Being designed in response to imaging challenges, the Roboscope is the product of a collaboration between Marc Tramier’s team (FBI Bretagne-Loire node) with Julia Bonnet-Gélébart, research engineer, Jacques Pécréaux’s team of the Institut Génétique & Développement de Rennes (IGDR), and the Inscoper company, spin-off of the lab. This technology could become a great timesaver for fluorescence microscopy.

Allowing the automation of fluorescence microscope acquisitions, the Roboscope is an embedded technology based on a deep learning algorithm. To be precise, it is a predesigned event-driven acquisition (PEDA) based on a learning automatization of any cellular changes tracked by fluorescence. Catching rare and fast cellular events then becomes possible!

The use of the Roboscope would also save precious time of research, providing users with results without the need to stand by the microscope during acquisition. This technology goes beyond as they will be able to recover the data already classified and with only the specific points of illumination that they have previously triggered. 

A broad range of applications

The teams have almost finished to develop an entire algorithm monitoring the cell cycle progression in mitosis. These events specific to the cellular division correspond to major challenges in the control and treatment of cancer progression (Kops, 2005). As the cell cycle study is needed to understand several biological processes helping the development of targeted drugs, the technology aims to monitor efficiently and automatically simple cell models through their division cycle. 

And this is not its only benefit: this automatized fluorescence microscopy acquisition can be adapted in very diverse fields. From a cell cycle progression analysis to specific analysis, organelles, proteins and biological events can be tracked or activated within cells. A noteworthy advantage of the integrated device that – we hope – will be deployed widely in the future. 

Workflow of a Roboscope experiment. 1. The user annotate a bench of images with different class of interest to be detected. 2. The pre-trained Convolutional Neural Network is adjusted for the experiment by fine tuning and/or transfer learning. 3. The algorithm is transfered on embedded systems to perform real-time image analysis during microscopy acquisition. 4. The biological application with event-driven acquisition is defined and started by the user in order to start, interrupt and parametrize different acquisition sequence following real-time image analysis and event classification.

Since 2019, the “Cristal collectif” medal rewards teams supporting research with their technical expertise, the collective dimension, their innovation and outreach. Both nationally and internationally recognised, the Bordeaux Imaging Center (BIC) from the France-BioImaging node of Bordeaux received this award for providing access to innovating technologies and for the quality of its training. The BIC was commended for its investment in training, especially for its partnership with the International School of Neurosciences, a unique partnership in Europe. The CNRS also has particularly highlighted the core facility’s activities of research and development in implementing new techniques and image analysis. Among its achievements, the BIC has succeeded to optimize a homemade Lattice Light Sheet, which has the benefit of being a good compromise between resolution, acquisition speed, imaging depth and low phototoxicity.

© Gautier Dufau

Laureates :

  • Lysiane Brocard, Plant Unit manager
  • Fabrice Cordelières, Image analysis manager
  • Mathieu Ducros, R&D Lattice Light Sheet Microscopy manager
  • Mónica Fernández Monreal, R&D CLEM manager
  • Étienne Gontier, Electronic Unit manager
  • Sabrina Lacomme, Transmission Electron Microscopy manager
  • Florian Levet, R&D software manager
  • Sébastien Marais, Confocal and Two-photon Microscopy manager
  • Magali Mondin, Super-resolution Microscopy manager
  • Melina Petrel, Cryo-preparation and immunomarking manager
  • Christel Poujol, Photonic Unit manager
  • Isabelle Svahn, Scanning Electron Microscopy manager
  • Jérémie Teillon, Clarification and Light-Sheet Microscopy manager

More information: www.cnrs.fr/fr/talent/index

Imagerie-Gif core facility, from our Ile-de-France Sud node, is pleased to announce the acquisition of a Scanning Ion Beam Electron Microscope (FIB-SEM) and a Lattice Structured Illumination Microscope (SIM) Elyra 7. For the occasion, the core facility is organizing “3D Res/volution“, a scientific event on high-resolution 3D imaging on December 15, 2022 from 2:00 pm to 5:00 pm at B21 amphitheatre. This event will be a great opportunity to introduce to you the possibilities of these 2 new systems available at Imagerie-Gif.

Free but mandatory registration: https://evento.renater.fr/survey/3d-res-volution-day-8pdzetbi

Initiated a few years ago, the Inria-IPL-NAVISCOPE (“Image guided NAvigation and Visualization data sets in live cell imaging and microscopy”) project aims at overcoming challenges of bioimaging observation. Virtual and augmented reality could become the new way to visualize and analyze microscope image renders.

