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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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2
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Hiraki-Kajiyama T, Miyasaka N, Ando R, Wakisaka N, Itoga H, Onami S, Yoshihara Y. An atlas and database of neuropeptide gene expression in the adult zebrafish forebrain. J Comp Neurol 2024; 532:e25619. [PMID: 38831653 DOI: 10.1002/cne.25619] [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: 03/29/2023] [Revised: 03/21/2024] [Accepted: 04/16/2024] [Indexed: 06/05/2024]
Abstract
Zebrafish is a useful model organism in neuroscience; however, its gene expression atlas in the adult brain is not well developed. In the present study, we examined the expression of 38 neuropeptides, comparing with GABAergic and glutamatergic neuron marker genes in the adult zebrafish brain by comprehensive in situ hybridization. The results are summarized as an expression atlas in 19 coronal planes of the forebrain. Furthermore, the scanned data of all brain sections were made publicly available in the Adult Zebrafish Brain Gene Expression Database (https://ssbd.riken.jp/azebex/). Based on these data, we performed detailed comparative neuroanatomical analyses of the hypothalamus and found that several regions previously described as one nucleus in the reference zebrafish brain atlas contain two or more subregions with significantly different neuropeptide/neurotransmitter expression profiles. Subsequently, we compared the expression data in zebrafish telencephalon and hypothalamus obtained in this study with those in mice, by performing a cluster analysis. As a result, several nuclei in zebrafish and mice were clustered in close vicinity. The present expression atlas, database, and anatomical findings will contribute to future neuroscience research using zebrafish.
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Affiliation(s)
- Towako Hiraki-Kajiyama
- Laboratory for Systems Molecular Ethology, RIKEN Center for Brain Science, Wako, Saitama, Japan
- Laboratory of Molecular Ethology, Graduate School of Life Science, Tohoku University, Sendai, Miyagi, Japan
| | - Nobuhiko Miyasaka
- Laboratory for Systems Molecular Ethology, RIKEN Center for Brain Science, Wako, Saitama, Japan
| | - Reiko Ando
- Support Unit for Bio-Material Analysis, Research Resources Division, RIKEN Center for Brain Science, Wako, Saitama, Japan
| | - Noriko Wakisaka
- Laboratory for Systems Molecular Ethology, RIKEN Center for Brain Science, Wako, Saitama, Japan
| | - Hiroya Itoga
- Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo, Japan
| | - Shuichi Onami
- Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo, Japan
- Life Science Data Sharing Unit, RIKEN Information R&D and Strategy Headquarters, Kobe, Hyogo, Japan
| | - Yoshihiro Yoshihara
- Laboratory for Systems Molecular Ethology, RIKEN Center for Brain Science, Wako, Saitama, Japan
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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] [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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Moore J, Basurto-Lozada D, Besson S, Bogovic J, Bragantini J, Brown EM, Burel JM, Casas Moreno X, de Medeiros G, Diel EE, Gault D, Ghosh SS, Gold I, Halchenko YO, Hartley M, Horsfall D, Keller MS, Kittisopikul M, Kovacs G, Küpcü Yoldaş A, Kyoda K, le Tournoulx de la Villegeorges A, Li T, Liberali P, Lindner D, Linkert M, Lüthi J, Maitin-Shepard J, Manz T, Marconato L, McCormick M, Lange M, Mohamed K, Moore W, Norlin N, Ouyang W, Özdemir B, Palla G, Pape C, Pelkmans L, Pietzsch T, Preibisch S, Prete M, Rzepka N, Samee S, Schaub N, Sidky H, Solak AC, Stirling DR, Striebel J, Tischer C, Toloudis D, Virshup I, Walczysko P, Watson AM, Weisbart E, Wong F, Yamauchi KA, Bayraktar O, Cimini BA, Gehlenborg N, Haniffa M, Hotaling N, Onami S, Royer LA, Saalfeld S, Stegle O, Theis FJ, Swedlow JR. OME-Zarr: a cloud-optimized bioimaging file format with international community support. Histochem Cell Biol 2023; 160:223-251. [PMID: 37428210 PMCID: PMC10492740 DOI: 10.1007/s00418-023-02209-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/16/2023] [Indexed: 07/11/2023]
Abstract
A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself-OME-Zarr-along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain-the file format that underlies so many personal, institutional, and global data management and analysis tasks.
