1
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Lee RM, Eisenman LR, Khuon S, Aaron JS, Chew TL. Believing is seeing - the deceptive influence of bias in quantitative microscopy. J Cell Sci 2024; 137:jcs261567. [PMID: 38197776 DOI: 10.1242/jcs.261567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2024] Open
Abstract
The visual allure of microscopy makes it an intuitively powerful research tool. Intuition, however, can easily obscure or distort the reality of the information contained in an image. Common cognitive biases, combined with institutional pressures that reward positive research results, can quickly skew a microscopy project towards upholding, rather than rigorously challenging, a hypothesis. The impact of these biases on a variety of research topics is well known. What might be less appreciated are the many forms in which bias can permeate a microscopy experiment. Even well-intentioned researchers are susceptible to bias, which must therefore be actively recognized to be mitigated. Importantly, although image quantification has increasingly become an expectation, ostensibly to confront subtle biases, it is not a guarantee against bias and cannot alone shield an experiment from cognitive distortions. Here, we provide illustrative examples of the insidiously pervasive nature of bias in microscopy experiments - from initial experimental design to image acquisition, analysis and data interpretation. We then provide suggestions that can serve as guard rails against bias.
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Affiliation(s)
- Rachel M Lee
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Leanna R Eisenman
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Satya Khuon
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Jesse S Aaron
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Teng-Leong Chew
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
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2
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Jambor HK. A community-driven approach to enhancing the quality and interpretability of microscopy images. J Cell Sci 2023; 136:jcs261837. [PMID: 38095680 DOI: 10.1242/jcs.261837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2023] Open
Abstract
Scientific publications in the life sciences regularly include image data to display and communicate revelations about cellular structure and function. In 2016, a set of guiding principles known as the 'FAIR Data Principles' were put forward to ensure that research data are findable, accessible, interoperable and reproducible. However, challenges still persist regarding the quality, accessibility and interpretability of image data, and how to effectively communicate microscopy data in figures. This Perspective article details a community-driven initiative that aims to promote the accurate and understandable depiction of light microscopy data in publications. The initiative underscores the crucial role of global and diverse scientific communities in advancing the standards in the field of biological images. Additionally, the perspective delves into the historical context of scientific images, in the hope that this look into our past can help ongoing community efforts move forward.
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Affiliation(s)
- Helena Klara Jambor
- National Center for Tumor Diseases - University Cancer Center (NCT-UCC), Universitätsklinikum Carl Gustav Carus an der Technischen Universität Dresden, Dresden 01307, Germany
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3
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Kemmer I, Keppler A, Serrano-Solano B, Rybina A, Özdemir B, Bischof J, El Ghadraoui A, Eriksson JE, Mathur A. Building a FAIR image data ecosystem for microscopy communities. Histochem Cell Biol 2023; 160:199-209. [PMID: 37341795 PMCID: PMC10492678 DOI: 10.1007/s00418-023-02203-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/27/2023] [Indexed: 06/22/2023]
Abstract
Bioimaging has now entered the era of big data with faster-than-ever development of complex microscopy technologies leading to increasingly complex datasets. This enormous increase in data size and informational complexity within those datasets has brought with it several difficulties in terms of common and harmonized data handling, analysis, and management practices, which are currently hampering the full potential of image data being realized. Here, we outline a wide range of efforts and solutions currently being developed by the microscopy community to address these challenges on the path towards FAIR bioimaging data. We also highlight how different actors in the microscopy ecosystem are working together, creating synergies that develop new approaches, and how research infrastructures, such as Euro-BioImaging, are fostering these interactions to shape the field.
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Affiliation(s)
- Isabel Kemmer
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - Antje Keppler
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - Beatriz Serrano-Solano
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - Arina Rybina
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - Buğra Özdemir
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - Johanna Bischof
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - Ayoub El Ghadraoui
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - John E Eriksson
- Euro-BioImaging ERIC Statutory Seat, Tykistökatu 6, P.O. Box 123, 20521, Turku, Finland
| | - Aastha Mathur
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany.
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4
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Poger D, Yen L, Braet F. Big data in contemporary electron microscopy: challenges and opportunities in data transfer, compute and management. Histochem Cell Biol 2023; 160:169-192. [PMID: 37052655 PMCID: PMC10492738 DOI: 10.1007/s00418-023-02191-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/21/2023] [Indexed: 04/14/2023]
Abstract
The second decade of the twenty-first century witnessed a new challenge in the handling of microscopy data. Big data, data deluge, large data, data compliance, data analytics, data integrity, data interoperability, data retention and data lifecycle are terms that have introduced themselves to the electron microscopy sciences. This is largely attributed to the booming development of new microscopy hardware tools. As a result, large digital image files with an average size of one terabyte within one single acquisition session is not uncommon nowadays, especially in the field of cryogenic electron microscopy. This brings along numerous challenges in data transfer, compute and management. In this review, we will discuss in detail the current state of international knowledge on big data in contemporary electron microscopy and how big data can be transferred, computed and managed efficiently and sustainably. Workflows, solutions, approaches and suggestions will be provided, with the example of the latest experiences in Australia. Finally, important principles such as data integrity, data lifetime and the FAIR and CARE principles will be considered.
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Affiliation(s)
- David Poger
- Microscopy Australia, The University of Sydney, Sydney, NSW, 2006, Australia.
| | - Lisa Yen
- Microscopy Australia, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Filip Braet
- Australian Centre for Microscopy and Microanalysis, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Medical Sciences (Molecular and Cellular Biomedicine), The University of Sydney, Sydney, NSW, 2006, Australia
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5
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Dekker J, Alber F, Aufmkolk S, Beliveau BJ, Bruneau BG, Belmont AS, Bintu L, Boettiger A, Calandrelli R, Disteche CM, Gilbert DM, Gregor T, Hansen AS, Huang B, Huangfu D, Kalhor R, Leslie CS, Li W, Li Y, Ma J, Noble WS, Park PJ, Phillips-Cremins JE, Pollard KS, Rafelski SM, Ren B, Ruan Y, Shav-Tal Y, Shen Y, Shendure J, Shu X, Strambio-De-Castillia C, Vertii A, Zhang H, Zhong S. Spatial and temporal organization of the genome: Current state and future aims of the 4D nucleome project. Mol Cell 2023; 83:2624-2640. [PMID: 37419111 PMCID: PMC10528254 DOI: 10.1016/j.molcel.2023.06.018] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 06/10/2023] [Accepted: 06/12/2023] [Indexed: 07/09/2023]
Abstract
The four-dimensional nucleome (4DN) consortium studies the architecture of the genome and the nucleus in space and time. We summarize progress by the consortium and highlight the development of technologies for (1) mapping genome folding and identifying roles of nuclear components and bodies, proteins, and RNA, (2) characterizing nuclear organization with time or single-cell resolution, and (3) imaging of nuclear organization. With these tools, the consortium has provided over 2,000 public datasets. Integrative computational models based on these data are starting to reveal connections between genome structure and function. We then present a forward-looking perspective and outline current aims to (1) delineate dynamics of nuclear architecture at different timescales, from minutes to weeks as cells differentiate, in populations and in single cells, (2) characterize cis-determinants and trans-modulators of genome organization, (3) test functional consequences of changes in cis- and trans-regulators, and (4) develop predictive models of genome structure and function.
