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Neural network-assisted segmentation of mating and budding events in S. cerevisiae

To ensure error-free duplication of all (epi)genetic information once per cell cycle, DNA replication follows a cell type and developmental stage specific spatio-temporal program. Here, we analyze the spatio-temporal DNA replication progression in (un)differentiated mouse embryonic stem (mES) cells. Whereas telomeres replicate throughout S-phase, we observe mid-S phase replication of (peri)centromeric heterochromatin in mES cells, which switches to late S-phase replication upon differentiation correlating with increase in condensation and decrease in acetylation of chromatin. We also find synchronous duplication of the Y chromosome, marking the end of S-phase, irrespectively of the pluripotency state. Using a combination of single-molecule and super-resolution microscopy, we measure molecular properties of the mES cell replicon, the number of replication foci active in parallel and their spatial clustering in mES cells versus somatic cells. We conclude that each replication nanofocus in mES cells corresponds to an individual replicon, with approximately up to one quarter representing unidirectional forks. Furthermore, with molecular combing and genome-wide origin mapping analyses we find that mES cells activate twice as many origins spaced at half the distance than somatic cells. Altogether, our results highlight fundamental developmental differences on progression of genome replication and origin activation in pluripotent cells.

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

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] . EDAM-bioimaging is an extension of EDAM dedicated to bioimage analysis, bioimage informatics, and bioimaging. It is being developed in collaboration between the ELIXIR research infrastructure and the NEUBIAS and COMULIS COST Actions, in close contact with the Euro-BioImaging research infrastructure and the Global BioImaging network.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, workflows, and training materials, fostering open science and "reproducible" bioimage analysis.New developments in EDAM-bioimaging at the time of publication [3] include among others:A concise but relatively comprehensive ontology of Machine learning, Artificial intelligence, and Clustering (to the level relevant in particular in bioimaging, biosciences, and also scientific data analysis in general)Added and refined topics and synonyms within Sample preparation and Tomography, and finalised coverage of imaging techniques (all of these to the high-level extent that influences choices of downstream analysis, i.e. the scope of EDAM)EDAM-bioimaging continues being under active development, with a growing and diversifying community of contributors. It is used in BIII.eu, the registry of bioimage analysis tools, workflows, and training materials, and emerging also in descriptions of Debian Med packages available in Debian and Bio-Linux, and tools in bio.tools. 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 publication is version alpha06 [3], 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] Matúš Kalaš, Laure Plantard, Martin Jones, Nataša Sladoje, Marie-Charlotte Domart, Matthia Karreman, Arrate Muñoz-Barrutia, Raf Van de Plas, Ivana Vrhovac Madunić, Dean Karaica, Laura Nicolás Sáenz, Estibaliz Gómez de Marisca, Daniel Sage, Robert Haase Joakim Lindblad, and all contributors to previous versions (2020). edamontology/edam-bioimaging: alpha06 (Version alpha06). Zenodo. DOI: 10.5281/zenodo.3695725 Open Access