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Affiliation(s)
- Josh Moore
- German BioImaging-Gesellschaft für Mikroskopie und Bildanalyse e.V., Constance, Germany.
| | | | - Sébastien Besson
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | - John Bogovic
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Eva M Brown
- Allen Institute for Cell Science, Seattle, WA, USA
| | - Jean-Marie Burel
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | - Xavier Casas Moreno
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | | | | | - David Gault
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | | | - Ilan Gold
- Harvard Medical School, Boston, MA, USA
| | | | - Matthew Hartley
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Cambridge, UK
| | - Dave Horsfall
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | | | - Mark Kittisopikul
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Gabor Kovacs
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | - Aybüke Küpcü Yoldaş
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Cambridge, UK
| | - Koji Kyoda
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | | | - Tong Li
- Wellcome Sanger Institute, Hinxton, UK
| | - Prisca Liberali
- Friedrich Miescher Institute for Biomedical Imaging, Basel, Switzerland
| | - Dominik Lindner
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | | | - Joel Lüthi
- Friedrich Miescher Institute for Biomedical Imaging, Basel, Switzerland
| | | | | | - Luca Marconato
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | | | | | - Khaled Mohamed
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | - William Moore
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | - Nils Norlin
- Department of Experimental Medical Science & Lund Bioimaging Centre, Lund University, Lund, Sweden
| | - Wei Ouyang
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | | | - Giovanni Palla
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | | | | | - Tobias Pietzsch
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Stephan Preibisch
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | | | | | - Nicholas Schaub
- Information Technology Branch, National Center for Advancing Translational Science, National Institutes of Health, Bethesda, USA
| | | | | | | | | | | | | | - Isaac Virshup
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Petr Walczysko
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | | | - Erin Weisbart
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Frances Wong
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | - Kevin A Yamauchi
- Department of Biosystems Science and Engineering, ETH Zürich, Zürich, Switzerland
| | | | - Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Nathan Hotaling
- Information Technology Branch, National Center for Advancing Translational Science, National Institutes of Health, Bethesda, USA
| | - Shuichi Onami
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | | | - Stephan Saalfeld
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Oliver Stegle
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jason R Swedlow
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
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Robotic data acquisition with deep learning enables cell image-based prediction of transcriptomic phenotypes. Proc Natl Acad Sci U S A 2023; 120:e2210283120. [PMID: 36577074 PMCID: PMC9910600 DOI: 10.1073/pnas.2210283120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Single-cell whole-transcriptome analysis is the gold standard approach to identifying molecularly defined cell phenotypes. However, this approach cannot be used for dynamics measurements such as live-cell imaging. Here, we developed a multifunctional robot, the automated live imaging and cell picking system (ALPS) and used it to perform single-cell RNA sequencing for microscopically observed cells with multiple imaging modes. Using robotically obtained data that linked cell images and the whole transcriptome, we successfully predicted transcriptome-defined cell phenotypes in a noninvasive manner using cell image-based deep learning. This noninvasive approach opens a window to determine the live-cell whole transcriptome in real time. Moreover, this work, which is based on a data-driven approach, is a proof of concept for determining the transcriptome-defined phenotypes (i.e., not relying on specific genes) of any cell from cell images using a model trained on linked datasets.
