Automated clustering reveals CD4⁺ T cell subset imbalances in rheumatoid arthritis

Abstract

Background:

Despite the report of an imbalance between CD4⁺ T helper (Th) cell subsets in rheumatoid arthritis (RA), patient stratification for precision medicine has been hindered by the discovery of ever more Th cell subsets, as well as contradictory association results.

Objectives:

To capture previously reported Th imbalance in RA with deep immunophenotyping techniques; to compare hypothesis-free unsupervised automated clustering with hypothesis-driven conventional biaxial gating and explore if Th cell heterogeneity accounts for conflicting association results.

Methods:

Unstimulated and stimulated peripheral blood mononuclear cells from 10 patients with RA and 10 controls were immunophenotyped with a 37-marker panel by mass cytometry (chemokine receptors, intra-cellular cytokines, intra-nuclear transcription factors). First, conventional biaxial gating and standard definitions of Th cell subsets were applied to compare subset frequencies between cases and controls. Second, unsupervised clustering was performed with FlowSOM and analysed using mixed-effects modelling of Associations of Single Cells (MASC).

Results:

Conventional analytical techniques fail to identify classical Th subset imbalance, while unsupervised automated clustering, by allowing for unusual marker combinations, identified an imbalance between pro- and anti-inflammatory subsets. For example, a pro-inflammatory Th1-like (IL-2⁺ T-bet⁺) subset and an unconventional but pro-inflammatory IL-17⁺ T-bet⁺ subset were significantly enriched in RA (odds ratio=5.7, p=2.2 x 10^-3; odds ratio=9.7, p=1.5x10^-3, respectively). In contrast, a FoxP3⁺ IL-2⁺ HLA-DR⁺ Treg-like subset was reduced in RA (odds ratio=0.1, p=7.7x10^-7).

Conclusion:

Taking an unbiased approach to large dataset analysis using automated clustering algorithms captures non-canonical CD4⁺ T cell subset imbalances in RA blood.

1 Introduction

Current treatment strategies for rheumatoid arthritis (RA) standardise treatments across patient groups, but as a heterogeneous disease, differences will exist in the underlying immune mechanisms. As a consequence, not all patients will respond similarly to the same drug with only 60-70% having a response to any biologic drug (1). Given that an increase of pro-inflammatory and/or a decrease of anti-inflammatory CD4⁺ T cell subsets has been reported in some patients, these cell types could represent therapeutic targets and aid patient stratification for precision medicine. However, which CD4⁺ T cell subset is associated with the disease, and in what proportion of patients, remains unclear.

The study of T cells in RA was revolutionised after the Th1/Th2 paradigm was proposed by Mossman et al. in 1986 (2). This paradigm was described as a dichotomy between type 1 T helper cells (Th1), characterised by interferon-γ (IFN-γ) and tumour necrosis factor (TNF), and type 2 T helper cells (Th2). RA pathogenesis predominantly involves Th1 cytokines, and the Th1 immune response is antagonised by Th2 cytokines [reviewed in (3)]. The Th1/Th2 paradigm was modified further when pathogenic IL-17-producing T helper cells (Th17) and regulatory T cells (Tregs) were discovered to play critical roles in initiating and regulating autoimmunity, respectively [reviewed in (4)]. Increases in Th1 and Th17 cells which antagonise, and are antagonised by, Th2 and Treg cells has been the focus of much research in autoimmune diseases including RA (5, 6). More recently, interferon-producing Th1 memory cells were found to be associated with RA using novel techniques of analysing high-dimensional single-cell data (7). A meta-analysis found that circulating Treg cells, as defined by both FOXP3 and CD25, were decreased in RA patients in 9 combined studies (8). However, other studies found no decrease in Treg frequency or function in RA (9). To add to the complexity of the paradigm, there are an ever-growing list of T helper cells [Th9 cells (10); Th22 cells (11); T follicular helper cells (Tfh) (12); peripheral helper T cells (Tph) (13)]. Critically, it has now been shown that many of these T cell types exhibit plasticity. It was previously thought that the mutually exclusive expression of a master transcription factor determined the fate of T cells, with T-bet, GATA3, RORγt and FoxP3 determining the fate of Th1, Th2, Th17 and Treg cells respectively. However, co-expression of these transcription factors may temporarily alter the effector function of T cell subsets [reviewed in (14)] and many of these T cells might also be transitional and therefore appear only in very low frequencies.

