1
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Li T, Horsfall D, Basurto-Lozada D, Roberts K, Prete M, Lawrence JEG, He P, Tuck E, Moore J, Yoldas AK, Babalola K, Hartley M, Ghazanfar S, Teichmann SA, Haniffa M, Bayraktar OA. WebAtlas pipeline for integrated single-cell and spatial transcriptomic data. Nat Methods 2024:10.1038/s41592-024-02371-x. [PMID: 39160302 DOI: 10.1038/s41592-024-02371-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/21/2024]
Affiliation(s)
- Tong Li
- Wellcome Sanger Institute, Hinxton, UK
| | - David Horsfall
- Wellcome Sanger Institute, Hinxton, UK
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Daniela Basurto-Lozada
- Wellcome Sanger Institute, Hinxton, UK
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | | | | | | | - Peng He
- Wellcome Sanger Institute, Hinxton, UK
| | | | - Josh Moore
- German BioImaging - Gesellschaft für Mikroskopie und Bildanalyse e.V., Konstanz, Germany
| | | | | | | | - Shila Ghazanfar
- School of Mathematics and Statistics, The University of Sydney, Sydney, New South Wales, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Hinxton, UK
- University of Cambridge, Cambridge, UK
| | - Muzlifah Haniffa
- Wellcome Sanger Institute, Hinxton, UK.
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK.
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2
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Keikhosravi A, Almansour F, Bohrer CH, Fursova NA, Guin K, Sood V, Misteli T, Larson DR, Pegoraro G. High-throughput image processing software for the study of nuclear architecture and gene expression. Sci Rep 2024; 14:18426. [PMID: 39117696 PMCID: PMC11310328 DOI: 10.1038/s41598-024-66600-1] [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: 10/27/2023] [Accepted: 07/02/2024] [Indexed: 08/10/2024] Open
Abstract
High-throughput imaging (HTI) generates complex imaging datasets from a large number of experimental perturbations. Commercial HTI software programs for image analysis workflows typically do not allow full customization and adoption of new image processing algorithms in the analysis modules. While open-source HTI analysis platforms provide individual modules in the workflow, like nuclei segmentation, spot detection, or cell tracking, they are often limited in integrating novel analysis modules or algorithms. Here, we introduce the High-Throughput Image Processing Software (HiTIPS) to expand the range and customization of existing HTI analysis capabilities. HiTIPS incorporates advanced image processing and machine learning algorithms for automated cell and nuclei segmentation, spot signal detection, nucleus tracking, nucleus registration, spot tracking, and quantification of spot signal intensity. Furthermore, HiTIPS features a graphical user interface that is open to integration of new analysis modules for existing analysis pipelines and to adding new analysis modules. To demonstrate the utility of HiTIPS, we present three examples of image analysis workflows for high-throughput DNA FISH, immunofluorescence (IF), and live-cell imaging of transcription in single cells. Altogether, we demonstrate that HiTIPS is a user-friendly, flexible, and open-source HTI software platform for a variety of cell biology applications.
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Affiliation(s)
- Adib Keikhosravi
- High-Throughput Imaging Facility, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Faisal Almansour
- Cell Biology of Genomes, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
- Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical School, Washington, DC, 20057, USA
| | - Christopher H Bohrer
- System Biology of Gene Expression, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Nadezda A Fursova
- System Biology of Gene Expression, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Krishnendu Guin
- Cell Biology of Genomes, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Varun Sood
- Cell Biology of Genomes, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
- System Biology of Gene Expression, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Tom Misteli
- Cell Biology of Genomes, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Daniel R Larson
- System Biology of Gene Expression, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA
| | - Gianluca Pegoraro
- High-Throughput Imaging Facility, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA.
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3
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Wetzker C, Froschauer C, Massino C, Reinhardt K. Drosophila melanogaster sperm turn more oxidative in the female. J Exp Biol 2024; 227:jeb247775. [PMID: 39023115 DOI: 10.1242/jeb.247775] [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: 03/22/2024] [Accepted: 07/04/2024] [Indexed: 07/20/2024]
Abstract
Males and females of many species store sperm for extended periods. During storage, sperm are predicted to undergo cellular and functional changes, especially towards glycolytic energy metabolism because oxygen radicals derived from oxidative phosphorylation can affect sperm motility and fertilisation ability. However, not all species can use both major energy metabolism pathways. Here, we examined the fruit fly Drosophila melanogaster and asked whether sperm metabolism can be fuelled by both glycolysis and oxidative phosphorylation, and to what extent metabolism changes during storage. Inhibiting glycolysis in vitro led to a more oxidative state of sperm; inhibiting oxidative phosphorylation increased the glycolytic component, assessed by multi-photon autofluorescence lifetime imaging (FLIM) of NAD(P)H. We further examined sperm in male and female sperm storage organs using FLIM of NAD(P)H and FAD. In intact storage organs, we found that, unexpectedly, (i) sperm were more oxidative in females than in males, and (ii) oxidative phosphorylation increased with storage duration in females. Our observation that the relative contribution of the two major energy metabolic pathways in D. melanogaster sperm differs in males and females and over storage time has important evolutionary implications.
