysis identified several new subsets, in addition to the already defined CD3+CD4+ Th, Treg, and Tr1 cell populations, for a total of 11 Th cell, 4 Treg, and 1 Tr1 cell subsets. Some of these subsets share markers previously thought to be selective for Treg, Th1, Th2, Th17, and Tfh cells, including CD194 (CCR4)+FOXP3+ Treg and CD183 (CXCR3)+T-bet+ Th17 cell subsets. Unsupervised clustering displayed a phenotypic organization of CD3+CD4+ T cells that confirmed their diversity but showed interrelation between the different subsets, including similarity between Th1–Th2–Tfh cell populations and Th17 cells, as well as similarity of Th2 cells with Treg cells. In conclusion, the use of single-cell mass cytometry provides a systems-level characterization of CD3+CD4+ T cells in healthy human blood, which represents an important baseline reference to investigate abnormalities of different subsets in immune- mediated pathologies. The Journal of Immunology, 2018, 200: 000–000.

uman CD3+CD4+ Th and T regulatory (Treg) cells are responses, peripherally induced T regulatory type 1 (Tr1) cells and involved in the effector or modulatory function of the thymic-derived FOXP3-expressing Treg cells, are essential to sup- H immune response (1–5). Th1 cells enhance cytotoxic press undesired immune responses and maintain immune homeo- immune responses against intracellular pathogens and are involved stasis (4, 6–8). Immune dysregulation may result in alterations in in autoimmunity (1, 3). Th2 cells enhance IgE class-switching and the frequency, function, or location of any one of these CD3+CD4+ immune responses against helminths and are involved in allergy T cell subsets, which determine the severity of different pathologies (1, 3). Th17 cells enhance inflammatory immune responses against (4,9,10). some bacteria and fungi and are involved in chronic inflammation Different pathogens can induce diverse T cell responses (3, 11). (2, 3). T follicular helper (Tfh) cells support B cell maturation and With high encounter of pathogens over time, humans share a large humoral immunity and, like other Th subsets, may be involved in repertoire of Th1, Th2, Th17, and Tfh cell populations within the primary or acquired immunodeficiencies (5). In addition to the Th memory compartment, as well as Tr1 and Treg cell populations. Intense investigation of Th cell lineages based on their unique cytokine profiles has helped to define combinations of surface and *Division of Stem Cell Transplantation and Regenerative Medicine, Department of † chemokine receptors, as well as transcription factors, specific for Pediatrics, Stanford University, Stanford, CA 94305; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305; and each population (12, 13). Likewise, Treg cells were initially iden- ‡Division of Hematology/Oncology, Department of Pediatrics, Stanford University, tified by their immune suppressive function, followed by intensive Stanford, CA 94305 investigation for specific surface markers and transcription fac- 1 M.A.K. and L.C.A.H. contributed equally to this work. tors. Table I (14–27) details each Th, Treg, and Tr1 cell pop- 2 R.B. and M.-G.R. contributed equally to this work. ulation’s defining markers, function, and the additional surface Received for publication July 17, 2017. Accepted for publication October 26, 2017. receptors’ and transcription factors’ expression attributed to each This work was supported by grants and awards to M.-G.R. from the following main population. organizations: Alex Lemonade Stand Foundation, Emerson Collective, Rising Tide, The collective characterization of alterations in different subsets in and CureSearch. This work was also supported by a generous gift to the Stanford Center for Genetic Immune Diseases. The data were acquired using instruments in immune-mediated diseases remains difficult because of the large the Stanford Shared FACS Facility, obtained using National Institutes of Health S10 phenotypic diversity (11, 12) across all CD3+CD4+ T cell pop- Shared Instrument Grant S10D016318-01. ulations, as well as the lack of a comprehensive analysis in healthy Address correspondence and reprint requests to Prof. Maria-Grazia Roncarolo, Stan- conditions. In addition, the concept of lineage definition and sta- ford University, Lorry I. Lokey Stem Cell Research Building, 265 Campus Drive West, Room G3021A, Stanford, CA 94305. E-mail address: [email protected] bility has been largely challenged by evidence of T cells with The online version of this article contains supplemental material. common molecular characteristics across different subsets (28), Abbreviations used in this article: CM, cell culture medium; CSM, cell staining especially in pathologic conditions. This can occur by transition or + + medium; CyTOF, cytometry by time-of-flight; MST, minimum spanning tree; reprogramming between distinct CD3 CD4 Tcellsubsets.Inad- PCA, principal component analysis; RT, room temperature; Tfh, T follicular helper; dition, the possible plasticity between these different Th and Treg Tr1, T regulatory type 1; Treg, T regulatory. cell populations adds a further layer of complexity to the under- Copyright Ó 2017 by The American Association of Immunologists, Inc. 0022-1767/17/$35.00 standing of the immune landscape. Therefore, elucidating the

