Semaphorin 3D autocrine signaling mediates the metastatic role of annexin A2 in pancreatic cancer.

Most patients with pancreatic ductal adenocarcinoma (PDA) present with metastatic disease at the time of diagnosis or will recur with metastases after surgical treatment. Semaphorin-plexin signaling mediates the migration of neuronal axons during development and of blood vessels during angiogenesis. The expression of the gene encoding semaphorin 3D (Sema3D) is increased in PDA tumors, and the presence of antibodies against the pleiotropic protein annexin A2 (AnxA2) in the sera of some patients after surgical resection of PDA is associated with longer recurrence-free survival. By knocking out AnxA2 in a transgenic mouse model of PDA (KPC) that recapitulates the progression of human PDA from premalignancy to metastatic disease, we found that AnxA2 promoted metastases in vivo. The expression of AnxA2 promoted the secretion of Sema3D from PDA cells, which coimmunoprecipitated with the co-receptor plexin D1 (PlxnD1) on PDA cells. Mouse PDA cells in which SEMA3D was knocked down or ANXA2-null PDA cells exhibited decreased invasive and metastatic potential in culture and in mice. However, restoring Sema3D in AnxA2-null cells did not entirely rescue metastatic behavior in culture and in vivo, suggesting that AnxA2 mediates additional prometastatic mechanisms. Patients with primary PDA tumors that have abundant Sema3D have widely metastatic disease and decreased survival compared to patients with tumors that have relatively low Sema3D abundance. Thus, AnxA2 and Sema3D may be new therapeutic targets and prognostic markers of metastatic PDA.

A cell biologist's guide to high resolution imaging.

Fluorescence microscopy is particularly well suited to the study of cell biology, due to its noninvasive nature, high sensitivity detection of specific molecules, and high spatial and temporal resolution. In recent years, there has been an important transition from imaging the static distributions of molecules as a snapshot in time in fixed material to live-cell imaging of the dynamics of molecules in cells: in essence visualizing biochemical processes in living cells. Furthermore, in the last 5 years, there have been important advances in so-called "super-resolution" imaging methods that have overcome the resolution limits imposed by the diffraction of light in optical systems. Live-cell imaging is now beginning to deliver in unprecedented detail, bridging the resolution gap between electron microscopy and light microscopy. We discuss the various factors that limit the spatial and temporal resolution of microscopy and how to overcome them, how to best prepare specimens for high resolution imaging, and the choice of fluorochromes. We also summarize the pros and cons of the different super-resolution techniques and introduce some of the key data analysis tasks that a cell biologist employing high resolution microscopy is typically interested in.

The gypsy insulator of Drosophila melanogaster, together with its binding protein suppressor of Hairy-wing, facilitate high and precise expression of transgenes in Arabidopsis thaliana.

The variation of expression pattern exhibited by a transgene as a result of random integration, known as position effect, is, among other mechanisms, a particular challenge to reverse genetics. We present a strategy to counteract position effect in Arabidopsis thaliana by flanking the transgenes with the gypsy insulator from Drosophila melanogaster. In addition, Suppressor of Hairy-wing [Su(Hw)], the binding protein of the gypsy insulator, was coexpressed. Results indicated that the gypsy insulators could efficiently improve the expression levels of reporter genes driven by various kinds of promoters by 8- to 13-fold. Coexpression of the Su(Hw) protein led to a more uniform expression level of transgenes, as the coefficient of variation of expression levels was reduced further. The gypsy-Su(Hw) system enhanced expression levels, but did not alter the specificity of promoter activities, as experimentally evidenced by the promoters of the PIN and the AFB gene families. Interestingly, the gypsy insulator was also able to improve the expression of a selectable marker gene outside the insulated region, which facilitated the screen of transformants. Our system will likely decrease the number of lines that experimenters need to create and examine for a given transgene by contributing to relatively high and precise expression of transgenes in plants. Certain features of the gypsy insulator in Arabidopsis also provide new perspectives on the insulator field.

Boosting accuracy of automated classification of fluorescence microscope images for location proteomics.

BACKGROUND: Detailed knowledge of the subcellular location of each expressed protein is critical to a full understanding of its function. Fluorescence microscopy, in combination with methods for fluorescent tagging, is the most suitable current method for proteome-wide determination of subcellular location. Previous work has shown that neural network classifiers can distinguish all major protein subcellular location patterns in both 2D and 3D fluorescence microscope images. Building on these results, we evaluate here new classifiers and features to improve the recognition of protein subcellular location patterns in both 2D and 3D fluorescence microscope images. RESULTS: We report here a thorough comparison of the performance on this problem of eight different state-of-the-art classification methods, including neural networks, support vector machines with linear, polynomial, radial basis, and exponential radial basis kernel functions, and ensemble methods such as AdaBoost, Bagging, and Mixtures-of-Experts. Ten-fold cross validation was used to evaluate each classifier with various parameters on different Subcellular Location Feature sets representing both 2D and 3D fluorescence microscope images, including new feature sets incorporating features derived from Gabor and Daubechies wavelet transforms. After optimal parameters were chosen for each of the eight classifiers, optimal majority-voting ensemble classifiers were formed for each feature set. Comparison of results for each image for all eight classifiers permits estimation of the lower bound classification error rate for each subcellular pattern, which we interpret to reflect the fraction of cells whose patterns are distorted by mitosis, cell death or acquisition errors. Overall, we obtained statistically significant improvements in classification accuracy over the best previously published results, with the overall error rate being reduced by one-third to one-half and with the average accuracy for single 2D images being higher than 90% for the first time. In particular, the classification accuracy for the easily confused endomembrane compartments (endoplasmic reticulum, Golgi, endosomes, lysosomes) was improved by 5-15%. We achieved further improvements when classification was conducted on image sets rather than on individual cell images. CONCLUSIONS: The availability of accurate, fast, automated classification systems for protein location patterns in conjunction with high throughput fluorescence microscope imaging techniques enables a new subfield of proteomics, location proteomics. The accuracy and sensitivity of this approach represents an important alternative to low-resolution assignments by curation or sequence-based prediction.