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Affiliation(s)
- Cornelia Wetzker
- Applied Zoology, Faculty of Biology, Technische Universität Dresden, 01062 Dresden, Germany
- B CUBE - Center for Molecular Bioengineering, Technische Universität Dresden, 01307 Dresden, Germany
| | - Christin Froschauer
- Applied Zoology, Faculty of Biology, Technische Universität Dresden, 01062 Dresden, Germany
| | - Christian Massino
- Applied Zoology, Faculty of Biology, Technische Universität Dresden, 01062 Dresden, Germany
| | - Klaus Reinhardt
- Applied Zoology, Faculty of Biology, Technische Universität Dresden, 01062 Dresden, Germany
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4
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Gómez-de-Mariscal E, Grobe H, Pylvänäinen JW, Xénard L, Henriques R, Tinevez JY, Jacquemet G. CellTracksColab is a platform that enables compilation, analysis, and exploration of cell tracking data. PLoS Biol 2024; 22:e3002740. [PMID: 39116189 PMCID: PMC11335138 DOI: 10.1371/journal.pbio.3002740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 08/20/2024] [Accepted: 07/08/2024] [Indexed: 08/10/2024] Open
Abstract
In life sciences, tracking objects from movies enables researchers to quantify the behavior of single particles, organelles, bacteria, cells, and even whole animals. While numerous tools now allow automated tracking from video, a significant challenge persists in compiling, analyzing, and exploring the large datasets generated by these approaches. Here, we introduce CellTracksColab, a platform tailored to simplify the exploration and analysis of cell tracking data. CellTracksColab facilitates the compiling and analysis of results across multiple fields of view, conditions, and repeats, ensuring a holistic dataset overview. CellTracksColab also harnesses the power of high-dimensional data reduction and clustering, enabling researchers to identify distinct behavioral patterns and trends without bias. Finally, CellTracksColab also includes specialized analysis modules enabling spatial analyses (clustering, proximity to specific regions of interest). We demonstrate CellTracksColab capabilities with 3 use cases, including T cells and cancer cell migration, as well as filopodia dynamics. CellTracksColab is available for the broader scientific community at https://github.com/CellMigrationLab/CellTracksColab.
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Affiliation(s)
| | - Hanna Grobe
- Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, Turku, Finland
| | - Joanna W. Pylvänäinen
- Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku and Åbo Akademi University, Turku, Finland
| | - Laura Xénard
- Institut Pasteur, Université Paris Cité, Image Analysis Hub, Paris, France
- Institut Pasteur, Université Paris Cité, INSERM UMR1225, Pathogenesis of Vascular Infections, Paris, France
| | - Ricardo Henriques
- Instituto Gulbenkian de Ciência, Oeiras, Portugal
- UCL Laboratory for Molecular Cell Biology, University College London, London, United Kingdom
| | - Jean-Yves Tinevez
- Institut Pasteur, Université Paris Cité, Image Analysis Hub, Paris, France
| | - Guillaume Jacquemet
- Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku and Åbo Akademi University, Turku, Finland
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- Turku Bioimaging, University of Turku and Åbo Akademi University, Turku, Finland
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5
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Vijayan A, Mody TA, Yu Q, Wolny A, Cerrone L, Strauss S, Tsiantis M, Smith RS, Hamprecht FA, Kreshuk A, Schneitz K. A deep learning-based toolkit for 3D nuclei segmentation and quantitative analysis in cellular and tissue context. Development 2024; 151:dev202800. [PMID: 39036998 PMCID: PMC11273294 DOI: 10.1242/dev.202800] [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: 02/19/2024] [Accepted: 06/17/2024] [Indexed: 07/23/2024]
Abstract
We present a new set of computational tools that enable accurate and widely applicable 3D segmentation of nuclei in various 3D digital organs. We have developed an approach for ground truth generation and iterative training of 3D nuclear segmentation models, which we applied to popular CellPose, PlantSeg and StarDist algorithms. We provide two high-quality models trained on plant nuclei that enable 3D segmentation of nuclei in datasets obtained from fixed or live samples, acquired from different plant and animal tissues, and stained with various nuclear stains or fluorescent protein-based nuclear reporters. We also share a diverse high-quality training dataset of about 10,000 nuclei. Furthermore, we advanced the MorphoGraphX analysis and visualization software by, among other things, providing a method for linking 3D segmented nuclei to their surrounding cells in 3D digital organs. We found that the nuclear-to-cell volume ratio varies between different ovule tissues and during the development of a tissue. Finally, we extended the PlantSeg 3D segmentation pipeline with a proofreading tool that uses 3D segmented nuclei as seeds to correct cell segmentation errors in difficult-to-segment tissues.
