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Renaud O, Aulner N, Salles A, Halidi N, Brunstein M, Mallet A, Aumayr K, Terjung S, Levy D, Lippens S, Verbavatz JM, Heuser T, Santarella-Mellwig R, Tinevez JY, Woller T, Botzki A, Cawthorne C, Munck S. Staying on track - Keeping things running in a high-end scientific imaging core facility. J Microsc 2024; 294:276-294. [PMID: 38656474 DOI: 10.1111/jmi.13304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 03/19/2024] [Accepted: 04/08/2024] [Indexed: 04/26/2024]
Abstract
Modern life science research is a collaborative effort. Few research groups can single-handedly support the necessary equipment, expertise and personnel needed for the ever-expanding portfolio of technologies that are required across multiple disciplines in today's life science endeavours. Thus, research institutes are increasingly setting up scientific core facilities to provide access and specialised support for cutting-edge technologies. Maintaining the momentum needed to carry out leading research while ensuring high-quality daily operations is an ongoing challenge, regardless of the resources allocated to establish such facilities. Here, we outline and discuss the range of activities required to keep things running once a scientific imaging core facility has been established. These include managing a wide range of equipment and users, handling repairs and service contracts, planning for equipment upgrades, renewals, or decommissioning, and continuously upskilling while balancing innovation and consolidation.
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Affiliation(s)
- Oliver Renaud
- Cell and Tissue Imaging Platform (PICT-IBiSA, France-BioImaging), Institut Curie, Université PSL, Sorbonne Université, CNRS, Inserm, Paris, France
| | - Nathalie Aulner
- Centre de Ressources et Recherches Technologiques (UTechS-PBI, C2RT), Institut Pasteur, Université Paris Cité, Photonic Bio-Imaging, Paris, France
| | - Audrey Salles
- Centre de Ressources et Recherches Technologiques (UTechS-PBI, C2RT), Institut Pasteur, Université Paris Cité, Photonic Bio-Imaging, Paris, France
| | - Nadia Halidi
- Advanced Light Microscopy Unit, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Maia Brunstein
- Bioimaging Core Facility, Centre de Ressources et Recherches Technologiques (C2RT), Institut Pasteur, Université Paris Cité, Inserm, Institut de l'Audition, Paris, France
| | - Adeline Mallet
- Centre de Ressources et Recherches Technologiques (UBI, C2RT), Institut Pasteur, Université Paris Cité, Ultrastructural BioImaging, Paris, France
| | - Karin Aumayr
- BioOptics Facility, Research Institute of Molecular Pathology (IMP) Campus-Vienna-Biocenter 1, Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Dr. Bohr-Gasse 3, Vienna, Austria
- Gregor Mendel Institute of Molecular Plant Biology, Austrian Academy of Sciences (GMI), Dr. Bohr-Gasse 3, Vienna, Austria
| | - Stefan Terjung
- Advanced Light Microscopy Facility, EMBL Heidelberg, Heidelberg, Germany
| | - Daniel Levy
- Cell and Tissue Imaging Platform (PICT-IBiSA, France-BioImaging), Institut Curie, Université PSL, Sorbonne Université, CNRS, Inserm, Paris, France
| | | | - Jean-Marc Verbavatz
- Institut Jacques Monod (Imagoseine), Université Paris Cité, CNRS, Paris, France
| | - Thomas Heuser
- Vienna Biocenter Core Facilities GmbH (VBCF), Wien, Austria
| | | | - Jean-Yves Tinevez
- Image Analysis Hub, Institut Pasteur, Université de Paris Cité, Paris, France
| | - Tatiana Woller
- VIB Technology Training, Data Core, VIB BioImaging Core, VIB, Ghent, Belgium
- Neuroscience Department, KU Leuven, Leuven, Belgium
| | | | - Christopher Cawthorne
- Department of Imaging and Pathology, Nuclear Medicine and Molecular Imaging, KU Leuven, Leuven, Belgium
| | - Sebastian Munck
- Neuroscience Department, KU Leuven, Leuven, Belgium
- VIB BioImaging Core, VIB, Leuven, Belgium
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Tranfield EM, Lippens S. Future proofing core facilities with a seven-pillar model. J Microsc 2024; 294:411-419. [PMID: 38700841 DOI: 10.1111/jmi.13314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Revised: 04/23/2024] [Accepted: 04/23/2024] [Indexed: 05/21/2024]
Abstract
