Spectral binning as an approach to post-acquisition processing of high resolution FIE-MS metabolome fingerprinting data.
Spectral binning as an approach to post-acquisition processing of high resolution FIE-MS metabolome fingerprinting data.
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光谱合并作为高分辨率FIE-MS代谢组指纹数据采集后处理的方法。
DOI：
10.1007/s11306-022-01923-6
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发表时间：
2022-08-02
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流动注入电喷雾高分辨率质谱（FIE-HRMS）指纹识别产生复杂的高维数据集，这需要专业的计算机软件工具在分析之前处理数据。目前的光谱分箱作为一个务实的方法后采集处理的FIE-HRMS代谢指纹数据。开发了光谱分箱方法，包括消除单次扫描m/z事件、光谱分箱和输注曲线上光谱的平均值。然后提取每个区间的模态精确m/z。使用四种不同的生物基质和31种已知化学标准品的混合物，通过FIE-HRMS使用Exactive Orbitrap进行分析，对这种方法进行了评估。开发了分组纯度和中心性指标，以分别客观评估单个分组内准确m/z的分布和位置。发现最佳光谱合并宽度为0.01 amu。发现与化学标准品混合物的预测电离产物匹配的80.8%的提取的准确m/z具有低于3ppm的误差。开源R包binneR是作为该方法的用户友好实现而开发的。这能够在55秒内使用4个中央处理器（CPU）工作器处理100个数据文件，最大内存使用量为1.36 GB。光谱合并是一种快速、稳健的FIE-HRMS数据采集后处理方法。开源R包binneR允许用户使用标准台式计算机上的可用资源有效地处理FIE-HRMS实验数据。在线版本包含补充材料，可通过10.1007/s11306 - 022 - 01923 - 6获得。
Flow infusion electrospray high resolution mass spectrometry (FIE-HRMS) fingerprinting produces complex, high dimensional data sets which require specialist in-silico software tools to process the data prior to analysis. Present spectral binning as a pragmatic approach to post-acquisition procession of FIE-HRMS metabolome fingerprinting data. A spectral binning approach was developed that included the elimination of single scan m/z events, the binning of spectra and the averaging of spectra across the infusion profile. The modal accurate m/z was then extracted for each bin. This approach was assessed using four different biological matrices and a mix of 31 known chemical standards analysed by FIE-HRMS using an Exactive Orbitrap. Bin purity and centrality metrics were developed to objectively assess the distribution and position of accurate m/z within an individual bin respectively. The optimal spectral binning width was found to be 0.01 amu. 80.8% of the extracted accurate m/z matched to predicted ionisation products of the chemical standards mix were found to have an error of below 3 ppm. The open-source R package binneR was developed as a user friendly implementation of the approach. This was able to process 100 data files using 4 Central Processing Units (CPU) workers in only 55 seconds with a maximum memory usage of 1.36 GB. Spectral binning is a fast and robust method for the post-acquisition processing of FIE-HRMS data. The open-source R package binneR allows users to efficiently process data from FIE-HRMS experiments with the resources available on a standard desktop computer. The online version contains supplementary material available at 10.1007/s11306-022-01923-6.