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Affiliation(s)
- Athul Vijayan
- Plant Developmental Biology, TUM School of Life Sciences, Technical University of Munich, Freising 85354, Germany
| | - Tejasvinee Atul Mody
- Plant Developmental Biology, TUM School of Life Sciences, Technical University of Munich, Freising 85354, Germany
| | - Qin Yu
- European Molecular Biology Laboratory, Heidelberg 69117, Germany
- Collaboration for joint PhD degree between European Molecular Biology Laboratory and Heidelberg University, Faculty of Biosciences, Heidelberg 69117, Germany
| | - Adrian Wolny
- European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Lorenzo Cerrone
- Interdsisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg 69120, Germany
| | - Soeren Strauss
- Department of Comparative Developmental and Genetics, Max Planck Institute for Plant Breeding Research, Cologne 50829, Germany
| | - Miltos Tsiantis
- Department of Comparative Developmental and Genetics, Max Planck Institute for Plant Breeding Research, Cologne 50829, Germany
| | - Richard S. Smith
- Department of Comparative Developmental and Genetics, Max Planck Institute for Plant Breeding Research, Cologne 50829, Germany
- Computational and Systems Biology, The John Innes Centre, Norwich Research Park, Norwich NR4 7UH, UK
| | - Fred A. Hamprecht
- Interdsisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg 69120, Germany
| | - Anna Kreshuk
- European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Kay Schneitz
- Plant Developmental Biology, TUM School of Life Sciences, Technical University of Munich, Freising 85354, Germany
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6
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Carraro C, Montgomery JV, Klimmt J, Paquet D, Schultze JL, Beyer MD. Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs. Front Mol Neurosci 2024; 17:1414886. [PMID: 38952421 PMCID: PMC11215216 DOI: 10.3389/fnmol.2024.1414886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 06/04/2024] [Indexed: 07/03/2024] Open
Abstract
Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics is particularly urgent considering the long list of late-stage drug candidate failures. Although our knowledge on the pathogenic mechanisms driving neurodegeneration is growing, additional efforts are required to achieve a better and ultimately complete understanding of the pathophysiological underpinnings of NDDs. Beyond the etiology of NDDs being heterogeneous and multifactorial, this process is further complicated by the fact that current experimental models only partially recapitulate the major phenotypes observed in humans. In such a scenario, multi-omic approaches have the potential to accelerate the identification of new or repurposed drugs against a multitude of the underlying mechanisms driving NDDs. One major advantage for the implementation of multi-omic approaches in the drug discovery process is that these overarching tools are able to disentangle disease states and model perturbations through the comprehensive characterization of distinct molecular layers (i.e., genome, transcriptome, proteome) up to a single-cell resolution. Because of recent advances increasing their affordability and scalability, the use of omics technologies to drive drug discovery is nascent, but rapidly expanding in the neuroscience field. Combined with increasingly advanced in vitro models, which particularly benefited from the introduction of human iPSCs, multi-omics are shaping a new paradigm in drug discovery for NDDs, from disease characterization to therapeutics prediction and experimental screening. In this review, we discuss examples, main advantages and open challenges in the use of multi-omic approaches for the in vitro discovery of targets and therapies against NDDs.
