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MS1 ion current‐based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts

The rapidly‐advancing field of pharmaceutical and clinical research calls for systematic, molecular‐level characterization of complex biological systems. To this end, quantitative proteomics represents a powerful tool but an optimal solution for reliable large‐cohort proteomics analysis, as frequent...

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Detalles Bibliográficos
Autores principales: Wang, Xue, Shen, Shichen, Rasam, Sailee Suryakant, Qu, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6849792/
https://www.ncbi.nlm.nih.gov/pubmed/30920002
http://dx.doi.org/10.1002/mas.21595
Descripción
Sumario:The rapidly‐advancing field of pharmaceutical and clinical research calls for systematic, molecular‐level characterization of complex biological systems. To this end, quantitative proteomics represents a powerful tool but an optimal solution for reliable large‐cohort proteomics analysis, as frequently involved in pharmaceutical/clinical investigations, is urgently needed. Large‐cohort analysis remains challenging owing to the deteriorating quantitative quality and snowballing missing data and false‐positive discovery of altered proteins when sample size increases. MS1 ion current‐based methods, which have become an important class of label‐free quantification techniques during the past decade, show considerable potential to achieve reproducible protein measurements in large cohorts with high quantitative accuracy/precision. Nonetheless, in order to fully unleash this potential, several critical prerequisites should be met. Here we provide an overview of the rationale of MS1‐based strategies and then important considerations for experimental and data processing techniques, with the emphasis on (i) efficient and reproducible sample preparation and LC separation; (ii) sensitive, selective and high‐resolution MS detection; iii)accurate chromatographic alignment; (iv) sensitive and selective generation of quantitative features; and (v) optimal post‐feature‐generation data quality control. Prominent technical developments in these aspects are discussed. Finally, we reviewed applications of MS1‐based strategy in disease mechanism studies, biomarker discovery, and pharmaceutical investigations.