Deep phenotyping of Tregs identifies an immune signature for idiopathic aplastic anemia and predicts response to treatment

Treatment options for idiopathic aplastic anemia (AA) include allogeneic hematopoietic stem cell transplantation or immunosuppressive therapy (IST), and recently eltrombopag for refractory severe AA.^1-3  Some patients are ineligible for hematopoietic stem cell transplantation because of older age or lack of a suitable donor, and after IST, one-third of patients fail to respond and 35% relapse after responding. Up to 20% of patients transform to myelodysplastic syndrome or acute myeloid leukemia after IST.^3-7  Additional novel therapeutic approaches are needed for AA patients who fail to respond to IST, and along with this, more robust diagnostic tests that predict response to IST.

We, and others, have shown a reduction in the number and function of regulatory T cells (Tregs) in AA.^8-11  Tregs from AA patients also secrete pro-inflammatory cytokines.⁸  Correlation between number and function of Tregs and response to standard IST has not been fully investigated, and it is unclear whether the dominant Treg subpopulation in AA is more of a T conventional (T_con) subtype or are genuinely functional Tregs.

The aims of this study were to (1) identify an immune signature for AA compared with healthy individuals, based on Treg subpopulations and T_con; (2) identify the immune signature that predicts response to IST at time of diagnosis of AA; and (3) examine the expandability and characteristics of Treg subpopulations that may form the basis for a novel therapeutic approach to AA patients who are refractory to IST. For this, we used a novel deep-phenotyping strategy using multiparameter mass cytometry (known as cytometry by time-of-flight [CyTOF]) and automated clustering method including t-distributed stochastic neighbor embedding (t-SNE)¹²  to use SNE visually (viSNE) as a means of identifying cell populations¹³  in combination with spanning-tree progression analysis of density-normalized events (SPADE).¹⁴  This enabled us to identify 2 distinct subpopulations of human Tregs in AA and healthy age-matched donors (HDs) and to demonstrate clear differences in AA that predicted response to IST. We also showed that AA Tregs can be expanded in vitro and that they are stable and functional.

Thirty-nine AA patients at diagnosis and 31 HDs were recruited (Table 1). Peripheral blood mononuclear cells (PBMCs) from 16 patients (12 IST responders and 4 nonresponders) were used for initial CyTOF analysis; samples from another 15 patients (11 IST responders and 4 nonresponders) were used as validation cohort. PBMCs from an additional 8 patients were used for functional assays in addition to 4 patients from the initial cohort. Response to IST was evaluated at 6 months posttherapy. Median age was 45 years (range, 20-72 years). IST-eligible AA patients were randomly invited to participate in this study before commencement of therapy. All patients <35 years of age were screened for Fanconi anemia.

We designed a panel of antibodies based on surface markers, transcription factors, and cytokines (see supplemental Table 1, available on the Blood Web site). Each antibody was tagged with a rare metal isotope and its function verified by conventional flow cytometry before mass cytometry (supplemental Methods). The CyTOF-2 mass cytometer (Fluidigm) was used for data acquisition. Acquired data were normalized based on normalization beads (Ce 140, Eu151, Eu153, Ho165, and Lu175).¹⁵  Automated clustering was performed on a subset of 800 000 cells sampled from all individuals. The number of cells sampled from each individual was proportional to the total number of cells in that sample. Collected cells were stained with metal-conjugated antibodies with or without a 4-hour stimulation with phorbol myristate acetate (PMA) and ionomycin in the presence of brefeldin. Intracellular staining for transcription factors and cytokines was performed after fixation and permeabilization according to the manufacturer's instructions (eBioscience). Conventional flow cytometry data were analyzed by polychromatic flow cytometer (LSRFortessa, BD Biosciences).

Data were initially processed and analyzed using Cytobank.¹⁶  The stand-alone analysis tool cyt was also used for performing t-SNE dimensionality reduction and merging distinct FCS files.¹³  We analyzed mass cytometry complex data using viSNE¹²  to visually identify delineated subpopulations¹³  in combination with SPADE¹⁴  and heat maps¹⁵  to distinguish CD4⁺ T-cell subpopulations, in particular Tregs¹⁵  (see supplemental Methods).

