Luo Ping, Yin Peiyuan, Zhang Weijian, Zhou Lina, Lu Xin, Lin Xiaohui, Xu Guowang
Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
School of Computer Science and Technology, Dalian University of Technology, Dalian, China.
J Chromatogr A. 2016 Mar 11;1437:127-136. doi: 10.1016/j.chroma.2016.01.078. Epub 2016 Feb 4.
Liquid chromatography-mass spectrometry (LC-MS) is now a main stream technique for large-scale metabolic phenotyping to obtain a better understanding of genomic functions. However, repeatability is still an essential issue for the LC-MS based methods, and convincing strategies for long time analysis are urgently required. Our former reported pseudotargeted method which combines nontargeted and targeted analyses, is proved to be a practical approach with high-quality and information-rich data. In this study, we developed a comprehensive strategy based on the pseudotargeted analysis by integrating blank-wash, pooled quality control (QC) sample, and post-calibration for the large-scale metabolomics study. The performance of strategy was optimized from both pre- and post-acquisition sections including the selection of QC samples, insertion frequency of QC samples, and post-calibration methods. These results imply that the pseudotargeted method is rather stable and suitable for large-scale study of metabolic profiling. As a proof of concept, the proposed strategy was applied to the combination of 3 independent batches within a time span of 5 weeks, and generated about 54% of the features with coefficient of variations (CV) below 15%. Moreover, the stability and maximal capability of a single analytical batch could be extended to at least 282 injections (about 110h) while still providing excellent stability, the CV of 63% metabolic features was less than 15%. Taken together, the improved repeatability of our strategy provides a reliable protocol for large-scale metabolomics studies.
液相色谱 - 质谱联用（LC-MS）如今是大规模代谢表型分析的主流技术，有助于更深入地了解基因组功能。然而，重复性仍是基于LC-MS的方法的关键问题，因此迫切需要适用于长时间分析且令人信服的策略。我们之前报道的结合非靶向和靶向分析的伪靶向方法，已被证明是一种能产生高质量、信息丰富数据的实用方法。在本研究中，我们通过整合空白清洗、混合质量控制（QC）样品以及用于大规模代谢组学研究的后校准，开发了一种基于伪靶向分析的综合策略。该策略的性能在采集前和采集后阶段均得到优化，包括QC样品的选择、QC样品的插入频率以及后校准方法。这些结果表明，伪靶向方法相当稳定，适用于大规模代谢谱研究。作为概念验证，所提出的策略应用于在5周时间跨度内的3个独立批次的组合，生成了约54%变异系数（CV）低于15%的特征。此外，单个分析批次的稳定性和最大能力可扩展至至少282次进样（约110小时），同时仍保持出色的稳定性，63%代谢特征的CV小于15%。综上所述，我们策略提高的重复性为大规模代谢组学研究提供了可靠的方案。