Microscopes have been an invaluable tool throughout the history of the life sciences, as they allow researchers to observe the miniscule details of living systems in space and time. However, modern biology studies complex and non-obvious phenotypes and their distributions in populations and thus requires that microscopes evolve from visual aids for anecdotal observation into instruments for objective and quantitative measurements. To this end, many cutting-edge developments in microscopy are fuelled by innovations in the computational processing of the generated images. Computational tools can be applied in the early stages of an experiment, where they allow for reconstruction of images with higher resolution and contrast or more colors compared to raw data. In the final analysis stage, state-of-the-art image analysis pipelines seek to extract interpretable and humanly tractable information from the high-dimensional space of images. In the work presented in this thesis, I performed super-resolution microscopy and wrote image analysis pipelines to derive quantitative information about multiple biological processes. I contributed to studies on the regulation of DNMT1 by implementing machine learning-based segmentation of replication sites in images and performed quantitative statistical analysis of the recruitment of multiple DNMT1 mutants. To study the spatiotemporal distribution of DNA damage response I performed STED microscopy and could provide a lower bound on the size of the elementary spatial units of DNA repair. In this project, I also wrote image analysis pipelines and performed statistical analysis to show a decoupling of DNA density and heterochromatin marks during repair. More on the experimental side, I helped in the establishment of a protocol for many-fold color multiplexing by iterative labelling of diverse structures via DNA hybridization. Turning from small scale details to the distribution of phenotypes in a population, I wrote a reusable pipeline for fitting models of cell cycle stage distribution and inhibition curves to high-throughput measurements to quickly quantify the effects of innovative antiproliferative antibody-drug-conjugates. The main focus of the thesis is BigStitcher, a tool for the management and alignment of terabyte-sized image datasets. Such enormous datasets are nowadays generated routinely with light-sheet microscopy and sample preparation techniques such as clearing or expansion. Their sheer size, high dimensionality and unique optical properties poses a serious bottleneck for researchers and requires specialized processing tools, as the images often do not fit into the main memory of most computers. BigStitcher primarily allows for fast registration of such many-dimensional datasets on conventional hardware using optimized multi-resolution alignment algorithms. The software can also correct a variety of aberrations such as fixed-pattern noise, chromatic shifts and even complex sample-induced distortions. A defining feature of BigStitcher, as well as the various image analysis scripts developed in this work is their interactivity. A central goal was to leverage the user's expertise at key moments and bring innovations from the big data world to the lab with its smaller and much more diverse datasets without replacing scientists with automated black-box pipelines. To this end, BigStitcher was implemented as a user-friendly plug-in for the open source image processing platform Fiji and provides the users with a nearly instantaneous preview of the aligned images and opportunities for manual control of all processing steps. With its powerful features and ease-of-use, BigStitcher paves the way to the routine application of light-sheet microscopy and other methods producing equally large datasets.

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] . EDAM-bioimaging is an extension of EDAM dedicated to bioimage analysis, bioimage informatics, and bioimaging. It is being developed in collaboration between the ELIXIR research infrastructure and the NEUBIAS and COMULIS COST Actions, in close contact with the Euro-BioImaging research infrastructure and the Global BioImaging network.

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

Light-sheet imaging of cleared and expanded samples creates terabyte-sized datasets that consist of many unaligned three-dimensional image tiles, which must be reconstructed before analysis. We developed the BigStitcher software to address this challenge. BigStitcher enables interactive visualization, fast and precise alignment, spatially resolved quality estimation, real-time fusion and deconvolution of dual-illumination, multitile, multiview datasets. The software also compensates for optical effects, thereby improving accuracy and enabling subsequent biological analysis.

Histone H2AX phosphorylation is an early signalling event triggered by DNA double-strand breaks (DSBs). To elucidate the elementary units of phospho-H2AX-labelled chromatin, we integrate super-resolution microscopy of phospho-H2AX during DNA repair in human cells with genome-wide sequencing analyses. Here we identify phospho-H2AX chromatin domains in the nanometre range with median length of ∼75 kb. Correlation analysis with over 60 genomic features shows a time-dependent euchromatin-to-heterochromatin repair trend. After X-ray or CRISPR-Cas9-mediated DSBs, phospho-H2AX-labelled heterochromatin exhibits DNA decondensation while retaining heterochromatic histone marks, indicating that chromatin structural and molecular determinants are uncoupled during repair. The phospho-H2AX nano-domains arrange into higher-order clustered structures of discontinuously phosphorylated chromatin, flanked by CTCF. CTCF knockdown impairs spreading of the phosphorylation throughout the 3D-looped nano-domains. Co-staining of phospho-H2AX with phospho-Ku70 and TUNEL reveals that clusters rather than nano-foci represent single DSBs. Hence, each chromatin loop is a nano-focus, whose clusters correspond to previously known phospho-H2AX foci., Phosphorylated histone H2AX is an early signalling event of DNA double-strand breaks. Here the authors use super-resolution microscopy and ChIP-seq and identify ‘nano-domains' – chromatin loops decorated by γH2AX and flanked by CTCF.