The plasticity of T cell subsets and heterogeneity of RA may also explain why clinical trials targeting specific cytokines, for instance IL-17, have not reached their primary endpoint (15). Stratified medicine may allow clinicians to identify RA patients who have predominantly IL-17-driven disease to improve the design of such trials, as researchers have successfully shown in psoriatic arthritis (16). Exploring peripheral blood immunophenotypes by flow cytometry in RA has been shown to mirror findings in the synovial compartment (17) and still represents the most widely used and cost-effective technique to enumerate immune cell populations. The advent of mass cytometry (CyTOF) has vastly increased the number of cellular markers which can be detected simultaneously (18) and together with the development of high-throughput automated methods of data analysis (19–23), rare and novel cell types involved in the aetiology of RA have been recently identified (7, 13, 24, 25). Automated cell clustering algorithms allow for unbiased marker combinations and therefore the hypothesis-free discovery of unanticipated cell subsets, relevant to disease and precision medicine (7, 22, 26, 27). We postulate that Th cell plasticity and overlap between subsets and definitions explain conflicting associations and lack of reproducibility in small sample sizes. Therefore, we developed a 37 marker T cell mass cytometry panel to encompass most definitions for the most studied CD4⁺ T cell subsets to date (Th1, Th2, Th17 and Treg).

The aims of this study are first, to confirm the T helper cell subset imbalance previously described in RA; second, to explore whether this imbalance is detectable in a small sample size, if the standard definition of Th subsets is relaxed in favour of Th cell plasticity (unbiased marker combinations) and third, to test whether innovative techniques (mass cytometry and unsupervised clustering algorithm) facilitate the identification of pro- and anti-inflammatory CD4⁺ T cell subsets over standard techniques (flow cytometry and manual bi-axial gating) in a small sample size.

2 Methods

2.1 Patient and public involvement

The Versus Arthritis Centre for Genetics and Genomics in Manchester has a Research User Group (RUG) of patients with various rheumatologic conditions, which includes RA. Members of the RUG meet regularly to review the research carried out in the Centre. They highlighted the importance of understanding basic mechanisms of disease from a patient’s perspective. The RUG supports research of basic disease mechanisms to identify biomarkers for stratification into treatment response categories. Some of the following comments were made: “the sooner anyone with RA can get on the correct treatment, the better”; “Personalising from onset would be perfect”; “If only a blood test is needed, then even better”.

2.2 Patient selection and sample collection

A cohort of 10 RA patients stabilised on therapy and 10 healthy volunteers were recruited from the National Repository (North West Ethics committee approval MREC 99/8/84) at the Manchester Royal Infirmary (NIHR portfolio ID 7881). Peripheral Blood Mononuclear Cells (PBMC) were isolated from 18 ml of blood by density gradient centrifugation using Ficoll-Paque plus (GE Healthcare Life Sciences) and cryopreserved at -150°C.

2.3 Mass cytometry antibody panel

We adapted a previously published mass cytometry T cell panel (13) to encompass surface (chemokine receptors), intra-cytoplasmic (cytokines) and intra-nuclear markers (transcription factors) used to define Th and Treg subsets (Table 1).

2.4 T cell enrichment and stimulation

Thawed PBMCs were rested at 37°C for one hour prior to enrichment of CD3⁺ T cells by positive selection using magnetic cell separation (MACS) with CD3 MicroBeads (Miltenyi Biotec). T cell receptor stimulation was achieved with Dynabeads Human T-Activator CD3/CD28 beads (Fisher Scientific UK Ltd) at a concentration of 1 bead per 2 cells, in the presence of brefeldin A and monensin (both Fisher Scientific UK Ltd) and incubated for 4.5 hours at 37°C.

2.5 Mass cytometry staining protocol

CD3⁺ T cells were incubated with cisplatin prior to incubation with an extracellular antibody cocktail (detailed protocol in Supplementary Methods). Subsequently, cells were fixed, permeabilised and stained for intracellular antigens for 30 minutes on ice. A solution of Intercalator-Ir was added to each well. Samples were run on Helios mass cytometers by the Longwood Medical Area CyTOF Core Facility at the Dana-Farber Cancer Institute, Boston, USA.

2.6 Data pre-processing

The Nolan Normalizer MATLAB plugin (28) was used to normalise the signal in each channel to the signals from the Maxpar Calibration beads.

2.7 Traditional gating analysis

Traditional biaxial gating was carried out using FlowJo V.10.8 (BD Biosciences) to identify well-described CD4⁺ Th and Treg subsets using standard definitions (29) (example gating strategy in Supplementary Figure 1). The percentages of positive cells are represented as a proportion of the parent T cell population (CD4⁺ or CD8⁺). Geometric mean intensity is used to quantify cytokine expression on an individual cell level. The Mann-Whitney U test was used to compare RA and Healthy Controls (HC). All p-values are unadjusted (tests are not independent).