www.jimmunol.org/cgi/doi/10.4049/jimmunol.1701025 2 PERIPHERAL CD3+CD4+ T CELL SUBSETS BY MASS CYTOMETRY interrelationship between CD3+CD4+ Th and Treg cell populations consent, in accordance with the Stanford University Institutional Review is critical to provide a deeper understanding of the immune system, Board–approved protocol (IRB-34131). which will pave the way for a comprehensive analysis in immune- PBMCs were isolated by Ficoll-Hypaque (GE Healthcare) density gradient and immunostained fresh or processed further for long-term mediated diseases (4, 11, 28, 29). storage. CD4+ T cells were isolated by negative selection using mag- Cytometry by time-of-flight (CyTOF) overcomes the limitations netic beads (EasySep Human CD4+ T Cell Enrichment Kit, STEMCELL of fluorophore tags by detecting metal-tagged Abs attached to single Technologies). Isolated CD4+ T cell and PBMC samples were cryo- 3 6 cells, which allows simultaneous multiparameter interrogation (30). preserved in stocks of 10 10 cells per milliliter of FBS with 10% (v/v) DMSO for $4 mo. Cells were thawed in warm CM and rested overnight in The advent of this technology has opened opportunities to study CM at 37˚C, 5% CO2 before staining. functional and phenotypic single-cell diversity in depth (31–33), including specific CD3+CD4+ T cell subsets (34, 35); it also has Mass cytometric immunoassay significant promise for the advancement of clinical diagnosis and Human PBMCs and isolated CD4+ T cells were stained for mass cytometry, prognosis (36, 37). as described by Bodenmiller et al. (38). Samples were analyzed individually, without barcoding or stimulation. Each sample was resuspended to In this study, we used CyTOF to simultaneously analyze 6 + + + 2 2 3 10 cells per 200 ml of PBS in FACS tubes and incubated for 1–2 min CD3 CD4 Th1 (CD183 [CXCR3] CD194 [CCR4] ), Th2 at room temperature (RT) with 1000:1 parts Cisplatin-198Pt (Fluidigm). The + 2 + + (CCR4 CXCR3 ), Th17 (CD196 [CCR6] CD161 ), Tfh (CD185 reaction was blocked with 1:5 parts CM, followed immediately by fixation. 2 [CXCR5]+), Treg (CD25hiCD127lo/ FOXP3+), and Tr1 (CD223 Every wash with CSM was performed at 800 3 g, RT. We normalized cell [LAG3]+ CD49b+) cells from the peripheral blood of healthy do- volume (100 ml) before adding surface Ab mixture. After permeabilization nors. By evaluating differential expression of transcription factors (15 min on ice, with a final concentration $ 90% [v/v] methanol), we washed the cells twice with CSM and normalized cell volume again before and chemokine, activation, and inhibitory receptors, we further adding intracellular Ab mixture. Only 20-min RT incubations of Cell-ID characterized each baseline population and identified new subsets Intercalator-Ir (Fluidigm) were performed. within. Using data-driven techniques to challenge the distinction CyTOF setup and sample acquisition of defined human Th, Treg, and Tr1 cell populations, we found shared phenotypes among these subsets, suggesting a continuum Cells were passed through a Falcon Tube Strainer Cap (Fisher) and diluted 3 6 of Th and Treg cell populations. Altogether, we present a precise to 0.5 10 cells/ml EQ Four Element Calibration Beads (Fluidigm) + + prediluted with 1:9 parts deionized water. Cells were injected at 45 ml/min and novel approach to show the multiplicity of CD3 CD4 Th, into a CyTOF 2 mass cytometer (Fluidigm) outfitted with a Super Sampler Treg, and Tr1 cells in healthy human blood. The purpose of this (Victorian Airship and Scientific Apparatus) and operated with software study is to establish a comprehensive visualization of the dynamic v6.0.626. The signal intensity was normalized to EQ calibration beads composition of CD3+CD4+ T cell subsets at the same time in using the same software, and beads were automatically removed. healthy donors, independently of their functional characterization, Data analysis which could be used as a baseline to study patients with immune Data collected in .fcs file format were normalized for intra- and interfile dysregulation. signal drift using CyTOF software v6.0626 and then analyzed on Cytobank (39). Our gating scheme excluded calibration beads by gating on 140 Ce2 Materials and Methods events and excluded cell aggregates using DNA signal (193Ir versus 191Ir) and event length, as described (40). Residual non-CD3+CD4+ T cells were Reagents removed by gating CD82CD3+CD4+ T cell events in purified CD4+ T cell Sterile X-VIVO 15 with gentamicin and L-glutamine (Lonza), supplemented and PBMC samples, either fresh or frozen. To identify