Biomolecular Medicine, Department of Surgery and Cancer, Imperial College London, South Kensington, London SW7 2AZ, United Kingdom.
Anal Chem. 2013 Feb 5;85(3):1415-23. doi: 10.1021/ac302330a. Epub 2013 Jan 15.
The acquisition of localized molecular spectra with mass spectrometry imaging (MSI) has a great, but as yet not fully realized, potential for biomedical diagnostics and research. The methodology generates a series of mass spectra from discrete sample locations, which is often analyzed by visually interpreting specifically selected images of individual masses. We developed an intuitive color-coding scheme based on hyperspectral imaging methods to generate a single overview image of this complex data set. The image color-coding is based on spectral characteristics, such that pixels with similar molecular profiles are displayed with similar colors. This visualization strategy was applied to results of principal component analysis, self-organizing maps and t-distributed stochastic neighbor embedding. Our approach for MSI data analysis, combining automated data processing, modeling and display, is user-friendly and allows both the spatial and molecular information to be visualized intuitively and effectively.
利用质谱成像（MSI）获取局部分子光谱具有巨大的潜力，但尚未得到充分实现，可用于生物医学诊断和研究。该方法从离散的样本位置生成一系列质谱，通常通过直观地解释特定选择的个别质量的图像进行分析。我们开发了一种基于高光谱成像方法的直观颜色编码方案，以生成此复杂数据集的单个概述图像。图像的颜色编码基于光谱特征，使得具有相似分子特征的像素显示为相似的颜色。这种可视化策略应用于主成分分析、自组织映射和 t 分布随机邻域嵌入的结果。我们的 MSI 数据分析方法结合了自动化数据处理、建模和显示，用户友好，可直观有效地显示空间和分子信息。
