{'fraction_non_alphanumeric': 0.01287001287001287, 'fraction_numerical': 0.0006435006435006435, 'mean_word_length': 5.911111111111111, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Application of high dimensional flow cytometry and unsupervised analysis to define the immune cell landscape of early childhood respiratory and blood compartments

The cellular landscape of the paediatric respiratory system remains largely uncharacterised and as a result, the mechanisms of highly prevalent childhood respiratory diseases remain poorly understood. A major limitation in defining mechanisms of disease has been the availability of tissue samples collected in early life, as well as technologies that permit deep immune analysis from limited sample volumes. In this work, we developed new experimental methods and applied unsupervised analytical tools to profile the local (bronchoalveolar lavage) and systemic (whole blood) immune response in childhood respiratory disease. We quantified and comprehensively phenotyped immune cell populations across blood and lung compartments in young children (under 6 years of age), showed that inflammatory cells in the BAL express higher levels of activation and migration markers relative to their systemic counterparts, and applied new analytical tools to reveal novel tissue-resident macrophage and infiltrating monocyte populations in the paediatric lung. To our knowledge, this is the first description of the use of these methods for paediatric respiratory samples. Combined with matched analysis of the systemic immune cell profile, the application of these pipelines will increase our understanding of childhood lung disease with potential to identify clinically relevant disease biomarkers.