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Affiliation(s)
- Job Dekker
- University of Massachusetts Chan Medical School, Boston, MA, USA; Howard Hughes Medical Institute, Chevy Chase, MD, USA.
| | - Frank Alber
- University of California, Los Angeles, Los Angeles, CA, USA
| | | | | | - Benoit G Bruneau
- Gladstone Institutes, San Francisco, CA, USA; University of California, San Francisco, San Francisco, CA, USA
| | | | | | | | | | | | | | | | | | - Bo Huang
- University of California, San Francisco, San Francisco, CA, USA
| | - Danwei Huangfu
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Reza Kalhor
- Johns Hopkins University, Baltimore, MD, USA
| | | | - Wenbo Li
- University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yun Li
- University of North Carolina, Gillings School of Global Public Health, Chapel Hill, NC, USA
| | - Jian Ma
- Carnegie Mellon University, Pittsburgh, PA, USA
| | | | | | | | - Katherine S Pollard
- Gladstone Institutes, San Francisco, CA, USA; University of California, San Francisco, San Francisco, CA, USA; Chan Zuckerberg Biohub, San Francisco, San Francisco, CA, USA
| | | | - Bing Ren
- University of California, San Diego, La Jolla, CA, USA
| | - Yijun Ruan
- Zhejiang University, Hangzhou, Zhejiang, China
| | | | - Yin Shen
- University of California, San Francisco, San Francisco, CA, USA
| | | | - Xiaokun Shu
- University of California, San Francisco, San Francisco, CA, USA
| | | | | | | | - Sheng Zhong
- University of California, San Diego, La Jolla, CA, USA.
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6
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Hu J, Serra‐Picamal X, Bakker G, Van Troys M, Winograd‐Katz S, Ege N, Gong X, Didan Y, Grosheva I, Polansky O, Bakkali K, Van Hamme E, van Erp M, Vullings M, Weiss F, Clucas J, Dowbaj AM, Sahai E, Ampe C, Geiger B, Friedl P, Bottai M, Strömblad S. Multisite assessment of reproducibility in high-content cell migration imaging data. Mol Syst Biol 2023; 19:e11490. [PMID: 37063090 PMCID: PMC10258559 DOI: 10.15252/msb.202211490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 03/27/2023] [Accepted: 03/30/2023] [Indexed: 04/18/2023] Open
Abstract
High-content image-based cell phenotyping provides fundamental insights into a broad variety of life science disciplines. Striving for accurate conclusions and meaningful impact demands high reproducibility standards, with particular relevance for high-quality open-access data sharing and meta-analysis. However, the sources and degree of biological and technical variability, and thus the reproducibility and usefulness of meta-analysis of results from live-cell microscopy, have not been systematically investigated. Here, using high-content data describing features of cell migration and morphology, we determine the sources of variability across different scales, including between laboratories, persons, experiments, technical repeats, cells, and time points. Significant technical variability occurred between laboratories and, to lesser extent, between persons, providing low value to direct meta-analysis on the data from different laboratories. However, batch effect removal markedly improved the possibility to combine image-based datasets of perturbation experiments. Thus, reproducible quantitative high-content cell image analysis of perturbation effects and meta-analysis depend on standardized procedures combined with batch correction.
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Affiliation(s)
- Jianjiang Hu
- Department of Biosciences and NutritionKarolinska InstitutetStockholmSweden
| | | | - Gert‐Jan Bakker
- Department of Medical BioSciencesRadboud University Medical CenterNijmegenThe Netherlands
| | | | - Sabina Winograd‐Katz
- Department of Immunology and Regenerative BiologyWeizmann Institute of ScienceRehovotIsrael
| | - Nil Ege
- The Francis Crick InstituteLondonUK
| | - Xiaowei Gong
- Department of Biosciences and NutritionKarolinska InstitutetStockholmSweden
| | - Yuliia Didan
- Department of Biosciences and NutritionKarolinska InstitutetStockholmSweden
| | - Inna Grosheva
- Department of Immunology and Regenerative BiologyWeizmann Institute of ScienceRehovotIsrael
| | - Omer Polansky
- Department of Immunology and Regenerative BiologyWeizmann Institute of ScienceRehovotIsrael
| | - Karima Bakkali
- Department of Biomolecular MedicineGhent UniversityGhentBelgium
| | | | - Merijn van Erp
- Department of Medical BioSciencesRadboud University Medical CenterNijmegenThe Netherlands
| | - Manon Vullings
- Department of Medical BioSciencesRadboud University Medical CenterNijmegenThe Netherlands
| | - Felix Weiss
- Department of Medical BioSciencesRadboud University Medical CenterNijmegenThe Netherlands
| | | | | | | | - Christophe Ampe
- Department of Biomolecular MedicineGhent UniversityGhentBelgium
| | - Benjamin Geiger
- Department of Immunology and Regenerative BiologyWeizmann Institute of ScienceRehovotIsrael
| | - Peter Friedl
- Department of Medical BioSciencesRadboud University Medical CenterNijmegenThe Netherlands
| | - Matteo Bottai
- Division of Biostatistics, Institute of Environmental MedicineKarolinska InstitutetStockholmSweden
| | - Staffan Strömblad
- Department of Biosciences and NutritionKarolinska InstitutetStockholmSweden
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7
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Gorman C, Punzo D, Octaviano I, Pieper S, Longabaugh WJR, Clunie DA, Kikinis R, Fedorov AY, Herrmann MD. Interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology. Nat Commun 2023; 14:1572. [PMID: 36949078 PMCID: PMC10033920 DOI: 10.1038/s41467-023-37224-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/08/2023] [Indexed: 03/24/2023] Open
Abstract
The exchange of large and complex slide microscopy imaging data in biomedical research and pathology practice is impeded by a lack of data standardization and interoperability, which is detrimental to the reproducibility of scientific findings and clinical integration of technological innovations. We introduce Slim, an open-source, web-based slide microscopy viewer that implements the internationally accepted Digital Imaging and Communications in Medicine (DICOM) standard to achieve interoperability with a multitude of existing medical imaging systems. We showcase the capabilities of Slim as the slide microscopy viewer of the NCI Imaging Data Commons and demonstrate how the viewer enables interactive visualization of traditional brightfield microscopy and highly-multiplexed immunofluorescence microscopy images from The Cancer Genome Atlas and Human Tissue Atlas Network, respectively, using standard DICOMweb services. We further show how Slim enables the collection of standardized image annotations for the development or validation of machine learning models and the visual interpretation of model inference results in the form of segmentation masks, spatial heat maps, or image-derived measurements.
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Affiliation(s)
- Chris Gorman
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | | | | | | | | | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrey Y Fedorov
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Markus D Herrmann
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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8
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Kunis S, Bernhardt K, Hensel M. Setting up a data management infrastructure for bioimaging. Biol Chem 2023; 404:433-439. [PMID: 36853922 DOI: 10.1515/hsz-2022-0304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 01/27/2023] [Indexed: 03/01/2023]
Abstract
While the FAIR (Findable, Accessible, Interoperable, and Re-usable) principles are well accepted in the scientific community, there are still many challenges in implementing them in the day-to-day scientific process. Data management of microscopy images poses special challenges due to the volume, variety, and many proprietary formats. In particular, appropriate metadata collection, a basic requirement for FAIR data, is a real challenge for scientists due to the technical and content-related aspects. Researchers benefit here from interdisciplinary research network with centralized data management. The typically multimodal structure requires generalized data management and the corresponding acquisition of metadata. Here we report on the establishment of an appropriate infrastructure for the research network by a Core Facility and the development and integration of a software tool MDEmic that allows easy and convenient processing of metadata of microscopy images while providing high flexibility in terms of customization of metadata sets. Since it is also in the interest of the core facility to apply standards regarding the scope and serialization formats to realize successful and sustainable data management for bioimaging, we report on our efforts within the community to define standards in metadata, interfaces, and to reduce the barriers of daily data management.
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Affiliation(s)
- Susanne Kunis
- Department of Biology/Chemistry, Center of Cellular Nanoanalytics, Integrated Bioimaging Facility iBiOs, Osnabrück University, Babarastrasse 11, D-49076 Osnabrück, Germany
| | - Karen Bernhardt
- Department of Biology/Chemistry, IT, Osnabrück University, D-49076 Osnabrück, Germany
| | - Michael Hensel
- Department of Biology/Chemistry, Center of Cellular Nanoanalytics and Microbiology Division, Osnabrück University, D-49076 Osnabrück, Germany
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9
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Abstract
Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.
