Department of Analytical Biochemistry, Groningen Research Institute of Pharmacy, University of Groningen, 9713 AV Groningen, The Netherlands.
Institute of Biochemistry and Center for Molecular Biosciences Innsbruck, University of Innsbruck, 6020 Innsbruck, Austria.
Anal Chem. 2022 Aug 9;94(31):10893-10906. doi: 10.1021/acs.analchem.2c01036. Epub 2022 Jul 26.
With increasing sensitivity and accuracy in mass spectrometry, the tumor phosphoproteome is getting into reach. However, the selection of quantitation techniques best-suited to the biomedical question and diagnostic requirements remains a trial and error decision as no study has directly compared their performance for tumor tissue phosphoproteomics. We compared label-free quantification (LFQ), spike-in-SILAC (stable isotope labeling by amino acids in cell culture), and tandem mass tag (TMT) isobaric tandem mass tags technology for quantitative phosphosite profiling in tumor tissue. Compared to the classic SILAC method, spike-in-SILAC is not limited to cell culture analysis, making it suitable for quantitative analysis of tumor tissue samples. TMT offered the lowest accuracy and the highest precision and robustness toward different phosphosite abundances and matrices. Spike-in-SILAC offered the best compromise between these features but suffered from a low phosphosite coverage. LFQ offered the lowest precision but the highest number of identifications. Both spike-in-SILAC and LFQ presented susceptibility to matrix effects. Match between run (MBR)-based analysis enhanced the phosphosite coverage across technical replicates in LFQ and spike-in-SILAC but further reduced the precision and robustness of quantification. The choice of quantitative methodology is critical for both study design such as sample size in sample groups and quantified phosphosites and comparison of published cancer phosphoproteomes. Using ovarian cancer tissue as an example, our study builds a resource for the design and analysis of quantitative phosphoproteomic studies in cancer research and diagnostics.
随着质谱技术的灵敏度和准确性的提高,肿瘤磷酸蛋白质组学研究已经取得了进展。然而,选择最适合生物医学问题和诊断要求的定量技术仍然是一个反复试验的决策,因为没有研究直接比较过它们在肿瘤组织磷酸蛋白质组学中的性能。我们比较了无标记定量(LFQ)、掺入 SILAC(细胞培养中的稳定同位素标记氨基酸)和串联质量标签(TMT)等定量磷酸化位点分析技术在肿瘤组织中的应用。与经典的 SILAC 方法相比,掺入 SILAC 不受限于细胞培养分析,使其适用于肿瘤组织样品的定量分析。TMT 的准确性最低,但对不同磷酸化位点丰度和基质的精度和稳健性最高。掺入 SILAC 在这些特性之间提供了最佳的折衷,但存在磷酸化位点覆盖度低的问题。LFQ 的精度最低,但鉴定的磷酸化位点数量最多。掺入 SILAC 和 LFQ 都容易受到基质效应的影响。基于匹配运行(MBR)的分析增强了 LFQ 和掺入 SILAC 中技术重复之间的磷酸化位点覆盖率,但进一步降低了定量的精度和稳健性。定量方法的选择对于研究设计(例如样本组中的样本量和定量磷酸化位点)以及比较已发表的癌症磷酸蛋白质组学至关重要。以卵巢癌组织为例,我们的研究为癌症研究和诊断中定量磷酸蛋白质组学研究的设计和分析提供了资源。