Centralised core facilities have evolved into vital components of life science research, transitioning from a primary focus on centralising equipment to ensuring access to technology experts across all facets of an experimental workflow. Herein, we put forward a seven-pillar model to define what a core facility needs to meet its overarching goal of facilitating research. The seven equally weighted pillars are Technology, Core Facility Team, Training, Career Tracks, Technical Support, Community and Transparency. These seven pillars stand on a solid foundation of cultural, operational and framework policies including the elements of transparent and stable funding strategies, modern human resources support, progressive facility leadership and management as well as clear institute strategies and policies. This foundation, among other things, ensures a tight alignment of the core facilities to the vision and mission of the institute. To future-proof core facilities, it is crucial to foster all seven of these pillars, particularly focusing on newly identified pillars such as career tracks, thus enabling core facilities to continue supporting research and catalysing scientific advancement. Lay abstract: In research, there is a growing trend to bring advanced, high-performance equipment together into a centralised location. This is done to streamline how the equipment purchase is financed, how the equipment is maintained, and to enable an easier approach for research scientists to access these tools in a location that is supported by a team of technology experts who can help scientists use the equipment. These centralised equipment centres are called Core Facilities. The core facility model is relatively new in science and it requires an adapted approach to how core facilities are built and managed. In this paper, we put forward a seven-pillar model of the important supporting elements of core facilities. These supporting elements are: Technology (the instruments themselves), Core Facility Team (the technology experts who operate the instruments), Training (of the staff and research community), Career Tracks (for the core facility staff), Technical Support (the process of providing help to apply the technology to a scientific question), Community (of research scientist, technology experts and developers) and Transparency (of how the core facility works and the costs associated with using the service). These pillars stand on the bigger foundation of clear policies, guidelines, and leadership approaches at the institutional level. With a focus on these elements, the authors feel core facilities will be well positioned to support scientific discovery in the future.
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Affiliation(s)
- Erin M Tranfield
- VIB Bioimaging Core Ghent, VIB, Zwijnaarde, Belgium
- VIB Center for Inflammation Research, Ghent University, Zwijnaarde, Belgium
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Schmidt C, Boissonnet T, Dohle J, Bernhardt K, Ferrando-May E, Wernet T, Nitschke R, Kunis S, Weidtkamp-Peters S. A practical guide to bioimaging research data management in core facilities. J Microsc 2024; 294:350-371. [PMID: 38752662 DOI: 10.1111/jmi.13317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 05/21/2024]
Abstract
Bioimage data are generated in diverse research fields throughout the life and biomedical sciences. Its potential for advancing scientific progress via modern, data-driven discovery approaches reaches beyond disciplinary borders. To fully exploit this potential, it is necessary to make bioimaging data, in general, multidimensional microscopy images and image series, FAIR, that is, findable, accessible, interoperable and reusable. These FAIR principles for research data management are now widely accepted in the scientific community and have been adopted by funding agencies, policymakers and publishers. To remain competitive and at the forefront of research, implementing the FAIR principles into daily routines is an essential but challenging task for researchers and research infrastructures. Imaging core facilities, well-established providers of access to imaging equipment and expertise, are in an excellent position to lead this transformation in bioimaging research data management. They are positioned at the intersection of research groups, IT infrastructure providers, the institution´s administration, and microscope vendors. In the frame of German BioImaging - Society for Microscopy and Image Analysis (GerBI-GMB), cross-institutional working groups and third-party funded projects were initiated in recent years to advance the bioimaging community's capability and capacity for FAIR bioimage data management. Here, we provide an imaging-core-facility-centric perspective outlining the experience and current strategies in Germany to facilitate the practical adoption of the FAIR principles closely aligned with the international bioimaging community. We highlight which tools and services are ready to be implemented and what the future directions for FAIR bioimage data have to offer.
