Clinical laboratory testing for HER2 status in breast cancer tissues is critically important for therapeutic decision making. Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) is a powerful tool for investigating proteins through the direct and morphology-driven analysis of tissue sections. We hypothesized that MALDI-IMS may determine HER2 status directly from breast cancer tissues. Breast cancer tissues (n = 48) predefined for HER2 status were subjected to MALDI-IMS, and protein profiles were obtained through direct analysis of tissue sections. Protein identification was performed by tissue microextraction and fractionation followed by top-down tandem mass spectrometry. A discovery and an independent validation set were used to predict HER2 status by applying proteomic classification algorithms. We found that specific protein/peptide expression changes strongly correlated with the HER2 overexpression. Among these, we identified m/z 8404 as cysteine-rich intestinal protein 1. The proteomic signature was able to accurately define HER2-positive from HER2-negative tissues, achieving high values for sensitivity of 83%, for specificity of 92%, and an overall accuracy of 89%. Our results underscore the potential of MALDI-IMS proteomic algorithms for morphology-driven tissue diagnostics such as HER2 testing and show that MALDI-IMS can reveal biologically significant molecular details from tissues which are not limited to traditional high-abundance proteins.

Abstract. We report on a Raman microspectroscopic characterization of the inflammatory bowel diseases (IBD) Crohn's disease (CD) and ulcerative colitis (UC). Therefore, Raman maps of human colon tissue sections were analyzed by utilizing innovative chemometric approaches. First, support vector machines were applied to highlight the tissue morphology (¼Raman spectroscopic histopathology). In a second step, the biochemical tissue composition has been studied by analyzing the epithelium Raman spectra of sections of healthy control subjects (n ¼ 11), subjects with CD (n ¼ 14), and subjects with UC (n ¼ 13). These three groups exhibit significantly different molecular specific Raman signatures, allowing establishment of a classifier (support-vector-machine). By utilizing this classifier it was possible to separate between healthy control patients, patients with CD, and patients with UC with an accuracy of 98.90%. The automatic design of both classification steps (visualization of the tissue morphology and molecular classification of IBD) paves the way for an objective clinical diagnosis of IBD by means of Raman spectroscopy in combination with chemometric approaches.

Discrimination of nodular lesions in cirrhotic liver is a challenge in the histopathologic diagnostics. For this reason, there is an urgent need for new detection methods to improve the accuracy of the diagnosis of liver cancer. Raman imaging allows to determine the spatial distribution of a variety of molecules in cells or tissue label-free and to correlate this molecular information with the morphological structures at the same sample location. This study reports investigations of two liver cancer cell lines, - HepG2 and SK-Hep1, - as well as HepG2 cells in different cellular growth phases using Raman micro-spectroscopic imaging. Spectral data of all cells were recorded as a color-coded image and subsequentially analyzed by hierarchical cluster and principal component analysis. A support vector machine-based classification algorithm reliably predicts previously unknown cancer cells and cell cycle phases. By including selectively the Raman spectra of the cytoplasmic lipids in the classifier, the accuracy has been improved. The main spectral differences that were found in the comparative analysis can be attributed to a higher expression of unsaturated fatty acids in the hepatocellular carcinoma cells and during the proliferation phase. This corresponds to the already examined de novo lipogenesis in cells of liver cancer.