Freshly isolated live Tregs were stimulated with anti-CD3/CD28 beads (1:1 ratio) (Dynabeads human T-activator CD3/CD28, Life Technologies) and high-dose (1000 IU/mL) interleukin-2 (IL-2)(Proleukin, Novartis) for 4 weeks with all-trans-retinoic acid 2 μM (Sigma Aldrich) and rapamycin 100 nM (Rapa; Alfa-Aesar). Culture medium (XV Prime, Irvine Scientific, supplemented with AB serum 10%) and beads were replenished every week. After 4 weeks of expansion, cells were rested with decreasing doses of IL-2 for 5 days.¹⁷ 

DNA isolation method, primer sequences, and cycling conditions are shown in supplemental Figure MS1 and the supplemental Methods.

Amplification and sequencing of T-cell receptor B (TCR-B) complementarity determining region 3 (CDR3) was performed using immunoSEQ Platform (Adaptive Biotechnologies, Seattle, WA), as previously described (supplemental Methods). A power geometric (PG) index with Horvitz-Thompson type was used as correction for undersampling and Good-Turing coverage adjustment.^18-20 

RNA extraction method is included in the supplemental Methods. Analysis of differential gene expression was performed as previously described^21,22  (supplemental Methods).

Different statistical methods have been used that are explained in the “Results” and/or figure legends. P < .05 was considered as statistically significant in all cases. Statistics were calculated using SPSS, version 22, or R, version 3.2.2.

King's College Hospital Local Research Ethics Committee and Institutional Review Board of the National Heart, Lung, and Blood Institute approved the clinical studies of IST and sample collection, and informed written consent was obtained from patients.

We analyzed 31 AA patients at diagnosis and 5 HDs. Of these 31 patients, 16 were part of an initial test cohort and a further 15 part of a validation cohort. Metal-tagged antibodies against surface and intracellular markers were used in 2 separate panels (with or without stimulation with PMA/ionomycin) to stain T cells, including known markers for Tregs (CD25, CD127, FOXP3), naïve/memory subsets, homing/trafficking receptors, and differentiation/activation markers (supplemental Table 1). CD4⁺ and CD8⁺ T cells and B cells were clustered using this “whole panel” clustering approach (34 markers in panel 1 [supplemental Table 1]) to minimize bias. FOXP3⁺ cells were also clustered within CD4⁺ T cells (Figure 1; supplemental Figure 1). After gating for CD3⁺, CD4⁺, and CD8⁻, and merging all samples, viSNE was performed and cells clustered, based on 13 markers that most clearly clustered Tregs (Figure 2; supplemental Figure 2). Treg subpopulations were clustered and identified by high expression of CD25 and FOXP3 and low expression of CD127 (Figure 2A; supplemental Figure 1B). Identified Tregs expressed CD27^hi, CD45RA^lo, CD45RO^hi, CD95^hi, CD7^lo, CD28^hi, and CCR4^hi compared with the total CD4⁺ T-cell subpopulation (Figure 2). The frequency of total Tregs was significantly lower in AA patients compared with HDs (2.7% vs 5.7% of CD4⁺ T cells, P < .01), confirming our previously published findings.⁸  The numbers of Tregs were not significantly different between patients with severe AA/very severe AA, and nonsevere AA.

Although viSNE clustered the Treg population in 1 area, density plots revealed a heterogeneous distribution of cells. Two subpopulations within Tregs with a different frequency between AA and HDs were identified and designated as Treg A and Treg B (Figure 2C-D; supplemental Figure 1B-C). To confirm the presence of these 2 subpopulations and eliminate any bias, dimensionality reduction and automated unsupervised clustering methodology was applied independently by the bioinformatician; this confirmed the presence of 2 subpopulations within Tregs (Figure 2C; supplemental Figure 1B-C). These 2 subpopulations showed distinct markers in AA Tregs (16 patients, 12 IST responders and 4 nonresponders) as well as 5 HDs. Although both subpopulations were CD25^hi, FOXP3^hi, and CD127^lo compared with total CD4⁺ T cells, subpopulation B was additionally characterized by a lower expression of CD45RA (P < .0001), CD7 (P < .001), and CD27 (P < .05) and a higher expression of CCR4 (P < .0001), CCR6 (P < .0001), CD25 (P < .0001), CD28 (P < .01), CD45RO (P < .0001), CD95 (P < .0001), CXCR3 (P < .05), FOXP3 (P < .0001), and HLA-DR (P < .0001) compared with subpopulation A (Kruskal-Wallis 1-way analysis of variance by ranks).

