Ishiguro Hiromu, Mizuno Tadahaya, Uchida Yasuo, Sato Risa, Sasaki Hayate, Nemoto Shumpei, Terasaki Tetsuya, Kusuhara Hiroyuki
Graduate School of Pharmaceutical Sciences, the University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan.
Graduate School of Pharmaceutical Sciences, Tohoku University, Aoba, Aramaki, Aoba-ku, Sendai 980-8578, Japan.
NAR Genom Bioinform. 2023 Mar 11;5(1):lqad022. doi: 10.1093/nargab/lqad022. eCollection 2023 Mar.
Transcriptomic data of cultured cells treated with a chemical are widely recognized as useful numeric information that describes the effects of the chemical. This property is due to the high coverage and low arbitrariness of the transcriptomic data as profiles of chemicals. Considering the importance of posttranslational regulation, proteomic profiles could provide insights into the unrecognized aspects of the effects of chemicals. Therefore, this study aimed to address the question of how well the proteomic profiles obtained using data-independent acquisition (DIA) with the sequential window acquisition of all theoretical mass spectra, which can achieve comprehensive and arbitrariness-free protein quantification, can describe chemical effects. We demonstrated that the proteomic data obtained using DIA-MS exhibited favorable properties as profile data, such as being able to discriminate chemicals like the transcriptomic profiles. Furthermore, we revealed a new mode of action of a natural compound, harmine, through profile data analysis using the proteomic profile data. To our knowledge, this is the first study to investigate the properties of proteomic data obtained using DIA-MS as the profiles of chemicals. Our 54 (samples) × 2831 (proteins) data matrix would be an important source for further analyses to understand the effects of chemicals in a data-driven manner.
用化学物质处理的培养细胞的转录组数据被广泛认为是描述该化学物质作用的有用数值信息。这一特性归因于转录组数据作为化学物质谱的高覆盖率和低随意性。考虑到翻译后调控的重要性，蛋白质组谱可以为化学物质作用中未被认识的方面提供见解。因此，本研究旨在解决这样一个问题：使用数据非依赖采集（DIA）结合所有理论质谱的顺序窗口采集所获得的蛋白质组谱，这种能够实现全面且无随意性的蛋白质定量方法，在描述化学物质作用方面效果如何。我们证明，使用DIA-MS获得的蛋白质组数据作为谱数据具有良好的特性，例如能够像转录组谱一样区分化学物质。此外，我们通过对蛋白质组谱数据进行谱数据分析，揭示了一种天然化合物——骆驼蓬碱的新作用模式。据我们所知，这是第一项研究使用DIA-MS获得的蛋白质组数据作为化学物质谱的特性的研究。我们的54（样本）×2831（蛋白质）数据矩阵将成为以数据驱动方式进一步分析以了解化学物质作用的重要来源。