Despite incredible progresses in microscopy, imaging biomolecular dynamics in cells remains a challenge. A lack of sensitivity, limited recording speed, photobleaching and phototoxicity associated have restrained, for a long time, our capacity to study biomolecules in their natural environments. As microscopy image is commonly observed on 2D screens, it can narrow human capacities to grasp volumetric, complex, and discrete biological dynamics. Following new modes of visualization including virtual reality (VR)/augmented reality (AR) approaches, the NAVISCOPE project allows more accurate analysis and exploration of large time series of volumetric images, such as those produced by the latest 3D + time fluorescence microscopy.

Why should cell biologists be interested in this project?

The project to which 4 FBI-teams from the BI-IPDM node participate, aims at engineering a technology made with and for biologists. For VR/AR approaches to be adopted by the broader bioimaging community, it is, indeed, important that they are evaluated by the biologists, on their own datasets. 

The potentials of VR/AR technologies for scientists are numerous: navigating into multidimensional, large data sets with another view angle or perception, interacting with these data especially by selecting subregions, quantifying features of interests, etc. New VR/AR approaches also provide specific quantification tools to show distances, angles, counting, local density, and histogram profiler or include a selection of regions of interest for further analysis such as the 3D Timelines. Moreover, because communication with analysis software coded in Java or Python is now integrated, more post-treatment analysis is possible on selected features, providing a multifaceted and accessible tool for biologists.

A promising future ahead

In practice, immersion of the user within 3D + time microscopy data still represents an acculturation challenge for the concerned community. Thus, to promote a broader adoption of these approaches by biologists, further dialogue is needed between the bioimaging community and the VR&AR developers. Nonetheless, future innovation can already be foreseen as there are multiple way to upgrade this technology. For example, using eye-tracking (Günther et al., 2020) or haptic interfaces (Petit et al., 2020) can improve human perception by providing local sensations, which would improve the selection of responses in a 3D + time space. Besides, a better integration of multiple channels with high pixel resolution or the addition of vector representations could add information about the orientation, movement of molecules or organization of structures such as cytoskeleton elements or membrane lipids. The prospects initiated by the NAVISCOPE projects are, as mentioned above, endless and could be a technology that reshapes the way we see biology at the hearth.

Full article on:

Challenges of intracellular visualization using virtual and augmented reality

Valades-Cruz Cesar Augusto, Leconte Ludovic, Fouche Gwendal, Blanc Thomas, Van Hille Nathan, Fournier Kevin, Laurent Tao, Gallean Benjamin, Deslandes Francois, Hajj Bassam, Faure Emmanuel, Argelaguet Ferran, Trubuil Alain, Isenberg Tobias, Masson Jean-Baptiste, Salamero Jean, Kervrann Charleseub

Front. Bioinform. 2:997082.
https://doi.org/10.3389/fbinf.2022.997082

Developed by the Serpico Inria-CNRS-Institut Curie Joint Team, member of the IPDM-BioImage Informatics node of France-BioImaging (FBI), this open-source framework could be a huge step forward in bioimaging management and analysis.

Bioimaging has a broad range of applications, addressing a variety of biological models at diverse scales of life. Thus, descriptions of novel computational approaches are often focused on target case studies. To tackle any scenario in biological imaging is a major challenge, that needs the conception and the development of a unified solution.

With this in mind, the BioImageIT project aims at providing a middleware that integrates data management with analysis using existing softwares (Omero, BioFormats, Fiji, napari, Scipy, pytorch…). The mission of BioImageIT was to design a graphical user interface (GUI) that allows any scientist without coding skills to annotate and analyze datasets using various software. By being user-centered, open-source and cross-platform (Windows, MacOS, Linux), BioImageIT created a management tool that is definitely accessible and well documented.

Started in late 2019, the project, funded by France-BioImaging, is now being deployed in 10 FBI imaging facilities. As it is a first step, the BioImageIT project have the ambition to expand the dissemination of the middleware throughout France and even further, Europe.