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Affiliation(s)
- Hang Hu
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN47907USA
| | - Julia Laskin
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN47907USA
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10
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Scheele CLGJ, Herrmann D, Yamashita E, Celso CL, Jenne CN, Oktay MH, Entenberg D, Friedl P, Weigert R, Meijboom FLB, Ishii M, Timpson P, van Rheenen J. Multiphoton intravital microscopy of rodents. Nat Rev Methods Primers 2022; 2:89. [PMID: 37621948 PMCID: PMC10449057 DOI: 10.1038/s43586-022-00168-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/12/2022] [Indexed: 08/26/2023]
Abstract
Tissues are heterogeneous with respect to cellular and non-cellular components and in the dynamic interactions between these elements. To study the behaviour and fate of individual cells in these complex tissues, intravital microscopy (IVM) techniques such as multiphoton microscopy have been developed to visualize intact and live tissues at cellular and subcellular resolution. IVM experiments have revealed unique insights into the dynamic interplay between different cell types and their local environment, and how this drives morphogenesis and homeostasis of tissues, inflammation and immune responses, and the development of various diseases. This Primer introduces researchers to IVM technologies, with a focus on multiphoton microscopy of rodents, and discusses challenges, solutions and practical tips on how to perform IVM. To illustrate the unique potential of IVM, several examples of results are highlighted. Finally, we discuss data reproducibility and how to handle big imaging data sets.
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Affiliation(s)
- Colinda L. G. J. Scheele
- Laboratory for Intravital Imaging and Dynamics of Tumor Progression, VIB Center for Cancer Biology, KU Leuven, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | - David Herrmann
- Cancer Ecosystems Program, Garvan Institute of Medical Research and The Kinghorn Cancer Centre, Cancer Department, Sydney, New South Wales, Australia
- St. Vincent’s Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia
| | - Erika Yamashita
- Department of Immunology and Cell Biology, Graduate School of Medicine and Frontier Biosciences, Osaka University, Osaka, Japan
- WPI-Immunology Frontier Research Center, Osaka University, Osaka, Japan
- Laboratory of Bioimaging and Drug Discovery, National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan
| | - Cristina Lo Celso
- Department of Life Sciences and Centre for Hematology, Imperial College London, London, UK
- Sir Francis Crick Institute, London, UK
| | - Craig N. Jenne
- Snyder Institute for Chronic Diseases, University of Calgary, Calgary, Alberta, Canada
| | - Maja H. Oktay
- Department of Pathology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA
- Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA
- Integrated Imaging Program, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA
| | - David Entenberg
- Department of Pathology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA
- Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA
- Integrated Imaging Program, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA
| | - Peter Friedl
- Department of Cell Biology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Centre, Nijmegen, Netherlands
- David H. Koch Center for Applied Genitourinary Cancers, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Roberto Weigert
- Laboratory of Cellular and Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Franck L. B. Meijboom
- Department of Population Health Sciences, Sustainable Animal Stewardship, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
- Faculty of Humanities, Ethics Institute, Utrecht University, Utrecht, Netherlands
| | - Masaru Ishii
- Department of Immunology and Cell Biology, Graduate School of Medicine and Frontier Biosciences, Osaka University, Osaka, Japan
- WPI-Immunology Frontier Research Center, Osaka University, Osaka, Japan
- Laboratory of Bioimaging and Drug Discovery, National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan
| | - Paul Timpson
- Cancer Ecosystems Program, Garvan Institute of Medical Research and The Kinghorn Cancer Centre, Cancer Department, Sydney, New South Wales, Australia
- St. Vincent’s Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia
| | - Jacco van Rheenen
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, Netherlands
- Division of Molecular Pathology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, Netherlands
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11
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Faklaris O, Bancel-Vallée L, Dauphin A, Monterroso B, Frère P, Geny D, Manoliu T, de Rossi S, Cordelières FP, Schapman D, Nitschke R, Cau J, Guilbert T. Quality assessment in light microscopy for routine use through simple tools and robust metrics. J Biophys Biochem Cytol 2022; 221:213512. [PMID: 36173380 PMCID: PMC9526251 DOI: 10.1083/jcb.202107093] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 04/04/2022] [Accepted: 08/31/2022] [Indexed: 11/22/2022] Open
Abstract
Although there is a need to demonstrate reproducibility in light microscopy acquisitions, the lack of standardized guidelines monitoring microscope health status over time has so far impaired the widespread use of quality control (QC) measurements. As scientists from 10 imaging core facilities who encounter various types of projects, we provide affordable hardware and open source software tools, rigorous protocols, and define reference values to assess QC metrics for the most common fluorescence light microscopy modalities. Seven protocols specify metrics on the microscope resolution, field illumination flatness, chromatic aberrations, illumination power stability, stage drift, positioning repeatability, and spatial-temporal noise of camera sensors. We designed the MetroloJ_QC ImageJ/Fiji Java plugin to incorporate the metrics and automate analysis. Measurements allow us to propose an extensive characterization of the QC procedures that can be used by any seasoned microscope user, from research biologists with a specialized interest in fluorescence light microscopy through to core facility staff, to ensure reproducible and quantifiable microscopy results.
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Affiliation(s)
- Orestis Faklaris
- Montpellier Ressources Imagerie, Biocampus, University of Montpellier, CNRS, INSERM, Montpellier, France
| | - Leslie Bancel-Vallée
- Montpellier Ressources Imagerie, Biocampus, University of Montpellier, CNRS, INSERM, Montpellier, France
| | - Aurélien Dauphin
- Unite Genetique et Biologie du Développement U934, PICT-IBiSA, Institut Curie, INSERM, CNRS, PSL Research University, Paris, France
| | - Baptiste Monterroso
- Prism, Institut de Biologie Valrose, CNRS UMR 7277, INSERM 1091, University of Nice Sophia Antipolis - Parc Valrose, Nice, France
| | - Perrine Frère
- Plate-forme d'Imagerie de Tenon, UMR_S 1155, Hôpital Tenon, Paris, France
| | - David Geny
- Institut de Psychiatrie Et Neurosciences de Paris, INSERM U1266, Paris, France
| | - Tudor Manoliu
- Gustave Roussy, Université Paris-Saclay, Plate-forme Imagerie et Cytométrie, UMS AMMICa. Villejuif, France
| | - Sylvain de Rossi
- Montpellier Ressources Imagerie, Biocampus, University of Montpellier, CNRS, INSERM, Montpellier, France
| | - Fabrice P Cordelières
- University of Bordeaux, CNRS, INSERM, Bordeaux Imaging Center, UMS 3420, US 4, Bordeaux, France
| | - Damien Schapman
- Université of Rouen Normandie, INSERM, Plate-Forme de Recherche en Imagerie Cellulaire de Normandie, Rouen, France
| | - Roland Nitschke
- Life Imaging Center and Signalling Research Centres CIBSS and BIOSS, University Freiburg, Freiburg, Germany
| | - Julien Cau
- Montpellier Ressources Imagerie, Biocampus, University of Montpellier, CNRS, INSERM, Montpellier, France
| | - Thomas Guilbert
- Institut Cochin, INSERM (U1016), CNRS (UMR 8104), Universite de Paris (UMR-S1016), Paris, France
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12
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Schmidt C, Hanne J, Moore J, Meesters C, Ferrando-May E, Weidtkamp-Peters S. Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey. F1000Res 2022; 11:638. [PMID: 36405555 PMCID: PMC9641114 DOI: 10.12688/f1000research.121714.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/16/2022] [Indexed: 01/13/2023] Open
Abstract
Background: Knowing the needs of the bioimaging community with respect to research data management (RDM) is essential for identifying measures that enable adoption of the FAIR (findable, accessible, interoperable, reusable) principles for microscopy and bioimage analysis data across disciplines. As an initiative within Germany's National Research Data Infrastructure, we conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. Methods: An online survey was conducted with a mixed question-type design. We created a questionnaire tailored to relevant topics of the bioimaging community, including specific questions on bioimaging methods and bioimage analysis, as well as more general questions on RDM principles and tools. 203 survey entries were included in the analysis covering the perspectives from various life and biomedical science disciplines and from participants at different career levels. Results: The results highlight the importance and value of bioimaging RDM and data sharing. However, the practical implementation of FAIR practices is impeded by technical hurdles, lack of knowledge, and insecurity about the legal aspects of data sharing. The survey participants request metadata guidelines and annotation tools and endorse the usage of image data management platforms. At present, OMERO (Open Microscopy Environment Remote Objects) is the best known and most widely used platform. Most respondents rely on image processing and analysis, which they regard as the most time-consuming step of the bioimage data workflow. While knowledge about and implementation of electronic lab notebooks and data management plans is limited, respondents acknowledge their potential value for data handling and publication. Conclusion: The bioimaging community acknowledges and endorses the value of RDM and data sharing. Still, there is a need for information, guidance, and standardization to foster the adoption of FAIR data handling. This survey may help inspiring targeted measures to close this gap.
