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International audience; EDAM is a well-established ontology of operations, topics, types of data, and data formats that are used in bioinformatics and its neighbouring fields [1,2,3] . EDAM-bioimaging is an extension of EDAM dedicated to bioimage analysis, bioimage informatics, and bioimaging. It is developed in collaboration between the NEUBIAS and ELIXIR Europe|ELIXIR-EXCELERATE projects, in contact with Euro-BioImaging and Global BioImaging.EDAM-bioimaging contains an inter-related hierarchy of concepts including bioimage analysis and related operations, bioimaging topics and technologies, and bioimage data and their formats. The modelled concepts enable interoperable descriptions of software, publications, data, and workflows, fostering reliable and transparent, "reproducible" bioimage analysis.EDAM-bioimaging is under active development, with a few alpha releases publicly available. It is used in BISE|biii.eu, the bioimaging tools and resources information portal, and emerging also in descriptions of Debian Med packages available in Debian and Bio-Linux. Development of EDAM-bioimaging has been carried out in a successful open community manner, in a fruitful collaboration between numerous bioimaging experts and ontology developers. The last stable release at the time of poster submission is version alpha05 [4], and the live development version can be viewed and commented on WebProtégé (free registration required). New contributors are warmly welcome![1] Ison, J., Kalaš, M., Jonassen, I., Bolser, D., Uludag, M., McWilliam, H., Malone, J., Lopez, R., Pettifer, S. and Rice, P. (2013). EDAM: an ontology of bioinformatics operations, types of data and identifiers, topics and formats. Bioinformatics, 29(10): 1325-1332. DOI: 10.1093/bioinformatics/btt113 Open Access[2] Kalaš, M., Ménager, H., Schwämmle, V., Ison, J. and EDAM Contributors (2017). EDAM – the ontology of bioinformatics operations, types of data, topics, and data formats (2017 update) [version 1; not peer reviewed]. F1000Research, 6(ISCB Comm J):1181 (Poster) DOI: 10.7490/f1000research.1114459.1 Open Access[3] Kalaš, M., Ménager, H., Ison, J. and Willighagen, E. (2018). edamontology/edamontology: EDAM 1.21 (Version 1.21). Zenodo. DOI: 10.5281/zenodo.1325952 Open Access[4] Matúš Kalaš, Nataša Sladoje, Laure Plantard, Martin Jones, Leandro Aluisio Scholz, Joakim Lindblad, and contributors (2019). edamontology/edam-bioimaging: alpha05 (Version alpha05). Zenodo. DOI: 10.5281/zenodo.2557012 Open Access

Cell competition is a quality control mechanism that eliminates unfit cells. How cells compete is poorly understood, but it is generally accepted that molecular exchange between cells signals elimination of unfit cells. Here we report an orthogonal mechanism of cell competition, whereby cells compete through mechanical insults. We show that MDCK cells silenced for the polarity gene scribble (scribKD) are hypersensitive to compaction, that interaction with wild-type cells causes their compaction and that crowding is sufficient for scribKD cell elimination. Importantly, we show that elevation of the tumour suppressor p53 is necessary and sufficient for crowding hypersensitivity. Compaction, via activation of Rho-associated kinase (ROCK) and the stress kinase p38, leads to further p53 elevation, causing cell death. Thus, in addition to molecules, cells use mechanical means to compete. Given the involvement of p53, compaction hypersensitivity may be widespread among damaged cells and offers an additional route to eliminate unfit cells., Cell competition is a quality control mechanism to eliminate unfit cells. Here the authors show that physical compaction of less fit cells surrounded by healthy neighbours leads to increased expression of tumour suppressor p53 in the compacted cells, causing cell death.

International audience; The ontology of bioimage informatics operations, topics, data, and formats What? EDAM-bioimaging is an extension of the EDAM ontology, dedicated to bioimage analysis, bioimage informatics, and bioimaging. Why? EDAM-bioimaging enables interoperable descriptions of software, publications, data, and workflows, fostering reliable and transparent science. How? EDAM-bioimaging is developed in a community spirit, in a welcoming collaboration between numerous bioimaging experts and ontology developers. How can I contribute? We need your expertise! You can help by reviewing parts of EDAM-bioimaging, posting comments with suggestions, requirements, or needs for clarification, or participating in a Taggathon or another hackathon. Please see https://github.com/edamontology/edam-bioimaging#contributing. EDAM-bioimaging is developed in an interdisciplinary open collaboration supported by the hosting institutions, participating individuals, and NEUBIAS COST Action (CA15124) and ELIXIR-EXCELERATE (676559) funded by the Horizon 2020 Framework Programme of the European Union. https://github.com/edamontology/edam-bioimaging @edamontology /edamontology/edam-bioimaging

