Establishment and clinical application evaluations of a deep mining strategy of plasma proteomics based on...

Research on plasma proteomics has received extensive attention, because human plasma is an important sample for disease biomarker research due to its easy clinical accessibility and richness in biological information. Plasma samples contain a large number of leaked proteins from different tissues in the body, immune proteins and communication signal proteins. However, MS signal suppression from high-abundance proteins results in a large number of proteins that are present in low abundance in plasma not being detected by the LC-MS method. This situation makes it more difficult to study neurological diseases, where tissue sampling is difficult and body fluid samples such as plasma or cerebrospinal fluid are both affected by signal suppression. A large number of methods have been developed to deeply mine plasma proteomics information; however, their application limitations remain to some extent. Traditional immuno- or affinity-based depletion, fractionation and subproteome enrichment methods cannot meet the challenges of large clinical cohort applications due to limited time efficiency. In this study, a deep mining strategy of plasma proteomics was established by combing the protein corona formed by deep mining beads (DMB beads, hereafter referred to as magnetic covalent organic frameworks Fe3O4@TpPa-1), DIA-MS detection and the DIA-NN library searching method. By optimizing the enrichment step, mass spectrometry acquisition and data processing, the evaluation results of the deep mining strategy showed the following: depth, the strategy identified and quantified results of 2000+ proteins per plasma sample; stability, more than 87% of the enriched low-abundance proteins had CV < 20%; accuracy, good agreement between measured and theoretical values (1.81/2, 8.68/10, 38.36/50) for the gradient addition of E. coli proteins to a plasma sample; time efficiency, the processing time was reduced from >12h in the traditional method to <5h (incubation 30 min, washing 15 min, reductive/alkylation/digestion/desalting 4 h), and more importantly, 96 samples can be processed simultaneously in combination with the magnetic module of the automated device. The optimal strategy enables greater enrichment of neurological disease-related proteins, including SNCA and BDNF. Finally, the deep mining strategy was applied in a pilot study of multiple system atrophy (MSA) for biomarker discovery. The results showed that a total of 215 proteins were upregulated and 184 proteins were downregulated (p < 0.05) in the MSA group compared with the healthy control group. Eighteen proteins of these differentially expressed proteins were reported to be associated with neurological diseases or expressed specifically in brain tissue, 8 and 4 of which have reference concentrations of μg/L and ng/L, respectively. The alterations of ENPP2 and SLC2A1/Glut1 were reanalyzed by ELISA, further supporting the results of mass spectrometry. In conclusion, the results of the evaluation and application of the deep mining strategy showed promise for clinical research applications.