Trace, Machine Learning of Signal Images for Trace-Sensitive Mass Spectrometry: A Case Study from Single-Cell Metabolomics
Trace, Machine Learning of Signal Images for Trace-Sensitive Mass Spectrometry: A Case Study from Single-Cell Metabolomics
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DOI：
10.1021/acs.analchem.8b05985
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发表时间：
2019-05-07
影响因子：
7.4
通讯作者：
Nemes, Peter
中科院分区：
文献类型：
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作者：
Liu, Zhichao;Portero, Erika P.;Nemes, Peter
高分辨率质谱（HRMS）技术的最新发展使得能够在有限数量的样品（甚至是单细胞）中超灵敏地检测蛋白质、肽和代谢物。然而，从超灵敏研究产生的复杂数据集（m/z值，分离时间，信号丰度）中提取痕量丰度信号需要改进的数据处理算法。为了弥合这一差距，我们在这里开发了“Trace”，这是一个软件框架，它结合了机器学习（ML）来自动化特征选择和优化，以便从HRMS数据中提取痕量级信号。该方法进行了验证，使用初级（原始）和手动策划的数据集，从单细胞代谢组学研究的南非爪蛙（非洲爪蟾）胚胎，毛细管电泳电喷雾电离高分辨质谱。我们证明了Trace结合了灵敏度，准确性和鲁棒性以及高数据处理吞吐量来识别信号，包括我们在几个月内进行的单细胞毛细管电泳HRMS测量中先前确定为代谢物的信号。这些性能指标与开源（mzML）格式的MS数据兼容性相结合，使Trace成为一种有吸引力的软件资源，可促进采用超灵敏度高分辨率MS的研究的数据分析。
Recent developments in high-resolution mass spectrometry (HRMS) technology enabled ultrasensitive detection of proteins, peptides, and metabolites in limited amounts of samples, even single cells. However, extraction of trace-abundance signals from complex data sets (m/z value, separation time, signal abundance) that result from ultrasensitive studies requires improved data processing algorithms. To bridge this gap, we here developed "Trace", a software framework that incorporates machine learning (ML) to automate feature selection and optimization for the extraction of trace-level signals from HRMS data. The method was validated using primary (raw) and manually curated data sets from single-cell metabolomic studies of the South African clawed frog (Xenopus laevis) embryo using capillary electrophoresis electrospray ionization HRMS. We demonstrated that Trace combines sensitivity, accuracy, and robustness with high data processing throughput to recognize signals, including those previously identified as metabolites in single-cell capillary electrophoresis HRMS measurements that we conducted over several months. These performance metrics combined with a compatibility with MS data in open-source (mzML) format make Trace an attractive software resource to facilitate data analysis for studies employing ultrasensitive high-resolution MS.