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6
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Azuma Y, Okada H, Onami S. Systematic analysis of cell morphodynamics in C. elegans early embryogenesis. FRONTIERS IN BIOINFORMATICS 2023; 3:1082531. [PMID: 37026092 PMCID: PMC10070942 DOI: 10.3389/fbinf.2023.1082531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 03/07/2023] [Indexed: 04/08/2023] Open
Abstract
The invariant cell lineage of Caenorhabditis elegans allows unambiguous assignment of the identity for each cell, which offers a unique opportunity to study developmental dynamics such as the timing of cell division, dynamics of gene expression, and cell fate decisions at single-cell resolution. However, little is known about cell morphodynamics, including the extent to which they are variable between individuals, mainly due to the lack of sufficient amount and quality of quantified data. In this study, we systematically quantified the cell morphodynamics in 52 C. elegans embryos from the two-cell stage to mid-gastrulation at the high spatiotemporal resolution, 0.5 μm thickness of optical sections, and 30-second intervals of recordings. Our data allowed systematic analyses of the morphological features. We analyzed sphericity dynamics and found a significant increase at the end of metaphase in every cell, indicating the universality of the mitotic cell rounding. Concomitant with the rounding, the volume also increased in most but not all cells, suggesting less universality of the mitotic swelling. Combining all features showed that cell morphodynamics was unique for each cell type. The cells before the onset of gastrulation could be distinguished from all the other cell types. Quantification of reproducibility in cell-cell contact revealed that variability in division timings and cell arrangements produced variability in contacts between the embryos. However, the area of such contacts occupied less than 5% of the total area, suggesting the high reproducibility of spatial occupancies and adjacency relationships of the cells. By comparing the morphodynamics of identical cells between the embryos, we observed diversity in the variability between cells and found it was determined by multiple factors, including cell lineage, cell generation, and cell-cell contact. We compared the variabilities of cell morphodynamics and cell-cell contacts with those in ascidian Phallusia mammillata embryos. The variabilities were larger in C. elegans, despite smaller differences in embryo size and number of cells at each developmental stage.
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7
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Arata Y, Shiga I, Ikeda Y, Jurica P, Kimura H, Kiyono K, Sako Y. Insulin signaling shapes fractal scaling of C. elegans behavior. Sci Rep 2022; 12:10481. [PMID: 35729173 PMCID: PMC9213454 DOI: 10.1038/s41598-022-13022-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 05/03/2022] [Indexed: 11/08/2022] Open
Abstract
Fractal scaling in animal behavioral activity, where similar temporal patterns appear repeatedly over a series of magnifications among time scales, governs the complex behavior of various animal species and, in humans, can be altered by neurodegenerative diseases and aging. However, the mechanism underlying fractal scaling remains unknown. Here, we cultured C. elegans in a microfluidic device for 3 days and analyzed temporal patterns of C. elegans activity by fractal analyses. The residence-time distribution of C. elegans behaviors shared a common feature with those of human and mice. Specifically, the residence-time power-law distribution of the active state changed to an exponential-like decline at a longer time scale, whereas the inactive state followed a power-law distribution. An exponential-like decline appeared with nutrient supply in wild-type animals, whereas this decline disappeared in insulin-signaling-defective daf-2 and daf-16 mutants. The absolute value of the power-law exponent of the inactive state distribution increased with nutrient supply in wild-type animals, whereas the value decreased in daf-2 and daf-16 mutants. We conclude that insulin signaling differentially affects mechanisms that determine the residence time in active and inactive states in C. elegans behavior. In humans, diabetes mellitus, which is caused by defects in insulin signaling, is associated with mood disorders that affect daily behavioral activities. We hypothesize that comorbid behavioral defects in patients with diabetes may be attributed to altered fractal scaling of human behavior.
