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Abstract Background Psoriasis patients developing psoriatic arthritis (PsA) are thought to go through different phases. Understanding the underlying events in these phases is crucial to diagnose PsA early. Here, we have characterized the circulating memory T helper (Th) cells in psoriasis patients with or without arthralgia, psoriasis patients who developed PsA during follow-up (subclinical PsA), early PsA patients and healthy controls to elucidate their role in PsA development. Methods We used peripheral blood mononuclear cells of sex and age-matched psoriasis patients included in Rotterdam Joint Skin study (n=22), early PsA patients included in Dutch South West Early Psoriatic Arthritis Cohort (DEPAR) (n=23) and healthy controls (HC; n=17). We profiled memory Th cell subsets with flow cytometry and used the machine learning algorithm FlowSOM to interpret the data. Results Three of the 22 psoriasis patients developed PsA during 2-year follow-up. FlowSOM identified 12 clusters of memory Th cells, including Th1, Th2, Th17/22, and Th17.1 cells. All psoriasis and PsA patients had higher numbers of Th17/22 than healthy controls. Psoriasis patients without arthralgia had lower numbers of CCR6-CCR4+CXCR3+ memory Th cells and higher numbers of CCR6+CCR4-CXCR3-memory Th cells compared to HC. PsA patients had higher numbers of Th2 cells and CCR6+CCR4+CXCR3- cells, but lower numbers of CCR6+CCR4+CXCR3+ memory Th cells compared to HC. The number of CCR6+ Th17.1 cells negatively correlated with tender joint counts and the number of CCR6+ Th17 cells positively correlated with skin disease severity. Conclusions Unsupervised clustering analysis revealed differences in circulating memory Th cells between psoriasis and PsA patients compared to HC; however, no specific subset was identified characterizing subclinical PsA patients.

Abstract Background Chronic synovial inflammation is an important hallmark of inflammatory arthritis, but the cells and mechanisms involved are incompletely understood. Previously, we have shown that CCR6+ memory T-helper (memTh) cells and synovial fibroblasts (SF) activate each other in a pro-inflammatory feedforward loop, which potentially drives persistent synovial inflammation in inflammatory arthritis. However, the CCR6+ memTh cells are a heterogeneous population, containing Th17/Th22 and Th17.1 cells. Currently, it is unclear which of these subpopulations drive SF activation and how they should be targeted. In this study, we examined the individual contribution of these CCR6+ memTh subpopulations to SF activation and examined ways to regulate their function. Methods Th17/Th22 (CXCR3−CCR4+), Th17.1 (CXCR3+CCR4−), DP (CXCR3+CCR4+), and DN (CXCR3−CCR4−) CCR6+ memTh, cells sorted from PBMC of healthy donors or treatment-naïve early rheumatoid arthritis (RA) patients, were cocultured with SF from RA patients with or without anti-IL17A, anti-IFNγ, or 1,25(OH)2D3. Cultures were analyzed by RT-PCR, ELISA, or flow cytometry. Results Th17/Th22, Th17.1, DP, and DN cells equally express RORC but differ in production of TBX21 and cytokines like IL-17A and IFNγ. Despite these differences, all the individual CCR6+ memTh subpopulations, both from healthy individuals and RA patients, were more potent in activating SF than the classical Th1 cells. SF activation was partially inhibited by blocking IL-17A, but not by inhibiting IFNγ or TBX21. However, active vitamin D inhibited the pathogenicity of all subpopulations leading to suppression of SF activation. Conclusions Human CCR6+ memTh cells contain several subpopulations that equally express RORC but differ in TBX21, IFNγ, and IL-17A expression. All individual Th17 subpopulations are more potent in activating SF than classical Th1 cells in an IFNγ-independent manner. Furthermore, our data suggest that IL-17A is not dominant in this T cell-SF activation loop but that a multiple T cell cytokine inhibitor, such as 1,25(OH)2D3, is able to suppress CCR6+ memTh subpopulation-driven SF activation.

