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A simplified optimization approach for sample preparation workflow in LC‐MS‐based quantitative proteomic analysis: Biological samples to peptides

蛋白质组学 工作流程 定量蛋白质组学 计算机科学 样品制备 样品(材料) 数据科学 生化工程 化学 色谱法 工程类 数据库 生物化学 基因
作者
Surendra Fartade,Tarang Jadav,Niraj Rajput,Pinaki Sengupta
出处
期刊:Archiv Der Pharmazie [Wiley]
卷期号:358 (3): e2400911-e2400911 被引量:2
标识
DOI:10.1002/ardp.202400911
摘要

Quantitative proteomics, an integral subfield within proteomics, is pivotal for elucidating complex biological processes. By integrating with other omics data, quantitative proteomics facilitates system-level analysis and significantly advances our understanding of cellular networks and disease mechanisms. The ongoing advancements in quantitative proteomics technology significantly boost its importance by improving analytical accuracy. This review focuses on quantitative proteomics employing liquid chromatography-mass spectrometry (LC-MS), a cornerstone technique renowned for its sensitivity, selectivity, accuracy, and throughput. The efficacy of LC-MS proteomics is heavily reliant on sample preparation, which encompasses protein extraction, total protein estimation, reduction, alkylation, digestion, and cleanup. For the very first time, this article provides a detailed examination of sample preparation methods offering insights and guidelines that researchers can utilize to refine their experimental protocols which were not critically evaluated before. By optimizing sample preparation workflows, researchers can enhance the robustness and reproducibility of their proteomic studies. By understanding the complexities of sample preparation in quantitative proteomics, researchers can optimize their experimental workflow to improve the robustness and reproducibility of their results. This review provides a comprehensive overview of sample preparation strategies in quantitative proteomics using LC-MS, discussing the underlying principles and key considerations for each step. By delving into the complexities of sample preparation, this article aims to aid researchers in optimizing their workflows to achieve robust and reproducible results, which ultimately drive innovations and breakthroughs in biomedical research and healthcare.
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