Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, Singapore, 138668, Singapore.
Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore.
Metabolomics. 2018 Jul 9;14(7):98. doi: 10.1007/s11306-018-1394-0.
Given a raw LC-MS dataset, it is often required to rapidly generate initial hypotheses, in conjunction with other 'omics' datasets, without time-consuming lipid verifications. Furthermore, for meta-analysis of many datasets, it may be impractical to conduct exhaustive confirmatory analyses. In other cases, samples for validation may be difficult to obtain, replicate or maintain. Thus, it is critical that the computational identification of lipids is of appropriate accuracy, coverage, and unbiased by a researcher's experience and prior knowledge.
We aim to prescribe a systematic framework for lipid identifications, without usage of their characteristic retention-time by fully exploiting their underlying mass features.
Initially, a hybrid technique, for deducing both common and distinctive daughter ions, is used to infer parent lipids from deconvoluted spectra. This is followed by parent confirmation using basic knowledge of their preferred product ions. Using the framework, we could achieve an accuracy of ~ 80% by correctly identified 101 species from 18 classes in Chinese hamster ovary (CHO) cells. The resulting inferences could explain the recombinant-producing capability of CHO-SH87 cells, compared to non-producing CHO-K1 cells. For comparison, a XCMS-based study of the same dataset, guided by a user's ad-hoc knowledge, identified less than 60 species of 12 classes from thousands of possibilities.
We describe a systematic LC-MS-based framework that identifies lipids for rapid hypothesis generation.
给定一个原始的 LC-MS 数据集，通常需要快速生成初始假设，并与其他“组学”数据集结合使用，而无需耗时的脂质验证。此外，对于许多数据集的元分析，进行详尽的确认分析可能不切实际。在其他情况下，验证样本可能难以获得、复制或维持。因此，计算识别脂质的准确性、覆盖范围以及不受研究人员经验和先验知识的影响至关重要。
我们旨在规定一个系统的脂质识别框架，而不使用其特征保留时间，而是充分利用其潜在的质量特征。
最初，我们使用一种混合技术，用于推断出共有的和独特的子离子，从解卷积光谱中推断出母体脂质。然后，使用它们的优先产物离子的基本知识来确认母体。使用该框架，我们可以从 CHO 细胞中正确识别 101 个来自 18 个类别的物种，准确率约为 80%。由此推断出，与不产 CHO-K1 细胞相比，CHO-SH87 细胞的重组生产能力。相比之下，基于 XCMS 的相同数据集研究，在用户的临时知识指导下，从数千种可能性中仅鉴定出 12 个类别的不到 60 种物种。
我们描述了一种基于 LC-MS 的系统框架，用于快速生成假设。