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Affiliation(s)
- Yukinobu Arata
- Cellular Informatics Laboratory, Cluster for Pioneering Research (CPR), RIKEN, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan.
| | - Itsuki Shiga
- Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka, 560-8531, Japan
| | - Yusaku Ikeda
- Cellular Informatics Laboratory, Cluster for Pioneering Research (CPR), RIKEN, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
- Department of Mechanical Engineering, School of Engineering, Tokai University, 4-1-1 Kitakaname, Hiratsuka, Kanagawa, 259-1292, Japan
| | - Peter Jurica
- Cellular Informatics Laboratory, Cluster for Pioneering Research (CPR), RIKEN, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Hiroshi Kimura
- Department of Mechanical Engineering, School of Engineering, Tokai University, 4-1-1 Kitakaname, Hiratsuka, Kanagawa, 259-1292, Japan
| | - Ken Kiyono
- Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka, 560-8531, Japan
| | - Yasushi Sako
- Cellular Informatics Laboratory, Cluster for Pioneering Research (CPR), RIKEN, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
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8
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Cuny AP, Schlottmann FP, Ewald JC, Pelet S, Schmoller KM. Live cell microscopy: From image to insight. BIOPHYSICS REVIEWS 2022; 3:021302. [PMID: 38505412 PMCID: PMC10903399 DOI: 10.1063/5.0082799] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 03/18/2022] [Indexed: 03/21/2024]
Abstract
Live-cell microscopy is a powerful tool that can reveal cellular behavior as well as the underlying molecular processes. A key advantage of microscopy is that by visualizing biological processes, it can provide direct insights. Nevertheless, live-cell imaging can be technically challenging and prone to artifacts. For a successful experiment, many careful decisions are required at all steps from hardware selection to downstream image analysis. Facing these questions can be particularly intimidating due to the requirement for expertise in multiple disciplines, ranging from optics, biophysics, and programming to cell biology. In this review, we aim to summarize the key points that need to be considered when setting up and analyzing a live-cell imaging experiment. While we put a particular focus on yeast, many of the concepts discussed are applicable also to other organisms. In addition, we discuss reporting and data sharing strategies that we think are critical to improve reproducibility in the field.
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Affiliation(s)
| | - Fabian P. Schlottmann
- Interfaculty Institute of Cell Biology, University of Tuebingen, 72076 Tuebingen, Germany
| | - Jennifer C. Ewald
- Interfaculty Institute of Cell Biology, University of Tuebingen, 72076 Tuebingen, Germany
| | - Serge Pelet
- Department of Fundamental Microbiology, University of Lausanne, 1015 Lausanne, Switzerland
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9
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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 PMCID: PMC8606015 DOI: 10.1038/s41592-021-01166-8] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. We propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy. We hope this publication and the proposed Recommended Metadata for Biological Images (REMBI) will stimulate discussions about their implementation and future extension.
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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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10
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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] [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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11
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Swedlow JR, Kankaanpää P, Sarkans U, Goscinski W, Galloway G, Malacrida L, Sullivan RP, Härtel S, Brown CM, Wood C, Keppler A, Paina F, Loos B, Zullino S, Longo DL, Aime S, Onami S. A global view of standards for open image data formats and repositories. Nat Methods 2021; 18:1440-1446. [PMID: 33948027 DOI: 10.1038/s41592-021-01113-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Jason R Swedlow
- Divisions of Computational Biology and Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, UK.