BioImageIT overview. a, Schematic view of BioImageIT architecture. The BioImageIT core is composed of data management and data processing functionalities. Users can access plugins by a script editor, Jupyter or the BioImageIT graphical interface (GUI). Data management functionalities exploit local files, remote files or databases such as OMERO. Data processing can perform computations in remote jobs, containers, or local runners. Image analysis is provided by plugins written in different languages. Developers can implement their own plugins in BioImageIT and design their own Graphical Interface. (b-i) LLSM processing workflow gathered in BioImageIT. Hela cell line expressing CD-M6PR-eGFP were stained with Tubulin TrackerTM Deep Red for Microtubules. b, Due to the geometry of LLS scanning, raw 3D images are skewed. c, g, First, realignment (deskew) of raw stacks is performed using Pycudadecon. d, h, Richardson Lucy deconvolution is performed using Pycudadecon. e, CD-M6PR-eGFP vesicles are tracked using Trackmate(FiJi). f, i, Deconvolved stacks and tracks are rendered using napari.

Prigent, S., Valades-Cruz, C.A., Leconte, L. et al. BioImageIT: Open-source framework for integration of image data management with analysis. Nat Methods (2022).
https://doi.org/10.1038/s41592-022-01642-9

Save the date! The Electron Microscopy facility of Imagerie-Gif (I2BC, France-BioImaging), the Cryo-Electron Microscopy facility (I2BC, FRISBI) and the Cimex facility of Ecole Polytechnique are organizing a 5-day workshop from October 3rd to October 7th, 2022 on Transmission Electron Microscopy to explore the architecture of a virus in all its forms.

The aim of this workshop is to propagate knowledge about transmission electron microscopy applications and to outline the advantages of transversal studies combining structural biology and cell biology. Indeed, structural biology and cell biology approaches both use TEM but are rarely merged in the same studies.

The workshop will focus on the advantages of combining both approaches, which can be easily performed with the same equipment. The workshop will focus on a biological object whose study requires such multiscale approaches: a virus. The virus will be studied in vitro to resolve its high-resolution 3D structure, and will be observed in infected cells to determine the infection and replication mechanisms in situ.

The workshop targets students and young researchers. The training will focus on a given biological object, a virus, which will be studied by two complementary approaches:

  • Single particle analysis by cryo-electron microscopy, allowing high-resolution 3D reconstruction of particles purified in vitro. This part will be performed on a 200kV TEM on the Cimex facility.
  • Cellular tomography of infected cells with observation of the virus replication sites in situ and analysis of its interaction with cellular membranes. This workshop will cover the workflow from sample preparation and resin sections realisation, to acquisition and analysis of tomograms with a 120kV TEM.

Attendees will have a theoretical and practical overview of these two complementary techniques. The practical training will be particularly emphasised, to ensure that attendees will be able to apply the knowledge acquired in the workshop for their further research projects.


Susbscription here: https://www.azur-colloque.fr/DR04/inscription/preinscription/245

Preliminary programme

Understanding how development is coordinated in multiple tissues and gives rise to fully functional organs or whole organisms necessitates microscopy tools. Over the last decade numerous advances have been made in live-imaging, enabling high resolution imaging of whole organisms at cellular resolution. Yet, these advances mainly rely on mounting the specimen in agarose or aqueous solutions, precluding imaging of organisms whose oxygen uptake depends on ventilation.

Engineers from the Institut Curie and Institut Jacques Monod implemented a multi-view multi-scale microscopy strategy based on confocal spinning disk microscopy, called Multi-View confocal microScopy (MuViScopy).

MuViScopy enables live-imaging of multiple organs with cellular resolution using sample rotation and confocal imaging without the need of sample embedding. They illustrated the capacity of MuViScopy by live-imaging Drosophila melanogaster pupal development throughout metamorphosis, highlighting how internal organs are formed and multiple organ development is coordinated. They foresee that MuViScopy will open the path to better understand developmental processes at the whole organism scale in living systems that require gas exchange by ventilation.

3D fusion reconstruction of eight different angles at 10x magnification. Animation of a 3D reconstruction after fusion of images acquired from eight angles by the MuViScope of an Ecad:3xGFP Drosophila pupa at 28 hAPF with a 10x objective. The animation starts with a 180° rotation along the A-P axis and then sequentially shows the eight different angles from a top view: 0° (red), 45° (orange), 90° (yellow), 135° (green), 180° (light blue), 225° (dark blue), 270° (purple) and 315° (pink). Fusion was performed using Huygens Fuser (SVI) and 3D visualization with Imaris software. The acquisition parameters are detailed in Table S1. Scale bar: 200 μm.