2.8 Automated clustering workflow

The CyTOF clustering workflow from Nowicka et al. (20) was modified to include an extended quality control approach and a different statistical framework for association testing (see below). The analysis was performed in R (v4.1.0) and all plots were produced with ggplot2 (v3.3.5) (30), unless stated otherwise. The parent population for the automated analysis was CD3⁺ cells, with cleaned CD4⁺ and CD8⁺ populations combined.

2.8.1 Quality control steps

Normalised cytometric data was manually inspected using FlowJo V.10.8 (BD Biosciences) to ensure that at least 10 cell events were identified in a group in at least 3 samples. IL-5 was excluded due to the absence of any positive cell groups (Supplementary Figure 2). Furthermore, only samples with at least 1000 live single T cells were included in subsequent analyses. Cell events with extreme expression were excluded per individual marker. After the removal of unsuccessful markers and extreme events, analysis was performed on data transformed with the inverse hyperbolic sine (arcsinh) function (cofactor = 5). Finally, to identify outlying samples and potential batch effects, multidimensional scaling (MDS) and principal component analysis (PCA) plots were produced from median expression of panel markers in each sample.

2.8.2 Clustering with FlowSOM/ConsensusClusterPlus

The dataset was downsampled to an equal number of randomly selected cells (n) from each sample, where n was equal to the number of cells in the smallest sample. The automated clustering steps were performed with FlowSOM (v2.0.0) (19) and ConsensusClusterPlus (v1.56.0) (23) using agglomerative hierarchical consensus clustering. Cells were clustered into 20 populations using Euclidean distance and average linkage (Supplementary Figure 3). Heatmaps of median marker expression for each cluster were generated with pheatmap (v1.0.12) (31). For visualisation purposes, these values represent the median of the arcsinh-transformed, 0 – 1 scaled expression data. For 0 – 1 scaling, the 0.01 and 0.99 quantiles for each marker were used as the lower and upper boundary, respectively.

2.8.3 Cluster visualisation with t-SNE

The similarity of single cells in two-dimensional space was visualised with the dimensionality reduction technique t-stochastic neighbour embedding (t-SNE), implemented with Rtsne (v0.15) (32). Clusters were annotated manually by their surface phenotype.

2.9 Statistical analysis

To identify clusters with differential representation in RA samples compared to healthy controls, we used mixed-effects modelling of associations of single cells (MASC) (7) for each cluster, including age and sex of donors as fixed-effects covariates (justification in the quality control section), sample ID as a random-effect covariate and the case-control status as the contrast term. Clusters that are significantly enriched or depleted in RA are defined as those with a P value ≤ 0.05 after Bonferroni correction (n = number of clusters tested).

2.10 Data sharing

All mass cytometry data is freely available at https://flowrepository.org/id/FR-FCM-Z5RM (33).

3 Results

3.1 Study design and subject characteristics

PBMCs from 10 RA patients and 10 healthy controls (Table 2) were left unstimulated or stimulated with anti-CD3/CD28 beads (n = 40 samples) prior to deep immunophenotyping by mass cytometry.

Manual gating with traditional statistical analysis fails to identify large differences between blood from patients with RA and healthy controls.

Sequential biaxial gating of CyTOF data was used to identify commonly described CD4⁺ and CD8⁺ T cell subsets as defined by expression of surface markers, transcription factors and/or intracellular cytokines. Conventional statistical analysis with Mann-Whitney U found no significant differences in the proportions of Th CD4⁺ subsets between RA and HC (Figure 1); only Treg (defined as CD4⁺ FoxP3⁺) were markedly decreased in RA. No CD8⁺ T cell subsets were found to be differentially abundant in RA (Figure 2).

Figure 1

Figure 2

3.2 CyTOF automated clustering pipeline quality control steps

Low quality cells with extreme expression values were excluded after individual appraisal of expression distributions for each marker. This step removed 0.34% (4,146/1,230,364) of cells from the full dataset, with between 0.09% and 1.4% of cells removed from each sample. Diagnostic MDS and PCA plots suggest a differential trend between RA and HC (MDS2 in Figure 3A, PC1 and PC2 in Figure 3B), indicating slight differences in marker expression between the two groups (Supplementary Figure 4). Data structure of this kind is likely caused by study design or technical artefacts (e.g. batch effects), which requires correction, when the potential confounder is identified. As age is known to associate with the frequency of several cell types (e.g. naïve T cell populations), we studied the effect of age on data structure: Figure 3C shows that samples largely cluster by age group, similarly to previous reports (34), suggesting that the structure in the data is attributable to participant age rather than technical batch effects. Therefore, we included age as a covariate for statistical association testing.

