The last decade has seen significant advances in the application of quantitative mass spectrometry-based proteomics technologies to tackle important questions in plant biology. The current standard for quantitative proteomics in plants is the use of data-dependent acquisition (DDA) analysis with or without the use of chemical labels. However, the DDA approach preferentially measures higher abundant proteins, and often requires data imputation due to quantification inconsistency between samples. In this study we systematically benchmarked a recently developed library-free data-independent acquisition (directDIA) method against a state-of-the-art DDA label-free quantitative proteomics workflow for plants. We next developed a novel acquisition approach combining MS1-level BoxCar acquisition with MS2-level directDIA analysis that we call BoxCarDIA. DirectDIA achieves a 33% increase in protein quantification over traditional DDA, and BoxCarDIA a further 8%, without any changes in instrumentation, offline fractionation, or increases in mass-spectrometer acquisition time. BoxCarDIA, especially, offers wholly reproducible quantification of proteins between replicate injections, thereby addressing the long-standing missing-value problem in label-free quantitative proteomics. Further, we find that the gains in dynamic range sampling by directDIA and BoxCarDIA translate to deeper quantification of key, low abundant, functional protein classes (e.g., protein kinases and transcription factors) that are underrepresented in data acquired using DDA. We applied these methods to perform a quantitative proteomic comparison of dark and light grown Arabidopsis cell cultures, providing a critical resource for future plant interactome studies. Our results establish BoxCarDIA as the new method of choice in quantitative proteomics using Orbitrap-type mass-spectrometers, particularly for proteomes with large dynamic range such as that of plants.

中文翻译：

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免图书馆的BoxCarDIA解决了无标签定量蛋白质组学中的缺失值问题

在过去的十年中，基于质谱的蛋白质组学技术在解决植物生物学中的重要问题方面取得了重大进展。植物中蛋白质组学的当前标准是在不使用化学标签的情况下使用依赖数据的数据采集（DDA）分析。但是，DDA方法优先测量较高丰度的蛋白质，由于样品之间的定量不一致，通常需要进行数据估算。在这项研究中，我们针对植物的最新DDA无标签定量蛋白质组学工作流程，系统地对最近开发的无库数据独立获取（directDIA）方法进行了基准测试。接下来，我们开发了一种新颖的采集方法，将MS1级BoxCar采集与MS2级DirectDIA分析相结合，我们将其称为BoxCarDIA。与传统的DDA相比，DirectDIA的蛋白质定量增加了33％，而BoxCarDIA的蛋白质定量又增加了8％，而仪器，脱机分馏或质谱仪获取时间没有任何变化。特别是BoxCarDIA，可以在重复进样之间提供完全可重复的蛋白质定量，从而解决了无标记定量蛋白质组学中长期存在的缺失值问题。此外，我们发现DirectDIA和BoxCarDIA在动态范围采样中获得的收益转化为对关键，低丰度，功能性蛋白类别（例如，蛋白激酶和转录因子）的更深入量化，而这些量化在使用DDA采集的数据中并未得到充分体现。我们应用这些方法对深色和浅色拟南芥细胞培养物进行了蛋白质组学定量比较，为将来的植物相互作用组研究提供重要资源。我们的结果使BoxCarDIA成为使用Orbitrap型质谱仪的定量蛋白质组学选择的新方法，特别是对于具有大动态范围的蛋白质组（例如植物）。