Olivier Leroy, Eric van Leen, Philippe Girard, Aurélien Villedieu, Christian Hubert, Floris Bosveld, Yohanns Bellaïche, Olivier Renaud; Multi-view confocal microscopy enables multiple organ and whole organism live-imagingDevelopment 15 February 2022; 149 (4): dev199760. doi: https://doi-org.insb.bib.cnrs.fr/10.1242/dev.199760

The MuViScope was co-funded by France-BioImaging.

Cryogenic electron tomography (cryo-ET) visualizes the 3D spatial distribution of macromolecules at nanometer resolution inside native cells. However, automated identification of macromolecules inside cellular tomograms is challenged by noise and reconstruction artifacts, as well as the presence of many molecular species in the crowded volumes. 

To overcome these obstacles, an international team of scientists from France, Spain and Germany, under the leadership of Charles Kervrann, from France BioImaging BioImage Informatics Node, developed a deep learning-based framework to quickly identify multiple classes of macromolecules in cryo-ET volumes. This DeepFinder pro­gramnow published in Nature Methods, builds upon convolutional neural networks that have already proven highly valuable in the microscopy field.

Overview of DeepFinder (from Moebel, E., et al., Nat Methods 18, 1386–1394 (2021).

a) The DeepFinder workflow consists of a training stage (stage I) and an analysis (or inference) stage (stage II). These two stages correspond to five steps (represented by blue boxes) to locate macromolecular complexes within crowded cells.

b) Ribosome localization with DeepFinder in a cryo-electron tomogram of a C. reinhardtii cell.
Tomographic slice with superimposed segmented cell membrane (gray) and ribosomes classified with respect to their binding states: membrane-bound (blue) and cytosolic (yellow).

c) Tomographic slices showing coordinates
of detected ribosomes (colors correspond to b). The positions and classes were determined by analyzing the segmentation map shown in b. This
analysis used 48 tomograms for training, one for validation and eight for testing. Scale bar, 60 nm.

Once trained, the inference stage of DeepFinder is faster than template matching and performs better than other competitive deep learning methods at identifying macromolecules of various sizes in both synthetic and experimental datasets. On cellular cryo-ET data, DeepFinder localized membrane-bound and cytosolic ribosomes (roughly 3.2 MDa), ribulose 1,5-bisphosphate carboxylase–oxygenase (roughly 560 kDa soluble complex) and photosystem II (roughly 550 kDa membrane complex) with an accuracy comparable to expert-supervised ground truth annotations. DeepFinder is therefore a promising algorithm for the semiautomated analysis of a wide range of molecular targets in cellular tomograms. It also serves as a prime example illustrating the importance of developing efficient, customized AI tools to accelerate knowledge generation in the biomedical life sciences.

DeepFinder has been implemented as a free, open-source program with an accessible graphical user interface.

The team is currently working on adapting it to fluorescence microscopy.

Moebel, E., Martinez-Sanchez, A., Lamm, L. et al. Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms. Nat Methods 18, 1386–1394 (2021). https://doi.org/10.1038/s41592-021-01275-4

Quantifying translation in space and time during development

During development, precise control of gene expression allows the reproducible establishment of patterns, leading to the formation of organs at the right time and place.

The establishment of developmental patterns has been primarily studied at the transcriptional level. In comparison, the fate of these transcripts received little attention.

Dufourt*, Bellec* et al deployed the SunTag labeling method to image the dynamics of translation of individual mRNA molecules in living Drosophila embryos. This led to the discovery of translation factories and unmasked important heterogeneities in the efficiency of translation between identical mRNAs, demonstrating a novel layer of fine-tuning of gene expression.

Imaging translation dynamics in live embryos reveals spatial heterogeneities.

Dufourt J, Bellec M, Trullo A, Dejean M, De Rossi S, Favard C, Lagha M.

Science. 29 avril 2021 doi: 10.1126/science.abc3483.

Contact:

Mounia Lagha (CNRS)

mounia.lagha@igmm.cnrs.fr

+33 434359653

twitter : drosoigmm

Institut de Génétique Moléculaire de Montpellier (Univ.Montpellier/CNRS) 1919 route de Mende, 34090 Montpellier

For several weeks now, the H2P2 histopathology platform located in Rennes (France BioImaging Bretagne-Loire Node) has become the European reference for the integral solution of Cell DIVE (https://www.leica-microsystems.com/products/light-microscopes/p/cell-dive/).