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Affiliation(s)
- Christian Schmidt
- Enabling Technology, German Cancer Research Center, Heidelberg, Baden-Württemberg, Germany,Bioimaging Center, University of Konstanz, Konstanz, Germany,
| | - Janina Hanne
- German BioImaging - Society for Microscopy and Image Analysis e.V., Konstanz, Germany,
| | - Josh Moore
- German BioImaging - Society for Microscopy and Image Analysis e.V., Konstanz, Germany,Open Microscopy Environment Consortium, University of Dundee, Dundee, UK
| | - Christian Meesters
- High Performance Computing, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Elisa Ferrando-May
- Enabling Technology, German Cancer Research Center, Heidelberg, Baden-Württemberg, Germany,Bioimaging Center, University of Konstanz, Konstanz, Germany,German BioImaging - Society for Microscopy and Image Analysis e.V., Konstanz, Germany
| | - Stefanie Weidtkamp-Peters
- German BioImaging - Society for Microscopy and Image Analysis e.V., Konstanz, Germany,Center for Advanced Imaging, Heinrich Heine University Dusseldorf, Dusseldorf, Germany
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13
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Ropelewski AJ, Rizzo MA, Swedlow JR, Huisken J, Osten P, Khanjani N, Weiss K, Bakalov V, Engle M, Gridley L, Krzyzanowski M, Madden T, Maiese D, Mandal M, Waterfield J, Williams D, Hamilton CM, Huggins W. Standard metadata for 3D microscopy. Sci Data 2022; 9:449. [PMID: 35896564 PMCID: PMC9329339 DOI: 10.1038/s41597-022-01562-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 07/13/2022] [Indexed: 01/04/2023] Open
Abstract
Recent advances in fluorescence microscopy techniques and tissue clearing, labeling, and staining provide unprecedented opportunities to investigate brain structure and function. These experiments' images make it possible to catalog brain cell types and define their location, morphology, and connectivity in a native context, leading to a better understanding of normal development and disease etiology. Consistent annotation of metadata is needed to provide the context necessary to understand, reuse, and integrate these data. This report describes an effort to establish metadata standards for three-dimensional (3D) microscopy datasets for use by the Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative and the neuroscience research community. These standards were built on existing efforts and developed with input from the brain microscopy community to promote adoption. The resulting 3D Microscopy Metadata Standards (3D-MMS) includes 91 fields organized into seven categories: Contributors, Funders, Publication, Instrument, Dataset, Specimen, and Image. Adoption of these metadata standards will ensure that investigators receive credit for their work, promote data reuse, facilitate downstream analysis of shared data, and encourage collaboration.
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Affiliation(s)
- Alexander J Ropelewski
- Biomedical Applications Group, Pittsburgh Supercomputing Center, 300 S Craig Street, Pittsburgh, PA, 15213, USA
| | - Megan A Rizzo
- Department of Physiology, University of Maryland School of Medicine, 660 West Redwood Street, Baltimore, MD, 21201, USA
| | - Jason R Swedlow
- Centre for Gene Regulation & Expression, Division of Computational Biology, University of Dundee, Nethergate, Dundee, Scotland, DD1 4HN, United Kingdom
| | - Jan Huisken
- Morgridge Institute for Research, 330 N Orchard Street, Madison, WI, 53715, USA
| | - Pavel Osten
- Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY, 11724, USA
| | - Neda Khanjani
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine of University of Southern California, 1975 Zonal Avenue, Los Angeles, CA, 90033, USA
| | - Kurt Weiss
- Morgridge Institute for Research, 330 N Orchard Street, Madison, WI, 53715, USA
| | - Vesselina Bakalov
- Bioinformatics and Computational Biology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, 27709, USA
| | - Michelle Engle
- Bioinformatics and Computational Biology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, 27709, USA
| | - Lauren Gridley
- Bioinformatics and Computational Biology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, 27709, USA
| | - Michelle Krzyzanowski
- Bioinformatics and Computational Biology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, 27709, USA
| | - Tom Madden
- Bioinformatics and Computational Biology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, 27709, USA
| | - Deborah Maiese
- Bioinformatics and Computational Biology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, 27709, USA
| | - Meisha Mandal
- Bioinformatics and Computational Biology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, 27709, USA
| | - Justin Waterfield
- Bioinformatics and Computational Biology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, 27709, USA
| | - David Williams
- Bioinformatics and Computational Biology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, 27709, USA
| | - Carol M Hamilton
- Bioinformatics and Computational Biology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, 27709, USA
| | - Wayne Huggins
- Bioinformatics and Computational Biology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, 27709, USA.
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14
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15
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Schapiro D, Yapp C, Sokolov A, Reynolds SM, Chen YA, Sudar D, Xie Y, Muhlich J, Arias-Camison R, Arena S, Taylor AJ, Nikolov M, Tyler M, Lin JR, Burlingame EA, Chang YH, Farhi SL, Thorsson V, Venkatamohan N, Drewes JL, Pe'er D, Gutman DA, Herrmann MD, Gehlenborg N, Bankhead P, Roland JT, Herndon JM, Snyder MP, Angelo M, Nolan G, Swedlow JR, Schultz N, Merrick DT, Mazzili SA, Cerami E, Rodig SJ, Santagata S, Sorger PK. MITI minimum information guidelines for highly multiplexed tissue images. Nat Methods 2022; 19:262-267. [PMID: 35277708 PMCID: PMC9009186 DOI: 10.1038/s41592-022-01415-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The imminent release of tissue atlases combining multi-channel microscopy with single cell sequencing and other omics data from normal and diseased specimens creates an urgent need for data and metadata standards that guide data deposition, curation and release. We describe a Minimum Information about highly multiplexed Tissue Imaging (MITI) standard that applies best practices developed for genomics and other microscopy data to highly multiplexed tissue images and traditional histology.
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Affiliation(s)
- Denis Schapiro
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University Hospital and Heidelberg University, Heidelberg, Germany
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Clarence Yapp
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Image and Data Analysis Core, Harvard Medical School, Boston, MA, USA
| | - Artem Sokolov
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | - Yu-An Chen
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | - Damir Sudar
- Quantitative Imaging Systems LLC, Portland, OR, USA
| | - Yubin Xie
- Program in Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jeremy Muhlich
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | - Raquel Arias-Camison
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | - Sarah Arena
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | | | | | - Madison Tyler
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | - Jia-Ren Lin
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | - Erik A Burlingame
- Oregon Health and Science University, Portland, OR, USA
- Indica Labs, Albuquerque, NM, USA
| | - Young H Chang
- Oregon Health and Science University, Portland, OR, USA
| | - Samouil L Farhi
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Julia L Drewes
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Dana Pe'er
- Program in Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Markus D Herrmann
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Peter Bankhead
- Edinburgh Pathology, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Joseph T Roland
- Vanderbilt University School of Medicine, Nashville, TN, USA
| | - John M Herndon
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Michael Angelo
- School of Medicine, Stanford University, Stanford, CA, USA
| | - Garry Nolan
- School of Medicine, Stanford University, Stanford, CA, USA
| | - Jason R Swedlow
- Division of Computational Biology and Centre for Gene Regulation and Expression, University of Dundee, Dundee, UK
| | - Nikolaus Schultz
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | | | | | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Sandro Santagata
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA.
