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.

Fertility issues are common amongst women with rheumatoid arthritis (RA). Interleukin 6 (IL-6) and tumor necrosis factor alpha (TNFα), known key players in RA pathogenesis, have been associated with reproductive disorders. This study investigates the role of these cytokines in decreased fertility in women with active RA. Preconception cytokine measurements of 61 patients from the PARA-cohort, a prospective study on RA and pregnancy, were studied in relation to time to pregnancy as a measure for fertility. IL-6 levels were higher in patients with a time to pregnancy longer than 1 year (p = 0.016). Survival analysis of patients stratified by high or low serum IL-6 levels, shows a prolonged time to pregnancy in the high IL-6 group (p = 0.045). Univariate cox regression analysis of IL-6 in relation to time to pregnancy as well as multivariate cox regression analysis correcting for age, disease activity, nulliparity, NSAID use and prednisone use were performed, with hazards ratios for log transformed IL-6 of 0.68 (95% CI: 0.51–0.93, p = 0.015) and 0.66 (95% CI: 0.43–0.99, p = 0.044), respectively. For TNFα, no association with time to pregnancy was found. This study shows that high IL-6, but not TNFα, is associated with decreased fertility in women with RA. This finding provides a rationale to therapeutically target the IL-6 pathway in the time period before pregnancy. More research in the form of large cohort studies on drug safety and the effect of bDMARDS on fertility is needed for implementation of treatment strategies directed at fertility issues in women with RA.

Background:Fertility issues are common in women with rheumatoid arthritis (RA). Decreased fertility in these patients is associated with high disease activity and the use of certain medication [1]. However, immunological mechanisms behind this phenomenon remain unresolved.Objectives:This study aims to identify inflammation-related proteins associated with decreased fertility in women with rheumatoid arthritis and a wish to conceive.Methods:Patients were derived from the PARA-study, a prospective cohort on RA and pregnancy. High-multiplex immunoassay technology with qPCR readout (Olink Proteomics, Uppsala, Sweden) was used to assess 92 inflammation-related proteins in serum obtained before pregnancy of 186 women with RA and a wish to conceive. Measured protein levels were imputed into multivariable cox regression models with time to pregnancy (TTP) as dependent variable. This model was corrected for known confounders age, nulliparity, NSAID use, prednisone use and past methotrexate use [2].Results:Our analyses show prolonged TTP to be associated with increased expression of pro-inflammatory cytokines (TNF, IL-6, IL-18), chemokines (CCL23, CCL19, CXCL10, MCP-3, CXCL9) and T cell stimulating factors (TNFRSF9, CDCP-1). Furthermore, increased factors associated with angiogenesis (VEGF-A) and bone and collagen damage (RANKL, MMP-1) were found. Lastly, decreased fertility is associated with increased immune regulatory factors (IL-10, IL10RB, PD-L1). After false discovery rate (FDR) correction for multiple testing, IL-10, CCL23, MCP-3 and CDCP-1 remained statistically significant (adjusted P<0.05). Results are depicted in table 1.Conclusion:This study shows a pro-inflammatory proteomic signature, including a counterbalance of increased immune regulatory proteins, to be associated with prolonged TTP. These findings provide more insight into the immunological pathways involved in fertility in RA patients.References:[1]Smeele HTW, Dolhain R. Current perspectives on fertility, pregnancy and childbirth in patients with Rheumatoid Arthritis. Semin Arthritis Rheum. 2019;49(3S):S32-S5.[2]Brouwer J, Hazes JM, Laven JS, Dolhain RJ. Fertility in women with rheumatoid arthritis: influence of disease activity and medication. Ann Rheum Dis. 2015;74(10):1836-41.Table 1.Significant multivariable cox regression results with time to pregnancy as dependent variable, corrected for age, nulliparity, NSAID use, prednisone use and past MTX use. HR = hazard ratio, FDR = false discovery rate.ProteinHRPP FDR adjustedFunctionIL-100,640,0010,026Immune regulatoryCCL230,510,0010,026T cell/monocyte migrationMCP-30,740,0010,026Monocyte migrationCDCP10,560,0020,039T cell migrationCCL190,770,0050,077DC/T cell/B cell migrationTNFRSF90,630,0070,090T cell costimulatorVEGF-A0,70,0140,154AngiogenesisCXCL100,820,0210,185T cell/monocyte/NK/DC migrationRANKL0,760,0220,185Osteoclast activation, DC survivalIL-60,880,0240,185Pro-inflammatory cytokine, stimulation of acute phase responsePD-L10,550,0270,189Immune suppressionIL-180,70,0350,193Type 1 response activatorIL-70,690,0380,208Lymphocyte maturationIL-10RB0,480,0390,208IL-10 receptorMMP-10,730,0450,208Collagen breakdownTNF0,440,0480,208Pro-inflammatory cytokine, necrosisCXCL90,260,0490,208Th1 polarizationAcknowledgements:The authors are grateful to all patients who participated in the PARA study. Additionally, they thank Yaël de Man, Fleur van de Geijn, Esther Gasthuis, Florentien de Steenwinkel, Jenny Brouwer and all laboratory workers and research assistants for their contribution to the data collection.Disclosure of Interests:Margot Bongenaar: None declared, Hieronymus TW Smeele: None declared, Erik Lubberts Grant/research support from: CHDR, Galapagos, Radboud Dolhain Speakers bureau: Yes, for UCB, Roche, Abbvie, Genzyme, Novartis, Consultant of: Yes, Galapagos, Grant/research support from: Yes, UCB