Cell Dive device on H2P2 platform

This technology brings great expectations for the research teams and the private companies with which we work. Leica’s Cell DIVE technology provides an in-depth solution for characterizing the tissue microenvironment using multiplexed imaging technology. Up to 60 biomarkers can be revealed in one tissue sample. An extensive list of antibodies is already validated and users can customize their own panel! The multiplexed Cell DIVE technology is based on successive immunolabeling of 4 antibodies conjugated with 4 fluorochromes (Cy2, Cy3, Cy5 and Cy7). The slides are digitized (x20 objective) as things progress and a final compiled image is obtained and can be analysed with the Halo Image Analysis Platform. This software allows users to do segmentation to highlight clusters, to define specific cell phenotypes, to analyse neighbourhood, heatmap…

For example, in cancer treatment research, researchers need a better understanding of the cellular architecture of normal and diseased tissues to develop better treatments and more accurately predict disease progression. 

This technology has been developed by scientists for scientists over the last decade and several publications are available to date (https://www.leica-microsystems.com/products/light-microscopes/p/cell-dive/publications/).

Images from human tonsil with 9 biomarkers.

For more information about the Cell Dive technology or to discuss your project, you can contact Nicolas Mouchet nicolas.mouchet@univ-rennes1.fr

Direct and simultaneous observation of transcription and chromosome architecture in single cells with Hi-M

Andrés M. Cardozo Gizzi, Sergio M. Espinola, Julian Gurgo, Christophe Houbron, Jean-Bernard Fiche, Diego I. Cattoni, Marcelo Nollmann

Simultaneous observation of 3D chromatin organization and transcription at the single cell level and with high spatial resolution may hold the key to unveil the mechanisms regulating embryonic development, cell differentiation and even disease. We have recently developed Hi-M, a technology that allows for the sequential labelling, 3D imaging and localization of multiple genomic DNA loci together with RNA expression in single cells within whole, intact Drosophila embryos. Importantly, Hi-M enables simultaneous detection of RNA expression and chromosome organization without requiring sample unmounting and primary probe re-hybridization. Here, we provide a step-by-step protocol describing the design of probes, the preparation of samples, the stable immobilization of embryos into microfluidics chambers, and the complete procedure for image acquisition. The combined RNA/DNA fluorescence in situ hybridization procedure takes 4-5 days including embryo collection. In addition, we describe image analysis software to segment nuclei, detect genomic spots, correct for drift and produce Hi-M matrices. A typical Hi-M experiment takes 1-2 days to complete all rounds of labelling and imaging and 4 additional days for image analysis. This technology can be easily expanded to investigate cell differentiation in cultured cells, or organization of chromatin within complex tissues.

DOI https://doi.org/10.1038/s41596-019-0269-9

Contact: Marcelo Nolmann marcnol@gmail.com

ATP-driven separation of liquid phase condensates in bacteria

B. Guilhas, J.C. Walter, J. Rech, G. David, N.-O. Walliser, J. Palmeri, C., Mathieu-Demaziere, A. Parmeggiani, J.Y. Bouet, A. Le Gall1, M. Nollmann

Liquid-liquid phase separated (LLPS) states are key to compartmentalise components in the absence of membranes, however it is unclear whether LLPS condensates are actively and specifically organized in the sub-cellular space and by which mechanisms. Here, we address this question by focusing on the ParABS DNA segregation system, composed of a centromeric-like sequence (parS), a DNA-binding protein (ParB) and a motor (ParA). We show that parS-ParB associate to form nanometer-sized, round condensates. ParB molecules diffuse rapidly within the nucleoid volume, but display confined motions when trapped inside ParB condensates. Single ParB molecules are able to rapidly diffuse between different condensates, and nucleation is strongly favoured by parS. Notably, the ParA motor is required to prevent the fusion of ParB condensates. These results describe a novel active mechanism that splits, segregates and localises non-canonical LLPS condensates in the sub-cellular space.

Guilhas et al. revealed that the bacterial DNA segregation apparatus behaves as a non-canonical phase separation system. This apparatus employs an ATP-powered motor that splits nanometer-sized condensates and localizes them robustly within the nucleoid to ensure faithful transmission of genetic material.

DOI: https://doi.org/10.1016/j.molcel.2020.06.034

Contact: Marcelo Nolmann marcnol@gmail.com