Refining Platelet Purification Methods: Enhancing Proteomics for Clinical Applications

Background: Platelet proteomics offers valuable insights for clinical research, yet isolating high-purity platelets remains a challenge. Current methods often lead to contamination or platelet loss, compromising data quality and reproducibility. Objectives: This study aimed to optimize a platelet isolation technique that yields high-purity samples with minimal loss and to identify the most effective mass spectrometry-based proteomic method for analyzing platelet proteins with optimal coverage and sensitivity. Methods: We refined an isolation protocol by adjusting centrifugation time to reduce blood volume requirements while preserving platelet yield and purity. Using this optimized method, we evaluated three proteomic approaches: Label-free Quantification with Data-Independent Acquisition (LFQ-DIA), Label-free Quantification with Data-Dependent Acquisition (LFQ-DDA), and Tandem Mass Tag labeling with DDA (TMT-DDA). Results: LFQ-DIA demonstrated superior protein coverage and sensitivity compared to LFQ-DDA and TMT-DDA. The refined isolation protocol effectively minimized contamination and platelet loss. Additionally, age-related differences in platelet protein composition were observed, highlighting the importance of using age-matched controls in biomarker discovery studies. Conclusions: The optimized platelet isolation protocol provides a cost-effective and reliable method for preparing high-purity samples for proteomics. LFQ-DIA is the most suitable approach for comprehensive platelet protein analysis. Age-related variation in platelet proteomes underscores the need for demographic matching in clinical proteomic research.