a given Th cell or with 5% (w/v) sterile human serum from male AB plasma (Sigma-Aldrich), Treg cell subset, we use a combination of one to three markers without was used as cell culture medium (CM). Cell staining medium (CSM) con- excluding any of the markers used to define the other subsets. sisted of PBS with 0.05% (w/v) BSA (MACS BSA Solution; Miltenyi Every heat map represents differential marker expression between cell populations by normalizing the mean marker intensity of a given cell Biotec) and 0.02% (w/v) sodium azide. Unlabeled carrier protein–free Abs + + were purchased from BD Biosciences (San Jose, CA), BioLegend (San population to CD3 CD4 T cells (arcsinh ratio). This differential expression was then transformed to z-score during visualization in R (ggplots Diego, CA), eBioscience (San Diego, CA), and R&D Systems (Minneapolis, + + MN). Our in-house Ab conjugated with lanthanide isotopes was made using package) for all target populations, including the original CD3 CD4 Maxpar X8 Ab labeling kits (Fluidigm), according to the manufacturer’s T cell distribution (data not shown). instructions, except for diluting the final Ab conjugate with PBS-based Ab We applied viSNE (41) in Cytobank to each fresh or frozen sample cohort, using the default settings for number of iterations, perplexity, and Stabilizer (Boca Scientific). Isotope-labeled Abs used for mass cytometric + + staining are listed below. The Abs were purchased from Fluidigm unless theta. We used equal downsampling of total CD3 CD4 T cells or pro- otherwise noted. The following Abs were used for surface staining: 141Pr- portional downsampling of Th, Treg, and Tr1 cell populations. In each CCR6 (G034E3), 143Nd-CD45RA (HI100), 145Nd-CD4 (RPA-T4), 148Nd- case, 60,000–80,000 events were analyzed from frozen or fresh samples, LAG3 (polyclonal; R&D Systems), 149Sm-CCR4 (205410), 155Gd-CD62L across all donors. The parameters used in our viSNE analysis were selected (DREG-56; BioLegend), 161Dy-CD49b (AK-7; BD Biosciences), 163Dy- based on the optimal separation of the baseline populations on the viSNE CXCR3 (G025H7), 164Dy-CD161 (HP-3G10), 165Ho-TIGIT (MBSA43; map. The 12 markers sufficient to distinguish the Th and Treg cell subsets eBioscience), 166Er-ICOS (DX29; BD Biosciences), 167Er-CD226 (11A8; were CD161, CCR6, CCR4, CXCR3, CXCR5, CD25, CD127, CD45RA, BioLegend), 168Er-CD8a (SK1), 169Tm-CD25 (2A3), 170Er-CTLA-4 FOXP3, GATA3, T-bet, and RORC2. Additionally, LAG3 and CD49b were (14D3), 171Yb-CXCR5 (51505), 173Yb-CD3 (UCHT1; BioLegend), necessary to distinguish the Tr1 cell subset. 175Lu–PD-1 (EH12.2H7), and 176Yb-CD127 (A019D5). The following The distinction and overlap of the different subsets within each baseline Abs were used for intracellular staining: 147Sm-pSTAT5 (47), 151Eu- population was calculated based on the viSNE analysis (Fig. 2B). Gates RORC2 (AFKJS-9; eBioscience), 152Sm-GATA3 (TWAJ; eBioscience), were manually drawn based on the position of each subset on the tSNE1 153Eu-pSTAT1 (58D6), 158Gd-pSTAT3 (4/P-STAT3), 160Gd–T-bet (4B10), and tSNE2 axes with polygon gate on Cytobank; an illustrative repre- and 162Dy-FOXP3 (PCH101). The selection of these 26 markers was based sentation of these gates was created with Adobe Photoshop CS6 by col- on data in the literature reporting chemokine receptors, transcription factors, oring the viSNE map blue (ungated cells) and gray (gated cells) and by activation markers, and the proportion of Th cell and Treg cell subsets highlighting the edges of each subpopulation of the gated cells in black. identified by flow cytometry in human peripheral blood, as described in We then calculated the percentage of events from each subset that fell into Table I. another subset gate, to develop an overlap matrix. The scaled Venn dia- gram of the overlapping subsets (Fig. 3B) was drawn such that the fre- Single-cell suspensions of human PBMCs and isolated quency of each subset of CD3+CD4+ T cells (at a minimum of 1%) is CD3+CD4+ T cells represented by the circle diameter. To maintain consistency for the events used for viSNE and FlowSOM Blood samples from healthy adults of both genders (median age 56 y, range (42), we concatenated the generated viSNE files using Cytobank’s con- 30–69 y for 9 of 11 donors tested) were purchased from the Stanford Blood catenation tool (FCSConcat), before importing these files into the R Center or obtained from volunteers within the laboratory, after informed (v3.3.0) environment. We digitally labeled each donor and Th, Treg, and The Journal of Immunology 3