| | - Pasi Kankaanpää
- Turku BioImaging, Åbo Akademi University and University of Turku, Turku, Finland.,Euro-BioImaging ERIC, Turku, Finland
| | - Ugis Sarkans
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, UK
| | - Wojtek Goscinski
- Monash eResearch Centre, Monash University, Melbourne, Victoria, Australia
| | - Graham Galloway
- National Imaging Facility, The University of Queensland, Brisbane, Queensland, Australia
| | - Leonel Malacrida
- Advanced Bioimaging Unit, Institut Pasteur Montevideo and Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
| | - Ryan P Sullivan
- Microscopy Australia, The University of Sydney, Sydney, Australia
| | - Steffen Härtel
- National Center for Health Information Systems (CENS), Center for Medical Informatics and Telemedicine (CIMT), and Biomedical Neuroscience Institute (BNI), Faculty of Medicine, University of Chile, Santiago, Chile
| | - Claire M Brown
- Advanced BioImaging Facility (ABIF), McGill University and Canada BioImaging, Montreal, Quebec, Canada
| | - Christopher Wood
- Laboratorio Nacional de Microscopía Avanzada, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, México
| | - Antje Keppler
- Euro-BioImaging Bio-Hub, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Federica Paina
- Department of Physiological Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Ben Loos
- Department of Physiological Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Sara Zullino
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.,Euro-BioImaging ERIC, Torino, Italy
| | - Dario Livio Longo
- Institute of Biostructures and Bioimaging (IBB), National Research Council of Italy (CNR), Torino, Italy
| | - Silvio Aime
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.,Euro-BioImaging ERIC, Torino, Italy
| | - Shuichi Onami
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
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12
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Imakubo M, Takayama J, Okada H, Onami S. Statistical image processing quantifies the changes in cytoplasmic texture associated with aging in Caenorhabditis elegans oocytes. BMC Bioinformatics 2021; 22:73. [PMID: 33596821 PMCID: PMC7890843 DOI: 10.1186/s12859-021-03990-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 01/31/2021] [Indexed: 12/30/2022] Open
Abstract
Background Oocyte quality decreases with aging, thereby increasing errors in fertilization, chromosome segregation, and embryonic cleavage. Oocyte appearance also changes with aging, suggesting a functional relationship between oocyte quality and appearance. However, no methods are available to objectively quantify age-associated changes in oocyte appearance. Results We show that statistical image processing of Nomarski differential interference contrast microscopy images can be used to quantify age-associated changes in oocyte appearance in the nematode Caenorhabditis elegans. Max–min value (mean difference between the maximum and minimum intensities within each moving window) quantitatively characterized the difference in oocyte cytoplasmic texture between 1- and 3-day-old adults (Day 1 and Day 3 oocytes, respectively). With an appropriate parameter set, the gray level co-occurrence matrix (GLCM)-based texture feature Correlation (COR) more sensitively characterized this difference than the Max–min Value. Manipulating the smoothness of and/or adding irregular structures to the cytoplasmic texture of Day 1 oocyte images reproduced the difference in Max–min Value but not in COR between Day 1 and Day 3 oocytes. Increasing the size of granules in synthetic images recapitulated the age-associated changes in COR. Manual measurements validated that the cytoplasmic granules in oocytes become larger with aging. Conclusions The Max–min value and COR objectively quantify age-related changes in C. elegans oocyte in Nomarski DIC microscopy images. Our methods provide new opportunities for understanding the mechanism underlying oocyte aging.
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Affiliation(s)
- Momoko Imakubo
- Department of Computational Science, Graduate School of System Informatics, Kobe University, Kobe, Hyogo, 657-8501, Japan.,Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo, 650-0047, Japan.,Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe, Hyogo, 650-0047, Japan
| | - Jun Takayama
- Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe, Hyogo, 650-0047, Japan
| | - Hatsumi Okada
- Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo, 650-0047, Japan.,Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe, Hyogo, 650-0047, Japan
| | - Shuichi Onami
- Department of Computational Science, Graduate School of System Informatics, Kobe University, Kobe, Hyogo, 657-8501, Japan. .,Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo, 650-0047, Japan. .,Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe, Hyogo, 650-0047, Japan.
