Department of Chemistry, Purdue University, West Lafayette, IN 47907, USA.
Chemistry. 2011 Mar 1;17(10):2897-902. doi: 10.1002/chem.201001692. Epub 2011 Jan 31.
Diagnosis of human bladder cancer in untreated tissue sections is achieved by using imaging data from desorption electrospray ionization mass spectrometry (DESI-MS) combined with multivariate statistical analysis. We use the distinctive DESI-MS glycerophospholipid (GP) mass spectral profiles to visually characterize and formally classify twenty pairs (40 tissue samples) of human cancerous and adjacent normal bladder tissue samples. The individual ion images derived from the acquired profiles correlate with standard histological hematoxylin and eosin (H&E)-stained serial sections. The profiles allow us to classify the disease status of the tissue samples with high accuracy as judged by reference histological data. To achieve this, the data from the twenty pairs were divided into a training set and a validation set. Spectra from the tumor and normal regions of each of the tissue sections in the training set were used for orthogonal projection to latent structures (O-PLS) treated partial least-square discriminate analysis (PLS-DA). This predictive model was then validated by using the validation set and showed a 5% error rate for classification and a misclassification rate of 12%. It was also used to create synthetic images of the tissue sections showing pixel-by-pixel disease classification of the tissue and these data agreed well with the independent classification that uses histological data by a certified pathologist. This represents the first application of multivariate statistical methods for classification by ambient ionization although these methods have been applied previously to other MS imaging methods. The results are encouraging in terms of the development of a method that could be utilized in a clinical setting through visualization and diagnosis of intact tissue.
利用解吸电喷雾电离质谱（DESI-MS）的成像数据结合多元统计分析，可在未经处理的组织切片中诊断人类膀胱癌。我们使用独特的 DESI-MS 甘油磷脂（GP）质谱谱图来直观地描述和正式分类二十对（40 个组织样本）人类癌性和相邻正常膀胱组织样本。从获得的图谱中得出的各个离子图像与标准组织学苏木精和伊红（H＆E）染色的连续切片相关。这些图谱允许我们根据参考组织学数据准确地对组织样本的疾病状态进行分类。为此，将二十对数据分为训练集和验证集。使用训练集中每个组织切片的肿瘤和正常区域的光谱进行正交投影到潜在结构（O-PLS）处理的偏最小二乘判别分析（PLS-DA）。然后使用验证集验证该预测模型，其分类错误率为 5％，误分类率为 12％。它还用于创建组织切片的合成图像，显示组织的逐像素疾病分类，并且这些数据与使用组织学数据的独立分类很好地吻合，该分类由认证病理学家进行。这代表了环境电离多元统计方法用于分类的首次应用，尽管这些方法以前已应用于其他 MS 成像方法。就开发可通过可视化和诊断完整组织在临床环境中利用的方法而言，结果令人鼓舞。
