Machine learning analysis of the bleomycin-mouse model reveals the compartmental and temporal inflammatory pulmonary fingerprint

Abstract

The bleomycin mouse-model is the extensively used model to study pulmonary fibrosis, however, the inflammatory cell kinetics and their compartmentalisation is still incompletely understood. Here we assembled historical flow cytometry data, totalling 303 samples and 16 inflammatory-cell populations, and applied advanced data modelling 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. Multi-colour imaging revealed the spatial-temporal cell distribution and the close association of T cells with deposited collagen. Unbiased immunophenotyping and data modelling exposed the dynamic shifts in immune-cell composition over the course of bleomycin-triggered lung injury. These results and workflow provides a reference point for future investigations, and can easily be applied in the analysis of other datasets.