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Tolerogenic dendritic cell (tolDC) therapies aim to restore self-tolerance in patients suffering from autoimmune diseases. Phase 1 clinical trials with tolDC have shown the feasibility and safety of this approach, but have also highlighted a lack of understanding of their distribution in vivo. Fluorine-19 magnetic resonance imaging (19F-MRI) promises an attractive cell tracking method because it allows for detection of 19F-labelled cells in a non-invasive and longitudinal manner. Here, we tested the suitability of nanoparticles containing 19F (19F-NP) for labelling of therapeutic human tolDC for detection by 19F-MRI. We found that tolDC readily endocytosed 19F-NP with acceptable effects on cell viability and yield. The MRI signal-to-noise ratios obtained are more than sufficient for detection of the administered tolDC dose (10 million cells) at the injection site in vivo, depending on the tissue depth and the rate of cell dispersal. Importantly, 19F-NP labelling did not revert tolDC into immunogenic DC, as confirmed by their low expression of typical mature DC surface markers (CD83, CD86), low secretion of pro-inflammatory IL-12p70, and low capacity to induce IFN-γ in allogeneic CD4+ T cells. In addition, the capacity of tolDC to secrete anti-inflammatory IL-10 was not diminished by 19F-NP labelling. We conclude that 19F-NP is a suitable imaging agent for tolDC. With currently available technologies, this imaging approach does not yet approach the sensitivity required to detect small numbers of migrating cells, but could have important utility for determining the accuracy of injecting tolDC into the desired target tissue and their efflux rate.

Analysis of Imaging Mass Cytometry (IMC) data and other low-resolution multiplexed tissue imaging technologies is often confounded by poor single cell segmentation and sub-optimal approaches for data visualisation and exploration. This can lead to inaccurate identification of cell phenotypes, states or spatial relationships compared to reference data from single cell suspension technologies. To this end we have developed the "OPTIMAL" framework to determine the best approaches for cell segmentation, parameter transformation, batch effect correction, data visualisation/clustering and spatial neighbourhood analysis. Using a panel of 27 metal-tagged antibodies recognising well characterised phenotypic and functional markers to stain the same FFPE human tonsil sample Tissue Microarray (TMA) over 12 temporally distinct batches we tested a total of four cell segmentation models, a range of different arcsinh cofactor parameter transformation values, five different dimensionality reduction algorithms and two clustering methods. Finally we assessed the optimal approach for performing neighbourhood analysis. We found that single cell segmentation was improved by the use of an Ilastik-derived probability map but that issues with poor segmentation were only really evident after clustering and cell type/state identification and not always evident when using "classical" bi-variate data display techniques. The optimal arcsinh cofactor for parameter transformation was 1 as it maximised the statistical separation between negative and positive signal distributions and a simple Z-score normalisation step after arcsinh transformation eliminated batch effects. Of the five different dimensionality reduction approaches tested, PacMap gave the best data structure with FLOWSOM clustering out-performing Phenograph in terms of cell type identification. We also found that neighbourhood analysis was influenced by the method used for finding neighbouring cells with a "disc" pixel expansion outperforming a "bounding box" approach combined with the need for filtering objects based on size and image-edge location. Importantly OPTIMAL can be used to assess and integrate with any existing approach to IMC data analysis and, as it creates .FCS files from the segmentation output, allows for single cell exploration to be conducted using a wide variety of accessible software and algorithms familiar to conventional flow cytometrists.

Background:CD4+ T cells reacting to post-translationally modified, citrullinated self-antigens are thought to play a central role in the pathogenesis of rheumatoid arthritis (RA)1. This is evidenced by a strong HLA class II association, the success of therapeutic co-stimulation blockade and the detection of autoantigen specific T-cells using HLA class II multimers2. These cells may represent a target for antigen-specific, tolerogenic therapies and their in-depth phenotyping may provide the means by which to monitor such treatment.Objectives:To identify the citrullinated-peptide (cit-peptide) induced cytokine repertoire of antigen-specific memory CD4+ T cells in both healthy controls (HCs) and ACPA positive RA patients using intracellular cytokine staining and flow cytometry. Of note, the HLA-types of both HCs and RA patients were not known.Methods:Cryopreserved peripheral blood mononuclear cells (PBMC) from both HCs (n = 8) and RA patients (n = 13) with both early (untreated) and established disease were thawed and labelled with a proliferation tracking dye (PTD). Labelled PBMC were then either incubated alone or with a pool of cit-peptides for 9-days, followed by a 5-hour restimulation with PMA and ionomycin, where cytokine secretion was blocked for the final 4-hours using brefeldin-A. Cells were then harvested, permeabilised and stained for T cell surface markers and intracellular cytokines including IFN-γ, IL-4, IL-21 and IL-17. Stained cells were immediately acquired using a BD Fortessa X20, where antigen-specific CD4+ T cells were identified as the viable CD45RO+ (memory) CD4+ T cell population that had proliferated (PTDlow) in response to the cit-peptides. Stimulation indices (SI) were calculated as the percentage of proliferated memory CD4+ T cells in the stimulated wells divided by the percentage in the unstimulated conditions, and cit-peptide responders were defined as those with an SI > 2.0. Net cytokine production was measured by subtracting the percentage cytokine production from unstimulated CD4+ CD45RO+ PTDlow cells, from those stimulated with the cit-peptides.Results:Comparable proliferative responses were observed in both donor groups in response to stimulation with the cit-peptide pool, where 37 % of HCs and 31 % of RA patients responded with an SI > 2.0 (Fig. 1A). While little cytokine production was observed in the cit-peptide responding HC T cells, for responding RA donors, cit-peptide responsive CD4+ memory T cells were predominantly IFN-γ and IL-21 producing (Fig. 1B and 1C). In contrast, these donors did not produce significant levels of either IL-17 or IL-4 (Fig. 1D and 1E).Conclusion:Cit-peptides were able to induce proliferation in both HCs and RA memory CD4+ T cells which, amongst the RA donors only, were of a Th1/Tfh subtype. In contrast, and while based only on a small sample, cit-peptides did not induce either IL-17 or IL-4 production in either donor group, suggesting a lack of Th17/Th2 responses. Not all donors responded to the peptide pool, possibly reflecting the limited number of pooled cit-peptides or to a lack of confirmed HLA-DRB1*04:01 positive donors, as peptides were selected for their specificity on this basis. Future work will therefore include HLA-typing, as well as the inclusion of additional citrullinated-epitopes to demonstrate autoreactivity in a wider cross-section of patients. Further phenotyping of the cit-peptide specific T cells will be performed, and future plans will be to study the assay data alongside clinical outcomes to assess its value for immune monitoring.References:[1]Malmström, V et al Nat Rev Immunol. 2017; 17(1):60-75.[2]Gerstner, C et al BMC Immunol. 2020; 21(27):1-14.Disclosure of Interests:Jessica Swift: None declared, James Stanway: None declared, Ioana Nicorescu: None declared, Catharien Hilkens: None declared, Frederik Stevenaert Employee of: Janssen, Amy Anderson Grant/research support from: Pfizer, GSK and Janssen, Arthur Pratt Grant/research support from: Pfizer, GSK and Janssen, John D Isaacs Speakers bureau: Abbvie, Gilead, Roche, UC, Consultant of: Abbvie, Gilead, Roche, UC, Grant/research support from: Pfizer, GSK and JanssenFigure 1.Citrullinated-peptide specific memory CD4+ T cell proliferation (A) and net % cytokine production of IFN-γ (B), IL-21 (C), IL-17 (D) and IL-4 (E) positive cells.