Department of Chemistry, University of Turin, Turin 10125, Italy.
Analyst. 2012 May 21;137(10):2374-80. doi: 10.1039/c2an35122f. Epub 2012 Apr 11.
Desorption electrospray ionization (DESI) is an ambient mass spectrometry (MS) technique that can be operated in an imaging mode. It is known to provide valuable information on disease state and grade based on lipid profiles in tissue sections. Comprehensive exploration of the spatial and chemical information contained in 2D MS images requires further development of methods for data treatment and interpretation in conjunction with multivariate analysis. In this study, we employ an interactive approach based on principal component analysis (PCA) to interpret the chemical and spatial information obtained from MS imaging of human bladder, kidney, germ cell and prostate cancer and adjacent normal tissues. This multivariate strategy facilitated distinction between tumor and normal tissue by correlating the lipid information with pathological evaluation of the same samples. Some common lipid ions, such as those of m/z 885.5 and m/z 788.5, nominally PI(18 : 0/20 : 4) and PS(18 : 0/18 : 1), as well as ions of free fatty acids and their dimers, appeared to be highly characterizing for different types of human cancers, while other ions, such as those of m/z 465.5 (cholesterol sulfate) for prostate cancer tissue and m/z 795.5 (seminolipid 16 : 0/16 : 0) for germ tissue, appeared to be extremely selective for the type of tissue analyzed. These data confirm that lipid profiles can reflect not only the disease/health state of tissue but also are characteristic of tissue type. The manual interactive strategy presented here is particularly useful to visualize the information contained in hyperspectral MS images by automatically connecting regions of PCA score space to pixels of the 2D physical object. The procedures developed in this study consider all the spectral variables and their inter-correlations, and guide subsequent investigations of the mass spectra and single ion images to allow one to maximize characterization between different regions of any DESI-MS image.
解吸电喷雾电离（DESI）是一种可在成像模式下操作的环境质谱（MS）技术。它已知能够根据组织切片中的脂质谱提供有关疾病状态和分级的有价值信息。全面探索 2D MS 图像中包含的空间和化学信息需要进一步开发与多元分析相结合的数据处理和解释方法。在这项研究中，我们采用基于主成分分析（PCA）的交互式方法来解释从人膀胱、肾、生殖细胞和前列腺癌以及相邻正常组织的 MS 成像中获得的化学和空间信息。这种多元策略通过将脂质信息与相同样本的病理评估相关联，促进了肿瘤组织和正常组织之间的区分。一些常见的脂质离子，如 m/z885.5 和 m/z788.5 的离子，PI（18：0/20：4）和 PS（18：0/18：1）的名义离子，以及游离脂肪酸及其二聚体的离子，似乎对不同类型的人类癌症具有高度特征性，而其他离子，如前列腺癌组织中的 m/z465.5（胆固醇硫酸盐）和生殖组织中的 m/z795.5（半脂 16：0/16：0）离子，似乎对分析的组织类型极为特异。这些数据证实，脂质谱不仅可以反映组织的疾病/健康状态，而且还可以反映组织类型。本文提出的手动交互策略特别有助于通过自动将 PCA 得分空间的区域连接到 2D 物理对象的像素来可视化高光谱 MS 图像中包含的信息。本研究中开发的程序考虑了所有光谱变量及其相互关系，并指导了随后对质谱和单离子图像的研究，以允许在任何 DESI-MS 图像的不同区域之间最大程度地进行特征化。