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13
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Saunders DC, Messmer J, Kusmartseva I, Beery ML, Yang M, Atkinson MA, Powers AC, Cartailler JP, Brissova M. Pancreatlas: Applying an Adaptable Framework to Map the Human Pancreas in Health and Disease. PATTERNS 2020; 1:100120. [PMID: 33294866 PMCID: PMC7691395 DOI: 10.1016/j.patter.2020.100120] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 08/31/2020] [Accepted: 09/14/2020] [Indexed: 12/14/2022]
Abstract
Human tissue phenotyping generates complex spatial information from numerous imaging modalities, yet images typically become static figures for publication, and original data and metadata are rarely available. While comprehensive image maps exist for some organs, most resources have limited support for multiplexed imaging or have non-intuitive user interfaces. Therefore, we built a Pancreatlas resource that integrates several technologies into a unique interface, allowing users to access richly annotated web pages, drill down to individual images, and deeply explore data online. The current version of Pancreatlas contains over 800 unique images acquired by whole-slide scanning, confocal microscopy, and imaging mass cytometry, and is available at https://www.pancreatlas.org. To create this human pancreas-specific biological imaging resource, we developed a React-based web application and Python-based application programming interface, collectively called Flexible Framework for Integrating and Navigating Data (FFIND), which can be adapted beyond Pancreatlas to meet countless imaging or other structured data-management needs. Human organ phenotyping databases benefit from intuitive user interfaces Pancreatlas resource enables exploration of bioimaging data from human pancreas The front-end framework of Pancreatlas, FFIND, is modular and easily adaptable FFIND provides structured data-exploration capabilities across countless domains
Scientists need cost-effective yet fully featured database solutions that facilitate large dataset sharing in a structured and easily digestible manner. Flexible Framework for Integrating and Navigating Data (FFIND) is a data-agnostic web application that is designed to easily connect existing databases with data-browsing clients. We used FFIND to build Pancreatlas, an online imaging resource containing datasets linking imaging data with clinical data to facilitate advances in the understanding of diabetes, pancreatitis, and pancreatic cancer. FFIND architecture, which is available as open-source software, can be easily adapted to meet other field- or project-specific needs; we hope it will help data scientists reach a broader audience by reducing the development life cycle and providing familiar interactivity in communicating data and underlying stories.
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Affiliation(s)
- Diane C Saunders
- Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - James Messmer
- Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Irina Kusmartseva
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, Diabetes Institute, University of Florida, Gainesville, FL, USA
| | - Maria L Beery
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, Diabetes Institute, University of Florida, Gainesville, FL, USA
| | - Mingder Yang
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, Diabetes Institute, University of Florida, Gainesville, FL, USA
| | - Mark A Atkinson
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, Diabetes Institute, University of Florida, Gainesville, FL, USA.,Department of Pediatrics, College of Medicine, Diabetes Institute, University of Florida, Gainesville, FL, USA
| | - Alvin C Powers
- Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.,VA Tennessee Valley Healthcare System, Nashville, TN, USA.,Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA
| | - Jean-Philippe Cartailler
- Creative Data Solutions Shared Resource, Center for Stem Cell Biology, Vanderbilt University, Nashville, TN, USA
| | - Marcela Brissova
- Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
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14
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Kishimoto K, Furukawa KT, Luz-Madrigal A, Yamaoka A, Matsuoka C, Habu M, Alev C, Zorn AM, Morimoto M. Bidirectional Wnt signaling between endoderm and mesoderm confers tracheal identity in mouse and human cells. Nat Commun 2020; 11:4159. [PMID: 32855415 PMCID: PMC7453000 DOI: 10.1038/s41467-020-17969-w] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 07/24/2020] [Indexed: 12/20/2022] Open
Abstract
The periodic cartilage and smooth muscle structures in mammalian trachea are derived from tracheal mesoderm, and tracheal malformations result in serious respiratory defects in neonates. Here we show that canonical Wnt signaling in mesoderm is critical to confer trachea mesenchymal identity in human and mouse. At the initiation of tracheal development, endoderm begins to express Nkx2.1, and then mesoderm expresses the Tbx4 gene. Loss of β-catenin in fetal mouse mesoderm causes loss of Tbx4+ tracheal mesoderm and tracheal cartilage agenesis. The mesenchymal Tbx4 expression relies on endodermal Wnt activation and Wnt ligand secretion but is independent of known Nkx2.1-mediated respiratory development, suggesting that bidirectional Wnt signaling between endoderm and mesoderm promotes trachea development. Activating Wnt, Bmp signaling in mouse embryonic stem cell (ESC)-derived lateral plate mesoderm (LPM) generates tracheal mesoderm containing chondrocytes and smooth muscle cells. For human ESC-derived LPM, SHH activation is required along with WNT to generate proper tracheal mesoderm. Together, these findings may contribute to developing applications for human tracheal tissue repair.
