Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is a powerful imaging method for generating molecular maps of biological samples and has numerous applications in biomedical research. A key challenge in MALDI MSI is to reliably map observed mass peaks to theoretical masses, which can be difficult due to mass shifts that occur during the measurement process. In this paper, we propose MassShiftNet, a novel self-supervised learning framework for mass recalibration. We train a neural network on a data dependent and specifically augmented training data set to directly estimate and correct the mass shift in the observed spectra. In our evaluation, we show that this method is both able to reduce the absolute mass error and to increase the relative mass alignment between peptide MSI spectra acquired from FFPE-fixated tissue using a MALDI time-of-flight (TOF) instrument.

中文翻译：

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使用基于神经网络的重新校准改进 MALDI MSI 中的质量校准


基质辅助激光解吸/电离质谱成像（MALDI MSI）是一种用于生成生物样品分子图谱的强大成像方法，在生物医学研究中具有广泛的应用。 MALDI MSI 的一个关键挑战是将观察到的质量峰可靠地映射到理论质量，由于测量过程中发生的质量变化，这可能很困难。在本文中，我们提出了 MassShiftNet，一种用于大规模重新校准的新型自监督学习框架。我们在依赖于数据且专门增强的训练数据集上训练神经网络，以直接估计和校正观察到的光谱中的质量偏移。在我们的评估中，我们表明该方法既能够减少绝对质量误差，又能够增加使用 MALDI 飞行时间 (TOF) 仪器从 FFPE 固定组织获取的肽 MSI 光谱之间的相对质量对准。