The club of INBSs is an informal consortium of the coordinations from 16 Research Infrastructures. It was initiated one year ago.

Why a CLUB?

  • Past institutional initiatives (DGRI, ANR…) stopped in 2019
  • Specificities and problems common to INBS (mostly distributed with complex perimeters and parent institutions). 21 Infrastructures in the 2018 roadmap of the Ministry of Research (16 members of the Club in October 2021) out of 70, for all disciplines.
  • Sharing of information on: MESRI WGs, ESFRI, Calls (from competition, to coordinated actions)
  • Overlapping service offer instead of benefiting from complementary technologies and skills between INBSs
  • Developing common activities; what, why and how?

What did we do this year ?

Online meetings ( agenda, reports, documentations on https://uncloud.univ-nantes.fr/)

  • January 21st 2020

New roadmaps (national, European).   Régine (FLI) and Isabelle (IDMIT) propose themselves as facilitators.  Changed to “Feedback from ESR/equipex+ for INBS RIs”.

  • February 23rd 2021

Meso-Centers and Data Center / Data management and openness. Perrine (FBI) and Jean François (IFB) as facilitators with invited participants.

  • April 6th 2021

Full costs and pricing “upcoming exercises”. Members of the MESRI WG on pricing, Myriam Ferro (Profi) and Yann Herault (Cellphedia) as facilitators

  • May 31st 2021

First propositions for the organization of a DATA-INBS symposium in December 2021

  • July 7, 2021

Indicators and Impact (Facilitated by MetaBoHUB, FBI and Cellphedia with invited participants): Automated  Indicator Harvesting; Hierarchizing Relevant Indicators for INBS;  KPIs and Impacts  ; Sustainable Development Goals, are we concerned?

  • September  7th, 2021

Focus on year-end meetings:

The NSAF (New Africa-France Summit in Montpellier. October) presentation of INBSs initiatives

RDV Carnot (Village des IR) and Symposium

RDV-Carnot-Programme-Sante_ANGLAISDownload

Organization of the 1st open Meeting of the CLUB, 16 and 17 of December: « Les données des Infrastructures en Biologie et Santé: enjeux et perspectives ».

More information and free but mandatory registration at:

Les données des Infrastructures en Biologie et Santé: enjeux et perspectives – 16-17 Déc.

Deadline for face to face registration: 15th of November

The National Infrastructures in Biology and Health (INBS) club is organizing its first symposium on a theme that concerns them all: the life of the data generated within them.

This two half-day symposium will be in French and will have multiple objectives:

  • Practices and developments in progress or implemented by INBS or Research Infrastructures of other disciplines around the management of our data (PGD structure),
  • Ideas for improvement and cohesion for the future, regarding access to storage and scientific computing structures,
  • Moral and ethical responsibility towards these data (FAIRisation, ethical OpenScience…) and their valorisation,
  • Finally, we will also focus this first meeting on the exchange of experiences between the INBS and with our respective Institutions.

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 pro­gram, now 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

Contact

BioImage Informatics 2021 is an annual meeting in the processing, analysis, and extraction of information and knowledge from biological images. This conference will be held in virtual from November 29 to December 1, 2021, and is organised by Jean-Christophe Olivo-Marin (Institut Pasteur), Charles Kervrann (Inria), Jean Salamero (Institut Curie) and Jean-Yves Tinevez (Institut Pasteur).

This year’s edition of the BioImage Informatics conference will happen fully online, and rely on a very nice website built specially for the conference. There will be a dedicated space for poster presentations where presenters will be able to interact with the audience, leave a video or materials when they are not here, etc…

There will be a space for job fair and general announcements as well.

BioImage Informatics 2021 meeting will include, but not be limited to, the following topics:

  • Advanced analytical solutions for bioimage processing and analysis
  • Statistical spatial analysis of cellular or molecular distributions
  • Applications of machine and deep learning to analysis of cellular structure and related functions
  • Quantification of dynamic images and transport phenomena Automation of data acquisition and analysis
  • Dynamic cell imaging and biological processes
  • Reconstruction and analysis of structure and function of biological networks
  • Registration, correlation and fusion of multimodality data

BioImage Informatics will feature a variety of types of presentations: invited talks (45’), selected talks from abstracts (20’) and posters.