In addition to total Tregs, the percentage of B Tregs was significantly lower in AA patients compared with HDs (40.8% ± 13.7% vs 72.2% ± 15%, P = .008).

When patients were stratified into IST responders (n = 12) and nonresponders (n = 4), although the overall frequency of Treg B was lower in both responder and non-responding patients at the time of diagnosis compared with HDs (n = 5) (48.8% ± 6.1% and 28.9% ± 2.7% vs 72.2% ± 6.7%, P = .005, P < .0001), nonresponders had significantly higher Treg A and lower Treg B cells compared with responders (63.5% ± 4.5 vs 38.8% ± 5.0%, P < .005 for Treg A, 28.9% ± 2.7 vs 48.8% ± 6.1, P < .05 for Treg B) (Figure 2E, Table 2).

To investigate the overlap between the Treg subpopulations with Treg subpopulations identified by Miyara et al,²³  Tregs were gated based on CD45RA and FOXP3 expression. Although subpopulation A and B mainly overlapped with subpopulations I (CD45RA^hi, FOXP3^lo) and II (CD45RA^lo, FOXP3^hi), respectively, subpopulation III (CD45RA^lo, FOXP3^lo) was spread over both population A and B areas, and some cells clustered outside Treg area (Figure 2F).

Following IST response, the frequency of population A was significantly reduced in responders (from 38.8% ± 5.0% to 19.2% ± 2.4, P < .01), but was not significant in nonresponder patients. Treg B frequency was significantly higher in responders compared with nonresponders (59.9% ± 3.4% vs 21.8% ± 4.3, P < .0001) and closer to HDs (Table 2). Treg population A or B was not significantly different between patients with severe/very severe (n = 11) and nonsevere AA (n = 5).

The cytokine profile of Tregs following stimulation with PMA, ionomycin, and brefeldin A was investigated. Treg subpopulations A and B were identified within Tregs (supplemental Figure 3). Stimulated Treg clusters expressed higher CD25 (P < .001) and FOXP3 (P = .001) and lower CD127 (P = .001) compared with total CD4⁺ T cells. The majority of CD4⁺ T cells with pro-inflammatory cytokine properties clustered outside the “Treg area”; thus, Treg clusters expressed negligible amounts of pro-inflammatory cytokines interferon-γ (IFN-γ), and IL-17 (Figure 3A). Tregs expressed significantly higher IL-10 compared with total CD4⁺ T cells (P = .002); however, the IL-10 expression was not significantly different between the 2 Treg subpopulations. Although tumor necrosis factor-α (TNF-α)–expressing cells were clustered within non-Treg subpopulations, a cluster of CD4⁺ TNF-α⁺ T cells clustered within the Treg A subpopulation (Figure 3B). These TNF-α⁺ cells expressed significantly higher CD127 (P < .001), and lower IL-10 (P < .001), CD279 (P < .001), HLA-DR (P = .003), CD38 (P < .001), CD25 (P < .001), and FOXP3 (P < .001) compared with total Tregs. Although at the time of diagnosis, the frequency of TNF-α⁺ cells was not significantly different between IST responders and nonresponders, IST responders had significantly lower TNF-α⁺ cells compared with nonresponders following response to IST (0.32% vs 1.81%, P = .008) (Figure 3C).

Conventional flow cytometry was performed on peripheral blood from a separate validation cohort of 15 AA patients (11 IST responders and 4 nonresponders) at the time of diagnosis to confirm that the identified markers were sufficient to detect Treg subpopulations and whether the CyTOF-identified combination of markers was still predictive for IST response. PBMCs were stained with anti-CD4, CD25, CD127, FOXP3, CD95, CCR4, and CD45RA and, similar to the initial cohort, Treg A cells were significantly higher in nonresponders compared with responders (37.82% ± 5.11% vs 18.33 ± 2.31, P = .006), whereas Treg B cells were significantly higher in responders (69.97% ± 3.02% vs 49.34% ± 4.92%, P = .01) (supplemental Figure 4).