Tr1 cell populations to track the origin of each cell in later visualizations Next, we used one to three markers to identify Th1 (CXCR3+ by creating a designated channel for each parameter. CCR42), Th2 (CCR4+CXCR32), Th17 (CCR6+CD161+), Tfh Each file was converted into a flow object using flowCore from Bio- (CXCR5+), Treg (CD25hiCD127lo/2FOXP3+) (Supplemental Fig. conductor (http://bioconductor.org/packages/release/bioc/html/flowCore. + + html) and then analyzed with the FlowSOM algorithm (42). Considering 1C), and memory Tr1 (LAG3 CD49b ) cell populations the expression of the same 12 or 14 markers used in the previous viSNE (Supplemental Fig. 1D). The frequencies of these cell populations analysis, 100 clusters were created using a self-organizing map based– are shown in Supplemental Table I and are within range of what algorithm and then visualized in a minimum spanning tree (MST). The was previously reported using flow cytometry (24, 26, 50). The MST shows similar clusters adjacent to each other; however, because most of the edges have similar weights, no unique MST configuration was found frequencies of Th1, Th17, and Treg cell populations were found, for our dataset. In addition, the distance between clusters does not provide by CyTOF, to be significantly different between fresh and frozen any information. samples as a result of the lower expression of CXCR3, CD161, We then applied principal component analysis (PCA) (stats package and and FOXP3, respectively, in frozen samples. Th2, Th17, and Tfh ggbiplot package for visualization in R) to the resulting 100 clusters (nodes). cells were predominantly memory cells, as defined by the lack of The PCA plot displays a simplified representation of these nodes, grouping expression of CD45RA (86.9 6 6.67% of Th2, 88.3 6 3.93% of each Th and Treg cell population inside ellipses that represent the normal 2 distribution of $68% of the nodes within each population, capturing all Th17, and 76.9 6 4.75% of Tfh cells were CD45RA , n = 8), data points within 1 SD from the mean. We have also calculated the whereas Th1 cells and Treg cells were more equally distributed correlation of the parameters with each component (determined by the between naive and memory compartments (61.9 6 9.34% of vectors coming from the center of the PCA plot). The length of each vector 6 2 represents the contribution of each variable to each principal component. Th1 cells and 57.7 10.2% of Treg cells were CD45RA , n = 8). FlowSOM and PCA were run in triplicate to validate these findings. The These data show that CyTOF analysis recapitulated the frequencies structure of our model remained consistent, and it showed reproducible of CD3+CD4+ T cell subsets observed in total PBMCs by stan- overlap across all iterations of PCA. dard flow cytometry (24, 26, 43–48, 50). These data also high- For consistency and efficiency in our data workflow, we used Shiny, a light that Th1, Th17, and Treg cell populations are better identified web application framework for R, to integrate multiple R packages for our mass cytometry data analysis in a user-friendly and automated manner. in fresh samples. Statistical analysis Expanded characterization of CD3+CD4+ Th, Treg, and Tr1 cell populations by CyTOF identifies further diversity We compared the frequency of each baseline population of CD3+CD4+ T cells between fresh and frozen samples using the Welch unpaired t test We next measured the expression of the master transcription factors available in GraphPad Prism v7. Statistical significance was determined T-bet, GATA3, RORC2, and FOXP3 in unstimulated Th1, Th2, with a = 0.05. Each population was analyzed individually, without as- suming a consistent SD. Th17, Tfh, and Treg cell populations (referred to hereafter as + Similarity among Th1, Th2, Th17, Tfh, and Treg cell populations was baseline populations) using frozen isolated CD4 T cell samples determined by the number of events from each population within shared (Fig. 1A). We found the highest expression of T-bet, GATA3, and nodes after clustering. Only nodes (N) with $120 events ($0.15%) of the FOXP3 in Th1, Th2, and Treg cell populations, respectively. No target population (X) were considered statistically relevant, when cal- specific RORC2 expression was detected in the nonactivated Th17 culating similarity between X and the four other populations (a–d). The population. This finding may be due to the fact that RORC2 ex- proportion of ai–di was weighted by the frequency of X (xi) for each node, and the sum of these weighted proportions was used to calculate pression is activation dependent and more transient compared with the percentage similarity of X to a–d. Tr1 cells were calculated in the other transcription factors. In addition to these transcription fac- same way, except Tr1 cell similarity was calculated for five other pop- tors, we included p-STAT1, p-STAT3, and p-STAT5 in our anal- ulations (Th1, Th2, Th17, Tfh, and Treg cells) as opposed to the pre- + vious four. The following three equations summarize our calculation ysis of frozen CD4 T cell samples to monitor active signaling for similarity: pathways. No basal p-STAT1 or p-STAT3 expression was observed among the baseline populations. p-STAT5 expression was ni Proportion; ai ¼ where ni is the event count of subset a in node N: elevated within Treg cells (25), but it was not significantly dif- N i ferent from that of other baseline populations (data not shown). + x We next included, in our analysis of frozen isolated CD4 T cell Weight; y ¼ i where x is the event countof population X in node N: i X i samples, surface markers relevant to further characterize the baseline populations, such as the coinhibitory receptors PD-1, " # n TIGIT, and CTLA-4, the costimulatory receptor ICOS, and the + yiðaÞ Similarity ¼ i¼1 i 3 100% for N with T cell activation and adhesion markers CD226, LAG3, CD49b, X;a +n y ða þ b þ c þ dÞ i¼1 i i and CD62L. The differential marker expression of each baseline % xi 0:15 of events: population for each individual (n = 8) is shown in Fig. 1B. Th1 cells had elevated expression of CD279 (PD-1) (7/8 individuals) and CD226 (5/8 individuals). Th2 cells had elevated expression of Results CD226 (7/8 individuals) and LAG3 (6/8 individuals). Th17 cells CyTOF detects Th cell, Treg, and Tr1 cell populations within + + had elevated expression of CD278 (ICOS), CD226, LAG3, and CD3 CD4 T cells CD49b (8/8 individuals for all). Tfh cells had elevated expression Our initial CyTOF analysis focused on validating the chemokine of PD-1 (8/8 individuals), TIGIT (8/8 individuals), ICOS (7/8 and surface receptor frequencies for circulating CD3+CD4+ Th1, individuals), and CD62L (6/8 individuals). Treg cells had ele- Th2, Th17, Tfh, Treg, and memory Tr1 cell subsets using the vated expression of TIGIT and CTLA-4 (8/8 individuals for both). previously identified markers (2–8) (Table I). The gating strategy Lastly, there was elevated expression of PD-1, ICOS, CD226, and to identify frequencies of CXCR3+, CCR4+, CCR6+, CD161+, and CD62L (3/3 individuals for all) within the memory Tr1 cell FOXP3+ cells within the CD3+CD4+ T cell population is shown in population. Of note, CXCR3 was expressed in Tfh cells (7/8 in- Supplemental Fig. 1A and 1B. Frequencies were within range of dividuals) and Tr1 cells (3/3 individuals), and CCR4 was those in previous reports (43–49) (Supplemental Table I) and were expressed in Treg cells (6/8 individuals) and Tr1 cells (3/3 indi- comparable between fresh and frozen samples, with the exception viduals). In addition, Th17 cells expressed CXCR3 and CCR4 in of CD161 and FOXP3, which were better captured on fresh all donors tested, but the intensity of expression varied among samples. donors. Furthermore, CD161 was expressed on memory Tr1 cells 4 PERIPHERAL CD3+CD4+ T CELL SUBSETS BY MASS CYTOMETRY

