Analysis of colorectal adenocarcinoma tissue by desorption electrospray ionization mass spectrometric imaging

作者：Gerbig Stefanie; Golf Ottmar; Balog Julia; Denes Julia; Baranyai Zsolt; Zarand Attila; Raso Erzsebet; Timar Jozsef; Takats Zoltan^*

来源：Analytical and Bioanalytical Chemistry, 2012, 403(8): 2315-2325.

DOI：10.1007/s00216-012-5841-x

• Desorption electrospray ionization
• Mass spectrometry imaging
• Phospholipid profile
• Multivariate statistical analysis
• Colorectal adenocarcinoma
• KRAS mutation

Negative ion desorption electrospray ionization (DESI) was used for the analysis of an ex vivo tissue sample set comprising primary colorectal adenocarcinoma samples and colorectal adenocarcinoma liver metastasis samples. Frozen sections (12 mu m thick) were analyzed by means of DESI imaging mass spectrometry (IMS) with spatial resolution of 100 mu m using a computer-controlled DESI imaging stage mounted on a high resolution Orbitrap mass spectrometer. DESI-IMS data were found to predominantly feature complex lipids, including phosphatidyl-inositols, phophatidyl-ethanolamines, phosphatidyl-serines, phosphatidyl-ethanolamine plasmalogens, phosphatidic acids, phosphatidyl-glycerols, ceramides, sphingolipids, and sulfatides among others. Molecular constituents were identified based on their exact mass and MS/MS fragmentation spectra. An identified set of molecules was found to be in good agreement with previously reported DESI imaging data. Different histological tissue types were found to yield characteristic mass spectrometric data in each individual section. Histological features were identified by comparison to hematoxylin-eosin stained neighboring sections. Ions specific to certain histological tissue types (connective tissue, smooth muscle, healthy mucosa, healthy liver parenchyma, and adenocarcinoma) were identified by semi-automated screening of data. While each section featured a number of tissue-specific species, no potential global biomarker was found in the full sample set for any of the tissue types. As an alternative approach, data were analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA) which resulted in efficient separation of data points based on their histological types. A pixel-by-pixel tissue identification method was developed, featuring the PCA/LDA analysis of authentic data set, and localization of unknowns in the resulting 60D, histologically assigned LDA space. Novel approach was found to yield results which are in 95% agreement with the results of classical histology. KRAS mutation status was determined for each sample by standard molecular biology methods and a similar PCA/LDA approach was developed to assess the feasibility of the determination of this important parameter using solely DESI imaging data. Results showed that the mutant and wild-type samples fully separated. DESI-MS and molecular biology results were in agreement in 90% of the cases.

• 出版日期2012-6