Our marker panels also identified and clustered conventional CD4⁺ T_con. The Treg population was first gated out on a viSNE plot and SPADE clustering based on CD45RA, CD45RO, CD27, and CD62L performed. T_con subpopulations were defined as naïve (CD45RA⁺ CD45RO⁻ CD27^hi), memory (CD45RA⁻ CD45RO⁺ CD27^lo), central memory (CD45RA⁻ CD45RO⁺ CD27^lo CD62L^hi), effector memory (CD45RA⁻ CD45RO⁺ CD27^lo CD62L^lo), effector (CD45RA⁻ CD45RO⁺ CD27^lo), and terminal effectors (CD45RA⁺ CD45RO⁻ CD27^lo). Subset frequencies were not significantly different between IST responders (n = 12) and nonresponders (n = 4) at the time of diagnosis. However, effector CD4⁺ T cells (T_e) expressed a significantly higher CD161 level in nonresponders compared with responders (P < .01) (Figure 3D-E; supplemental Table 2).

To assess the function of the Treg subpopulations, HD CD4⁺ Tregs were sorted based on CD25, CD127, and CD95 expression, which showed the highest expression of differences between subpopulations A and B on viSNE clusters, excluding intracellular markers to avoid fixation and permeabilization of the cells. Sorted cells were CD4⁺CD25^hi CD127^lo CD95⁻, CD45RA^hi, CCR4^lo (subpopulation A) and CD4⁺CD25^hi CD127^lo CD95⁺, CD45RA^lo, and CCR4^hi (subpopulation B). To confirm these markers were enough to identify Treg subpopulations, viSNE runs were performed based on these markers and both subpopulations were identified (supplemental Figure 5). Treg B cells were significantly more functional compared with those of subpopulation A in suppression of both IFN-γ and TNF-α secretion by T_con (P < .05, Figure 4A).

Genomic DNA from sorted Tregs A and B was used for TCR Vβ chain CDR3 high-throughput sequencing.^19,20  On average, subpopulations A and B had 33 773 and 26 090 unique TCR sequences, respectively, and 233 sequences were common to both (r = 0.008) (supplemental Figure 6). T_con also shared 137 and 383 sequences with subpopulations A and B, respectively (r = 0.154 and r = 0.092, respectively). In assessing a naturally, highly diverse TCR subpopulations, undersampling can introduce bias.^18,24  We therefore used PG index for pairwise comparison of TCR repertoire overlap.¹⁸  Overlap between Treg A, B, and T_con was small, suggesting these subpopulations were distinct (Figure 4B).

Whole GEP data^25,26  showed both Treg A and Treg B had different GEP compared with T_con. Nevertheless, when principal component analysis was performed, Treg B and T_con had the most differences, whereas the Treg A subpopulation showed a transcriptional profile in between Treg B and T_con (supplemental Figure 7).

Comparing GEPs of Treg subpopulations to T_con using the human Treg’s gene signature²⁷  showed both Treg A and B subpopulations were significantly enriched in genes upregulated in human Tregs, including IL-2RA, FOXP3, IKZF2, TIGIT, and CTLA4 (false discovery rate [FDR] < 0.0001) for both Treg subpopulations compared with T_con. Treg B cells were enriched with Treg-related memory/activation genes compared with Treg A, including JAKMIP1, CCR8, TRIB1, and GZMK (FDR < 0.0001, Figure 4C-E). Thus, although both Treg subpopulations were enriched with Treg-associated genes, Tregs B were characterized by an activation gene signature in agreement with mass cytometry findings.

Gene set enrichment functional analysis²²  highlighted several gene sets as significantly overexpressed in the Treg B subpopulation, including G2M checkpoint (FDR < 0.0001), mitosis (FDR = 0.015), M phase of mitotic cell cycle (FDR = 0.018) IL-2–STAT5 signaling (FDR = 0.023), and immune response genes (FDR = 0.032) (Table 3). Protein interaction networks of proteins encoded by messenger RNA (mRNA) that are enriched in Treg B subpopulation cells were also mapped (supplemental Figure 8A-B). Ontology analyses of the functions of these protein complexes (supplemental Table 3, supplemental Figure 9) showed they are involved in mitotic functions, DNA replication, and cell-cycle–dependent transcription. MKI67 mRNA (encodes nuclear Ki67 protein) was enriched in Treg B subpopulation cells. This is important because the Ki67 protein is often used as a marker of proliferating cells.^28,29 

