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

Analysis of imaging mass cytometry (IMC) data and other low-resolution multiplexed tissue imaging technologies is often confounded by poor single-cell segmentation and suboptimal approaches for data visualization 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 "OPTimized Imaging Mass cytometry AnaLysis (OPTIMAL)" framework to benchmark any approaches for cell segmentation, parameter transformation, batch effect correction, data visualization/clustering, and spatial neighborhood analysis. Using a panel of 27 metal-tagged antibodies recognizing well-characterized phenotypic and functional markers to stain the same Formalin-Fixed Paraffin Embedded (FFPE) human tonsil sample tissue microarray over 12 temporally distinct batches we tested several cell segmentation models, a range of different arcsinh cofactor parameter transformation values, 5 different dimensionality reduction algorithms, and 2 clustering methods. Finally, we assessed the optimal approach for performing neighborhood 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" bivariate data display techniques. The optimal arcsinh cofactor for parameter transformation was 1 as it maximized the statistical separation between negative and positive signal distributions and a simple Z-score normalization 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 neighborhood analysis was influenced by the method used for finding neighboring 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 and allows for single-cell exploration to be conducted using a wide variety of accessible software and algorithms familiar to conventional flow cytometrists.

Tolerogenic dendritic cell reporting: Has a minimum information model made a difference?

Minimum information models are reporting frameworks that describe the essential information that needs to be provided in a publication, so that the work can be repeated or compared to other work. In 2016, Minimum Information about Tolerogenic Antigen-Presenting cells (MITAP) was created to standardize the reporting on tolerogenic antigen-presenting cells, including tolerogenic dendritic cells (tolDCs). tolDCs is a generic term for dendritic cells that have the ability to (re-)establish immune tolerance; they have been developed as a cell therapy for autoimmune diseases or for the prevention of transplant rejection. Because protocols to generate these therapeutic cells vary widely, MITAP was deemed to be a pivotal reporting tool by and for the tolDC community. In this paper, we explored the impact that MITAP has had on the tolDC field. We did this by examining a subset of the available literature on tolDCs. Our analysis shows that MITAP is used in only the minority of relevant papers (14%), but where it is used the amount of metadata available is slightly increased over where it is not. From this, we conclude that MITAP has been a partial success, but that much more needs to be done if standardized reporting is to become common within the discipline.

Fluorine labelling of therapeutic human tolerogenic dendritic cells for 19F-magnetic resonance imaging.

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.

Potential epigenetic therapeutics for atherosclerosis treatment.

Notwithstanding the intense efforts into the understanding and prevention of cardiovascular disease (CVD), its complex pathology remains the leading cause of mortality worldwide. The pivotal role of epigenetic changes in the control of gene expression has been profiled in several diseases, such as cancer and inflammatory disorders. In the last decade, increasing evidence has also linked aberrant epigenetic modulation as a contributor to CVD development. Differential profiles of DNA methylation, histone methylation and acetylation have consistently been observed in tissues and cells (comprising the aortic lesions, vascular endothelium and monocytes) from patients with CVD. This highlights the therapeutic potential of epigenetic drugs for cardiovascular treatment.