Development of Novel Ionstar-Based Methods for Large-Scale Tissue and Plasma Proteomics with High Sensitivity and Reproducibility
MetadataShow full item record
Quantitative proteomics concomitantly analyze numerous proteins in biological samples and thus allows the depiction of a global picture of a dynamically changing proteome, which is inaccessible to conventional reductionist approaches. Nonetheless, the prevalent quantitative proteomics approaches appear to be inadequate to address the growing demands for proteomics research targeting tissues and plasma as subjects, which is critical for clinical and pharmaceutical research. We have devised a unique MS1-based quantitative strategy termed as IonStar for comprehensive and reliable proteome-wide quantification in large sample cohorts, and proved it highly applicable for tissue and plasma proteomics, as was substantiated by a number of studies. Compared with cellular proteomics, however, the performance of IonStar in tissue and plasma proteomics is suboptimal to some extents, manifested as compromised quantitative depth and lower sampling capacity. These drawbacks may limit the use of IonStar in clinical and pharmaceutical studies requiring considerably large sample number and quantitative data quality if not well addressed. Therefore, we targeted the three stages of the IonStar strategy and designed a number of novel techniques to improve the performance of IonStar in tissue and plasma proteomics, including: i) an exhaustive and reproducible sample preparation protocol, termed as Surfactant-aided Extraction/Precipitation/On-pellet Digestion, to allow efficient protein extraction and proteolytic peptide yields in large-cohort tissue samples with excellent recovery for hydrophobic membrane proteins; ii) a dual column, dual-flow RPLC configuration with an optimized selective trapping/delivery strategy to achieve high-capacity sample loading with enhanced robustness and reproducibility for tissue sample analysis; iii) a modified data processing pipeline incorporating new post-search algorithms with in-house developed R packages for better peptide identification. Implemented with these new techniques, the new-generation IonStar was employed in three proof-of-concept, large-scale tissue/plasma proteomics studies: i) a large-scale tissue proteomics study investigating molecular changes underlying neuroprotective effects of two drugs against traumatic brain injury ; ii) a micro-scale, cell-type specific dual-omics ( i.e. proteomics and metabolomics) study of laser capture microdissected cells from clinical biopsies; iii) a plasma proteomics study to discover and validate novel circulating biomarker for predicting risks for sudden cardiac death. Together, the new-generation IonStar has exhibited excellent applicability as well as versatility for the comprehensive and in-depth proteomic characterization of tissue and plasma samples, pertaining to the needs to decipher the enigma of the aberrant proteome changes underlying various diseases.