文章基本信息

标题：Machine Learning Analysis of the Bleomycin Mouse Model Reveals the Compartmental and Temporal Inflammatory Pulmonary Fingerprint
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作者：Natalie Bordag ; Valentina Biasin ; Diana Schnoegl 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2020
卷号：23
期号：12
页码：1-31
DOI：10.1016/j.isci.2020.101819
语种：English
出版社：Elsevier
摘要：SummaryThe bleomycin mouse model is the extensively used model to study pulmonary fibrosis; however, the inflammatory cell kinetics and their compartmentalization is still incompletely understood. Here we assembled historical flow cytometry data, totaling 303 samples and 16 inflammatory-cell populations, and applied advanced data modeling and machine learning methods to conclusively detail these kinetics.Three days post-bleomycin, the inflammatory profile was typified by acute innate inflammation, pronounced neutrophilia, especially of SiglecF+neutrophils, and alveolar macrophage loss. Between 14 and 21 days, rapid responders were increasingly replaced by T and B cells and monocyte-derived alveolar macrophages. Multicolour imaging revealed the spatial-temporal cell distribution and the close association of T cells with deposited collagen.Unbiased immunophenotyping and data modeling exposed the dynamic shifts in immune-cell composition over the course of bleomycin-triggered lung injury. These results and workflow provide a reference point for future investigations and can easily be applied in the analysis of other datasets.Graphical AbstractDisplay OmittedHighlights•The inflammatory cell landscape continually evolves after bleomycin exposure•Data modeling provides the most complete description of immune cell-trajectories•Chronic inflammation persists in the late-stage bleomycin-treated mice•Distinct inflammatory cells changes are observed in BALF and lung tissueImmunology; Immune Response; Artificial Intelligence