Jean-Yves Tinevez

Bio: Jean-Yves Tinevez is an academic researcher from Pasteur Institute. The author has contributed to research in topics: Neural stem cell & Software. The author has an hindex of 19, co-authored 67 publications receiving 35036 citations. Previous affiliations of Jean-Yves Tinevez include Curie Institute & Max Planck Society.

Fiji: an open-source platform for biological-image analysis

Abstract: Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

Objective comparison of particle tracking methods

Abstract: Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.

Role of cortical tension in bleb growth

Abstract: Blebs are spherical membrane protrusions often observed during cell migration, cell spreading, cytokinesis, and apoptosis, both in cultured cells and in vivo. Bleb expansion is thought to be driven by the contractile actomyosin cortex, which generates hydrostatic pressure in the cytoplasm and can thus drive herniations of the plasma membrane. However, the role of cortical tension in bleb formation has not been directly tested, and despite the importance of blebbing, little is known about the mechanisms of bleb growth. In order to explore the link between cortical tension and bleb expansion, we induced bleb formation on cells with different tensions. Blebs were nucleated in a controlled manner by laser ablation of the cortex, mimicking endogenous bleb nucleation. Cortical tension was modified by treatments affecting the level of myosin activity or proteins regulating actin turnover. We show that there is a critical tension below which blebs cannot expand. Above this threshold, the maximal size of a bleb strongly depends on tension, and this dependence can be fitted with a model of the cortex as an active elastic material. Together, our observations and model allow us to relate bleb shape parameters to the underlying cellular mechanics and provide insights as to how bleb formation can be biochemically regulated during cell motility.

TNF and IL-1 exhibit distinct ubiquitin requirements for inducing NEMO–IKK supramolecular structures

Abstract: Nuclear factor κB (NF-κB) essential modulator (NEMO), a regulatory component of the IκB kinase (IKK) complex, controls NF-κB activation through its interaction with ubiquitin chains We show here that stimulation with interleukin-1 (IL-1) and TNF induces a rapid and transient recruitment of NEMO into punctate structures that are anchored at the cell periphery These structures are enriched in activated IKK kinases and ubiquitinated NEMO molecules, which suggests that they serve as organizing centers for the activation of NF-κB These NEMO-containing structures colocalize with activated TNF receptors but not with activated IL-1 receptors We investigated the involvement of nondegradative ubiquitination in the formation of these structures, using cells deficient in K63 ubiquitin chains or linear ubiquitin chain assembly complex (LUBAC)-mediated linear ubiquitination Our results indicate that, unlike TNF, IL-1 requires K63-linked and linear ubiquitin chains to recruit NEMO into higher-order complexes Thus, different mechanisms are involved in the recruitment of NEMO into supramolecular complexes, which appear to be essential for NF-κB activation

NIH Image to ImageJ: 25 years of image analysis

Abstract: For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.

ImageJ2: ImageJ for the next generation of scientific image data

Abstract: ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software’s ability to handle the requirements of modern science. We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called “ImageJ2” in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ’s development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites.

scikit-image: Image processing in Python

Abstract: scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image. More information can be found on the project homepage, http://scikit-image.org.

QuPath: Open source software for digital pathology image analysis

Abstract: QuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. In addition to offering a comprehensive panel of tumor identification and high-throughput biomarker evaluation tools, QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. Furthermore, QuPath’s flexible design makes it suitable for a wide range of additional image analysis applications across biomedical research.