Inhibition of macropinocytosis blocks antigen presentation of type II collagen in vitro and in vivo in HLA-DR1 transgenic mice

Professional antigen-presenting cells, such as dendritic cells, macrophages and B cells have been implicated in the pathogenesis of rheumatoid arthritis, constituting a possible target for antigen-specific immunotherapy. We addressed the possibility of blocking antigen presentation of the type II collagen (CII)-derived immunodominant arthritogenic epitope CII(259–273 )to specific CD4 T cells by inhibition of antigen uptake in HLA-DR1-transgenic mice in vitro and in vivo. Electron microscopy, confocal microscopy, subcellular fractionation and antigen presentation assays were used to establish the mechanisms of uptake, intracellular localization and antigen presentation of CII by dendritic cells and macrophages. We show that CII accumulated in membrane fractions of intermediate density corresponding to late endosomes. Treatment of dendritic cells and macrophages with cytochalasin D or amiloride prevented the intracellular appearance of CII and blocked antigen presentation of CII(259–273 )to HLA-DR1-restricted T cell hybridomas. The data suggest that CII was taken up by dendritic cells and macrophages predominantly via macropinocytosis. Administration of amiloride in vivo prevented activation of CII-specific polyclonal T cells in the draining popliteal lymph nodes. This study suggests that selective targeting of CII internalization in professional antigen-presenting cells prevents activation of autoimmune T cells, constituting a novel therapeutic strategy for the immunotherapy of rheumatoid arthritis

IRF4 transcription factor-dependent CD11b+ dendritic cells in human and mouse control mucosal IL-17 cytokine responses.

Mouse and human dendritic cells (DCs) are composed of functionally specialized subsets, but precise interspecies correlation is currently incomplete. Here, we showed that murine lung and gut lamina propria CD11b+ DC populations were comprised of two subsets: FLT3- and IRF4-dependent CD24(+)CD64(-) DCs and contaminating CSF-1R-dependent CD24(-)CD64(+) macrophages. Functionally, loss of CD24(+)CD11b(+) DCs abrogated CD4+ T cell-mediated interleukin-17 (IL-17) production in steady state and after Aspergillus fumigatus challenge. Human CD1c+ DCs, the equivalent of murine CD24(+)CD11b(+) DCs, also expressed IRF4, secreted IL-23, and promoted T helper 17 cell responses. Our data revealed heterogeneity in the mouse CD11b+ DC compartment and identifed mucosal tissues IRF4-expressing DCs specialized in instructing IL-17 responses in both mouse and human. The demonstration of mouse and human DC subsets specialized in driving IL-17 responses highlights the conservation of key immune functions across species and will facilitate the translation of mouse in vivo findings to advance DC-based clinical therapies

OPTIMAL: An OPTimised Imaging Mass cytometry AnaLysis framework for benchmarking segmentation and data exploration.

Funder: Medical Research CouncilFunder: UK Research and Innovation; doi: http://dx.doi.org/10.13039/100014013Funder: JGW Patterson Foundation; doi: http://dx.doi.org/10.13039/100010089Analysis 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 benchmark any 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 several 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