Table I. CD3+CD4+ Th and Treg cell populations: bona ﬁde markers and function

Population Deﬁning Markers Immune Target/Function Alternative Molecules References Th1 CXCR3+CCR42 Intracellular microbes CD226, PD-1, T-bet (3, 14, 15–17) Th2 CCR4+CXCR32 Extracellular parasites GATA3 (3, 14) Th17 CCR6+CD161+ Extracellular microbes/commensal ICOS, RORC2 (2, 18–20) Tfh CXCR5+ B cell maturation ICOS, PD-1, CD62L (5, 21–23) FOXP3 Treg FOXP3+CD25hiCD127lo/2 Tolerance/immune suppression TIGIT, CTLA-4, p-STAT5 (4, 7, 8, 24, 25) Memory Tr1 CD45RA2LAG3+CD49b+ Peripheral Tolerance/myeloid killing ICOS, PD-1, CD226 (4, 6, 26, 27)

(3/3 individuals), but these cells did not coexpress CCR6 like the functions, respectively. We found no substantial differences in Th17 cell population. Interestingly, Th1, Th2, and Th17 cells marker expression among baseline populations between fresh displayed more activation markers, whereas Tfh cells, Treg cells, and frozen samples. and Tr1 cells were more positive for coinhibitory and costimu- We further characterized these baseline populations to determine latory receptors, likely indicative of their effector and regulatory whether the observed proportional changes are due to the presence

FIGURE 1. Expanded 20-marker characterization of CD3+CD4+ Th, Treg, and Tr1 cell baseline populations in healthy donors. (A) Master transcription factor expression of each baseline population (each number indicates raw mean intensity [MI] across eight donors). (B) Marker mean intensity for Th1, Th2, Th17, Tfh, and Treg cell baseline populations (n = 8, frozen samples isolated CD4+ T cells) and for memory Tr1 cell populations (n = 3, fresh samples gated CD4+ T cells from PBMCs) was normalized (arcsinh ratio) based on the expression in CD3+CD4+ T cells for each donor. Differential expression (z-score) for population-defining markers, coinhibitory or costimulatory, and activation-associated receptors across each donor’s baseline populations is shown. Memory Tr1 cell populations within fresh samples are shown to the right. Black boxes highlight the defining marker profile for the baseline populations (upper portion of heat map), and “+” signs in the lower half of the heat map indicate positive differential expression across donors. The Journal of Immunology 5 of small populations of positive cells among the whole or to overall Fig. 3A shows the differential expression of 20 markers for each differences in expression within populations. of the 17 subsets across all individuals (n = 8). Indeed, within each Additional markers reveal phenotypic overlap among Th1, baseline population, we identified multiple subsets with differen- Th2, Th17, Tfh, and Treg cell subsets tial expression of transcription factors, costimulatory molecules, and inhibitory or activating receptors. For example, within the Th2 We next analyzed CD3+CD4+ T cells using viSNE (41). We initially population, we identified subsets with differential expression of considered the differential expressionof23markers,includingsur- GATA3 and FOXP3, which were clearly noticeable at the single- face markers, chemokine receptors, transcription factors, and sig- cell level (Supplemental Fig. 3B). A subset of Treg cells naling and activation molecules. Then, by sequentially excluding these groups of markers from the analysis, one at a time, we deter- expressing GATA3 under baseline conditions and after activation mined that only 12 markers are sufficient to separate the five baseline has been described in mice (51, 52). The coexpression of tran- populations. The CD3+CD4+ T cell viSNE landscape, based on the scription factors GATA3 and FOXP3 demonstrates the interrela- expression of these 12 markers, plus two markers that distinguish Tr1 tionship between Treg and Th2 cell populations. In contrast to cells (LAG3 and CD49B), is shown in Supplemental Fig. 2. what was reported previously (53–56), we did not detect over- Ultimately, using viSNE, we analyzed CD3+CD4+ T cells (frozen lapping Th1 or Th17 features in the Treg cell population. The samples) based on their unique combined expression of CD161, distinction and overlap of the different subsets were calculated CCR6, CCR4, CXCR3, CD25, CD127, CXCR5, and CD45RA plus based on the viSNE analysis described in Fig. 2B and in Materials the transcription factors FOXP3, T-bet, GATA3, and RORC2. Fig. 2A and Methods (Data analysis). Fig. 3B shows that the space oc- shows an example of the workflow used to further characterize the cupied on the viSNE map by Th1 subset 2 completely encom- different baseline population subsets by viSNE. We asked whether passed Tfh cell subset 3, a Th1-Tfh cell subset previously baseline populations, as traditionally gated, were a single population described as Tfh1 (15). Th2 subset 1 and Treg cell subset 1 were of cells by showing where they lay on the viSNE map (Fig. 2B). We found a total of 17 subsets, indicating that each baseline population superimposable on the viSNE map, indicating that they are occupies more than one area on the viSNE map; therefore, each identical. Similarly, Th17 subset 2 encompassed Tfh cell subset 2 population encompasses more than one subset. The frequency of the and Th1 cell subset 1. The latter Th17-Th1 cell subset demon- 17 subsets was quite consistent among donors (Supplemental Fig. strates the high degree of plasticity of Th17 cells. This finding is 3A). The unselected CD3+CD4+ T cells were mostly naive T cells, consistent with previous reports showing the ability of Th17 cells as indicated by the CD45RA expression level (Fig. 2A). to acquire Th1 cell characteristics (57, 58).

