Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data

Abstract

The proteomic analyses of human blood and blood-derived products (e.g. plasma) offers an attractive avenue to translate research progress from the laboratory into the clinic. However, due to its unique protein composition, performing proteomics assays with plasma is challenging. Plasma proteomics has regained interest due to recent technological advances, but challenges imposed by both complications inherent to studying human biology (e.g. interindividual variability), analysis of biospecimen (e.g. sample variability), as well as technological limitations remain. As part of the Human Proteome Project (HPP), the Human Plasma Proteome Project (HPPP) brings together key aspects of the plasma proteomics pipeline. Here, we provide considerations and recommendations concerning study design, plasma collection, quality metrics, plasma processing workflows, mass spectrometry (MS) data acquisition, data processing and bioinformatic analysis. With exciting opportunities in studying human health and disease though this plasma proteomics pipeline, a more informed analysis of human plasma will accelerate interest whilst enhancing possibilities for the incorporation of proteomics-scaled assays into clinical practice.

High-throughput approaches for precision medicine in high-grade serous ovarian cancer

“…Considering that the 22 most abundant proteins in plasma account for 99% of the total mass of protein [115], detection of low-abundance proteins, often the most promising proteins for biomarker candidates, is hindered. Several preanalytical workflows have been developed to overcome this complexity, including the depletion of high-abundance proteins, sample fractionation and/or the enrichment for sub-proteomes [116]. N-glycosylation is a posttranslational modification that plays an important role in the stability, solubility and localization of proteins to the cell surface [117].…”

“…Provided that several preanalytical variables (e.g., time to freezing, storage duration, serum vs. plasma etc.) have been shown to influence molecular profiles [116,204], a lack of standardized sample collection and storage protocols can pose as a challenge and potential source of incompatibility for multisite investigations. Though these variables cannot be retrospectively regulated when using samples from biobanks, information about these preanalytical variables should be collected and examined as possible confounders during data analysis.…”

Computational advances of tumor marker selection and sample classification in cancer proteomics

“…Tumor heterogeneity, design of study, sample size sand selection of the reference sample should be considered for discovering tumor biomarker in proteomic study [45] , [46] , [47] . For example, the use of normal/healthy controls as a reference group also needs to be made cautiously [47] .…”

“…Tumor heterogeneity, design of study, sample size sand selection of the reference sample should be considered for discovering tumor biomarker in proteomic study [45] , [46] , [47] . For example, the use of normal/healthy controls as a reference group also needs to be made cautiously [47] . The inter-individual heterogeneity among tumors could affect proteins biomarker expression [48] and could result in inappropriate “normal controls” samples employed for tumor biomarker discovery [48] .…”

COVID-19: Nanomedicine Uncovers Blood-Clot Mystery

“…Human Plasma Proteome Project (HPPP) considerations and recommendations regarding plasma proteomics study design, sample collection, quality measures, processing workflows, MS data acquisition, data processing, and bioinformatic analysis have been reviewed elsewhere. 104 …”