Comparative Assessment of Quantification Methods for Tumor Tissue Phosphoproteomics

细胞培养中氨基酸的稳定同位素标记 磷酸蛋白质组学 定量蛋白质组学 稳健性(进化) 计算生物学 串联质谱法 蛋白质组 化学 无标记量化 蛋白质组学 等压标记
作者
Yang Zhang,Benjamin Dreyer,Natalia Govorukhina,Alexander M. Heberle,Saša Končarević,Christoph Krisp,Christiane A. Opitz,Pauline Pfänder,Rainer Bischoff,Harmut Schlüter,Marcel Kwiatkowski,Kathrin Thedieck,Peter Horvatovich
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:94 (31): 10893-10906
标识
DOI:10.1021/acs.analchem.2c01036
摘要

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.
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