Development of a widely targeted volatilomics method for profiling volatilomes in plants

Volatile organic compounds play essential roles in plant environment interactions as well as determining the fragrance of plants.Although gas chromatography-mass spectrometry-based untargeted metabolo-mics is commonly used to assess plant volatiles,it suffers from high spectral convolution,low detection sensitivity,a limited number of annotated metabolites,and relatively poor reproducibility.Here,we report a widely targeted volatilomics(WTV)method that involves using a“targeted spectra extraction”algorithm to address spectral convolution,constructing a high-coverage MS2 spectral tag library to expand volatile annotation,adapting a multiple reaction monitoring mode to improve sensitivity,and using regression models to adjust for signal drift.The newly developed method was used to profile the volatilome of rice grains.Compared with the untargeted method,the newly developed WTV method shows higher sensitivity(for example,the signal-to-noise ratio of guaicol increased from 4.1 to 18.8),high annotation coverage(the number of annotated volatiles increased from 43 to 132),and better reproducibility(the number of volatiles in quality control samples with relative standard deviation value below 30.0%increased from 14 to 92 after normalization).Using the WTV method,we studied the metabolic responses of tomato to environmental stimuli and profiled the volatilomes of different rice accessions.The results identified benzothiazole as a potential airborne signal priming tomato plants for enhanced defense and 2-nonanone and 2-heptanone as novel aromatic compounds contributing to rice fragrance.These case studies suggest that the widely targeted volatilomics method is more efficient than those currently used and may considerably promote plant volatilomics studies.

WTV2.0:A high-coverage plant volatilomics method with a comprehensive selective ion monitoring acquisition mode

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.