One of the aims of this study was to investigate the potential expandability of Tregs in AA. We first tested the IL-2 sensitivity of AA total Tregs based on STAT5 phosphorylation.³⁰  Freshly isolated PBMCs from 2 HDs and 6 AA patients (3 IST responders and 3 nonresponders, at diagnosis) were cultured in the presence of IL-2 at different concentrations (from 0.1 to 1000 IU/mL). Treg p-STAT5 expression significantly increased after 15 minutes’ culture with IL-2 (0.5 IU/mL) (median fluorescence intensities, 92.6 ± 35.1 pre-IL-2 vs 787 ± 109.6 post-IL-2, P < .001), confirming their responsiveness to IL-2. There was no difference between HDs and AA Tregs in response to IL-2 (Figure 5A).

To test their in vitro expandability, Tregs were obtained from 6 AA patients (3 IST responders and 3 nonresponders, at diagnosis) and 8 HDs and were cultured and stimulated with anti-CD3/CD28 beads (1:1 ratio) and high-dose IL-2 (1000 IU/mL) for 4 weeks with added all-trans-retinoic acid 2 μM and rapamycin 100 nM.¹⁷  AA Tregs expanded in a comparable rate to HD Tregs, with a median 33-fold increase (range, 29-149) compared with a 21-fold increase (range, 8-36) in HD. Expanded Tregs demonstrated more than 90% FOXP3⁺ expression in both AA and HDs (Figure 5 B-C).

Expanded Tregs were gradually (within 5 days) deprived of IL-2 following expansion and p-STAT5 evaluated in both AA and HDs Tregs to assess IL-2 dependency. Although expanded AA Tregs showed slightly lower p-STAT5 level following IL-2 deprivation, they responded to low-dose IL-2 and p-STAT5 and returned to a similar level as in HDs (data not shown).

To assess the suppressive activity of expanded Tregs, T_con were stained with a fluorescent proliferation dye (CellTrace Violet, Life Technologies) and cocultured with autologous expanded Tregs (1:1 ratio) for 5 days in the presence of anti-CD3/CD28 beads (T_con:Treg:beads = 20:20:1). Expanded AA Tregs suppressed proliferation of CD4⁺ T_con (an average reduction from 43% to 5%, P = .009) in both autologous and allogeneic conditions (Figure 5D). The suppressive activity of AA expanded Tregs was not significantly different from HD expanded Tregs.

To assess the stability of expanded Tregs, we investigated the methylation status of 15 cytosine guanine dinucleotide sites within the FOXP3 Treg-specific demethylated region (TSDR)³¹  by amplicon sequencing of bisulfite treated DNA on an Illumina MiSeq sequencing platform. TSDR cytosine guanine dinucleotide sites in expanded HDs and AA Tregs were >98% unmethylated, confirming the stability of expanded Tregs (Figure 6A).

TCR Vβ CDR3 high-throughput sequencing of expanded Tregs was used to investigate their clonality. The normalized Shannon entropy of expanded Treg repertoire was used to calculate the degree of clonality that, on average, was 0.12 (4 expanded Tregs, 1 being the most clonal and 0 with the most TCR diversity). Both AA and HD expanded Tregs showed a comparable level of TCR Vβ CDR3 diversity, as defined in Figure 6B and the supplemental Methods.

The (dis)similarity of in vitro expanded Tregs with Treg A and Treg B was investigated next. As the isolated Tregs were stimulated and treated in a Treg skewing environment, the expression level of some markers such as CD25 or FOXP3 were high in expanded Tregs, making it difficult to cluster expanded Tregs with Treg A or B of untouched Tregs for comparison. To overcome this technical issue, we used an alternative analysis approach based on distance calculation and relative expression of markers. The subpopulations of Tregs expanded in vitro were assessed using the Euclidian distance between the mean expression for each parameter in Tregs A and B (Figure 6C; supplemental Figure 10).

Using a 1-tail Welch’s 2-sample t test, we rejected the null hypothesis that the distance between Treg A and expanded Tregs is lower than in Treg B (P < 2.2 × 10⁻¹⁶), suggesting that expanded Tregs were more similar to subpopulation B than A.

