基于局部内源性生物信号的质谱成像的自适应像素质量重新校准,Analytical Chemistry

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基于局部内源性生物信号的质谱成像的自适应像素质量重新校准
Analytical Chemistry ( IF 6.7 ) Pub Date : 2021-02-14 , DOI: 10.1021/acs.analchem.0c05071
Raphaël La Rocca ₁ , Christopher Kune ₁ , Mathieu Tiquet ₁ , Lachlan Stuart ₂ , Gauthier Eppe ₁ , Theodore Alexandrov _{2,

3} , Edwin De Pauw ₁ , Loïc Quinton ₁

Affiliation

质谱成像（MSI）是一种强大而便捷的方法，可揭示不同生物样品的空间化学成分。仅当在整个图像和m / z范围内都保持较高的质量精度时，才可能对检测到的信号进行分子注释。但是，生物样品中像素之间的离子数量变化可能会导致检测到的m / z出现较小的波动值，称为质量转移。已知使用内部校准可提供最佳解决方案，以避免或至少减少质量偏移。他们为全球MSI采集而选择的“先验”容易出现误报检测，因此校正效果不佳。为了填补这一空白，这项工作描述了一种算法，该算法通过借助以数据自适应方式自动生成的特定于像素的内部校准离子列表来估计其质谱位移，从而分别重新校准每个光谱（https://github.com / LaRoccaRaphael / MSI_recalibration）。通过一个实际示例，我们将该方法应用于以高分辨率获得的斑马鱼全身切片，以证明质量偏移对数据分析的影响以及我们算法重新校准MSI数据的能力。此外，

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Adaptive Pixel Mass Recalibration for Mass Spectrometry Imaging Based on Locally Endogenous Biological Signals

Mass spectrometry imaging (MSI) is a powerful and convenient method for revealing the spatial chemical composition of different biological samples. Molecular annotation of the detected signals is only possible if a high mass accuracy is maintained over the entire image and the m/z range. However, the change in the number of ions from pixel-to-pixel of the biological samples could lead to small fluctuations in the detected m/z-values, called mass shift. The use of internal calibration is known to offer the best solution to avoid, or at least to reduce, mass shifts. Their “a priori” selection for a global MSI acquisition is prone to false positive detection and therefore to poor recalibration. To fill this gap, this work describes an algorithm that recalibrates each spectrum individually by estimating its mass shift with the help of a list of pixel-specific internal calibrating ions, automatically generated in a data-adaptive manner (https://github.com/LaRoccaRaphael/MSI_recalibration). Through a practical example, we applied the methodology to a zebrafish whole-body section acquired at a high mass resolution to demonstrate the impact of mass shift on data analysis and the capability of our algorithm to recalibrate MSI data. In addition, we illustrate the broad applicability of the method by recalibrating 31 different public MSI data sets from METASPACE from various samples and types of MSI and show that our recalibration significantly increases the numbers of METASPACE annotations (gaining from 20 up to 400 additional annotations), particularly the high-confidence annotations with a low false discovery rate.

更新日期：2021-03-02

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