Invited Speakers

  • Yonina Eldar, Weissmann Institute, Israel
  • Michael Liebling, EPFL/IDIAP, Switzerland
  • Emma Lundberg, KTH Royal Institute of Technology, Sweden
  • Jong Chul Ye, KAIST, Korea

Abstract submission and Registration for BioImage Informatics 2021 are now open!

  • All abstracts for selected talk and poster consideration must be submitted by October 15, 2021 and should not exceed 350 words (excluding authors and affiliations).
  • You may submit as many abstracts as you like.
  • At least one author of each paper or poster must register and attend the conference in order to be listed in the conference programme as a presenter. Authors will have the opportunity to edit an originally-submitted abstract before it is published in conference proceedings.
  • Only accepted abstracts and fully paid registration will be printed in the PDF programme book.

More details can be found at Home – BioImage Informatics 2021

A new version of TrackMate is available now, with major changes that improve its versatility. TrackMate now integrates state-of-the-art segmentation algorithms from machine-learning and deep-learning such as StarDist, Ilastik and Weka.

TrackMate[1] is a Fiji plugin that address cell or organelle tracking in Life-Science microscopy images. Its main goals are to be user-friendly, interoperable and to serve as a platform to accelerate the development of novel tracking algorithms and analysis pipelines.

With this new version we rewrote almost entirely TrackMate so that it can integrate state-of-the-art segmentation algorithms and benefit from their output. For instance, TrackMate can now store, display, save, load and exploit object contours in 2D.

We also made a new application programming interface that will facilitate and accelerate reusing TrackMate in other analysis pipelines and allow 3rd party contributors to add new segmentation algorithms in TrackMate in an easy way. We used this API ourselves to add 7 new segmentation algorithms to TrackMate:

For instance, the StarDist[2] algorithm is integrated as two different detectors. The first one uses the built-in deep-learning model that can segment cell nuclei in fluorescence image in a wide range of situation. The robustness of the StarDist algorithm in turn positively impacts the robustness of tracking and allows for better detection of cell divisions with TrackMate tracking algorithms. This will facilitate cell migration studies.

The TrackMate StarDist integration also allows for specifying and using a custom deep-learning model. For instance, we trained a specific model to detect T-cells imaged in bright-field microscopy and track them over time. Before the emergence of such detection algorithms, the tracking of label-free cells was difficult.

We also integrated the ilastik[3] segmentation software. A TrackMate user can input an ilastik classifier to detect objects then track them. We used them to study the bacterial growth of Neisseria meningitidis clones. The output of this analysis pipeline offers the lineage of each single cell along with its morphology and how it evolves across cell divisions.

The new capabilities of TrackMate can be used to address applications beyond tracking. For instance, it is now possible to use TrackMate to perform the segmentation of 3D objects using a slice-by-slice approach. This approach consists in segmenting objects in each 2D section of a 3D stack, then merging the segmentation results along Z in a subsequent step. This can be done in TrackMate, using the tracking step for merging. We implemented a novel tracking algorithm to foster this application, the overlap tracker. We could use this approach combining the cellpose[4] algorithm in 2D to segment 3D images of Arabidopsis thaliana floral meristem.

There are several other algorithms that are now offered to the TrackMate user, within a user-friendly software meant to interoperate with the key software of bioimage analysis. More importantly, TrackMate is an open-source academic software, and its new API will foster the development of new analysis pipeline with TrackMate and the integration of new algorithms by other developers, increasing the breadth of applications it can address for Life-Science researchers.

This new version of TrackMate is the product of a collaboration between the IAH facility (Institut Pasteur), part of the FBI Bioimage Informatics Node , the Jacquemet lab (Turku Bioscience Centre) , and the Dumenil lab (Institut Pasteur) .

