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.

中文翻译：

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WTV2.0：高覆盖度的植物挥发组学方法，具有全面的选择性离子监测采集模式


挥发性组学对于了解植物挥发物的生物功能和香味贡献至关重要。然而，使用当前非靶向和广泛靶向的挥发组学 (WTV) 方法实现的注释覆盖率受到低灵敏度和/或低采集覆盖率的限制。在这里，我们介绍了 WTV 2.0，它能够构建包含 2111 种植物挥发物的高覆盖率库，并报告开发综合选择性离子监测（cSIM）采集方法，包括选择具有最小离子数的特征定性离子针对每种化合物和优化的分割方法，可以获取大多数植物挥发物的最小但足够数量的离子，以及 cSIM 数据的自动定性和半定量分析。重要的是，我们开发的库和采集方法可以利用获得的 cSIM 数据，通过合并库中不存在的化合物来自我扩展。我们发现，与非靶向方法相比，WTV 2.0 将中位信噪比提高了 7.6 倍，与非靶向方法和 WTV 1.0 方法相比，番茄果实的注释覆盖率增加了一倍，并导致了薄荷呋喃作为一种新型药物的发现。百香果中的风味化合物。 WTV 2.0 是一个具有用户友好界面的 Python 库，适用于任何物种的挥发物和初级代谢物的分析。