Comparison of Lung Tissue‐Derived Extracellular Vesicles Using Multiple Dissociation Methods for Profiling Protein Biomarkers

Extracellular vesicles (EVs) operate as chemical messengers that facilitate intercellular communication. Emerging evidence has demonstrated that lung tissue-derived EVs play pivotal roles in pulmonary physiological processes and have potential as biomarkers and therapeutics for lung diseases. Multiple methods have been proposed for the isolation of lung tissue-derived EVs. However, the effects of different tissue pre-treatments on lung EV isolation and subsequent disease biomarker discovery have not yet been comprehensively investigated. In this study, we compared the physical characteristics, recovery yields, and protein compositions of EVs isolated from lung tissues using three methods based on different tissue dissociation principles. Methodologically, the beneficial roles of blood perfusion and gentle meshing were emphasized based on their impact on EV yield and purity. These results demonstrate that different methods enrich distinct subpopulations of EVs that exhibit significant differences in their protein cargo and surface properties. These disparities directly affect the diagnostic detection of marker proteins related to lung diseases, including lung tumors, asthma, and pulmonary fibrosis. Collectively, these findings highlight the variations in EV characteristics resulting from the applied approaches and offer compelling suggestions for guiding researchers in selecting a suitable isolation method based on downstream functional studies and clinical applications.

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Publication Details

Journal
Biotechnology Journal
Published
2024-09-01
DOI
https://doi.org/10.1002/biot.202400329
Citations
5
Primary Topic
Extracellular vesicles in disease
Type
article
Field-Weighted Citation Impact
1.17

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article

Comparison of Lung Tissue‐Derived Extracellular Vesicles Using Multiple Dissociation Methods for Profiling Protein Biomarkers

Meng Ye, Bowen Liu, Sheng Ye, Yue Han
5 citations
Biotechnology Journal
Extracellular vesicles in disease
1.17
article

Comparison of Lung Tissue‐Derived Extracellular Vesicles Using Multiple Dissociation Methods for Profiling Protein Biomarkers

Meng Ye, Bowen Liu, Sheng Ye, Yue Han
article en
5 citations

Abstract

Extracellular vesicles (EVs) operate as chemical messengers that facilitate intercellular communication. Emerging evidence has demonstrated that lung tissue-derived EVs play pivotal roles in pulmonary physiological processes and have potential as biomarkers and therapeutics for lung diseases. Multiple methods have been proposed for the isolation of lung tissue-derived EVs. However, the effects of different tissue pre-treatments on lung EV isolation and subsequent disease biomarker discovery have not yet been comprehensively investigated. In this study, we compared the physical characteristics, recovery yields, and protein compositions of EVs isolated from lung tissues using three methods based on different tissue dissociation principles. Methodologically, the beneficial roles of blood perfusion and gentle meshing were emphasized based on their impact on EV yield and purity. These results demonstrate that different methods enrich distinct subpopulations of EVs that exhibit significant differences in their protein cargo and surface properties. These disparities directly affect the diagnostic detection of marker proteins related to lung diseases, including lung tumors, asthma, and pulmonary fibrosis. Collectively, these findings highlight the variations in EV characteristics resulting from the applied approaches and offer compelling suggestions for guiding researchers in selecting a suitable isolation method based on downstream functional studies and clinical applications.

Biotechnology JournalVol. 19(9)
Westlake University (CN), Chinese University of Hong Kong, Shenzhen (CN), Beihang University (CN), Ministry of Industry and Information Technology (CN)
China Postdoctoral Science Foundation
Good health and well-being
Openalex Percentile: Top 22%
Extracellular vesicles in disease
1.17
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