Proteomics in Practice: A Case Study Highlighting Tandem Mass Tag‐Based MS for Quantitative Profiling of Extracellular Vesicles and Application to Irradiated Fibroblasts
Year: 2025
Type: article
Abstract: Extracellular vesicles (EVs) are critical mediators of intercellular communication, and valuable sources of biomarkers for diagnostic and therapeutic applications. However, the complexity and heterogeneity of EVs present significant challenges for their proteomic characterization. Major challenges of EV samples include low yield, technical variability, and the need for sensitive and high throughput quantification approaches. In this study, we implement a tandem mass tag (TMT)-based MS workflow for comprehensive, quantitative proteomic profiling of isolated EVs. Through comparison with label-free quantitation (LFQ), we highlight the potential pitfalls and limitations of methodological choices in EV proteomic analyses. Our study integrates standard EV isolation with robust TMT labeling and high-resolution MS, providing insights into practical EV analysis. Utilizing this approach, we profiled EVs isolated from human fibroblasts treated with ionizing radiation. The TMT workflow uncovered an EV proteomic signature reflective of the cellular origins and potential functional roles of irradiated fibroblasts, compared to the LFQ workflow. Our case study underscores the potential of TMT-based MS to overcome common barriers in EV proteomics, facilitating new discoveries in EV biology and advancing their application in biomarker development and translational research.
Source: PROTEOMICS
DOI: 10.1002/pmic.70103
Institutions: Vanderbilt University, Vanderbilt University Medical Center
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $4,650
Funders: National Science Foundation, Conquer Cancer Foundation, Concern Foundation, Vanderbilt University, Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, National Institutes of Health, National Science Foundation Graduate Research Fellowship Program
OpenAlex ID: W7117651968
