Vannary Meas-Yedid

Bio: Vannary Meas-Yedid is an academic researcher from Pasteur Institute. The author has contributed to research in topics: Image segmentation & Transplantation. The author has an hindex of 25, co-authored 65 publications receiving 2616 citations. Previous affiliations of Vannary Meas-Yedid include Centre national de la recherche scientifique.

Icy: an open bioimage informatics platform for extended reproducible research

Abstract: Icy is a collaborative platform for biological image analysis that extends reproducible research principles by facilitating and stimulating the contribution and sharing of algorithm-based tools and protocols between researchers. Current research in biology uses evermore complex computational and imaging tools. Here we describe Icy, a collaborative bioimage informatics platform that combines a community website for contributing and sharing tools and material, and software with a high-end visual programming framework for seamless development of sophisticated imaging workflows. Icy extends the reproducible research principles, by encouraging and facilitating the reusability, modularity, standardization and management of algorithms and protocols. Icy is free, open-source and available at http://icy.bioimageanalysis.org/ .

Olfactory discrimination learning increases the survival of adult-born neurons in the olfactory bulb.

Abstract: In the olfactory bulb (OB), new neurons are added throughout life, forming an integral part of the functioning circuit. Yet only some of them survive more than a month. To determine whether this turnover depends on olfactory learning, we examined the survival of adult newborn cells labeled with the cell division marker BrdU, administered before learning in an olfactory discrimination task. We report that discrimination learning increases the number of newborn neurons in the adult OB by prolonging their survival. Simple exposure to the pair of olfactory cues did not alter neurogenesis, indicating that the mere activation of sensory inputs during the learning task was insufficient to alter neurogenesis. The increase in cell survival after learning was not uniformly distributed throughout angular sectors of coronal sections of the OB. Monitoring odor activation maps using patterns of Zif268 immediate early gene expression revealed that survival was greater in regions more activated by the non-reinforced odorant. We conclude that sensory activation in a learning context not only controls the total number of newborn neurons in the adult OB, but also refines their precise location. Shaping the distribution of newborn neurons by influencing their survival could optimize the olfactory information processing required for odor discrimination.

On the digital trail of mobile cells

Abstract: Cell migration is a field of intense current research, where biologists increasingly rely on methods and expertise from physics and engineering. Signal processing approaches can contribute significantly to this research, most notably to help analyze the exploding quantity of imaging data produced with standard and new microscopy techniques. In this article, we first provide a brief background on the importance of understanding cell movements, then review a selection of current efforts on tracking moving cells, with an emphasis on deformable model approaches (some ideas expressed in this article have been previously discussed. We will point out some of the main difficulties posed by cellular imaging, discuss advantages and limitations of different tracking techniques, and suggest a few directions for future advances

Quantitative elastography of renal transplants using supersonic shear imaging: a pilot study

Abstract: Purpose To evaluate the reliability of quantitative ultrasonic measurement of renal allograft elasticity using supersonic shear imaging (SSI) and its relationship with parenchymal pathological changes.

αTAT1 catalyses microtubule acetylation at clathrin-coated pits

Abstract: In most eukaryotic cells microtubules undergo post-translational modifications such as acetylation of α-tubulin on lysine 40, a widespread modification restricted to a subset of microtubules that turns over slowly. This subset of stable microtubules accumulates in cell protrusions and regulates cell polarization, migration and invasion. However, mechanisms restricting acetylation to these microtubules are unknown. Here we report that clathrin-coated pits (CCPs) control microtubule acetylation through a direct interaction of the α-tubulin acetyltransferase αTAT1 (refs 8, 9) with the clathrin adaptor AP2. We observe that about one-third of growing microtubule ends contact and pause at CCPs and that loss of CCPs decreases lysine 40 acetylation levels. We show that αTAT1 localizes to CCPs through a direct interaction with AP2 that is required for microtubule acetylation. In migrating cells, the polarized orientation of acetylated microtubules correlates with CCP accumulation at the leading edge, and interaction of αTAT1 with AP2 is required for directional migration. We conclude that microtubules contacting CCPs become acetylated by αTAT1. In migrating cells, this mechanism ensures the acetylation of microtubules oriented towards the leading edge, thus promoting directional cell locomotion and chemotaxis.

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.

A SARS-CoV-2 protein interaction map reveals targets for drug repurposing.

Abstract: A newly described coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the causative agent of coronavirus disease 2019 (COVID-19), has infected over 2.3 million people, led to the death of more than 160,000 individuals and caused worldwide social and economic disruption1,2. There are no antiviral drugs with proven clinical efficacy for the treatment of COVID-19, nor are there any vaccines that prevent infection with SARS-CoV-2, and efforts to develop drugs and vaccines are hampered by the limited knowledge of the molecular details of how SARS-CoV-2 infects cells. Here we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins that physically associated with each of the SARS-CoV-2 proteins using affinity-purification mass spectrometry, identifying 332 high-confidence protein–protein interactions between SARS-CoV-2 and human proteins. Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (of which, 29 drugs are approved by the US Food and Drug Administration, 12 are in clinical trials and 28 are preclinical compounds). We screened a subset of these in multiple viral assays and found two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the sigma-1 and sigma-2 receptors. Further studies of these host-factor-targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19. A human–SARS-CoV-2 protein interaction map highlights cellular processes that are hijacked by the virus and that can be targeted by existing drugs, including inhibitors of mRNA translation and predicted regulators of the sigma receptors.

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.