MS‐based proteomics is expanding its role as a routine tool for biological discovery. Nevertheless, the task of accurately and precisely quantifying thousands of analytes in a single experiment remains challenging. In this study, the diagnostic accuracy of three popular data‐dependent methods for protein relative quantification (label‐free [LF], dimethyl labelling [DML] and tandem mass tags [TMT]) has been assessed using a mixed species proteome (three species) and five experimental replicates per condition. Data were produced using a quadrupole‐Orbitrap mass spectrometer and analysed using a single platform (the MaxQuant/Perseus software suite). The whole comparative analysis was repeated three times over a period of 6 months, in order to assess the consistency of the reported findings. As expected, label‐based methods reproducibly provided a lower false positives rate, whereas TMT and LF performed similarly, and significantly better than DML, in terms of proteome coverage using the same instrument time. Although parameters like proteome coverage and precision were consistent in between replicates, other parameters like sensitivity, intended as the capacity of correctly classifying true positives (regulated proteins), were found to be less reproducible, especially at challenging fold‐changes (1.5). Collectively, data suggest that an increased interest in data reproducibility would be desirable in the quantitative proteomics field.

中文翻译：

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三种基于 MS 的定量蛋白质组学方法的关键比较

基于 MS 的蛋白质组学正在扩大其作为生物发现常规工具的作用。然而，在单个实验中准确和精确地量化数千种分析物的任务仍然具有挑战性。在这项研究中，使用混合物种蛋白质组（三种物种) 和每个条件五个实验重复。数据使用四极杆 Orbitrap 质谱仪产生，并使用单一平台（MaxQuant/Perseus 软件套件）进行分析。整个比较分析在 6 个月内重复 3 次，以评估报告结果的一致性。正如预期的那样，基于标签的方法可重复提供较低的假阳性率，而 TMT 和 LF 的表现相似，并且在使用相同仪器时间的蛋白质组覆盖率方面明显优于 DML。尽管蛋白质组覆盖度和精确度等参数在重复之间保持一致，但发现其他参数（如灵敏度）的可重复性较差，尤其是在具有挑战性的倍数变化 (1.5) 时，旨在作为正确分类真阳性（调节蛋白）的能力。总的来说，数据表明，在定量蛋白质组学领域，人们对数据可重复性的兴趣增加是可取的。尽管蛋白质组覆盖度和精确度等参数在重复之间保持一致，但发现其他参数（如灵敏度）的可重复性较差，尤其是在具有挑战性的倍数变化 (1.5) 时，旨在作为正确分类真阳性（调节蛋白）的能力。总的来说，数据表明，在定量蛋白质组学领域，人们对数据可重复性的兴趣增加是可取的。尽管蛋白质组覆盖度和精确度等参数在重复之间保持一致，但发现其他参数（如灵敏度）的可重复性较差，尤其是在具有挑战性的倍数变化 (1.5) 时，旨在作为正确分类真阳性（调节蛋白）的能力。总的来说，数据表明，在定量蛋白质组学领域，人们对数据可重复性的兴趣增加是可取的。