Bringing TrackMate in the era of machine-learning and deep-learningDmitry Ershov, Minh-Son Phan, Joanna W. Pylvänäinen, Stéphane U. Rigaud, Laure Le Blanc, Arthur Charles-Orszag, James R. W. Conway, Romain F. Laine, Nathan H. Roy, Daria Bonazzi, Guillaume Duménil, Guillaume Jacquemet, Jean-Yves Tinevez bioRxiv 2021.09.03.458852; doi: https://doi.org/10.1101/2021.09.03.458852

Contact: Jean-Yves Tinevez

[1] https://imagej.net/plugins/trackmate/

[2] Alejandro F. Frangi, Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, and Gabor Fichtinger Uwe Schmidt, Martin Weigert, Coleman Broaddus, and Gene Myers. Cell detection with star-convex polygons. In Alejandro F. Frangi, Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, and Gabor Fichtinger, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, pages 265–273, Cham, 2018. Springer International Publishing. doi:10.1007/978-3-030-00934-2_30.

[3] Stuart Berg, Dominik Kutra, Thorben Kroeger, Christoph N Straehle, Bernhard X Kausler, Carsten Haubold, Martin Schiegg, Janez Ales, Thorsten Beier, Markus Rudy, Kemal Eren, Jaime I Cervantes, Buote Xu, Fynn Beuttenmueller, Adrian Wolny, Chong Zhang, Ullrich Koethe, Fred A Hamprecht, and Anna Kreshuk. ilastik: interactive machine learning for (bio)image analysis. Nature Methods, 16(12):1226–1232, 2019. ISSN 1548-7105. doi:10.1038/s41592-019-0582-9.

[4] Carsen Stringer, Tim Wang, Michalis Michaelos, and Marius Pachitariu. Cellpose: a generalist algorithm for cellular segmentation. Nature Methods, 18(1):100–106, jan 2021. doi:10.1038/s41592-020-01018-x.

Abstract submission is open for the Neurophotonics II Conference that will take place at Photonics Europe on April 3-7, 2022, in Strasbourg, France. 

This conference focuses on cutting edge research and techniques used to investigate the brain and nervous system.
Multiscale imaging and manipulating the living and intact brain are becoming important topics in neurophotonics. In this context, it’s mandatory to provide new strategies for optical measurements of neural function and develop tools such as optogenetics to enables the control of cellular function with light. Also, in terms of imaging, furthermore it’s often important to image the samples from nanoscale to whole organism scales, bridging the gap between technologies.

The conference aims to bring together engineers, optical and medical scientists, biologists, chemists, neuroscientists and physicians, bringing together researchers working in all aspects of neurophotonics. It will also serve as a forum to discuss existing and emerging techniques.

Topics include but are not limited to:

  • hybrid and multimodal approaches to neuroimaging
  • optical hemodynamic imaging and neuro-vascular interactions
  • mesoscopic, microscopic, and endoscopic imaging of neural structure and function
  • tissue scattering, clearing and de-scattering
  • superresolution microscopy and nanoscopy of the nervous system
  • novel reporters and actuators, optogenetics, bioluminescence
  • data analysis, machine learning, and image processing
  • analyzing circuitry, network function, and information processing
  • optics and brain disease
  • light shaping in the brain, holography
  • dissemination and commercialization of BRAIN technologies
  • closed loop optical neural interfaces.

 Timeline:

Abstract Due: 20 October 2021

Author Notification: 12 January 2022

Manuscript Due: 9 March 2022

Call for papers information

pe116callDownload

France BioImaging primary mission is to develop, promote, disseminate and provide access to innovative instruments and imaging technologies in the field of bioimaging to scientists. Fostering the technological transfers is at the heart of this mission, and for this France BioImaging relies on a strong association of leading R&D research teams with core facilities.

