Günther Ernst

Mass spectrometry meets chip technology: A new proteomic tool in cancer research?

DNA chip technologies are the most exiting genomic tools, which were developed within the last few years. It is, however, evident that knowledge of the gene sequence or the quantity of gene expression is not sufficient to predict the biological nature and function of a protein. This can be particularly important in cancer research where post‐translational modifications of a protein can specifically contribute to the disease. To address this problem, several proteomic tools have been developed. Currently the most widely used proteomic tool is two‐dimensional protein gel electrophoresis (2‐DE), which can display protein expression patterns to a high degree of resolution. As an alternative to 2‐DE, a preliminary study using a new technique was employed to generate protein expression patterns from whole tissue extracts. Surface‐enhanced laser desorption/ionization (SELDI) allows the retention of proteins on a solid‐phase chromatographic surface (Protein Chip® Array) with direct detection of retained proteins by time of flight‐mass spectrometry (TOF‐MS). Using this system, we analyzed eight cases of renal cell carcinoma (RCC) including normal, peripheral and central tumor tissue as well as four microdissected cases of cervical intraepithelial neoplasia (CIN) and three microdissected cases of cervix uteri carcinoma. Differentially expressed proteins were found by comparing the protein expression patterns generated using SELDI‐based TOF‐MS of tumor tissue with normal and neoplastic tissue, respectively. By applying this fast and powerful Protein Chip array technology it becomes possible to investigate complex changes at the protein level in cancer associated with tumor development and progression.

A Technical Triade for Proteomic Identification and Characterization of Cancer Biomarkers

Biomarkers are needed to elucidate the biological background and to improve the detection of cancer. Therefore, we have analyzed laser-microdissected cryostat sections from head and neck tumors and adjacent mucosa on ProteinChip arrays. Two differentially expressed proteins (P = 3.34 × 10−5 and 4.6 × 10−5) were isolated by two-dimensional gel electrophoresis and identified as S100A8 (calgranulin A) and S100A9 (calgranulin B) by in-gel proteolytic digestion, peptide mapping, tandem mass spectrometry analysis, and immunodepletion assay. The relevance of these single marker proteins was evaluated by immunohistochemistry. Positive tissue areas were reanalyzed on ProteinChip arrays to confirm the identity of these proteins. As a control, a peak with low P was identified as calgizzarin (S100A11) and characterized in the same way. This technical triade of tissue microdissection, ProteinChip technology, and immunohistochemistry opens up the possibility to find, identify, and characterize tumor relevant biomarkers, which will allow the movement toward the clonal heterogeneity of malignant tumors. Taking this approach, proteins were identified that might be responsible for invasion and metastasis.

Biomarker Discovery and Identification in Laser Microdissected Head and Neck Squamous Cell Carcinoma with ProteinChip® Technology, Two-dimensional Gel Electrophoresis, Tandem Mass Spectrometry, and Immunohistochemistry

Head and neck cancer is a frequent malignancy with a complex, and up to now not clear etiology. Therefore, despite of improvements in diagnosis and therapy, the survival rate with head and neck squamous-cell carcinomas is poor. For a better understanding of the molecular mechanisms behind the process of tumorigenesis and tumor progression, we have analyzed changes of protein expression between microdissected normal pharyngeal epithelium and tumor tissue by ProteinChip® technology. For this, cryostat sections from head and neck tumors (n = 57) and adjacent mucosa (n = 44) were laser-microdissected and analyzed on ProteinChip arrays. The derived mass spectrometry profiles exhibited numerous statistical differences. One peak significantly higher expressed in the tumor (p = 0.000029) was isolated by two-dimensional gel electrophoresis and identified as annexin V by in-gel proteolytic digestion, peptide mapping, tandem mass spectrometry analysis, and immuno-deplete assay. The relevance of this single marker protein was further evaluated by immunohistochemistry. Annexin-positive tissue areas were re-analyzed on ProteinChip arrays to confirm the identity of this protein. In this study, we could show that biomarker in head and neck cancer can be found, identified, and assessed by combination of ProteinChip technology, two-dimensional gel electrophoresis, and immunohistochemistry. In our experience, however, such studies only make sense if a relatively pure microdissected tumor tissue is used. Only then minute changes in protein expression between normal pharyngeal epithelium and tumor tissue can be detected, and it will become possible to educe a tumor-associated protein pattern that might be used as a marker for tumorigenesis and progression.

