ArticleMolecular & cellular proteomics : MCP2025
A Single-Step Protein Extraction for Lung Extracellular Matrix Proteomics Enabled by the Photocleavable Surfactant Azo and timsTOF Pro.
Article in Molecular & cellular proteomics : MCP, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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10 authors.
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Abstract
The extracellular matrix (ECM) is a dynamic, complex network of proteins, collectively known as the "matrisome," which not only provides essential structural support to cells and tissues but also regulates critical cellular processes. Dysregulation of ECM is implicated in many diseases, underscoring the need to characterize the matrisome to better understand disease mechanisms. We have previously developed a dual-step protocol enabled by the photocleavable surfactant Azo for the extraction of ECM proteins from tissue using pH-neutral decellularization followed by solubilization by Azo. While effective for characterization of ECM proteins, such a dual-step protocol requires two extracts per sample, limiting the throughput and complicating the comparison of protein quantitation across different extraction conditions. Here, we develop a single-step Azo-enabled protein extraction for the solubilization of ECM proteins from lung tissue to improve the throughput for studies with large sample sizes. Using this method, we identified 324 ECM proteins, including 137 core ECM and 187 ECM-associated proteins. Core ECM proteins including elastin, fibronectin, and fibrillar collagens were reproducibly identified and quantified. We observed a 94.6% overlap in the ECM proteins identified between the single-step and the dual-step Azo extracts, indicating the single-step Azo extraction achieves ECM protein coverage comparable to the dual-step extraction. Overall, we have demonstrated that this single-step Azo extraction is not only highly efficient but also comprehensive for ECM protein identification and quantification, making it a powerful method for ECM proteomics, especially for studies with large sample sizes.
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