Supplemental Figures S1â S6. (DOCX 1246 kb)

Cell polarity is regulated by evolutionarily conserved polarity factors whose precise higher-order organization at the cell cortex is largely unknown. Here we image frontally the cortex of live fission yeast cells using time-lapse and super-resolution microscopy. Interestingly, we find that polarity factors are organized in discrete cortical clusters resolvable to ~50–100 nm in size, which can form and become cortically enriched by oligomerization. We show that forced co-localization of the polarity factors Tea1 and Tea3 results in polarity defects, suggesting that the maintenance of both factors in distinct clusters is required for polarity. However, during mitosis, their co-localization increases, and Tea3 helps to retain the cortical localization of the Tea1 growth landmark in preparation for growth reactivation following mitosis. Thus, regulated spatial segregation of polarity factor clusters provides a means to spatio-temporally control cell polarity at the cell cortex. We observe similar clusters in Saccharomyces cerevisiae and Caenorhabditis elegans cells, indicating this could be a universal regulatory feature., Nature Communications, 4, ISSN:2041-1723

International audience; The study of membrane plasticity and the role of molecular "machines" in the control of biogenesis of the endo-cellular membranes have highlighted the crucial role of the "Rab" GTPases family as organizing centers of functional molecular platforms. Yet, to understand the regulation and coordination of these molecular assemblies, which are responsible for intracellular dynamic architectures, a more global vision, the development and the correlation of approaches at different spatial and temporal scales are needed. Considering the "fickle" nature of such dynamic architectures, the current performance of image acquisition systems and the analytical tools at our disposal, many technological challenges must be overcome. Dynamic aspects of perspectives described above require conceptual developments, particularly in the field of microscopy imaging. Moreover, to extract maximum information on the same sample, the development of an adapted microscopy, correlating different modalities, is needed. Last but not least, accurate image descriptors, allowing automatic detection and classification of molecular behavior in space and time, are indispensable. In this talk, we will focus on unsupervised change detection algorithms and new image modeling able to capture spatio-temporal regularities and geometries present in an image pair. In contrast to the usual pixel-wise methods and Markov Random Fields methods, we propose a patch-based formulation for modeling semi-local interactions and detecting local or regional changes in a microscopy image pair. By introducing dissimilarity measures to compare patches and binary local decisions, we design collaborative decision rules that use the total number of detections made by individual neighboring pixels, for different patch sizes. First, we will describe the patch-based representation for image pair analysis and present collaborative decision rules in neighborhoods. In addition, we will present the algorithm used to fuse binary decisions with statistical tests, at different spatial scales. Experimental results in video-microscopy (TIRF and wide-field imaging) demonstrate that the detection algorithm (with no optical flow computation) performs well at detecting meaningful changes and appearing/disappearing spots at the cell membrane. We also illustrate the approach for probabilistic local and global colocalization analysis of molecules in dual-color confocal images.

International audience; The study of membrane plasticity and the role of molecular "machines" in the control of biogenesis of the endo-cellular membranes have highlighted the crucial role of the "Rab" GTPases family as organizing centers of functional molecular platforms in membrane sub-domains. Yet, to understand the regulation and coordination of these molecular assemblies, which are responsible for cellular dynamic architectures, a more global vision, the development and the correlation of approaches at different spatial and temporal scales are needed. Focused biological models will be presented and will serve as guidelines that consider the mosaic of "Rabs domains" in particular at the plasma membrane within the context of recycling and exocytosis processes of fluorescently labeled cargos. In these In Vivo biological contexts, approaches aimed to measure and understand the multi-specific nature of these complexes have been developed. Our overall target is to model and assess the functional coordination between the different molecular machineries involved in the biogenesis of membrane sub-domains and their regulation. However, considering the "fickle" nature of such dynamic architectures, the current performance of image acquisition systems and the analytical tools at our disposal, many technological challenges must be overcome. Moreover, to extract maximum information on the same sample, the development of an adapted microscopy, correlating different modalities, is needed. Last but not least, accurate image descriptors, allowing automatic detection and classification of molecular behaviors in space and time, are indispensable.