Colon-Derived Liver Metastasis, Colorectal Carcinoma, and Hepatocellular Carcinoma Can Be Discriminated by the Ca2+-Binding Proteins S100A6 and S100A11

Background It is unknown, on the proteomic level, whether the protein patterns of tumors change during metastasis or whether markers are present that allow metastases to be allocated to a specific tumor entity. The latter is of clinical interest if the primary tumor is not known. Methodology/Principal Findings In this study, tissue from colon-derived liver metastases (n = 17) were classified, laser-microdissected, and analysed by ProteinChip arrays (SELDI). The resulting spectra were compared with data for primary colorectal (CRC) and hepatocellular carcinomas (HCC) from our former studies. Of 49 signals differentially expressed in primary HCC, primary CRC, and liver metastases, two were identified by immunodepletion as S100A6 and S100A11. Both proteins were precisely localized immunohistochemically in cells. S100A6 and S100A11 can discriminate significantly between the two primary tumor entities, CRC and HCC, whereas S100A6 allows the discrimination of metastases and HCC. Conclusions Both identified proteins can be used to discriminate different tumor entities. Specific markers or proteomic patterns for the metastases of different primary cancers will allow us to determine the biological characteristics of metastasis in general. It is unknown how the protein patterns of tumors change during metastasis or whether markers are present that allow metastases to be allocated to a specific tumor entity. The latter is of clinical interest if the primary tumor is not known.

MALDI-imaging segmentation is a powerful tool for spatial functional proteomic analysis of human larynx carcinoma

PurposeFor several decades, conventional histological staining and immunohistochemistry (IHC) have been the main tools to visualize and understand tissue morphology and structure. IHC visualizes the spatial distribution of individual protein species directly in tissue. However, a specific antibody is required for each protein, and multiplexing capabilities are extremely limited, rarely visualizing more than two proteins simultaneously. With the recent emergence of matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-imaging), it is becoming possible to study more complex proteomic patterns directly in tissue. However, the analysis and interpretation of large and complex MALDI-imaging data requires advanced computational methods. In this paper, we show how the recently introduced method of spatial segmentation can be applied to analysis and interpretation of a larynx carcinoma section and compare the spatial segmentation with the histological annotation of the same tissue section.MethodsMatrix-assisted laser desorption/ionization imaging is a label-free spatially resolved analytical technique, which allows detection and visualization of hundreds of proteins at once. Spatial segmentation of the MALDI-imaging data by clustering of spectra by their similarity was performed, automatically generating spatial a segmentation map of the tissue section, where regions of similar proteomic patterns were highlighted. The tissue was stained with the hematoxylin and eosin (H&E), histopathologically analyzed and annotated. The segmentation map was interpreted after its overlay with the H&E microscopy image.ResultsThe automatically generated segmentation map exhibits high correspondence to the detailed histological annotation of the larynx carcinoma tissue section. By superimposing, the segmentation map based on the proteomic profiles with H&E-stained microscopic images, we demonstrate precise localization of complex and histopathologically relevant tissue features in an automated way.ConclusionsThe combination of MALDI-imaging and automatic spatial segmentation is a useful approach in analyzing carcinoma tissue and provides a deeper insight into the functional proteomic organization of the respective tissue.

Multimodal nonlinear microscopic investigations on head and neck squamous cell carcinoma: toward intraoperative imaging.

Prognosis and appropriate treatment of head and neck squamous cell carcinoma (HNSCC) depend on the tumor type routinely derived by invasive histopathology. A promising noninvasive alternative is nonlinear optical imaging, which is capable of in vivo tissue visualization for tumor typing and grading.

