Reproducibility is a “proteomic dream” yet to be fully realized. A typical data analysis workflow utilizing extracted ion chromatograms (XICs) often treats the information path from identification to quantification as a one-way street. Here, we propose an XIC-centric approach in which the data flow is bidirectional: identifications are used to derive XICs whose information is in turn applied to validate the identifications. In this study, we employed liquid chromatography-mass spectrometry data from glycoprotein and human hair samples to illustrate the XIC-centric concept. At the core of this approach was XIC-based monoisotope repicking. Taking advantage of the intensity information for all detected isotopes across the whole range of an XIC peak significantly improved the accuracy and uncovered misidentifications originating from monoisotope assignment mistakes. It could also rescue non-top-ranked glycopeptide hits. Identification of glycopeptides is particularly susceptible to precursor mass errors for their low abundances, large masses, and glycans differing by 1 or 2 Da easily confused as isotopes. In addition, the XIC-centric strategy significantly reduced the problem of one XIC peak associated with multiple unique identifications, a source of quantitative irreproducibility. Taken together, the proposed approach can lead to improved identification and quantification accuracy and, ultimately, enhanced reproducibility in proteomic data analyses.

中文翻译：

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用于改进蛋白质组数据分析中的鉴定和定量的以 XIC 为中心的策略

可重复性是一个尚未完全实现的“蛋白质组梦想”。利用提取离子色谱图 (XIC) 的典型数据分析工作流程通常将从识别到定量的信息路径视为单向街道。在这里，我们提出了一种以 XIC 为中心的方法，其中数据流是双向的：使用标识来派生 XIC，而 XIC 的信息又用于验证标识。在本研究中，我们利用糖蛋白和人发样品的液相色谱-质谱数据来说明以 XIC 为中心的概念。该方法的核心是基于 XIC 的单同位素重选。利用 XIC 峰整个范围内所有检测到的同位素的强度信息，显着提高了准确性，并发现了源自单同位素分配错误的错误识别。它还可以挽救非顶级糖肽命中。糖肽的鉴定特别容易受到前体质量错误的影响，因为它们的丰度低、质量大，并且相差 1 或 2 Da 的聚糖很容易被误认为是同位素。此外，以 XIC 为中心的策略显着减少了与多个独特鉴定相关的一个 XIC 峰的问题，这是定量不可重复性的一个根源。总而言之，所提出的方法可以提高识别和定量的准确性，并最终提高蛋白质组数据分析的可重复性。