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Affiliation(s)
- Keishi Kishimoto
- Laboratory for Lung Development and Regeneration, Riken Center for Biosystems Dynamics Research (BDR), Kobe, 650-0047, Japan
- RIKEN BDR-CuSTOM Joint Laboratory, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
- Center for Stem Cell & Organoid Medicine (CuSTOM), Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
- Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
| | - Kana T Furukawa
- Laboratory for Lung Development and Regeneration, Riken Center for Biosystems Dynamics Research (BDR), Kobe, 650-0047, Japan
| | - Agustin Luz-Madrigal
- Center for Stem Cell & Organoid Medicine (CuSTOM), Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
- Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
| | - Akira Yamaoka
- Laboratory for Lung Development and Regeneration, Riken Center for Biosystems Dynamics Research (BDR), Kobe, 650-0047, Japan
| | - Chisa Matsuoka
- Laboratory for Lung Development and Regeneration, Riken Center for Biosystems Dynamics Research (BDR), Kobe, 650-0047, Japan
| | - Masanobu Habu
- Department of Cell Growth and Differentiation, Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, 606-8507, Japan
| | - Cantas Alev
- Department of Cell Growth and Differentiation, Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, 606-8507, Japan
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto, 606-8501, Japan
| | - Aaron M Zorn
- RIKEN BDR-CuSTOM Joint Laboratory, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
- Center for Stem Cell & Organoid Medicine (CuSTOM), Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
- Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
| | - Mitsuru Morimoto
- Laboratory for Lung Development and Regeneration, Riken Center for Biosystems Dynamics Research (BDR), Kobe, 650-0047, Japan.
- RIKEN BDR-CuSTOM Joint Laboratory, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA.
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15
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Kyoda K, Ho KHL, Tohsato Y, Itoga H, Onami S. BD5: An open HDF5-based data format to represent quantitative biological dynamics data. PLoS One 2020; 15:e0237468. [PMID: 32785254 PMCID: PMC7423140 DOI: 10.1371/journal.pone.0237468] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 07/27/2020] [Indexed: 11/18/2022] Open
Abstract
BD5 is a new binary data format based on HDF5 (hierarchical data format version 5). It can be used for representing quantitative biological dynamics data obtained from bioimage informatics techniques and mechanobiological simulations. Biological Dynamics Markup Language (BDML) is an XML (Extensible Markup Language)-based open format that is also used to represent such data; however, it becomes difficult to access quantitative data in BDML files when the file size is large because parsing XML-based files requires large computational resources to first read the whole file sequentially into computer memory. BD5 enables fast random (i.e., direct) access to quantitative data on disk without parsing the entire file. Therefore, it allows practical reuse of data for understanding biological mechanisms underlying the dynamics.
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Affiliation(s)
- Koji Kyoda
- Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
- Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe, Japan
| | - Kenneth H. L. Ho
- Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
- Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe, Japan
| | - Yukako Tohsato
- Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
- Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe, Japan
- Department of Information Science and Engineering, Ritsumeikan University, Shiga, Japan
| | - Hiroya Itoga
- Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Shuichi Onami
- Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
- Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe, Japan
- * E-mail: ,
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16
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Zaritsky A. Sharing and reusing cell image data. Mol Biol Cell 2018; 29:1274-1280. [PMID: 29851565 PMCID: PMC5994892 DOI: 10.1091/mbc.e17-10-0606] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Revised: 04/02/2018] [Accepted: 04/06/2018] [Indexed: 01/19/2023] Open
Abstract
The rapid growth in content and complexity of cell image data creates an opportunity for synergy between experimental and computational scientists. Sharing microscopy data enables computational scientists to develop algorithms and tools for data analysis, integration, and mining. These tools can be applied by experimentalists to promote hypothesis-generation and discovery. We are now at the dawn of this revolution: infrastructure is being developed for data standardization, deposition, sharing, and analysis; some journals and funding agencies mandate data deposition; data journals publish high-content microscopy data sets; quantification becomes standard in scientific publications; new analytic tools are being developed and dispatched to the community; and huge data sets are being generated by individual labs and philanthropic initiatives. In this Perspective, I reflect on sharing and reusing cell image data and the opportunities that will come along with it.
