Cell-autonomous induction of type I interferon must be stringently regulated. Rapid induction is key to control virus infection, whereas proper limitation of signaling is essential to prevent immunopathology and autoimmune disease. Using unbiased kinome-wide RNAi screening followed by thorough validation, we identified 22 factors that regulate RIG-I/IRF3 signaling activity. We describe a negative-feedback mechanism targeting RIG-I activity, which is mediated by death associated protein kinase 1 (DAPK1). RIG-I signaling triggers DAPK1 kinase activation, and active DAPK1 potently inhibits RIG-I stimulated IRF3 activity and interferon-beta production. DAPK1 phosphorylates RIG-I in vitro at previously reported as well as other sites that limit 5'ppp-dsRNA sensing and virtually abrogate RIG-I activation.
- MeSH
- buňky A549 MeSH
- fosforylace MeSH
- HEK293 buňky MeSH
- kultivované buňky MeSH
- lidé MeSH
- malá interferující RNA genetika MeSH
- myši MeSH
- proteinkinasy asociované se smrtí metabolismus MeSH
- proteinkinasy metabolismus MeSH
- receptory kyseliny retinové metabolismus MeSH
- signální transdukce MeSH
- zpětná vazba fyziologická MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- myši MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
Reliable 3D detection of diffraction-limited spots in fluorescence microscopy images is an important task in subcellular observation. Generally, fluorescence microscopy images are heavily degraded by noise and non-specifically stained background, making reliable detection a challenging task. In this work, we have studied the performance and parameter sensitivity of eight recent methods for 3D spot detection. The study is based on both 3D synthetic image data and 3D real confocal microscopy images. The synthetic images were generated using a simulator modeling the complete imaging setup, including the optical path as well as the image acquisition process. We studied the detection performance and parameter sensitivity under different noise levels and under the influence of uneven background signal. To evaluate the parameter sensitivity, we propose a novel measure based on the gradient magnitude of the F1 score. We measured the success rate of the individual methods for different types of the image data and found that the type of image degradation is an important factor. Using the F1 score and the newly proposed sensitivity measure, we found that the parameter sensitivity is not necessarily proportional to the success rate of a method. This also provided an explanation why the best performing method for synthetic data was outperformed by other methods when applied to the real microscopy images. On the basis of the results obtained, we conclude with the recommendation of the HDome method for data with relatively low variations in quality, or the Sorokin method for image sets in which the quality varies more. We also provide alternative recommendations for high-quality images, and for situations in which detailed parameter tuning might be deemed expensive.
MOTIVATION: Automatic tracking of cells in multidimensional time-lapse fluorescence microscopy is an important task in many biomedical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this article, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. RESULTS: The main contributions of the challenge include the creation of a comprehensive video dataset repository and the definition of objective measures for comparison and ranking of the algorithms. With this benchmark, six algorithms covering a variety of segmentation and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets. Given the diversity of the datasets, we do not declare a single winner of the challenge. Instead, we present and discuss the results for each individual dataset separately. AVAILABILITY AND IMPLEMENTATION: The challenge Web site (http://www.codesolorzano.com/celltrackingchallenge) provides access to the training and competition datasets, along with the ground truth of the training videos. It also provides access to Windows and Linux executable files of the evaluation software and most of the algorithms that competed in the challenge.