Figure 3

3.3 Automated clustering identifies well-described T cell subsets

Automated clustering was performed on downsampled quality-controlled CyTOF data; analyses of unstimulated and stimulated T cell datasets were performed separately. For both datasets, T cell subsets were identified from a parent population of CD3⁺ T cells, including both CD4⁺ and CD8⁺ populations. Within the unstimulated dataset, we identified 20 distinct T cell subsets, including 11 CD4⁺ and nine CD8⁺ (Figure 4A). Amongst the CD4⁺ populations are a FoxP3⁺ subset (subset 2U; a Treg-like subset), two IL-17⁺ subsets (subsets 10U and 1U; Th17 subsets), a Th1-like subset (subset 4U) and a CD38⁺ subset (subset 19U).

Figure 4

Although analysis was performed independently, clusters identified in the stimulated dataset were broadly similar to those of the unstimulated dataset (Figure 4B). As expected, t-SNE plots coloured by expression level of several markers highlight an increase in the number of cells expressing TNF upon stimulation (Figures 4C, D).

3.4 MASC identifies multiple T cell clusters with differential expression between RA and healthy samples

MASC was used to identify clusters with differential abundance between RA samples and HC, adjusting for the effects of age, gender, and donor. Six unstimulated cell subsets and eight stimulated subsets were found to be differentially abundant with MASC (Figure 5). The majority were CD4⁺ and, of these, three populations were present in both unstimulated and stimulated states, including enrichment of Th1 and Th17 subsets (CD4⁺ T-bet⁺ IL-17⁺ and CD4⁺ T-bet⁺ IL-2⁺ cell clusters) and depletion of a Treg-like subset (CD4⁺ FoxP3⁺ IL-2⁺) in RA (Table 3, Figure 6). Interestingly, a Th2 subset (CD4⁺ T_CM GATA3⁺) was decreased in RA (although it did not reach statistical significance).

Figure 5

Figure 6

Notably, Figures 7, 8 highlight the heterogeneity in subset proportions between individual patients (raw values in Supplementary Tables 1, 2). For example, the abundance of the CD4⁺ TNF⁺ cells range from < 1% to > 7% of cells in stimulated RA samples (Figure 8, subset 8S). Before any correction, naïve T cells were found to be increased in frequency in controls compared to cases, as expected by the age difference between the two groups. This difference disappeared after statistical adjustment for age implemented in MASC, demonstrating that the correction for the age-driven stratification observed in the PCA plots was sufficient in terms of statistical outcome.

Figure 7

Figure 8

Cell cluster phenotype, abundances, odds ratios, and P values for all cell clusters generated by the pipeline are presented in Table 3.

3.5 Agnostically defined cell clusters can be identified using conventional techniques

To provide internal validation of the automated pipeline’s significant clusters, manual gating and standard statistical analysis of agnostically defined clusters was performed. Based on the phenotypes presented in Figure 4 and the level of expression of each marker, gates were drawn by eye around all positive cells if the cluster had low expression of a marker on the heatmap, but only around high positive cells if the marker had high expression.

Using these definitions of T cell subsets and a standard statistical approach, manual gating was able to identify a clear imbalance between pro-inflammatory Th subsets and anti-inflammatory subsets in RA. For example, out of the unstimulated dataset, manual gating was able to identify that subset 2U (CD4⁺ Tcm FoxP3⁺ IL-2⁺), a Treg-like subset, is decreased in RA (P = 0.0004; Figure 9). The same population could also be identified in the stimulated dataset (subset 2Sb, CD4⁺ Tcm FoxP3⁺ IL-2⁺, P = 0.0002). Pro-inflammatory Th cells were also clearly identified and statistically significantly increased in RA (CD4⁺ Tcm Tbet⁺ IL-17⁺, subset 1U, P = 0.0172; and subset 1S, P = 0.0022; Figure 9). Therefore, innovative approaches easily capture known CD4⁺ T cell subset imbalances in RA blood, even in a small sample size, by allowing for unconventional marker combinations.

Figure 9

4 Discussion

We show the superiority of innovative immunophenotyping and automated analytical techniques over hypothesis-driven conventional biaxial gating (based on canonical biomarkers) to identify a T cell subset imbalance in RA peripheral blood in small sample sizes. Our strategy allows for unusual combinations of T cell markers, for instance T-bet and IL-17, capturing the plasticity of T helper cell populations, a more difficult task to achieve with standard gating strategies.