FIGURE 2. Expanded phenotyping of single CD3+CD4+ T cells using viSNE identifies 17 Th and Treg cell subsets. Ten thousand CD3+CD4+ T cells from each donor (n = 8, frozen samples) were organized by their combined expression of the defining markers: CD161, CCR6, CCR4, CXCR3, CXCR5, CD25, CD127, CD45RA, FOXP3, GATA3, T-bet, and RORC2. (A) An illustration of the workflow, showing the generated viSNE map of CD3+CD4+ T cells across donors colored by CD45RA expression (blue = low, red = high), followed by the manual selection of Th1, Th2, Th17, Tfh, and Treg cell baseline populations, which were then overlaid onto the same viSNE map. (B) Organization of each baseline population shown in gray; distinct regions of each population are outlined in black to illustrate the manually gated subsets (numbered 1–5, total = 17). 6 PERIPHERAL CD3+CD4+ T CELL SUBSETS BY MASS CYTOMETRY

FIGURE 3. Differential expression of 20 specific CD3+CD4+ T cell markers in the identified Th and Treg cell subsets. (A) Marker mean intensity for each subset was normalized (arcsinh ratio) to total CD3+CD4+ T cells for each donor (n = 8, frozen samples). Differential expression (z-score) for each marker across all subsets was calculated. Black boxes highlight the defining marker profile for the baseline populations, and “+” signs indicate positive differential expression. (B) Venn diagrams of each unique CD3+ CD4+ Th and Treg cell subset and of CD3+CD4+ Th and Treg overlapping cell subsets (total = 15), identified in combination with Fig. 2B and the calculation of percentage of overlap (see Materials and Methods). The single or shared defining marker expression for each subset is indicated by the color within each circle. The size and position of each circle closely represent each subset size (percentage of CD3+CD4+ T cells) and the percentage of overlap among subsets of distinct baseline populations.

Altogether, these Th and Treg cell subset phenotypes and their shows a consistent phenotypic overlap among baseline populations. overlap highlight specific interrelationships between the baseline For example, overlap of the Th2 subset with the Treg cell subset populations when expanded phenotyping is used simultaneously. (Fig. 3B) is reproduced in the PCA analysis (Supplemental Fig. 4B). Thus, using additional defining markers, and after eliminating those In summary, this unsupervised analysis confirms the specific over- overlapping subsets, we found a total of 15 unique CD3+CD4+ Th and lap among baseline populations previously observed by viSNE. Treg cell subsets. A summary of defining, shared, and differentially We then quantified similarity among the Th1, Th2, Th17, Tfh, expressed markers for each subset identified is shown in Table II. We and Treg cell populations after clustering to identify baseline list subsets with bona fide baseline population phenotypes, as well population interrelationships. The percentage similarity between as the other subsets sharing multiple Th and Treg cell phenotypes. each baseline population represents the proportion of cells that In conclusion, by viSNE analysis, which simultaneously engages shares the same phenotype. Because each node represents a single all known markers, we established a foundation on which to un- phenotype, we could count the number of phenotypically similar cells derstand the relationship among the baseline populations and to calculate similarity (see Materials and Methods; Statistical anal- their heterogeneity. ysis). Supplemental Fig. 4C displays the similarity between each pair of baseline populations. We found that Th1, Th2, and Tfh cell Unsupervised clustering predicts higher-level organization of + + populations shared high similarity with Th17 cells. Also, the Th2 CD3 CD4 Th cells and Treg cells population has the highest similarity with the Treg cell population. Given the overlap observed with extended phenotyping, we used an The similarity for each baseline population overall corresponds unsupervised clustering analysis, which is a more quantitative with the phenotypic organization of baseline populations displayed method, alternative to viSNE, not based on manual gating, to by PCA. Even small patterns of similarity are still apparent by confirm our findings and identify consistent interrelationships PCA, such as the Treg cell population being least similar to Th17, among the baseline populations. Using the FlowSOM algorithm, or Th1 being least similar to Th2. The largest distinction among we clustered CD3+CD4+ T cells (frozen samples) based on phe- baseline populations within the PCA was the grouping of Th2-Treg notypic similarity among baseline populations (see Materials and cell populations and Th1-Th17-Tfh cell populations on opposite Methods; Data analysis). Supplemental Fig. 4A displays 100 sides (Supplemental Fig. 4B). We found that the variables with the nodes visualized in an MST, each representing a cluster of cells that greatest contribution to this distinction were CCR4, CXCR3, and share expression of the same 12 parameters used in our viSNE CD161. In conclusion, these findings reflect the population in- analysis. Analysis of these nodes by PCA (Supplemental Fig. 4B) terrelationships in human peripheral blood. The Journal of Immunology 7

Table II. CD3+CD4+ Th and Treg baseline cell population subsets identiﬁed using viSNE

Baseline Cell Population Subset Shared/Defining Markers Differential Expression Profile Annotation Th1 2 CXCR3+CCR42 T-bet+PD-1+CD226+ Bona fide Th1 3 CD45RA+ CD45RA+ Th1 Th2 2 CCR4+CXCR32GATA3+ CCR6+CD127+CD226+LAG3+ CCR6+ Th2 CD45RA2 3 CD127+ICOS+CD226+LAG3+ Bona fide Th2 Th17 1 CCR6+CD161+CD127+ CXCR3+CCR4+ICOS+LAG3+ CCR4+CXCR3+ Th17 CD45RA2CD226+ CD49b+ Th17(2)-Th1(1) CXCR3+CCR42T-bet+GATA3low Th1-like Th17 Th17(2)-Tfh(2) CXCR5+RORC2+ICOS+CD49b+ Tfh-like Th17 Tfh 1 CXCR5+RORC2+/lowCD45RA2/ CXCR3+CCR4+CCR6+CD161+ CXCR3+CCR4+ Th17- low CD127+ICOS+LAG3+CD62L+ like Tfh 3 CXCR3+T-bet+PD-1+TIGIT+ Tfh1 ICOS+CD226+ 4 CD161+PD-1+ Tfh 5 CD45RAlow Tfh Treg Treg(1)-Th2(1) CD25+CD127low/2FOXP3+ CXCR32CCR4+CCR6+ Th2-like Treg TIGIT+/lowCTLA4+ CD45RA2GATA3+ICOS+ CD62L+ 2 CXCR3+CCR4+CCR6+CD45RA+ CXCR3+CCR4+CCR6+ CD49b+ Treg 3 CXCR3+CD45RA+RORC2+ CXCR3+ Treg CD62L+ 4 CD45RA+CD49b+ Bona fide Treg