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
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16
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Martínez-García M, Hernández-Lemus E. Data Integration Challenges for Machine Learning in Precision Medicine. Front Med (Lausanne) 2022; 8:784455. [PMID: 35145977 PMCID: PMC8821900 DOI: 10.3389/fmed.2021.784455] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 12/28/2021] [Indexed: 12/19/2022] Open
Abstract
A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.
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Affiliation(s)
- Mireya Martínez-García
- Clinical Research Division, National Institute of Cardiology ‘Ignacio Chávez’, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autnoma de Mexico, Mexico City, Mexico
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17
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Watson ER, Taherian Fard A, Mar JC. Computational Methods for Single-Cell Imaging and Omics Data Integration. Front Mol Biosci 2022; 8:768106. [PMID: 35111809 PMCID: PMC8801747 DOI: 10.3389/fmolb.2021.768106] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 11/29/2021] [Indexed: 12/12/2022] Open
Abstract
Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile at the cellular level. Although the use of imaging data is well established in biomedical research, its primary application has been to observe phenotypes at the tissue or organ level, often using medical imaging techniques such as MRI, CT, and PET. These imaging technologies complement omics-based data in biomedical research because they are helpful for identifying associations between genotype and phenotype, along with functional changes occurring at the tissue level. Single cell imaging can act as an intermediary between these levels. Meanwhile new technologies continue to arrive that can be used to interrogate the genome of single cells and its related omics datasets. As these two areas, single cell imaging and single cell omics, each advance independently with the development of novel techniques, the opportunity to integrate these data types becomes more and more attractive. This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomena like ageing. We include an emphasis on machine learning algorithms because of their ability to identify complex patterns in large multidimensional data.
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Affiliation(s)
| | - Atefeh Taherian Fard
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia
| | - Jessica Cara Mar
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia
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18
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Sanchez-Arias JC, Carrier M, Frederiksen SD, Shevtsova O, McKee C, van der Slagt E, Gonçalves de Andrade E, Nguyen HL, Young PA, Tremblay MÈ, Swayne LA. A Systematic, Open-Science Framework for Quantification of Cell-Types in Mouse Brain Sections Using Fluorescence Microscopy. Front Neuroanat 2021; 15:722443. [PMID: 34949993 PMCID: PMC8691181 DOI: 10.3389/fnana.2021.722443] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 10/28/2021] [Indexed: 02/03/2023] Open
Abstract
The ever-expanding availability and evolution of microscopy tools has enabled ground-breaking discoveries in neurobiology, particularly with respect to the analysis of cell-type density and distribution. Widespread implementation of many of the elegant image processing tools available continues to be impeded by the lack of complete workflows that span from experimental design, labeling techniques, and analysis workflows, to statistical methods and data presentation. Additionally, it is important to consider open science principles (e.g., open-source software and tools, user-friendliness, simplicity, and accessibility). In the present methodological article, we provide a compendium of resources and a FIJI-ImageJ-based workflow aimed at improving the quantification of cell density in mouse brain samples using semi-automated open-science-based methods. Our proposed framework spans from principles and best practices of experimental design, histological and immunofluorescence staining, and microscopy imaging to recommendations for statistical analysis and data presentation. To validate our approach, we quantified neuronal density in the mouse barrel cortex using antibodies against pan-neuronal and interneuron markers. This framework is intended to be simple and yet flexible, such that it can be adapted to suit distinct project needs. The guidelines, tips, and proposed methodology outlined here, will support researchers of wide-ranging experience levels and areas of focus in neuroscience research.
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Affiliation(s)
| | - Micaël Carrier
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada.,Axe Neurosciences, Centre de Recherche du CHU de Québec, Université de Laval, Québec City, QC, Canada
| | | | - Olga Shevtsova
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
| | - Chloe McKee
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
| | - Emma van der Slagt
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
| | | | - Hai Lam Nguyen
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
| | - Penelope A Young
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
| | - Marie-Ève Tremblay
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada.,Axe Neurosciences, Centre de Recherche du CHU de Québec, Université de Laval, Québec City, QC, Canada.,Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada.,Department of Molecular Medicine, Université de Laval, Québec City, QC, Canada.,Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Leigh Anne Swayne
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada.,Department of Cellular and Physiological Sciences, University of British Columbia, Vancouver, BC, Canada.,Department of Neurology and Neurosurgery, Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Program, Research Institute of the McGill University Health Centre, Montreal General Hospital, Montreal, QC, Canada
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19
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Kunis S, Hänsch S, Schmidt C, Wong F, Strambio-De-Castillia C, Weidtkamp-Peters S. MDEmic: a metadata annotation tool to facilitate management of FAIR image data in the bioimaging community. Nat Methods 2021; 18:1416-1417. [PMID: 34635849 PMCID: PMC9514507 DOI: 10.1038/s41592-021-01288-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Susanne Kunis
- Department of Biology/Chemistry, Centre for Cellular Nanoanalytics, University Osnabrueck, Osnabrueck, Germany.
| | - Sebastian Hänsch
- Centre for Advanced Imaging, University Duesseldorf, Duesseldorf, Germany
| | - Christian Schmidt
- Bioimaging Centre, Department of Biology, University of Konstanz, Konstanz, Germany
| | - Frances Wong
- Division of Computational Biology, Centre for Gene Regulation and Expression, University of Dundee, Dundee, UK
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20
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Hammer M, Huisman M, Rigano A, Boehm U, Chambers JJ, Gaudreault N, North AJ, Pimentel JA, Sudar D, Bajcsy P, Brown CM, Corbett AD, Faklaris O, Lacoste J, Laude A, Nelson G, Nitschke R, Farzam F, Smith CS, Grunwald D, Strambio-De-Castillia C. Towards community-driven metadata standards for light microscopy: tiered specifications extending the OME model. Nat Methods 2021; 18:1427-1440. [PMID: 34862501 PMCID: PMC9271325 DOI: 10.1038/s41592-021-01327-9] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments.