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17
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Azuma Y, Onami S. Biologically constrained optimization based cell membrane segmentation in C. elegans embryos. BMC Bioinformatics 2017. [PMID: 28629355 PMCID: PMC5477254 DOI: 10.1186/s12859-017-1717-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Recent advances in bioimaging and automated analysis methods have enabled the large-scale systematic analysis of cellular dynamics during the embryonic development of Caenorhabditis elegans. Most of these analyses have focused on cell lineage tracing rather than cell shape dynamics. Cell shape analysis requires cell membrane segmentation, which is challenging because of insufficient resolution and image quality. This problem is currently solved by complicated segmentation methods requiring laborious and time consuming parameter adjustments. RESULTS Our new framework BCOMS (Biologically Constrained Optimization based cell Membrane Segmentation) automates the extraction of the cell shape of C. elegans embryos. Both the segmentation and evaluation processes are automated. To automate the evaluation, we solve an optimization problem under biological constraints. The performance of BCOMS was validated against a manually created ground truth of the 24-cell stage embryo. The average deviation of 25 cell shape features was 5.6%. The deviation was mainly caused by membranes parallel to the focal planes, which either contact the surfaces of adjacent cells or make no contact with other cells. Because segmentation of these membranes was difficult even by manual inspection, the automated segmentation was sufficiently accurate for cell shape analysis. As the number of manually created ground truths is necessarily limited, we compared the segmentation results between two adjacent time points. Across all cells and all cell cycles, the average deviation of the 25 cell shape features was 4.3%, smaller than that between the automated segmentation result and ground truth. CONCLUSIONS BCOMS automated the accurate extraction of cell shapes in developing C. elegans embryos. By replacing image processing parameters with easily adjustable biological constraints, BCOMS provides a user-friendly framework. The framework is also applicable to other model organisms. Creating the biological constraints is a critical step requiring collaboration between an experimentalist and a software developer.
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Affiliation(s)
- Yusuke Azuma
- Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo, 650-0047, Japan
| | - Shuichi Onami
- Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo, 650-0047, Japan.
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18
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Takayama J, Fujita M, Onami S. In vivo Live Imaging of Calcium Waves and Other Cellular Processes during Fertilization in Caenorhabditis elegans. Bio Protoc 2017; 7:e2205. [PMID: 34541214 DOI: 10.21769/bioprotoc.2205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Revised: 01/14/2017] [Accepted: 03/01/2017] [Indexed: 11/02/2022] Open
Abstract
Fertilization calcium waves are a conserved trigger for animal development; however, genetic analysis of these waves has been limited due to the difficulty of imaging in vivo fertilization. Here we describe a protocol to image calcium dynamics during in vivo fertilization in the genetic animal model Caenorhabditis elegans. This protocol consists of germline microinjection of a chemical calcium indicator, worm immobilization, live imaging, and image processing that quantifies the calcium fluorescence in the oocyte region moving in the field-of-view during ovulation. This imaging protocol can also be used to image other cellular processes during in vivo fertilization in C. elegans, such as membrane fusion and cytoskeletal dynamics.
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Affiliation(s)
- Jun Takayama
- RIKEN Quantitative Biology Center, Laboratory for Developmental Dynamics, Kobe, Japan
| | - Masashi Fujita
- RIKEN Quantitative Biology Center, Laboratory for Developmental Dynamics, Kobe, Japan
| | - Shuichi Onami
- RIKEN Quantitative Biology Center, Laboratory for Developmental Dynamics, Kobe, Japan
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