Rongrong Li, Xinyi Jiao, Xiaolin Wu, Lei Xu, Lin Zhang, Lifeng Han, Guixiang Pai, Wei Mi, Jiang Wu, Liming Wang
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引用次数: 0
Abstract
Metabolites identification is the major bottleneck in untargeted LC-MS metabolomics, primarily due to the limited availability of MS2 information for most detected metabolites in data dependent acquisition (DDA) mode. To solve this problem, we have integrated the iterative, interval, and segmented window acquisition concepts to develop an innovative non-fixed segmented window interval data dependency acquisition (NFSWI-DDA) mode, which achieves comparable MS2 coverage to data independent acquisition (DIA) mode. This acquisition strategy harnesses the strengths of both DDA and DIA, which could provide extensive coverage and excellent reproducibility of MS2 spectra. Furthermore, utilizing the NFSWI-DDA data, we successfully acquired and identified a large-scale of multiple reaction monitoring (MRM) ion pairs, and transitioned them from high-resolution mass spectrometry (HRMS) to triple quadrupole mass spectrometry (TQ-MS). At last, a large-scale targeted metabolomics method was established practically. This method enables targeted analysis of 475 endogenous metabolites encompassing amino acids, nucleotides, bile acids, fatty acids, and carnitines, which could cover 9 major metabolic pathways as well as 65 secondary metabolic pathways. The established targeted method allows for semi-quantitative assessment of 475 metabolites while enabling quantitative analysis of 327 specific metabolites in biological samples. The method demonstrates immense potential in the detection of various biological samples, offering robust technical support and generating extensive data to advance applications in precision medicine and life sciences.
期刊介绍：
Talanta provides a forum for the publication of original research papers, short communications, and critical reviews in all branches of pure and applied analytical chemistry. Papers are evaluated based on established guidelines, including the fundamental nature of the study, scientific novelty, substantial improvement or advantage over existing technology or methods, and demonstrated analytical applicability. Original research papers on fundamental studies, and on novel sensor and instrumentation developments, are encouraged. Novel or improved applications in areas such as clinical and biological chemistry, environmental analysis, geochemistry, materials science and engineering, and analytical platforms for omics development are welcome.
Analytical performance of methods should be determined, including interference and matrix effects, and methods should be validated by comparison with a standard method, or analysis of a certified reference material. Simple spiking recoveries may not be sufficient. The developed method should especially comprise information on selectivity, sensitivity, detection limits, accuracy, and reliability. However, applying official validation or robustness studies to a routine method or technique does not necessarily constitute novelty. Proper statistical treatment of the data should be provided. Relevant literature should be cited, including related publications by the authors, and authors should discuss how their proposed methodology compares with previously reported methods.
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