Chi Yuanye, Mitchell Joshua M, Thapa Maheshwor, Zheng Shujian, Frohock Zackary, Li Yi, Smirnov Aleksandr, Du Xiuxia, Li Shuzhao
The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA.
University of North Carolina, Charlotte, NC 28223, USA.
bioRxiv. 2025 May 11:2025.05.07.652782. doi: 10.1101/2025.05.07.652782.
Blood analysis is the most common in biomedical applications and a reference metabolome will be critical for effective annotation and for guiding scientific investigations. However, compiling such a reference is hindered by many technical challenges, despite the availability of large amount of metabolomics data today. Based on a new set of data structures and tools, we have assembled a consensus serum metabolome (CSM) from over 100,000 mass spectrometry acquisitions of more than 200 million spectra. This provides a comprehensive survey of human blood chemistry, revealing the frequency dependent nature of metabolome and exposome. Major gaps are found between CSM and the current databases. The CSM enables community-level data alignment and significantly improves annotation quality of LC-MS metabolomics.
血液分析在生物医学应用中最为常见,而一个参考代谢组对于有效的注释和指导科学研究至关重要。然而,尽管如今有大量的代谢组学数据,但编制这样一个参考受到许多技术挑战的阻碍。基于一组新的数据结构和工具,我们从超过2亿个光谱的10万多次质谱采集数据中组装了一个共识血清代谢组(CSM)。这提供了对人体血液化学成分的全面调查,揭示了代谢组和暴露组的频率依赖性本质。在CSM和当前数据库之间发现了重大差距。CSM实现了社区层面的数据比对,并显著提高了液相色谱-质谱代谢组学的注释质量。