However, several bottlenecks exist and often hamper or prevent successful technology transfer:

  • A lack of human resource leads to difficulties in transferring and stabilizing the technology which is not enough user-friendly
  • A technology that is too specific, with not enough user base
  • A difficulty to contract with industry through institutional offices for industrial valuation
  • In the context of image analysis: the instability of open software economical model, inter-operability, large data handling and algorithm complexity

As a way to tackle these bottlenecks, France BioImaging launched in January 2021 its first “FBI Internal Call 2021: Technology transfer from the R&D teams to the core facilities” to promote the transfer of new technologies (instrumentation, probes, staining methods, software, data analysis or data visualization) from the R&D teams to the facilities of France BioImaging, for access and service to end-users. The outcome of the transfer project had to ensure for the prototype to be usable by the end-users until the interpretation of the data. The project had also to include a sustainability plan and a training plan to guide both facility staff and end-users toward autonomy.

The project selection was organized by the National Coordination of France-BioImaging and applications were assessed according to the following evaluation criteria:

  • Innovation and originality of the proposal
  • Scientific quality, implementation, timeline
  • Competitive positioning
  • Adequacy of resources with the proposed project
  • Economic impact and tech transfer potential and perspectives
  • Estimation of the user market and potential for user adoption
  • Plan for training and sustainability.

For the first edition of the “FBI Internal Call 2021: Technology transfer from the R&D teams to the core facilities”, 5 projects were selected:

  • Icy@FBI: Jean-Christophe Olivo-Marin (IPDM Node): Broadening the scope of applications of Icy (http://icy.bioimageanalysis.org/) by implementing several key new bioimage analysis components
  • BIC-HCS-SMLM: Jean-Baptiste Sibarita (Bordeaux Node), Technological transfer of a Single-Molecule-based High Content Screening platform to the Bordeaux Imaging Center
  • CloudFISH: Marcello Nollmann (Montpellier Node), A tool for the analysis of single-molecule RNA and DNA FISH images
  • MorphoNet: Emmanuel Faure (Montpellier Node), An interactive online morphological browser to explore complex multi-scale data
  • BioImageIT (https://bioimageit.github.io/#/about): Jean Salamero, Sylvain Prigent (IPDM Node), An open source framework for integration of image data management with analysis

Each selected project was awarded with a 80k€ grant for salary and/or equipment, and several positions are currently available: https://france-bioimaging.org/jobs/

This call will be renewed in 2023.

The 6th edition of Global BioImaging annual gathering will have the theme “Imaging Research Infrastructures in a time of change” and will take place on the 8th and 9th September 2021 as an online event.

The program of the event including the titles of the talks and the line-up of speakers will be available soon via the GBI web page: https://globalbioimaging.org/exchange-of-experience/exchange-of-experience-vi

Registration is open! The registration form is available here: https://www.surveymonkey.com/r/EoEVI

Save the date! The European Research Infrastructure Euro BioImaging (EuBI) is organizing an online User Forum on “Understanding and Fighting Cancer”.  The event takes place on June 17, 2021 from 14:00-17:00 CEST and will highlight the importance of cutting-edge imaging technologies in support of cancer research and showcase the specific expertise available at the EuBI Nodes.

In addition, keynote presentations from Kevin Brindle, University of Cambridge, and Frank Winkler, DKFZ, will further reveal the potential of biological and biomedical imaging technologies to boost cancer research. 

The full program is coming soon! In the meantime, you can register here.

For more information: https://www.eurobioimaging.eu/news/join-us-for-the-first-euro-bioimaging-user-forum-understanding-and-fighting-cancer/

When: Thursday, June 17th, 14:00-17:00 CEST

Where: Online

Please spread the word within your network! 

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

On May 25th, 2021, 15:00 CSET, our partner the French Network for Multidimensional Optical Fluorescence Microscopy will receive Edward S. Boyden* from the MIT, USA for a webinar on Expansion Microscopy: “Tools for analyzing and repairing biological systems”.

The presentation will be broadcast live on Youtube: https://youtu.be/Xc48aDLDZDI

*invited by Tudor Manoliu (Imaging and Cytometry platform-Gustave Roussy/ FBI Paris-Sud Node)

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