Unsupervised clustering reveals Tr1 cell similarity with Th and immune response, in health or immune-mediated diseases (1–5). Treg cell populations The identification of these subsets is based on specific surface To characterize Tr1 cells and their relationship with the other receptors, signaling pathways, transcription factors, and functional baseline populations, we proceeded with viSNE and unsupervised properties. Using CyTOF and high-dimensional single-cell analyses, we achieve a systems-level characterization by visualizing clustering analysis, including LAG3 and CD49b (which identify Tr1 + + cells), using fresh PBMC gated CD3+CD4+ T cells (Fig. 4). Tr1 cells the CD3 CD4 T cell subsets at the same time and by grouping were detected as a distinct population in three healthy donors, with them in phenotypically distinct populations using unsupervised a mean frequency of 3.05 6 1.94% of CD3+CD4+CD45RA2 clustering. Expanded phenotyping using known surface receptors T cells (Fig. 4A). In addition to the Tr1 cell subset, this analysis and master transcription factors for Th1, Th2, Th17, Tfh, Treg (defined in this study as baseline populations), and Tr1 cells was confirms the detection of the 15 subsets, previously identified in + + frozen samples, within the baseline populations shown in Figs. 2B performed on purified or gated CD3 CD4 T cells from fresh or and 3A with a similar expression profile. Moreover, we identified frozen samples obtained from healthy donors. Our data show that two additional populations using this 14-parameter analysis (12 we could identify distinct baseline populations, as previously markers plus LAG3 and CD49b) on fresh CD3+CD4+ Tcells:a described by flow cytometry (24, 26, 43–48, 50). However, we also + + identified heterogeneity within each baseline population, with a CXCR5 Th2 subset with low GATA3 expression and a CXCR5 + + CD127low Th17 subset (Fig. 4A), for a total of 18 unique subsets. total of 18 subsets identified by viSNE. Among these 18 CD3 CD4 The unsupervised clustering analysis using 14 markers showed T cell subsets, we found overlap of a Treg cell subset with a Th2 similarity between Th and Treg cell populations (Fig. 4B) in fresh cell subset, as well as overlap of a Th17 cell subset with Th1 and and frozen samples that was comparable to that previously observed Tfh cell subsets, reducing the number of the distinct subsets to with 12-marker analysis in frozen samples (Fig. 4B versus 16 which were found independently of donor variability. The clustering analysis further showed novel reproducible similarities among Supplemental Fig. 4C). The similarity between each cell population + + remained consistent among fresh and frozen samples, because the certain subsets. Overall, our in-depth characterization of CD3 CD4 pattern of overlap between Th and Treg cell populations remained T cell subsets reveals unprecedented diversity, but also inter- the same. These data suggest that the baseline populations and their relationships among different subsets. similarity are reproducible among donors and conditions. CyTOF analysis confirms that T-bet and FOXP3 are con- Fig. 4C displays an MST of 100 nodes for the unsupervised stitutively expressed in unstimulated Th1 cells and Treg cells, clustering and the calculated similarity of Tr1 to the Th and Treg respectively, as previously described (8, 16). Additionally, we cell populations. Tr1 cells were most similar to Th1 (34.46%) and found elevated GATA3 expression within circulating Th2 Tfh (30.90%) cells, followed by Th2 (18.11%) and Th17 (15.53%) cells, which has not been reported. ViSNE analysis shows low, but detectable, RORC2 expression in four of five Tfh cell cells. In contrast, Tr1 cells had no substantial similarity (0.61%) to + + Treg cells. In conclusion, based on CD49b and LAG3 expression, subsets identified, including the CD161 CCR6 Tfh cell subset. RORC2 expression hasbeenreportedingerminal we could depict Tr1 cells by viSNE and by unsupervised clus- + + + tering analyses in fresh and frozen samples. center CXCR5 ICOS Bcl-6 Tfh cells, associated with the early development of human Tfh and Th17 cells (17). Our results suggest that Tfh cells may retain the ability to express Discussion RORC2 outside the germinal center, and they align with the CD3+CD4+ Th1, Th2, Th17, Tfh, Treg, and Tr1 cell populations description of Th17-like (CXCR32CCR6+)Tfhcellsinpe- have been identified as major players in determining the type of ripheral blood (5, 59). 8 PERIPHERAL CD3+CD4+ T CELL SUBSETS BY MASS CYTOMETRY

FIGURE 4. Expanded phenotypic analysis and unsupervised clustering of CD3+CD4+ Th cells and Treg cells, with the addition of LAG3 and CD49b to identify Tr1 cells. The same viSNE analysis and FlowSOM clustering described in Fig. 2 and Supplemental Fig. 4 were performed on healthy donor fresh PBMC samples (n = 3) by the combined expression of 12 markers plus LAG3 and CD49b. (A) viSNE map of CD3+CD4+ T cells across donors colored by CD45RA expression (blue = low, red = high), followed by overlay of Th1, Th2, Th17, Tfh, Treg, and Tr1 cell populations (gray) onto total ungated CD4+ T cells (blue). (B) Similarity was calculated as described in Supplemental Fig. 4C, and proportions of cells with shared phenotype between each population are shown. (C) MST visualization of FlowSOM clustering (left panel). Each node represents one cluster (total = 100 nodes). Green circles highlight Tr1 cell populations. Similarity was calculated between Tr1 and the other Th and Treg cell populations (right panel). The percentage similarity of the Tr1 cell population with the other Th and Treg baseline cell populations is shown.