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Affiliation(s)
- Christian Schmidt
- Enabling Technology Department, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tom Boissonnet
- Center for Advanced Imaging, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Julia Dohle
- Center of Cellular Nanoanalytics, Integrated Bioimaging Facility iBiOs, University of Osnabrück, Osnabrück, Germany
| | - Karen Bernhardt
- Center of Cellular Nanoanalytics, Integrated Bioimaging Facility iBiOs, University of Osnabrück, Osnabrück, Germany
| | - Elisa Ferrando-May
- Enabling Technology Department, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Tobias Wernet
- Life Imaging Center, University of Freiburg, Freiburg, Germany
| | - Roland Nitschke
- Life Imaging Center, University of Freiburg, Freiburg, Germany
- CIBSS and BIOSS - Centres for Biological Signalling Studies, University of Freiburg, Freiburg, Germany
| | - Susanne Kunis
- Center of Cellular Nanoanalytics, Integrated Bioimaging Facility iBiOs, University of Osnabrück, Osnabrück, Germany
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Cimini BA, Tromans-Coia C, Stirling D, Sivagurunathan S, Senft R, Ryder P, Miglietta E, Llanos P, Jamali N, Diaz-Rohrer B, Dasgupta S, Cruz M, Weisbart E, Carpenter AE. A Postdoctoral Training Program in Bioimage Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.13.593910. [PMID: 38798545 PMCID: PMC11118354 DOI: 10.1101/2024.05.13.593910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
We herein describe a postdoctoral training program designed to train biologists with microscopy experience in bioimage analysis. We detail the rationale behind the program, the various components of the training program, and outcomes in terms of works produced and the career effects on past participants. We analyze the results of an anonymous survey distributed to past and present participants, indicating overall high value of all 12 rated aspects of the program, but significant heterogeneity in which aspects were most important to each participant. Finally, we propose this model as a template for other programs which may want to train experts in professional skill sets, and discuss the important considerations when running such a program. We believe that such programs can have extremely positive impact for both the trainees themselves and the broader scientific community.
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Affiliation(s)
- Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | | | - David Stirling
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | | | - Rebecca Senft
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | - Pearl Ryder
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | - Esteban Miglietta
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | - Paula Llanos
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | - Nasim Jamali
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | | | | | - Mario Cruz
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | - Erin Weisbart
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
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5
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Cimini BA. Creating and troubleshooting microscopy analysis workflows: Common challenges and common solutions. J Microsc 2024. [PMID: 38532662 DOI: 10.1111/jmi.13288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 02/29/2024] [Accepted: 03/04/2024] [Indexed: 03/28/2024]
Abstract
As microscopy diversifies and becomes ever more complex, the problem of quantification of microscopy images has emerged as a major roadblock for many researchers. All researchers must face certain challenges in turning microscopy images into answers, independent of their scientific question and the images they have generated. Challenges may arise at many stages throughout the analysis process, including handling of the image files, image pre-processing, object finding, or measurement, and statistical analysis. While the exact solution required for each obstacle will be problem-specific, by keeping analysis in mind, optimizing data quality, understanding tools and tradeoffs, breaking workflows and data sets into chunks, talking to experts, and thoroughly documenting what has been done, analysts at any experience level can learn to overcome these challenges and create better and easier image analyses.
