摘要
Volatilomics is essential for understanding the biological functions and fragrance contributions of plant volatiles.However,the annotation coverage achieved using current untargeted and widely targeted volatomics(WTV)methods has been limited by low sensitivity and/or low acquisition coverage.Here,we introduce WTV 2.0,which enabled the construction of a high-coverage library containing 2111 plant volatiles,and report the development of a comprehensive selective ion monitoring(cSIM)acquisition method,including the selection of characteristic qualitative ions with the minimal ion number for each compound and an optimized segmentation method,that can acquire the smallest but sufficient number of ions for most plant volatiles,as well as the automatic qualitative and semi-quantitative analysis of cSIM data.Importantly,the library and acquisition method we developed can be self-expanded by incorporating compounds not present in the library,utilizing the obtained cSIM data.We showed that WTV 2.0 increases the median signal-to-noise ratio by 7.6-fold compared with the untargeted method,doubled the annotation coverage compared with the untargeted and WTV 1.0 methods in tomato fruit,and led to the discovery of menthofuran as a novel flavor compound in passion fruit.WTV 2.0 is a Python library with a user-friendly interface and is applicable to profiling of volatiles and primary metabolites in any species.
基金
supported by key project of regional joint fund of National Natural Science FoundationNational Natural Science Foundation of China(U22A20476)
Hainan international science and technology cooperation research and development project(GHYF2023005)
Sanya Yazhou Sci-Tech City(SYND-2022-02).)
Hainan Yazhou Bay Seed Lab(Nono.B21HJ0903)
“111”Project111 Project(Nono.D20024).)
Hainan Provincial Natural Science Foundation of China Hainan Provincial Natural Science Foundation of China(320MS011).)‘PhD Scientific Research and Innovation Foundation of Sanya Yazhou Bay Science and Technology City(HSPHDSRF-2023-12-001).)’Basic Research Project in 2023 of Yazhouwan National Laboratory.
