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If new treatments for immune-mediated inflammatory diseases (IMIDs) are to emerge, then a radical new approach that moves the field from one that is based on clinical signs and symptoms to one that is based on immunological and molecular mechanisms is urgently needed. This requires a new way of thinking: that IMIDs should be approached as having shared common pathogenic cells and pathways, and that therapies should be targeted at these cells and processes rather than clinical features.
Could we have predicted that the second deadliest pandemic encountered since the influenza pandemic of 1918 would result in the highest mortality and adverse health outcomes among minority and underserved populations in the United States? Given the abundant evidence documenting the disproportionately high burden of preventable disease, disability, and injury among these underserved groups, our answer should echo a resounding ‘yes’.
High-dimensional cytometry experiments measuring 20–50 cellular markers have become routine in many laboratories. The increased complexity of these datasets requires added rigor during the experimental planning and the subsequent manual and computational data analysis to avoid artefacts and misinterpretation of results. Here we discuss pitfalls frequently encountered during high-dimensional cytometry data analysis and aim to provide a basic framework and recommendations for reporting and analyzing these datasets.