Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden.
Proteomics. 2021 Mar;21(6):e2000093. doi: 10.1002/pmic.202000093. Epub 2021 Feb 23.
Protein quantification via label-free mass spectrometry (MS) has become an increasingly popular method for predicting genome-wide absolute protein abundances. A known caveat of this approach, however, is the poor technical reproducibility, that is, how consistent predictions are when the same sample is measured repeatedly. Here, we measured proteomics data for Saccharomyces cerevisiae with both biological and inter-batch technical triplicates, to analyze both accuracy and precision of protein quantification via MS. Moreover, we analyzed how these metrics vary when applying different methods for converting MS intensities to absolute protein abundances. We demonstrate that our simple normalization and rescaling approach can perform as accurately, yet more precisely, than methods which rely on external standards. Additionally, we show that inter-batch reproducibility is worse than biological reproducibility for all evaluated methods. These results offer a new benchmark for assessing MS data quality for protein quantification, while also underscoring current limitations in this approach.
通过无标记质谱(MS)进行蛋白质定量已成为预测全基因组绝对蛋白质丰度的一种越来越受欢迎的方法。然而,这种方法的一个已知缺点是技术重复性差,也就是说,当对相同的样本进行重复测量时,预测结果的一致性如何。在这里,我们使用生物和批间技术重复测量了酿酒酵母的蛋白质组学数据,以分析通过 MS 进行蛋白质定量的准确性和精密度。此外,我们还分析了在将 MS 强度转换为绝对蛋白质丰度时应用不同方法时这些指标的变化。我们证明,我们的简单归一化和重新缩放方法可以与依赖外部标准的方法一样准确,但更精确。此外,我们还表明,对于所有评估的方法,批间重复性都比生物学重复性差。这些结果为评估 MS 数据质量提供了新的基准,同时也强调了该方法的当前局限性。