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Affiliation(s)
- Mathias Hammer
- RNA Therapeutics Institute, UMass Chan Medical School, Worcester, MA, USA
- Department of Biology, Technical University of Darmstadt, Darmstadt, Germany
| | | | - Alessandro Rigano
- Program in Molecular Medicine, UMass Chan Medical School, Worcester, MA, USA
| | - Ulrike Boehm
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - James J Chambers
- Institute for Applied Life Sciences, University of Massachusetts, Amherst, MA, USA
| | | | | | - Jaime A Pimentel
- Laboratorio Nacional de Microscopía Avanzada, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Damir Sudar
- Quantitative Imaging Systems LLC, Portland, OR, USA
| | - Peter Bajcsy
- National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Claire M Brown
- Advanced BioImaging Facility (ABIF), McGill University, Montreal, Quebec, Canada
| | | | - Orestis Faklaris
- MRI, BCM, University of Montpellier, CNRS, INSERM, Montpellier, France
| | | | - Alex Laude
- Bioimaging Unit, Newcastle University, Newcastle upon Tyne, UK
| | - Glyn Nelson
- Bioimaging Unit, Newcastle University, Newcastle upon Tyne, UK
| | - Roland Nitschke
- Life Imaging Center and Signalling Research Centres CIBSS and BIOSS, University of Freiburg, Freiburg, Germany
| | - Farzin Farzam
- RNA Therapeutics Institute, UMass Chan Medical School, Worcester, MA, USA
| | - Carlas S Smith
- Delft Center for Systems and Control and Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
| | - David Grunwald
- RNA Therapeutics Institute, UMass Chan Medical School, Worcester, MA, USA
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21
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Sasaki A. Recent advances in the standardization of fluorescence microscopy for quantitative image analysis. Biophys Rev. [DOI: 10.1007/s12551-021-00871-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 10/22/2021] [Indexed: 12/19/2022] Open
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22
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Abstract
PURPOSE Micron-scale computed tomography (micro-CT) imaging is a ubiquitous, cost-effective, and non-invasive three-dimensional imaging modality. We review recent developments and applications of micro-CT for preclinical research. METHODS Based on a comprehensive review of recent micro-CT literature, we summarize features of state-of-the-art hardware and ongoing challenges and promising research directions in the field. RESULTS Representative features of commercially available micro-CT scanners and some new applications for both in vivo and ex vivo imaging are described. New advancements include spectral scanning using dual-energy micro-CT based on energy-integrating detectors or a new generation of photon-counting x-ray detectors (PCDs). Beyond two-material discrimination, PCDs enable quantitative differentiation of intrinsic tissues from one or more extrinsic contrast agents. When these extrinsic contrast agents are incorporated into a nanoparticle platform (e.g. liposomes), novel micro-CT imaging applications are possible such as combined therapy and diagnostic imaging in the field of cancer theranostics. Another major area of research in micro-CT is in x-ray phase contrast (XPC) imaging. XPC imaging opens CT to many new imaging applications because phase changes are more sensitive to density variations in soft tissues than standard absorption imaging. We further review the impact of deep learning on micro-CT. We feature several recent works which have successfully applied deep learning to micro-CT data, and we outline several challenges specific to micro-CT. CONCLUSIONS All of these advancements establish micro-CT imaging at the forefront of preclinical research, able to provide anatomical, functional, and even molecular information while serving as a testbench for translational research.
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Affiliation(s)
- D.P. Clark
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Durham, NC 27710
| | - C.T. Badea
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Durham, NC 27710
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Nelson G, Boehm U, Bagley S, Bajcsy P, Bischof J, Brown CM, Dauphin A, Dobbie IM, Eriksson JE, Faklaris O, Fernandez-Rodriguez J, Ferrand A, Gelman L, Gheisari A, Hartmann H, Kukat C, Laude A, Mitkovski M, Munck S, North AJ, Rasse TM, Resch-Genger U, Schuetz LC, Seitz A, Strambio-De-Castillia C, Swedlow JR, Alexopoulos I, Aumayr K, Avilov S, Bakker GJ, Bammann RR, Bassi A, Beckert H, Beer S, Belyaev Y, Bierwagen J, Birngruber KA, Bosch M, Breitlow J, Cameron LA, Chalfoun J, Chambers JJ, Chen CL, Conde-Sousa E, Corbett AD, Cordelieres FP, Nery ED, Dietzel R, Eismann F, Fazeli E, Felscher A, Fried H, Gaudreault N, Goh WI, Guilbert T, Hadleigh R, Hemmerich P, Holst GA, Itano MS, Jaffe CB, Jambor HK, Jarvis SC, Keppler A, Kirchenbuechler D, Kirchner M, Kobayashi N, Krens G, Kunis S, Lacoste J, Marcello M, Martins GG, Metcalf DJ, Mitchell CA, Moore J, Mueller T, Nelson MS, Ogg S, Onami S, Palmer AL, Paul-Gilloteaux P, Pimentel JA, Plantard L, Podder S, Rexhepaj E, Royon A, Saari MA, Schapman D, Schoonderwoert V, Schroth-Diez B, Schwartz S, Shaw M, Spitaler M, Stoeckl MT, Sudar D, Teillon J, Terjung S, Thuenauer R, Wilms CD, Wright GD, Nitschke R. QUAREP-LiMi: A community-driven initiative to establish guidelines for quality assessment and reproducibility for instruments and images in light microscopy. J Microsc 2021; 284:56-73. [PMID: 34214188 PMCID: PMC10388377 DOI: 10.1111/jmi.13041] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 06/16/2021] [Indexed: 11/27/2022]
Abstract
A modern day light microscope has evolved from a tool devoted to making primarily empirical observations to what is now a sophisticated , quantitative device that is an integral part of both physical and life science research. Nowadays, microscopes are found in nearly every experimental laboratory. However, despite their prevalent use in capturing and quantifying scientific phenomena, neither a thorough understanding of the principles underlying quantitative imaging techniques nor appropriate knowledge of how to calibrate, operate and maintain microscopes can be taken for granted. This is clearly demonstrated by the well-documented and widespread difficulties that are routinely encountered in evaluating acquired data and reproducing scientific experiments. Indeed, studies have shown that more than 70% of researchers have tried and failed to repeat another scientist's experiments, while more than half have even failed to reproduce their own experiments. One factor behind the reproducibility crisis of experiments published in scientific journals is the frequent underreporting of imaging methods caused by a lack of awareness and/or a lack of knowledge of the applied technique. Whereas quality control procedures for some methods used in biomedical research, such as genomics (e.g. DNA sequencing, RNA-seq) or cytometry, have been introduced (e.g. ENCODE), this issue has not been tackled for optical microscopy instrumentation and images. Although many calibration standards and protocols have been published, there is a lack of awareness and agreement on common standards and guidelines for quality assessment and reproducibility. In April 2020, the QUality Assessment and REProducibility for instruments and images in Light Microscopy (QUAREP-LiMi) initiative was formed. This initiative comprises imaging scientists from academia and industry who share a common interest in achieving a better understanding of the performance and limitations of microscopes and improved quality control (QC) in light microscopy. The ultimate goal of the QUAREP-LiMi initiative is to establish a set of common QC standards, guidelines, metadata models and tools, including detailed protocols, with the ultimate aim of improving reproducible advances in scientific research. This White Paper (1) summarizes the major obstacles identified in the field that motivated the launch of the QUAREP-LiMi initiative; (2) identifies the urgent need to address these obstacles in a grassroots manner, through a community of stakeholders including, researchers, imaging scientists, bioimage analysts, bioimage informatics developers, corporate partners, funding agencies, standards organizations, scientific publishers and observers of such; (3) outlines the current actions of the QUAREP-LiMi initiative and (4) proposes future steps that can be taken to improve the dissemination and acceptance of the proposed guidelines to manage QC. To summarize, the principal goal of the QUAREP-LiMi initiative is to improve the overall quality and reproducibility of light microscope image data by introducing broadly accepted standard practices and accurately captured image data metrics.