Although the importance of Tregs in the pathophysiology of autoimmune diseases is well established, the definition and significance of Treg subpopulations is less clear. Identification of Treg subsets is challenging in autoimmune diseases because the number of Tregs is usually low and Tregs may express aberrant markers; in addition, gating strategies for Treg subpopulations are often subjective. Biomarkers that, first, identify AA patients from HDs and, second, identify at time of diagnosis who are less likely to respond to IST, have as yet not been identified. It is now possible to comprehensively characterize rare, complex populations of cells with minimal bias^15,32  using CyTOF to measure the expression level of more than 40 parameters at the single-cell level.^32-34  The complexity of Treg subsets in HDs has been previously demonstrated by mass cytometry on sorted Tregs³⁵ ; however, their biological importance has not been investigated. In the current study, by using this multidimensional phenotyping and unbiased approach, 2 distinct Treg subpopulations were characterized in HD and AA patients and the changes in these subsets predicted response to IST at diagnosis of AA. We sorted these cells based on their immunological markers and confirmed their dissimilar TCRs, gene expression signatures, and functions. Our analytical strategy eliminated the unavoidable subjectivity of Treg subpopulation definition based on 2-dimensional gating without unnecessary overclustering.

Within the CD25^hi, FOXP3^hi, and CD127^lo Treg population, AA Tregs expressed CD27^hi, CD45RA^lo, CD45RO^hi, CD95^hi, CD7^lo, CD28^hi, and CCR4^hi compared with the total CD4⁺ T cell. We have identified 2 well-defined subpopulations within this Treg population (Treg A and B). Although total Treg numbers were reduced in AA, Treg A was significantly higher in AA patients compared with HDs. In contrast, the number of Treg B subpopulation was significantly lower in AA patients compared with HDs (Table 2). Subpopulation B was characterized by a lower expression of CD45RA, CD7, and CD27 and a higher expression of CCR4, CCR6, CD25, CD28, CD45RO, CD95, CXCR3, FOXP3, and HLA-DR. The most significantly different markers were CD95, CCR4, and CD45RO.^36-38  The identified Treg subpopulations were compared with established Treg subpopulation definitions.²³  Although Tregs A and B overlap with Treg subpopulations I and II, respectively, our approach demarcates those Treg III cells that are closer to Tregs and combine them with subpopulation A or B based on their phenotype and eliminate cells that are closer to T_con and less likely to be regulatory. There is an unmet need for more robust predictive factors for response to IST at the time of AA diagnosis. Known predictive factors for response to IST include less severe disease, young age, and absolute reticulocyte and lymphocyte counts of ≥25 and ≥1.0 × 10⁹/L, respectively.³⁹  Short telomeres in children, but not in adults, also predict response to IST.^40,41  The presence of a PIGA mutation predicts for response to IST, as does a somatic BCOR/BCORL mutation. In contrast, somatic mutation of DNMT3A or ASXL1 is associated with worse outcomes following IST.⁴²  We have identified an immune signature that predicts for response to IST for an individual patient at time of diagnosis of AA. Nonresponders to IST were more likely to have higher Treg A compared with nonresponders, whereas responders had higher Treg B numbers compared with HDs (Figure 7). Treg B subpopulation characterized by a more “activated/memory” phenotype. We also tested this combination of identified markers in a separate validation cohort of AA patients using conventional flow cytometry and confirmed the predictive value of this combination for response to IST.

To explore the potential therapeutic application of expanded Tregs in the treatment of AA, we examined the characteristics and in vitro expandability of the Treg subpopulations in depth. Both AA and HDs Tregs were sensitive to IL-2 as assessed by STAT5 phosphorylation. The expansion rate of AA Tregs (3 of them from IST nonresponders) was not different from HDs after 4 weeks’ culture. Expanded Tregs were functional in both autologous and allogeneic settings, and TSDR of expanded Tregs indicated a stable phenotype. Treg B population was more functional in suppressing IFN-γ and TNF-α secretion by T_con compared with Treg A cells. Although both Treg subpopulations were significantly enriched with Treg-specific genes such as IL-2RA, FOXP3, IKZF2, TIGIT, and CTLA4 compared with T_con, nevertheless, TCR sequence overlaps between Treg A, Treg B, and T_con were minimal, suggesting possible distinct developmental origins. Functional GEP analysis revealed marked enrichment of Treg B subpopulation with G2M checkpoint and mitosis-related genes, suggesting that Tregs B are more prone to enter cell cycle. In studies of human primary lymphocytes and CD34⁺ cells, we showed that quiescent cells do not contain many proteins required for cell proliferation (eg, DNA synthesis, mitosis) or molecules that regulate cell cycle.^43-47  Expression of mRNA-encoding proteins involved in mitosis, DNA replication, and other proliferation functions suggest that the Treg B subpopulation is more likely to be proliferating, primed to proliferate, or has recently exited the cell cycle. The limitation of this study was the very low number of Tregs in AA patients, which made it technically difficult to individually assess the IL-2 sensitivity and in vitro expandability of patients’ Treg subpopulations. Nonetheless, the observation that IST-responder patients had a significantly higher frequency of Treg B following IST therapy compared with IST nonresponders would support the hypothesis that Treg B cells are more likely to proliferate and perhaps better control the immune response in AA, particularly following treatments with antithymocyte globulin–based immunosuppressive therapy, when the number of T cells is reduced and the ability of Tregs to proliferate would be crucial to reinstate a balanced immune response (Figure 7).