Th17 cells are known to be phenotypically unstable (35). In our study, T-bet⁺ IL-17⁺ T cells are increased in RA blood. These cells have been termed Th1-like Th17 cells in the literature, co-produce IL-17 and IFN-γ, and are known to contribute to inflammation in the context of autoimmunity (35–37). Interestingly, IFN-γ production in Th1-like Th17 cells has been shown to be repressed by miR-146a (37) and loss of function polymorphisms in the microRNA-146a (miR-146a) gene have been associated with an increased risk of developing RA and lupus in both European and Asian genetic association studies (38, 39); taken together, these data provide some mechanistic evidence that Th1-like Th17 cells are involved in the pathogenesis of RA. The methods outlined in this paper easily detect an increase of this subset in RA.

We also found a significant decrease in the number of IL-2⁺ FoxP3⁺ T cells (Treg-like cells) in RA samples compared to controls. It is well-established that IL-2 is critical in maintaining Treg function (40) and that some Treg produce IL-2 (41). However, the relative role of paracrine versus autocrine IL-2 secretion from nearby effector T cells (42) versus IL-2⁺ FoxP3 T cells (41) is unclear, and the low level or absence of CD25 expression on the IL-2⁺ FoxP3⁺ subsets in this data appears to conflict with classical Treg definitions (reviewed by (43). Other studies have characterised subpopulations of CD4⁺ FoxP3⁺ memory cells that produce IL-2 (44). Consistent with our findings, the authors show that these cells possess a phenotype that lies between Tregs and other T helper cells, where they exhibit lower expression of classical Treg markers, including CD25, than their non-IL-2-producing counterparts. Therefore, rather than identifying bona fide Tregs, the unsupervised analysis has potentially identified IL-2-producing Treg-like subpopulations that may be more relevant for RA. Regardless, these findings highlight the utility of automated analyses for capturing non-canonical cell types, better reflecting the heterogeneity and plasticity of T cells.

Due to our small sample size, our association results could theoretically represent false positive due to a sampling bias or caused by multiple testing. However, this is very unlikely, as first we confirm a plausible and previously reported imbalance of Th/Treg-like subsets in RA; second the mechanistic evidence supports our findings, and finally all statistical tests have been stringently corrected for multiple testing. These elements reinforce the credibility of our main conclusion: the superiority of hypothesis-free clustering of many markers in small sample sizes over standard hypothesis-driven bi-axial gating.

The hallmark of precision medicine and the primary aim of stratified medicine initiatives in RA (45) consists of stratifying patients into treatment response categories. Our study shows large heterogeneity across patients with some with a predominantly Th1 profile, others a Th17 profile, or a Th1-like Th17 profile, while others are mainly characterised by a low IL-2⁺ Treg-like profile. As an example, stratifying patients by their IL-2⁺ Treg titres may dictate response to low-dose IL-2 therapy, as has now been successfully trialled in lupus (46) and RA (47). Furthermore, a Japanese group found that stratified medicine for psoriatic arthritis patients increased response rates significantly (16). Immunophenotyped patients received ustekinumab if Th1-dominant and secukinumab if either Th17-dominant or with a Th1/Th17-hybrid phenotype resulting in clinically significant improvement in response rates (16). The same group have good one year follow-up data (48), suggesting that the immunophenotype could be the main predictor of drug response in precision medicine. Stratifying RA patients by their Th1/Th17 phenotype, or by the Th1-like Th17 phenotype highlighted in this study, may suggest that, despite the fact that previous trials of secukinumab in RA failed to reach their primary endpoint, it could be a viable treatment option in RA patient subgroups; further work is required to confirm this hypothesis (49).

We have shown that non-biased automated analysis of large immune datasets is successful in identifying Th cell imbalance in RA with implications for precision medicine. We now plan to expand on these findings by testing their role in patient stratification for treatment response studies.

5 Key messages

What is already known about this subject?

• Large cytometric datasets are difficult to analyse manually due to the multidimensional nature of the data.

• Manual data analysis may introduce significant bias and be underpowered to interrogate multidimensional data.

What does this study add?

• This study has shown that an unbiased automated clustering algorithm can successfully interrogate large cytometric datasets, finding differential expression of 2 rare T cell populations in RA patients (decreased IL-2⁺ Treg-like cells; increased Th1-like Th17 cells).

How might this impact on clinical practice?

• Understanding the underlying immunopathology of autoimmune disease is a prerequisite to finding new drug targets for treatment.

• As our knowledge of T cell heterogeneity expands, discovering rare T cell populations will directly feed into the concept of precision medicine, to: ‘treat arthritis, right first time’.

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