Deininger Sören-Oliver, Ebert Matthias P, Fütterer Arne, Gerhard Marc, Röcken Christoph
Bruker Daltonik GmbH, Bremen, Germany.
J Proteome Res. 2008 Dec;7(12):5230-6. doi: 10.1021/pr8005777.
Proteomics analyses have been exploited for the discovery of novel biomarkers for the early recognition and prognostic stratification of cancer patients. These analyses have now been extended to whole tissue sections by using a new tool, that is, MALDI imaging. This allows the spatial resolution of protein and peptides and their allocation to histoanatomical structures. Each MALDI imaging data set contains a large number of proteins and peptides, and their analysis can be quite tedious. We report here a new approach for the analysis of MALDI imaging results. Mass spectra are classified by hierarchical clustering by similarity and the resulting tissue classes are compared with the histology. The same approach is used to compare data sets of different patients. Tissue sections of gastric cancer and non-neoplastic mucosa obtained from 10 patients were forwarded to MALDI-Imaging. The in situ proteome expression was analyzed by hierarchical clustering and by principal component analysis (PCA). The reconstruction of images based on principal component scores allowed an unsupervised feature extraction of the data set. Generally, these images were in good agreement with the histology of the samples. The hierarchical clustering allowed a quick and intuitive access to the multidimensional information in the data set. It allowed a quick selection of spectra classes representative for different tissue features. The use of PCA for the comparison of MALDI spectra from different patients showed that the tumor and non-neoplastic mucosa are separated in the first three principal components. MALDI imaging in combination with hierarchical clustering allows the comprehensive analysis of the in situ cancer proteome in complex human cancers. On the basis of this cluster analysis, classification of complex human tissues is possible and opens the way for specific and cancer-related in situ biomarker analysis and identification.
蛋白质组学分析已被用于发现新型生物标志物，以实现癌症患者的早期识别和预后分层。目前，通过使用一种新工具——基质辅助激光解吸电离成像技术（MALDI成像），这些分析已扩展到全组织切片。这使得蛋白质和肽段能够实现空间分辨，并将它们定位到组织解剖结构上。每个MALDI成像数据集都包含大量的蛋白质和肽段，对其进行分析可能会相当繁琐。我们在此报告一种分析MALDI成像结果的新方法。质谱通过相似度进行层次聚类分类，然后将所得的组织类别与组织学结果进行比较。同样的方法用于比较不同患者的数据集。从10名患者获取的胃癌和非肿瘤性黏膜组织切片被送去进行MALDI成像。通过层次聚类和主成分分析（PCA）对原位蛋白质组表达进行分析。基于主成分得分重建图像能够对数据集进行无监督特征提取。总体而言，这些图像与样本的组织学结果高度吻合。层次聚类能够快速直观地获取数据集中的多维信息。它能够快速选择代表不同组织特征的光谱类别。使用PCA比较不同患者的MALDI光谱表明，肿瘤和非肿瘤性黏膜在前三个主成分中得以区分。MALDI成像与层次聚类相结合，能够对复杂人类癌症中的原位癌蛋白质组进行全面分析。基于这种聚类分析，可以对复杂的人体组织进行分类，为特异性和癌症相关的原位生物标志物分析与鉴定开辟道路。