In vitro expansion of Tregs for clinical use is an emerging cellular therapy in graft-versus-host disease, type I diabetes, and organ transplant rejection^48-52 ; nevertheless, the quality control of expanded Tregs is a time-consuming procedure. Our data comparing expanded Tregs with preexpansion Treg subpopulations could provide a robust and quick screening for quality control of cell therapy products and may serve as a predictive tool for expandability of Tregs in AA and other autoimmune diseases.

In summary, we have shown for the first time that a novel strategy for multidimensional deep phenotyping can reliably identify an immune signature for AA based on Treg subpopulations. This approach also identifies an immune signature that predicts for response to IST at time of diagnosis of AA and that may allow a more patient-specific approach to future treatment decision-making in severe AA. Our findings also pave the way for future novel therapeutic approaches such as expanded autologous Tregs and low-dose IL-2 in AA.

The authors thank King’s College London Genomic Centre for performing gene expression experiments; in particular, Matthew Arno. The authors also thank the hematology department tissue bank for processing patient samples and are grateful to Giovanna Lombardi, Cristiano Scotta, Behdad Afzali, and Claudia Kemper for their scientific input.

This work is supported by financial support from the Department of Health via the National Institute for Health Research comprehensive Biomedical Research Centre Award to Guy's & St Thomas' National Health Service Foundation Trust in partnership with King's College London and King's College Hospital National Health Service Foundation Trust. This work is also supported by a research program grant from Bloodwise UK (grant number 14017) and a grant from The Aplastic Anemia and MDS International Foundation, USA (research grant awarded to S.K.).

Contribution: S.K. designed, supervised, and performed experiments; analyzed and interpreted data; and wrote the paper. B.C. and T.S. designed and performed experiments, analyzed and interpreted data, and wrote the paper. P.P.A. validated metal-tagged antibodies. M.M.L. analyzed and interpreted the global gene expression (GEP) data and contributed in writing the paper. D.M. provided clinical data and contributed in writing the paper. K.E.D. contributed in data analysis. A.K. provided clinical data. C.B. performed experiments. X.F. provided clinical samples and data. A.S. analyzed data. S.A.M. performed experiments. R.M. provided bioinformatics and statistical analysis. E.d.R. supervised the bioinformatics and statistical analysis. S.H. provided mass cytometry data quality control and contributed in writing the paper; R.E. provided mass cytometry data acquisition and quality control. N.P. provided bioinformatics and statistical analysis. G.A.M.P. provided GEP data analysis. S.S.C. provided cell-cycle–related bioinformatics data analysis. N.S.B.T. provided cell-cycle–related data analysis and contributed in writing the paper; F.F. contributed in writing the paper. J.M.I. contributed in data analysis, interpreting data, and in writing the paper. N.S.Y. provided clinical samples and data and contributed in writing the paper. J.C.W.M. provided clinical samples and data, supervised the project, interpreted the results, and contributed in writing the paper. G.J.M. supervised the project, interpreted the results, and wrote the paper.

Conflict-of-interest disclosure: J.M.I. is a cofounder and board member at Cytobank Inc. The remaining authors declare no competing financial interests.

Correspondence: Ghulam J. Mufti, Department of Haematological Medicine, King’s College London, Rayne Institute, 123 Coldharbour Ln, London SE5 9NU, United Kingdom; e-mail: ghulam.mufti@kcl.ac.uk.