Salovska Barbora, Li Wenxue, Bernhardt Oliver M, Germain Pierre-Luc, Gandhi Tejas, Reiter Lukas, Liu Yansheng
Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA.
Cancer Biology Institute, Yale University School of Medicine, West Haven, CT 06516, USA.
bioRxiv. 2024 Oct 31:2024.10.28.620709. doi: 10.1101/2024.10.28.620709.
Measuring protein turnover is essential for understanding cellular biological processes and advancing drug discovery. The multiplex DIA mass spectrometry (DIA-MS) approach, combined with dynamic SILAC labeling (pulse-SILAC, or pSILAC), has proven to be a reliable method for analyzing protein turnover and degradation kinetics. Previous multiplex DIA-MS workflows have employed various strategies, including leveraging the highest isotopic labeling channels of peptides to enhance the detection of isotopic MS signal pairs or clusters. In this study, we introduce an improved and robust workflow that integrates a novel machine learning strategy and channel-specific statistical filtering, enabling dynamic adaptation to systematic or temporal variations in channel ratios. This allows comprehensive profiling of protein turnover throughout the pSILAC experiment without relying solely on the highest channel signals. Additionally, we developed , a data processing and analysis package optimized for pSILAC-DIA experiments, which estimates and visualizes peptide and protein degradation rates and dynamic profiles. Our integrative workflow was benchmarked on both 2-channel and 3-channel standard DIA datasets and across two mass spectrometry platforms, demonstrating its broad applicability. Finally, applying this workflow to an aneuploid cancer cell model before and after cisplatin resistance development demonstrated a strong negative correlation between transcript regulation and protein degradation for major protein complex subunits. We also identified specific protein turnover signatures associated with cisplatin resistance.
测量蛋白质周转率对于理解细胞生物学过程和推进药物发现至关重要。多重数据独立采集质谱法（DIA-MS）与动态稳定同位素标记氨基酸细胞培养法（pulse-SILAC，或pSILAC）相结合，已被证明是一种分析蛋白质周转率和降解动力学的可靠方法。以前的多重DIA-MS工作流程采用了各种策略，包括利用肽的最高同位素标记通道来增强对同位素质谱信号对或簇的检测。在本研究中，我们引入了一种改进的、强大的工作流程，该流程整合了一种新颖的机器学习策略和通道特异性统计过滤，能够动态适应通道比率的系统或时间变化。这使得在整个pSILAC实验中能够全面分析蛋白质周转率，而不单纯依赖于最高通道信号。此外，我们开发了一个针对pSILAC-DIA实验优化的数据处理和分析软件包，该软件包可估计并可视化肽和蛋白质的降解速率及动态概况。我们的整合工作流程在双通道和三通道标准DIA数据集以及两个质谱平台上进行了基准测试，证明了其广泛的适用性。最后，将此工作流程应用于顺铂耐药性发展前后的非整倍体癌细胞模型，结果表明主要蛋白质复合体亚基的转录调控与蛋白质降解之间存在强烈的负相关。我们还鉴定出了与顺铂耐药性相关的特定蛋白质周转特征。
