Whole lung proteome of an acute epithelial injury mouse model in comparison to spatially resolved proteomes.
Eva GriesserMartin GesellDaniel VeyelThorsten LamlaKerstin Geillinger-KästleWolfgang RistPublished in: Proteomics (2023)
Epithelial injury is one of the major drivers of acute pulmonary diseases. Recurring injury followed by aberrant repair is considered as the primary cause of chronic lung diseases, such as idiopathic pulmonary fibrosis (IPF). Preclinical in vivo models allow studying early disease-driving mechanisms like the recently established adeno-associated virus-diphtheria toxin receptor (AAV-DTR) mouse model of acute epithelial lung injury, which utilises AAV mediated expression of the human DTR. We performed quantitative proteomics of homogenised lung samples from this model and compared the results to spatially resolved proteomics data of epithelial cell regions from the same animals. In whole lung tissue proteins involved in cGAS-STING and interferon pathways, proliferation, DNA replication and the composition of the provisional extracellular matrix were upregulated upon injury. Besides epithelial cell markers SP-A, SP-C and Scgb1a1, proteins involved in cilium assembly, lipid metabolism and redox pathways were among downregulated proteins. Comparison of the bulk to spatially resolved proteomics data revealed a large overlap of protein changes and striking differences. Together our study underpins the broad usability of bulk proteomics and pinpoints to the benefit of sophisticated proteomic analyses of specific tissue regions or single cell types.
Keyphrases
- idiopathic pulmonary fibrosis
- mouse model
- liver failure
- mass spectrometry
- extracellular matrix
- label free
- single cell
- respiratory failure
- drug induced
- electronic health record
- aortic dissection
- endothelial cells
- gene therapy
- escherichia coli
- binding protein
- poor prognosis
- big data
- pulmonary hypertension
- interstitial lung disease
- signaling pathway
- machine learning
- stem cells
- systemic sclerosis
- social media
- mesenchymal stem cells
- health information
- healthcare
- acute respiratory distress syndrome
- deep learning
- artificial intelligence
- amino acid