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Affiliation(s)
- Glyn Nelson
- Bioimaging Unit, Newcastle University, Newcastle upon Tyne, UK
| | - Ulrike Boehm
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Steve Bagley
- Visualisation, Irradiation & Analysis, Cancer Research UK Manchester Institute, Alderley Park, Macclesfield, UK
| | - Peter Bajcsy
- National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | | | - Claire M Brown
- Advanced BioImaging Facility (ABIF), McGill University, Montreal, Quebec, Canada
| | - Aurélien Dauphin
- Unité Génétique et Biologie du Développement U934, PICT-IBiSA, Institut Curie/Inserm/CNRS/PSL Research University, Paris, France
| | - Ian M Dobbie
- Department of Biochemistry, University of Oxford, Oxford, Oxon, UK
| | - John E Eriksson
- Turku Bioscience Centre, Euro-Bioimaging ERIC, Turku, Finland
| | | | | | - Alexia Ferrand
- Imaging Core Facility, Biozentrum, University of Basel, Basel, Switzerland
| | - Laurent Gelman
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Ali Gheisari
- Light Microscopy Facility, CMCB Technology Platform, TU Dresden, Dresden, Germany
| | - Hella Hartmann
- Light Microscopy Facility, CMCB Technology Platform, TU Dresden, Dresden, Germany
| | - Christian Kukat
- FACS & Imaging Core Facility, Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Alex Laude
- Bioimaging Unit, Newcastle University, Newcastle upon Tyne, UK
| | - Miso Mitkovski
- Light Microscopy Facility, Max Planck Institute of Experimental Medicine, Goettingen, Germany
| | - Sebastian Munck
- VIB BioImaging Core & VIB-KU Leuven Center for Brain and Disease Research & KU Leuven Department for Neuroscience, Leuven, Flanders, Belgium
| | | | - Tobias M Rasse
- Scientific Service Group Microscopy, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
| | - Ute Resch-Genger
- Division Biophotonics, Federal Institute for Materials Research and Testing, Berlin, Germany
| | - Lucas C Schuetz
- European Molecular Biology Laboratory, Advanced Light Microscopy Facility, Heidelberg, Germany
| | - Arne Seitz
- Faculty of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Vaud, Switzerland
| | | | - Jason R Swedlow
- Divisions of Computational Biology and Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, UK
| | - Ioannis Alexopoulos
- General Instrumentation - Light Microscopy Facility, Faculty of Science, Radboud University, Nijmegen, The Netherlands
| | - Karin Aumayr
- BioOptics Facility, IMP - Research Institute of Molecular Pathology, Vienna, Austria
| | - Sergiy Avilov
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Gert-Jan Bakker
- Department of Cell Biology (route 283), Radboud Institute for Molecular Life Sciences, Nijmegen, The Netherlands
| | | | - Andrea Bassi
- Dipartimento di Fisica, Politecnico di Milano, Milan, Italy
| | - Hannes Beckert
- Microscopy Core Facility, Medizinische Fakultät, Universität Bonn, Bonn, Germany
| | | | - Yury Belyaev
- Microscopy Imaging Center, University of Bern, Bern, Switzerland
| | | | | | - Manel Bosch
- Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
| | | | - Lisa A Cameron
- Light Microscopy Core Facility, Department of Biology, Duke University, Durham, North Carolina, USA
| | - Joe Chalfoun
- National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - James J Chambers
- Institute for Applied Life Sciences, University of Massachusetts, Amherst, Massachusetts, USA
| | | | - Eduardo Conde-Sousa
- i3S - Instituto de InvestigaÇão e InovaÇão em Saúde, Universidade do Porto, Porto, Portugal.,INEB - Instituto de Engenharia Biomédica, Universidade do Porto, Porto, Portugal
| | | | | | - Elaine Del Nery
- BioPhenics High-Content Screening Laboratory (PICT-IBiSA), Translational Research Department, Institut Curie - PSL Research University, Paris, France
| | - Ralf Dietzel
- Omicron-Laserage Laserprodukte GmbH, Rodgau, Germany
| | | | | | | | - Hans Fried
- Light Microscope Facility, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | | | - Wah Ing Goh
- A*STAR Microscopy Platform, Research Support Centre, Agency for Science, Technology and Research, Singapore, Singapore
| | - Thomas Guilbert
- Institut Cochin, INSERM (U1016), CNRS (UMR 8104), Université de Paris (UMR-S1016), Paris, France
| | | | - Peter Hemmerich
- Core Facility Imaging, Leibniz Institute on Aging, Jena, Germany
| | | | - Michelle S Itano
- Neuroscience Microscopy Core, University of North Carolina, Chapel Hill, North Carolina, USA
| | | | - Helena K Jambor
- Mildred-Scheel Nachwuchszentrum, Universitätsklinikum Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Stuart C Jarvis
- Prior Scientific Instruments Limited, Cambridge, Cambridgeshire, UK
| | - Antje Keppler
- EMBL Heidelberg, Global BioImaging, Heidelberg, Germany
| | | | - Marcel Kirchner
- FACS & Imaging Core Facility, Max Planck Institute for Biology of Ageing, Cologne, Germany
| | | | - Gabriel Krens
- Bioimaging Facility, Institute of Science and Technology Austria, Klosterneuburg, Austria
| | - Susanne Kunis
- University Osnabrueck, Biology/Chemistry, Osnabrueck, Germany
| | | | - Marco Marcello
- Institute of Systems, Molecular & Integrative Biology, University of Liverpool, Liverpool, Merseyside, UK
| | - Gabriel G Martins
- Instituto Gulbenkian de Ciencia & Faculdade de Ciencias, University of Lisboa, Oeiras, Portugal
| | | | - Claire A Mitchell
- Warwick Medical School, University of Warwick, Coventry, West Midlands, UK
| | - Joshua Moore
- Divisions of Computational Biology and Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, UK
| | - Tobias Mueller
- Gregor Mendel Institute of Molecular Plant Biology (GMI), Vienna, Austria
| | | | - Stephen Ogg
- Medical Microbiology & Immunology, University of Alberta, Edmonton, Alberta, Canada
| | - Shuichi Onami
- RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo, Japan
| | | | - Perrine Paul-Gilloteaux
- Université de Nantes, CHU Nantes, Inserm, CNRS, SFR Santé, Inserm UMS 016, CNRS UMS 3556, F-44000 Nantes, France
| | - Jaime A Pimentel
- Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, Mexico
| | - Laure Plantard
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Santosh Podder
- Microscopy Facility, Department of Biology, Indian Institute of Science Education and Research Pune, Pune, India
| | | | | | - Markku A Saari
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Damien Schapman
- UNIROUEN, INSERM, PRIMACEN, Normandie University, Rouen, France
| | | | - Britta Schroth-Diez
- Light Microscopy Facility, Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | | | - Michael Shaw
- National Physical Laboratory, Teddington, Middlesex, UK
| | - Martin Spitaler
- Imaging Facility, Max Planck Institute of Biochemistry, Martinsried, Munich, Germany
| | | | - Damir Sudar
- Quantitative Imaging Systems, Portland, Oregon, USA
| | - Jeremie Teillon
- Bordeaux Imaging Center, Université de Bordeaux, Bordeaux, Gironde, France
| | - Stefan Terjung
- European Molecular Biology Laboratory, Advanced Light Microscopy Facility, Heidelberg, Germany
| | - Roland Thuenauer
- Technology Platform Microscopy and Image Analysis, Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany
| | | | - Graham D Wright
- A*STAR Microscopy Platform, Research Support Centre, Agency for Science, Technology and Research, Singapore, Singapore
| | - Roland Nitschke
- Life Imaging Center and BIOSS Centre for Biological Signaling Studies, Albert-Ludwigs-University Freiburg, Freiburg, Germany
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24
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Sarkans U, Chiu W, Collinson L, Darrow MC, Ellenberg J, Grunwald D, Hériché JK, Iudin A, Martins GG, Meehan T, Narayan K, Patwardhan A, Russell MRG, Saibil HR, Strambio-De-Castillia C, Swedlow JR, Tischer C, Uhlmann V, Verkade P, Barlow M, Bayraktar O, Birney E, Catavitello C, Cawthorne C, Wagner-Conrad S, Duke E, Paul-Gilloteaux P, Gustin E, Harkiolaki M, Kankaanpää P, Lemberger T, McEntyre J, Moore J, Nicholls AW, Onami S, Parkinson H, Parsons M, Romanchikova M, Sofroniew N, Swoger J, Utz N, Voortman LM, Wong F, Zhang P, Kleywegt GJ, Brazma A. REMBI: Recommended Metadata for Biological Images-enabling reuse of microscopy data in biology. Nat Methods 2021; 18:1418-1422. [PMID: 34021280 DOI: 10.1038/s41592-021-01166-8] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Ugis Sarkans
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
| | - Wah Chiu
- Department of Bioengineering, Stanford University, Stanford and SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | | | | | - Jan Ellenberg
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - David Grunwald
