Medizinische Universität Graz - Research portal
Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Bordag, N; Biasin, V; Schnoegl, D; Valzano, F; Jandl, K; Nagy, BM; Sharma, N; Wygrecka, M; Kwapiszewska, G; Marsh, LM.
Machine Learning Analysis of the Bleomycin Mouse Model Reveals the Compartmental and Temporal Inflammatory Pulmonary Fingerprint.
iScience. 2020; 23(12):101819-101819
Doi: 10.1016/j.isci.2020.101819
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- Leading authors Med Uni Graz
- Bordag Natalie
- Marsh Leigh
- Co-authors Med Uni Graz
- Biasin Valentina
- Jandl Katharina
- Kwapiszewska-Marsh Grazyna
- Nagy Miklos Bence
- Schnögl Diana
- Sharma Neha
- Valzano Francesco
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- Abstract:
- The 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. © 2020 The Author(s).