ObjectivesTo classify patients with rheumatoid arthritis (RA) in an earlier stage of the disease, the ACR/EULAR classification criteria were updated in 2010. These criteria might have led to an increased incidence of RA in the rheumatology clinic. Since a higher incidence increases the socio-economic burden of RA, it is worthwhile to evaluate whether there is a time effect.Materials and methodsA systematic review was conducted using Embase, Medline Ovid, Cochrane Central, and Web of Science from database inception to February 2021. Included were only articles that addressed incidence rates of rheumatoid arthritis from rheumatology outpatient clinics.ResultsOf the 6,289 publications only 243 publications on RA were found eligible for full-text review. Nine studies were included reporting incidence. The pooled incidence for RA was 11% (95% CI 6–16%) per year. Over time the incidence increased after the introduction of the 2010 ACR/EULAR classification criteria. Overall there was a high intragroup heterogeneity (I2 = 97.93%, p < 0.001), caused by geographical area, study design and differences in case definitions.ConclusionAlthough the incidence seems to increase after the introduction of the 2010 ACR/EULAR criteria, no conclusions can be drawn on this time effect due to heterogeneity.

Spectral flow cytometry is an upcoming technique that allows for extensive multicolor panels, enabling simultaneous investigation of a large number of cellular parameters in a single experiment. To fully explore the resulting high-dimensional single cell datasets, high-dimensional analysis is needed, as opposed to the common practice of manual gating in conventional flow cytometry. However, preparing spectral flow cytometry data for high-dimensional analysis can be challenging, because of several technical aspects. In this article, we will give insight into the pitfalls of handling spectral flow cytometry datasets. Moreover, we will describe a workflow to properly prepare spectral flow cytometry data for high dimensional analysis and tools for integrating new data at later time points. Using healthy control data as example, we will go through the concepts of quality control, data cleaning, transformation, correcting for batch effects, subsampling, clustering and data integration. This methods article provides an R-based pipeline based on previously published packages, that are readily available to use. Application of our workflow will aid spectral flow cytometry users to obtain valid and reproducible results.

Objectives Methotrexate (MTX) is currently the recommended first-line therapy for treating psoriatic arthritis (PsA), despite lacking clear evidence. No estimates of efficacy of MTX in usual care and no clear MTX responsive clinical or laboratory variables are currently available. This study describes the response to MTX monotherapy in newly diagnosed patients with PsA in usual care. Second, we compared clinical variables and cytokine profiles in patients responding and not responding to MTX monotherapy.Methods We used data collected in the Dutch southwest Early Psoriatic Arthritis cohoRt study to select patients with PsA with oligoarthritis or polyarthritis, and at least 1 year follow-up. We analysed disease activity at 6 months of patients who started MTX monotherapy and still used MTX monotherapy 1 year after diagnosis. Cytokine profiles were determined at baseline and after 3 and 6 months with a bead-based multi-immunoassay.Results We identified 219 patients of which 183 (84%) patients started MTX monotherapy within 6 months after diagnosis. 90 patients used MTX monotherapy throughout the first year of which 44 patients (24%) reached minimal disease activity(MDA) at 6 months, decreasing to 33 patients (18%) after 1 year. Non-responders had significantly higher concentrations of interleukin (IL) 23 and IL-10 before and during MTX therapy.Conclusions Our results showed that only 18% of patients with PsA are in sustained MDA after 1 year of MTX monotherapy and non-responders more often had IL-23-driven disease. Our results indicate the need for more treat-to-target and personalised therapy strategies in PsA.

In mouse, X-chromosome inactivation (XCI) can either be imprinted or random. Imprinted XCI (iXCI) is considered unstable and depending on continuous Xist expression, whereas random XCI (rXCI) is stably maintained even in the absence of Xist. Here we have systematically examined epigenetic modifications associated with the inactive X-chromosome (Xi) in Trophoblast Stem cells, eXtra-Embryonic Endoderm Cells, undifferentiated and differentiated Epiblast Like Stem Cells in order to understand intrinsic differences in epigenetic mechanisms involved in silencing of the inactive X-chromosome in lineages presenting iXCI and rXCI. Whereas euchromatic histone modifications are predominantly lost from the Xi territory in all cell types, the accumulation of heterochromatic modifications diverges in between the analysed cell lineages. Particularly, only the Xi of multipotent Trophoblast (iXCI) and Epiblast stem cells (rXCI) display a visible accumulation of Polycomb Repressive Complexes (PRCs), in contrast to the Xi in differentiated Epiblast Like Stem Cells and eXtra-embryonic Endoderm cells. Despite this, the histone modifications catalysed by PRCs, ubH2AK119 and H3K27me3, remain the best heterochromatic markers for the Xi in all assessed lineages. Heterochromatic chromatin modifications associated with the Xi are a reflection of the epigenetic landscape of the entire genome of the assessed cell regardless whether XCI is imprinted or random.