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Affiliation(s)
- Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
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Carpenter AE, Singh S. Bringing computation to biology by bridging the last mile. Nat Cell Biol 2024; 26:5-7. [PMID: 38228822 PMCID: PMC10880119 DOI: 10.1038/s41556-023-01286-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
Affiliation(s)
- Anne E Carpenter
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
| | - Shantanu Singh
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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Sivagurunathan S, Marcotti S, Nelson CJ, Jones ML, Barry DJ, Slater TJA, Eliceiri KW, Cimini BA. Bridging imaging users to imaging analysis - A community survey. J Microsc 2023:10.1111/jmi.13229. [PMID: 37727897 PMCID: PMC10950841 DOI: 10.1111/jmi.13229] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 08/24/2023] [Accepted: 09/13/2023] [Indexed: 09/21/2023]
Abstract
The 'Bridging Imaging Users to Imaging Analysis' survey was conducted in 2022 by the Center for Open Bioimage Analysis (COBA), BioImaging North America (BINA) and the Royal Microscopical Society Data Analysis in Imaging Section (RMS DAIM) to understand the needs of the imaging community. Through multichoice and open-ended questions, the survey inquired about demographics, image analysis experiences, future needs and suggestions on the role of tool developers and users. Participants of the survey were from diverse roles and domains of the life and physical sciences. To our knowledge, this is the first attempt to survey cross-community to bridge knowledge gaps between physical and life sciences imaging. Survey results indicate that respondents' overarching needs are documentation, detailed tutorials on the usage of image analysis tools, user-friendly intuitive software, and better solutions for segmentation, ideally in a format tailored to their specific use cases. The tool creators suggested the users familiarise themselves with the fundamentals of image analysis, provide constant feedback and report the issues faced during image analysis while the users would like more documentation and an emphasis on tool friendliness. Regardless of the computational experience, there is a strong preference for 'written tutorials' to acquire knowledge on image analysis. We also observed that the interest in having 'office hours' to get an expert opinion on their image analysis methods has increased over the years. The results also showed less-than-expected usage of online discussion forums in the imaging community for solving image analysis problems. Surprisingly, we also observed a decreased interest among the survey respondents in deep/machine learning despite the increasing adoption of artificial intelligence in biology. In addition, the community suggests the need for a common repository for the available image analysis tools and their applications. The opinions and suggestions of the community, released here in full, will help the image analysis tool creation and education communities to design and deliver the resources accordingly.
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Affiliation(s)
| | | | | | | | | | | | | | - Beth A Cimini
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Deschamps J, Dalle Nogare D, Jug F. Better research software tools to elevate the rate of scientific discovery or why we need to invest in research software engineering. FRONTIERS IN BIOINFORMATICS 2023; 3:1255159. [PMID: 37600971 PMCID: PMC10438982 DOI: 10.3389/fbinf.2023.1255159] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 07/25/2023] [Indexed: 08/22/2023] Open
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Ouyang W, Eliceiri KW, Cimini BA. Moving beyond the desktop: prospects for practical bioimage analysis via the web. FRONTIERS IN BIOINFORMATICS 2023; 3:1233748. [PMID: 37560357 PMCID: PMC10409478 DOI: 10.3389/fbinf.2023.1233748] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 07/12/2023] [Indexed: 08/11/2023] Open
Abstract
As biological imaging continues to rapidly advance, it results in increasingly complex image data, necessitating a reevaluation of conventional bioimage analysis methods and their accessibility. This perspective underscores our belief that a transition from desktop-based tools to web-based bioimage analysis could unlock immense opportunities for improved accessibility, enhanced collaboration, and streamlined workflows. We outline the potential benefits, such as reduced local computational demands and solutions to common challenges, including software installation issues and limited reproducibility. Furthermore, we explore the present state of web-based tools, hurdles in implementation, and the significance of collective involvement from the scientific community in driving this transition. In acknowledging the potential roadblocks and complexity of data management, we suggest a combined approach of selective prototyping and large-scale workflow application for optimal usage. Embracing web-based bioimage analysis could pave the way for the life sciences community to accelerate biological research, offering a robust platform for a more collaborative, efficient, and democratized science.
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Affiliation(s)
- Wei Ouyang
- Science for Life Laboratory, Department of Applied Physics, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Kevin W. Eliceiri
- Morgridge Institute for Research and Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI, United States
| | - Beth A. Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, United States
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