Chem Res Toxicol. 2022 Apr 18;35(4):606-615. doi: 10.1021/acs.chemrestox.1c00397. Epub 2022 Mar 15.
Matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS)-based lipid profiling is a powerful method to study the cytotoxicity of chemical exposure to microorganisms at the single cell level. We report here a combined approach of machine learning (ML) and microchip-based MALDI-time of flight (TOF) mass spectrometry to investigate the cytotoxic effect of herbicides on algae through single cell lipid profiling. Algal species was chosen as the target system, and its exposure to different doses of common chemical herbicides and the resulting cytotoxic behaviors under various stress conditions were characterized. A lipid library for has been established with 63 identified lipids that include glycosyldiacylglycerols and triacylglycerols. We demonstrated that major alternations occurred for lipids with functional groups of digalactosyldiacylglycerol (DGDG), triacylglycerol (TAG), and monogalactosyldiacylglycerol (MGDG). DGDG was shown to decrease upon exposure to herbicides of norflurazon and atrazine, while some MGDG and TAG lipids would increase for norflurazon. Compared to other algae, was more strongly impacted by norflurazon than atrazine while the latter was observed to have a greater effect on . Machine learning algorithms have been applied to improve the classification of herbicide impact and help identify lipid species affected by the chemical exposure. A total of 69 machine learning models were trained and tested for the identification of ideal algorithms in the classification process, in which flexible discriminant analysis and support vector machine model were found to be the most accurate and consistent. The ML algorithms accurately differentiated herbicide impact and have identified cytotoxic differences that were previously hidden. The results suggest that herbicides express toxicity among different algae likely on the basis of metabolic differences. The ML-assisted method proves to be highly effective and can provide an advanced technological platform for probing cytotoxicity for bacterial species and in metabolic pathway analysis.
基质辅助激光解吸电离飞行时间质谱（MALDI-TOF-MS）为基础的脂质谱分析是一种强大的方法，可用于研究化学暴露对微生物的细胞毒性。我们在这里报告了一种机器学习（ML）和基于微芯片的 MALDI-TOF 质谱相结合的方法，用于通过单细胞脂质谱分析研究除草剂对藻类的细胞毒性。选择藻类物种作为目标系统，研究了不同剂量的常见化学除草剂对藻类的暴露及其在不同胁迫条件下的细胞毒性行为。建立了一个关于 的脂质文库，其中包含 63 种已鉴定的脂质，包括糖基二酰基甘油和三酰基甘油。我们证明，具有二半乳糖二酰基甘油（DGDG）、三酰基甘油（TAG）和单半乳糖二酰基甘油（MGDG）功能基团的脂质会发生重大变化。暴露于氟磺胺草醚和莠去津除草剂后，DGDG 减少，而一些 MGDG 和 TAG 脂质会因氟磺胺草醚增加。与其他藻类相比，氟磺胺草醚对 的影响比莠去津更强，而后者对 的影响更大。机器学习算法已被应用于提高除草剂影响的分类，并帮助识别受化学暴露影响的脂质种类。总共训练和测试了 69 个机器学习模型，以确定分类过程中理想的算法，其中发现灵活判别分析和支持向量机模型最准确和一致。ML 算法能够准确地区分除草剂的影响，并识别出以前隐藏的细胞毒性差异。结果表明，除草剂在不同藻类中的表达毒性可能基于代谢差异。该 ML 辅助方法被证明是非常有效的，并可为细菌物种的细胞毒性探测和代谢途径分析提供先进的技术平台。
