Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany.
Department of Analytical Chemistry, University of Vienna, Vienna, Austria.
Proteomics. 2022 Apr;22(7):e2100136. doi: 10.1002/pmic.202100136. Epub 2022 Jan 19.
So far, mass spectrometry-based targeted proteomics is the most sensitive approach to answer and address specific biological questions in an accurate and quantitative fashion. However, the data analysis design used for such quantification varies in the field leading to discrepancies in the reported values. In this study, different quantification strategies based on calibration curves were evaluated and compared. The best accuracy and coefficient of variation was achieved by ratio to ratio calibration curves. We applied the ratio to ratio quantification approach to analyze very low abundant insulin signaling proteins such as PIK3RA (0.10-0.93 fmol/μg), AKT1 (0.1-0.39 fmol/μg), and the insulin receptor (0.22-2.62 fmol/μg) in a fat cell model and demonstrated the adaptation of this pathway at different states of insulin sensitivity.
到目前为止,基于质谱的靶向蛋白质组学是最敏感的方法,可以准确和定量地回答和解决特定的生物学问题。然而,该领域用于这种定量的数据分析设计存在差异,导致报告值存在差异。在这项研究中,评估和比较了基于校准曲线的不同定量策略。通过比率到比率校准曲线实现了最佳的准确性和变异系数。我们应用比率到比率定量方法来分析非常低丰度的胰岛素信号蛋白,如 PIK3RA(0.10-0.93 fmol/μg)、AKT1(0.1-0.39 fmol/μg)和胰岛素受体(0.22-2.62 fmol/μg)在脂肪细胞模型中,并证明了该途径在不同胰岛素敏感性状态下的适应性。