- RNA Therapeutics Institute, University of Massachusetts Medical School, Worcester, MA, USA
| | - Jean-Karim Hériché
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Andrii Iudin
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | | | - Terry Meehan
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.,Kymab Ltd., Babraham Research Campus, Cambridge, UK
| | - Kedar Narayan
- Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.,Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Ardan Patwardhan
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | | | - Helen R Saibil
- Institute of Structural and Molecular Biology, Birkbeck, University of London, London, UK
| | | | - Jason R Swedlow
- Division of Computational Biology and Centre for Gene Regulation and Expression, University of Dundee, Dundee, UK
| | - Christian Tischer
- Centre for Bioimage Analysis, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Virginie Uhlmann
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Paul Verkade
- School of Biochemistry, University of Bristol, Bristol, UK
| | - Mary Barlow
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | | | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Cesare Catavitello
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.,Ebury UK, London, UK
| | - Christopher Cawthorne
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | | | - Elizabeth Duke
- Diamond Light Source, Harwell Science and Innovation Campus, Harwell, UK.,European Molecular Biology Laboratory, Hamburg, Germany
| | - Perrine Paul-Gilloteaux
- Université de Nantes, CNRS, INSERM, l'institut du thorax, Nantes, France.,Université de Nantes, CHU Nantes, Inserm, CNRS, SFR Santé, Inserm UMS 016, CNRS UMS 3556, Nantes, France
| | - Emmanuel Gustin
- Janssen Pharmaceutical Companies of Johnson & Johnson, Beerse, Belgium
| | - Maria Harkiolaki
- Diamond Light Source, Harwell Science and Innovation Campus, Harwell, UK
| | - Pasi Kankaanpää
- Turku BioImaging, University of Turku and Åbo Akademi University, Turku, Finland.,Euro-BioImaging ERIC, Turku, Finland
| | | | - Jo McEntyre
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Josh Moore
- Division of Computational Biology and Centre for Gene Regulation and Expression, University of Dundee, Dundee, UK
| | | | - Shuichi Onami
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Helen Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Maddy Parsons
- Randall Centre for Cell and Molecular Biophysics, King's College London, London, UK
| | | | | | - Jim Swoger
- European Molecular Biology Laboratory, Barcelona, Spain
| | - Nadine Utz
- German BioImaging e.V., University of Konstanz, Konstanz, Germany
| | - Lenard M Voortman
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Frances Wong
- Division of Computational Biology and Centre for Gene Regulation and Expression, University of Dundee, Dundee, UK
| | - Peijun Zhang
- Diamond Light Source, Harwell Science and Innovation Campus, Harwell, UK.,Division of Structural Biology, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Gerard J Kleywegt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
| | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
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25
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Rigano A, Ehmsen S, Öztürk SU, Ryan J, Balashov A, Hammer M, Kirli K, Boehm U, Brown CM, Bellve K, Chambers JJ, Cosolo A, Coleman RA, Faklaris O, Fogarty KE, Guilbert T, Hamacher AB, Itano MS, Keeley DP, Kunis S, Lacoste J, Laude A, Ma WY, Marcello M, Montero-Llopis P, Nelson G, Nitschke R, Pimentel JA, Weidtkamp-Peters S, Park PJ, Alver BH, Grunwald D, Strambio-De-Castillia C. Micro-Meta App: an interactive tool for collecting microscopy metadata based on community specifications. Nat Methods 2021; 18:1489-1495. [PMID: 34862503 PMCID: PMC8648560 DOI: 10.1038/s41592-021-01315-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 09/30/2021] [Indexed: 12/31/2022]
Abstract
For quality, interpretation, reproducibility and sharing value, microscopy images should be accompanied by detailed descriptions of the conditions that were used to produce them. Micro-Meta App is an intuitive, highly interoperable, open-source software tool that was developed in the context of the 4D Nucleome (4DN) consortium and is designed to facilitate the extraction and collection of relevant microscopy metadata as specified by the recent 4DN-BINA-OME tiered-system of Microscopy Metadata specifications. In addition to substantially lowering the burden of quality assurance, the visual nature of Micro-Meta App makes it particularly suited for training purposes.
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Affiliation(s)
- Alessandro Rigano
- Program in Molecular Medicine, UMass Chan Medical School, Worcester, MA USA
| | - Shannon Ehmsen
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Serkan Utku Öztürk
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Joel Ryan
- grid.14709.3b0000 0004 1936 8649Advanced BioImaging Facility (ABIF), McGill University, Montreal, Quebec Canada
| | - Alexander Balashov
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Mathias Hammer
- RNA Therapeutics Institute, UMass Chan Medical School, Worcester, MA USA
| | - Koray Kirli
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Ulrike Boehm
- grid.443970.dJanelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Claire M. Brown
- grid.14709.3b0000 0004 1936 8649Advanced BioImaging Facility (ABIF), McGill University, Montreal, Quebec Canada
| | - Karl Bellve
- Program in Molecular Medicine, UMass Chan Medical School, Worcester, MA USA
| | - James J. Chambers
- grid.266683.f0000 0001 2166 5835Institute for Applied Life Sciences, University of Massachusetts, Amherst, MA USA
| | - Andrea Cosolo
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Robert A. Coleman
- grid.251993.50000000121791997Department of Anatomy and Structural Biology, Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine, Bronx, NY USA
| | - Orestis Faklaris
- grid.121334.60000 0001 2097 0141BioCampus Montpellier (BCM), University of Montpellier, CNRS, INSERM, Montpellier, France
| | - Kevin E. Fogarty
- Program in Molecular Medicine, UMass Chan Medical School, Worcester, MA USA
| | - Thomas Guilbert
- grid.508487.60000 0004 7885 7602Institut Cochin, Inserm U1016-CNRS UMR8104-Université de Paris, Paris, France
| | - Anna B. Hamacher
- grid.411327.20000 0001 2176 9917Center for Advanced Imaging, Heinrich-Heine University Duesseldorf, Düsseldorf, Germany
| | - Michelle S. Itano
- grid.10698.360000000122483208UNC Neuroscience Microscopy Core Facility, Department of Cell Biology and Physiology, Carolina Institute for Developmental Disabilities, and UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC USA
| | - Daniel P. Keeley
- grid.10698.360000000122483208UNC Neuroscience Microscopy Core Facility, Department of Cell Biology and Physiology, Carolina Institute for Developmental Disabilities, and UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC USA
| | - Susanne Kunis
- grid.10854.380000 0001 0672 4366Department of Biology/Chemistry and Center for Cellular Nanoanalytics, University Osnabrück, Osnabrück, Germany
| | | | - Alex Laude
- grid.1006.70000 0001 0462 7212Bioimaging Unit, Newcastle University, Newcastle upon Tyne, UK
| | - Willa Y. Ma
- grid.10698.360000000122483208UNC Neuroscience Microscopy Core Facility, Carolina Institute for Developmental Disabilities, and UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC USA
| | - Marco Marcello
- grid.10025.360000 0004 1936 8470Center for Cell Imaging, University of Liverpool, Liverpool, UK
| | - Paula Montero-Llopis
- grid.38142.3c000000041936754XMicroscopy Resources of the North Quad, University of Harvard Medical School, Boston, MA USA
| | - Glyn Nelson
- grid.1006.70000 0001 0462 7212Bioimaging Unit, Newcastle University, Newcastle upon Tyne, UK
| | - Roland Nitschke
- grid.5963.9Life Imaging Center and Signalling Research Centres CIBSS and BIOSS, University of Freiburg, Freiburg, Germany
| | - Jaime A. Pimentel
- grid.9486.30000 0001 2159 0001Laboratorio Nacional de Microscopía Avanzada, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Mexico
| | - Stefanie Weidtkamp-Peters
- grid.411327.20000 0001 2176 9917Center for Advanced Imaging, Heinrich-Heine University Duesseldorf, Düsseldorf, Germany
| | - Peter J. Park
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Burak H. Alver
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - David Grunwald
- RNA Therapeutics Institute, UMass Chan Medical School, Worcester, MA USA
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