Proteomic analysis of human papillomavirus-related oral squamous cell carcinoma: Identification of thioredoxin and epidermal-fatty acid binding protein as upregulated protein markers in microdissected tumor tissue

Human papillomavirus (HPV) infection has been identified as an etiologic agent for a subset of oral squamous cell carcinoma (OSCC) with increasing incidence. HPV DNA‐positivity may confer better prognosis but the related oncogenic mechanisms are unknown. For the identification of HPV relevant proteins, we analyzed microdissected cells from HPV DNA‐positive (n = 17) and HPV DNA‐negative (n = 7) OSCC tissue samples. We identified 18 proteins from tumor tissues by peptide fingerprint mapping and SELDI MS that were separated using 2‐DE. Among a number of signals that were detected as significantly different in the protein profiling analysis, we identified thioredoxin (TRX) and epidermal‐fatty acid binding protein as upregulated in HPV related tumor tissue. This study, investigating for the first time proteomic changes in microdissected HPV infected tumor tissue, provides an indication on the oncogenic potential of viruses.

The prognostic relevance of p16 inactivation in head and neck cancer.

The inactivation of the tumor suppressor gene p16 plays an important role in the development of malignant tumors. p16 loss can result from point mutations, loss of heterozygosity (LOH) or methylation of the promoter region. A total of 67 samples of tumor tissue from squamous cell carcinoma of the oral cavity, the pharynx and the larynx were analyzed for an inactivation of p16. The results of the molecular-biological investigations were correlated with the known clinical prognostic parameters after a follow-up period of approximately 3 years. Methylation of the promoter region and LOH were the main mechanisms of p16 inactivation. Point mutations presented as rare events. An inactivation of p16 did not have any statistical influence on tumor prognosis. Patients with a p16 gene inactivated by promoter methylation appeared to have a slightly lower tendency for local and regional recurrences. The inactivation of the tumor suppressor gene p16 plays a role in the carcinogenesis of head and neck cancer.

Proteohistography--direct analysis of tissue with high sensitivity and high spatial resolution using ProteinChip technology.

On the proteomic level, all tissues, tissue constituents, or even single cells are heterogeneous, but the biological relevance of this cannot be adequately investigated with any currently available technique. The analysis of proteins of small tissue areas by any proteomic approach is limited by the number of required cells. Increasing the number of cells only serves to lower the spatial resolution of expressed proteins. To enhance sensitivity and spatial resolution we developed Proteohistography. Laser microdissection was used to mark special areas of interest on tissue sections attached to glass slides. These areas were positioned under microscopic control directly on an affinity chromatographic ProteinChip Array so that cells were lysed and their released proteins bound on a spatially defined point. The ProteinChip System, surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), allows the laser to be steered to up to 215 distinct positions across the surface of the spot, enabling a high spatial resolution of measured protein profiles for the analyzed tissue area. Protein profiles of the single positions were visually plotted over the used tissue section to visualize distribution proteohistologically. Results show that the spatial distribution of detectable proteins could be used as a Proteohistogram for a given tissue area. Consequently, this procedure can provide additional information to both a matrix-assisted laser desorption/ionization (MALDI)-based approach and immunohistochemistry, as it is more sensitive, highly quantitative, and no specific antibody is needed. Hence, proteomic heterogeneity can be visualized even if proteins are not known or identified.

Spatial Segmentation of MALDI FT-ICR MSI Data: A Powerful Tool to Explore the Head and Neck Tumor In Situ Lipidome

AbstractMatrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI MSI) is a well-established analytical technique for determining spatial localization of lipids in biological samples. The use of Fourier-transform ion cyclotron resonance (FT-ICR) mass spectrometers for the molecular imaging of endogenous compounds is gaining popularity, since the high mass accuracy and high mass resolving power enables accurate determination of exact masses and, consequently, a more confident identification of these molecules. The high mass resolution FT-ICR imaging datasets are typically large in size. In order to analyze them in an appropriate timeframe, the following approach has been employed: the FT-ICR imaging datasets were spatially segmented by clustering all spectra by their similarity. The resulted spatial segmentation maps were compared with the histologic annotation. This approach facilitates interpretation of the full datasets by providing spatial regions of interest. The application of this approach, which has originally been developed for MALDI-TOF MSI datasets, to the lipidomic analysis of head and neck tumor tissue revealed new insights into the metabolic organization of the carcinoma tissue. Graphical Abstractᅟ