Comparing the Epidermal Growth Factor Interaction with Four Different Cell Lines: Intriguing Effects Imply Strong Dependency of Cellular Context

The interaction of the epidermal growth factor (EGF) with its receptor (EGFR) is known to be complex, and the common over-expression of EGF receptor family members in a multitude of tumors makes it important to decipher this interaction and the following signaling pathways. We have investigated the affinity and kinetics of 125I-EGF binding to EGFR in four human tumor cell lines, each using four culturing conditions, in real time by use of LigandTracer®

In situ quantification of HER2–protein tyrosine kinase 6 (PTK6) protein–protein complexes in paraffin sections from breast cancer tissues

BACKGROUND: Protein tyrosine kinase 6 (PTK6; breast tumour kinase) is overexpressed in up to 86% of the invasive breast cancers, and its association with the oncoprotein human epidermal growth factor receptor 2 (HER2) was shown in vitro by co-precipitation. Furthermore, expression of PTK6 in tumours is linked with the expression of HER2. METHOD AND RESULTS: In this study, we used the proximity ligation assay (PLA) technique on formalin-fixed paraffin sections from eighty invasive breast carcinoma tissue specimens to locate PTK6-HER2 protein-protein complexes. Proximity ligation assay signals from protein complexes were assessed quantitatively, and expression levels showed a statistically significant association with tumour size (P=0.015) and course of the cancer disease (P=0.012). CONCLUSION: Protein tyrosine kinase 6 forms protein complexes with HER2 in primary breast cancer tissues, which can be visualised by use of the PLA technique. Human epidermal growth factor receptor 2-PTK6 complexes are of prognostic relevance

Proximity assays for sensitive quantification of proteins

Proximity assays are immunohistochemical tools that utilise two or more DNA-tagged aptamers or antibodies binding in close proximity to the same protein or protein complex. Amplification by PCR or isothermal methods and hybridisation of a labelled probe to its DNA target generates a signal that enables sensitive and robust detection of proteins, protein modifications or protein–protein interactions. Assays can be carried out in homogeneous or solid phase formats and in situ assays can visualise single protein molecules or complexes with high spatial accuracy. These properties highlight the potential of proximity assays in research, diagnostic, pharmacological and many other applications that require sensitive, specific and accurate assessments of protein expression

Interaction proteomics of synapse protein complexes

The brain integrates complex types of information, and executes a wide range of physiological and behavioral processes. Trillions of tiny organelles, the synapses, are central to neuronal communication and information processing in the brain. Synaptic transmission involves an intricate network of synaptic proteins that forms the molecular machinery underlying transmitter release, activation, and modulation of transmitter receptors and signal transduction cascades. These processes are dynamically regulated and underlie neuroplasticity, crucial to learning and memory formation. In recent years, interaction proteomics has increasingly been used to elucidate the constituents of synaptic protein complexes. Unlike classic hypothesis-based assays, interaction proteomics detects both known and novel interactors without bias. In this trend article, we focus on the technical aspects of recent proteomics to identify synapse protein complexes, and the complementary methods used to verify the protein–protein interaction. Moreover, we discuss the experimental feasibility of performing global analysis of the synapse protein interactome

Colorectal cancer cell line proteomes are representative of primary tumors and predict drug sensitivity

Proteomics holds promise for individualizing cancer treatment. We analyzed to what extent the proteomic landscape of human colorectal cancer (CRC) is maintained in established CRC cell lines and the utility of proteomics for predicting therapeutic responses. Proteomic and transcriptomic analyses were performed on 44 CRC cell lines, compared against primary CRCs (n=95) and normal tissues (n=60), and integrated with genomic and drug sensitivity data. Cell lines mirrored the proteomic aberrations of primary tumors, in particular for intrinsic programs. Tumor relationships of protein expression with DNA copy number aberrations and signatures of post-transcriptional regulation were recapitulated in cell lines. The 5 proteomic subtypes previously identified in tumors were represented among cell lines. Nonetheless, systematic differences between cell line and tumor proteomes were apparent, attributable to stroma, extrinsic signaling, and growth conditions. Contribution of tumor stroma obscured signatures of DNA mismatch repair identified in cell lines with a hypermutation phenotype. Global proteomic data showed improved utility for predicting both known drug-target relationships and overall drug sensitivity as compared with genomic or transcriptomic measurements. Inhibition of targetable proteins associated with drug responses further identified corresponding synergistic or antagonistic drug combinations. Our data provide evidence for CRC proteomic subtype-specific drug responses. Proteomes of established CRC cell line are representative of primary tumors. Proteomic data tend to exhibit improved prediction of drug sensitivity as compared with genomic and transcriptomic profiles. Our integrative proteogenomic analysis highlights the potential of proteome profiling to inform personalized cancer medicine

Application guide for omics approaches to cell signaling

Research in signal transduction aims to identify the functions of different signaling pathways in physiological and pathological states. Traditional techniques using biochemical, genetic or cell biological approaches have made important contributions to our understanding of cellular signaling. However, the single-gene approach does not take into account the full complexity of cell signaling. With the availability of omics techniques, great progress has been made in understanding signaling networks. Omics approaches can be classified into two categories: 'molecular profiling', including genomic, proteomic, post-translational modification and interactome profiling; and 'molecular perturbation', including genetic and functional perturbations

G Protein-Coupled Receptor heterodimerization in the brain

G protein-coupled receptors (GPCRs) play critical roles in cellular processes and signaling and have been shown to form heteromers with diverge biochemical and/or pharmacological activities that are different from those of the corresponding monomers or homomers. However, despite extensive experimental results supporting the formation of GPCR heteromers in heterologous systems, the existence of such receptor heterocomplexes in the brain remains largely unknown, mostly because of the lack of appropriate methodology. Herein, we describe the in situ proximity ligation assay procedure underlining its high selectivity and sensitivity to image GPCR heteromers with confocal microscopy in brain sections. We describe here how the assay is performed and discuss advantages and disadvantages of this method compared with other available techniques

Classification of High Content Screening Data by Deep Convolutional Neural Networks

In drug discovery, high content screening (HCS) is an imaging-based method forcell-based screening of large libraries of drug compounds. HCS generates enormous amounts of images that need to be analysed and quantified by automated image analysis. This analysis is typically performed by a variety of algorithms segmenting cells and sub-cellular compartments and quantifying properties such as fluorescence intensities, morphological features, and textural characteristics. These quantified data can then be used to train a classifier to classify the imaged cells according to the phenotypic effects of the compounds. Recent developments in machine learning have enabled a new kind of image analysis in which classifiers based on convolutional neural networks can be trained on the image data directly, by passing the image quantification step. This has been shown to produce highly accurate predictions and simplify the analysis process. In this study, convolutional neural networks (CNNs) were used to classify HCS images of cells treated with a set of different drug compounds. A set of network architectures and hyper-parameters were explored in order to optimise the classification performance. The results were compared with the accuracies achieved with a classical image analysis pipeline in combination with a classifier. With this data set, the best CNN-based classifier achieved an accuracy of 91.3 %, where as classical image analysis combined with a random forest classifier achieved a classification accuracy of 78.8 %. In addition to the large increase in classification accuracy, CNNs have benefits such as being less biased when it comes to image quantification algorithm selection, and require less hands-on time during optimisation