Data-independent acquisition mass spectrometry (DIA-MS) for proteomic applications in oncology,Molecular Omics

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Data-independent acquisition mass spectrometry (DIA-MS) for proteomic applications in oncology
Molecular Omics ( IF 3.0 ) Pub Date : 2020-10-9 , DOI: 10.1039/d0mo00072h
Lukas Krasny ₁ , Paul H Huang

Affiliation

Data-independent acquisition mass spectrometry (DIA-MS) is a next generation proteomic methodology that generates permanent digital proteome maps offering highly reproducible retrospective analysis of cellular and tissue specimens. The adoption of this technology has ushered a new wave of oncology studies across a wide range of applications including its use in molecular classification, oncogenic pathway analysis, drug and biomarker discovery and unravelling mechanisms of therapy response and resistance. In this review, we provide an overview of the experimental workflows commonly used in DIA-MS, including its current strengths and limitations versus conventional data-dependent acquisition mass spectrometry (DDA-MS). We further summarise a number of key studies to illustrate the power of this technology when applied to different facets of oncology. Finally we offer a perspective of the latest innovations in DIA-MS technology and machine learning–based algorithms necessary for driving the development of high-throughput, in-depth and reproducible proteomic assays that are compatible with clinical diagnostic workflows, which will ultimately enable the delivery of precision cancer medicine to achieve optimal patient outcomes.

中文翻译：

用于肿瘤学中蛋白质组学应用的数据独立采集质谱 (DIA-MS)

数据独立采集质谱 (DIA-MS) 是下一代蛋白质组学方法，可生成永久数字蛋白质组图，提供高度可重复的细胞和组织样本回顾性分析。这项技术的采用在广泛的应用中迎来了一波新的肿瘤学研究浪潮，包括其在分子分类、致癌途径分析、药物和生物标志物发现以及治疗反应和耐药性机制的揭示。在这篇综述中，我们概述了 DIA-MS 中常用的实验工作流程，包括其当前的优势和局限性与传统的数据相关采集质谱 (DDA-MS)。我们进一步总结了一些关键研究，以说明这项技术在应用于肿瘤学的不同方面时的力量。最后，我们提供了 DIA-MS 技术和基于机器学习的算法的最新创新观点，这些算法推动了与临床诊断工作流程兼容的高通量、深入和可重复的蛋白质组学检测的开发，最终将使提供精准的癌症医学，以实现最佳的患者治疗效果。

更新日期：2020-12-09

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