Our extended characterization of each baseline population using unique divergence among the Th, Treg, and Tr1 cell populations in 23 markers (Fig. 3) confirmed the elevated expression of PD-1 and blood. CD226 in Th1 cells (60–62), TIGIT, CD62L, ICOS, and PD-1 in Our viSNE analysis confirms, in a single snapshot of the CD3+ Tfh cells (21–23, 63), TIGIT and CD152 (CTLA-4) in Treg cells CD4+ T cell immune landscape, the distinction of the different (64, 65), and CD226, ICOS, and PD-1 in Tr1 cells (26, 66). Of baseline populations, but, at the same time, highlights the het- note, we found that unstimulated Treg and memory Tr1 cell erogeneity within each baseline population, including the exis- populations differ in their expression of TIGIT and PD-1, with the tence of new previously undescribed subsets. Of the 16 identified former being strongly positive for TIGIT and the latter positive for Th, Treg, and Tr1 cell subsets using viSNE, we also described PD-1. In this analysis, the Treg cell population did not express overlapping phenotypes among Th cell and Treg cell subsets. We ICOS. However, the viSNE data show that a CD45RA2 Treg cell found a CCR6+ Th2 subset, which matches previously described subset does in fact express ICOS, as previously described for CCR6+CCR4+ Th17 cells (69). Similarly, within Th17 cells we memory FOXP3 Treg cells (67, 68). These data allude to the identified a Th1-like Th17 subset that matches the previously The Journal of Immunology 9 described CXCR3+T-bet+ Th17 cell population (70) and a Th17- Treg, and Tr1 cell diversity, heterogeneity, and interrelationships. like Tfh cell subset that matches the previously described CCR6+ We identified novel heterogeneity within each baseline population, Tfh cell population (59). Among Tfh cells, we saw a Th1-like Tfh as well as phenotypic overlap between some subsets. In addition, cell subset that matches the previously described CXCR3+ Tfh or this expanded single-cell analysis by CyTOF allows a better Tfh1 cell population (15). Lastly, among Treg cells, we found definition of CD3+CD4+ Treg cell populations and how they Th2-like Treg and CXCR3+ Treg cell subsets, which match pre- compare with Th cells. This unique opportunity to measure the viously described CCR4+ Treg cells and CXCR3+ Treg cells, re- similarity between each population using unsupervised clustering spectively (67, 71). We also found three novel subsets at low unveiled specific interrelationships, indicative of a comprehen- frequency, within the Th17, Tfh, and Treg cell populations, all sive organization of CD3+CD4+ Th cells and Treg cells. The sharing coexpression of CXCR3, CCR4, and CCR6 receptors. The interrelationships may indicate the existence of additional novel large number of subsets that we could find simultaneously by subsets with overlapping characteristics or, rather, suggest the viSNE speaks for the fact that the overlap between baseline presence of T cell subsets in transition between one another, populations is biologically relevant. which may represent their differentiation states. An alternative We initially found a vicinity continuum of CD3+CD4+ Tcell interpretation of our data is the plasticity within the CD3+CD4+ phenotypes, as indicated by the undivided population of cells T cell compartment in healthy conditions, which might represent displayed by viSNE. However, by looking at each Th, Treg, and a readiness within memory CD3+CD4+ T cells to quickly switch Tr1 cell population individually, it became apparent that the from one functional “program” to another in response to external CD3+CD4+ T cell viSNE map is still organized into multiple triggers, as recently suggested (12, 28). We are currently de- distinct subsets. Along with other single-cell analyses of T cells veloping a specific CyTOF panel to further characterize the in the blood, the described similarity based on shared marker identified subsets by determining their functional properties expression agrees with the idea that Th and Treg cell populations upon activation. may have flexible terminal differentiation (12). In this study, we Overall, these data in healthy human blood provide new insights did not evaluate the differentiation of these T cells, but, by into the CD3+CD4+ T cell network and will constitute a reference phenotype alone, we show the capability of monitoring the type for future studies aimed at evaluating Th, Treg, and Tr1 cell subset and frequency of cells sharing multiple Th, Treg, and Tr1 cell variations in disease conditions and in response to therapies. defining characteristics. By unsupervised clustering analysis, we confirm the heterogeneity within each baseline population and Acknowledgments the overlap among some subsets. We found that Th1 and Th2 We thank E. Hsieh for excellent technical support with the set-up of the cells have no similarities, Tfh cells have similarity with Th1 and mass cytometric assay, B. Carter for quality instrument operation, Anita Th17 cells, and Th17 cells are similar to Th1, Th2, and Tfh cells. Khant for assistance with panel design, and S. Wager for statistical advice. Interestingly, Treg cells were predominantly similar to Th2 cells. We also thank Prof. Jeff Bluestone for helpful scientific discussion and crit- This Treg-Th2 cell similarity is in line with the large majority of ical review of the manuscript. CCR4+CXCR32 Treg cell phenotypes previously identified by CyTOF (35). Likewise, the high degree of similarity between Disclosures Th17 cells, as well as Tfh cells, and other Th populations, but not The authors have no financial conflicts of interest. with Treg cells, is in line with previous descriptions of their heterogeneity (59, 69, 70). By simultaneously examining all Th, Treg, and Tr1 cell populations using unsupervised analyses, we References 1. Mosmann, T. R., and R. L. Coffman. 1989. TH1 and TH2 cells: different patterns reaffirmed the overlap and similarity between subsets observed of lymphokine secretion lead to different functional properties. Annu. Rev. by viSNE. Immunol. 7: 145–173. 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