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Affiliation(s)
- Caterina Carraro
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Jessica V. Montgomery
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
| | - Julien Klimmt
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Dominik Paquet
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Joachim L. Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
- PRECISE, Platform for Single Cell Genomics and Epigenomics at the German Center for Neurodegenerative Diseases and the University of Bonn and West German Genome Center, Bonn, Germany
| | - Marc D. Beyer
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- PRECISE, Platform for Single Cell Genomics and Epigenomics at the German Center for Neurodegenerative Diseases and the University of Bonn and West German Genome Center, Bonn, Germany
- Immunogenomics & Neurodegeneration, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
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7
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Shaw F, Minotto A, McTaggart S, Providence A, Harrison P, Paupério J, Rajan J, Burgin J, Cochrane G, Kilias E, Lawniczak MK, Davey R. COPO - Managing sample metadata for biodiversity: considerations from the Darwin Tree of Life project. Wellcome Open Res 2024; 7:279. [PMID: 39091415 PMCID: PMC11292180 DOI: 10.12688/wellcomeopenres.18499.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/24/2024] [Indexed: 08/04/2024] Open
Abstract
Large-scale reference genome sequencing projects for all of biodiversity are underway and common standards have been in place for some years to enable the understanding and sharing of sequence data. However, the metadata that describes the collection, processing and management of samples, and link to the associated sequencing and genome data, are not yet adequately developed and standardised for these projects. At the time of writing, the Darwin Tree of Life (DToL) Project is over two years into its ten-year ambition to sequence all described eukaryotic species in Britain and Ireland. We have sought consensus from a wide range of scientists across taxonomic domains to determine the minimal set of metadata that we collectively deem as critically important to accompany each sequenced specimen. These metadata are made available throughout the subsequent laboratory processes, and once collected, need to be adequately managed to fulfil the requirements of good data management practice. Due to the size and scale of management required, software tools are needed. These tools need to implement rigorous development pathways and change management procedures to ensure that effective research data management of key project and sample metadata is maintained. Tracking of sample properties through the sequencing process is handled by Lab Information Management Systems (LIMS), so publication of the sequenced data is achieved via technical integration of LIMS and data management tools. Discussions with community members on how metadata standards need to be managed within large-scale programmes is a priority in the planning process. Here we report on the standards we developed with respect to a robust and reusable mechanism of metadata collection, in the hopes that other projects forthcoming or underway will adopt these practices for metadata.
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Affiliation(s)
- Felix Shaw
- Earlham Institute, Norwich, Norfolk, NR4 7UH, UK
| | | | | | | | - Peter Harrison
- EMBL European Bioinformatics Institute, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Joana Paupério
- EMBL European Bioinformatics Institute, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Jeena Rajan
- EMBL European Bioinformatics Institute, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Josephine Burgin
- EMBL European Bioinformatics Institute, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Guy Cochrane
- EMBL European Bioinformatics Institute, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Estelle Kilias
- Department of Zoology, University of Oxford, Oxford, Oxfordshire, OX1 2JD, UK
| | | | - Robert Davey
- Earlham Institute, Norwich, Norfolk, NR4 7UH, UK
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8
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Soltwedel JR, Haase R. Challenges and opportunities for bioimage analysis core-facilities. J Microsc 2024; 294:338-349. [PMID: 37199456 DOI: 10.1111/jmi.13192] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 05/05/2023] [Accepted: 05/15/2023] [Indexed: 05/19/2023]
Abstract
Recent advances in microscopy imaging and image analysis motivate more and more institutes worldwide to establish dedicated core-facilities for bioimage analysis. To maximise the benefits research groups at these institutes gain from their core-facilities, they should be established to fit well into their respective environment. In this article, we introduce common collaborator requests and corresponding potential services core-facilities can offer. We also discuss potential competing interests between the targeted missions and implementations of services to guide decision makers and core-facility founders to circumvent common pitfalls.
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Affiliation(s)
| | - Robert Haase
- DFG Cluster of Excellence 'Physics of Life', TU Dresden, Germany
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9
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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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10
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Kenney M, Vasylieva I, Hood G, Cao-Berg I, Tuite L, Laghaei R, Smith MC, Watson AM, Ropelewski AJ. The Brain Image Library: A Community-Contributed Microscopy Resource for Neuroscientists. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.22.573024. [PMID: 38187527 PMCID: PMC10769375 DOI: 10.1101/2023.12.22.573024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Advancements in microscopy techniques and computing technologies have enabled researchers to digitally reconstruct brains at micron scale. As a result, community efforts like the BRAIN Initiative Cell Census Network (BICCN) have generated thousands of whole-brain imaging datasets to trace neuronal circuitry and comprehensively map cell types. This data holds valuable information that extends beyond initial analyses, opening avenues for variation studies and robust classification of cell types in specific brain regions. However, the size and heterogeneity of these imaging data have historically made storage, sharing, and analysis difficult for individual investigators and impractical on a broad community scale. Here, we introduce the Brain Image Library (BIL), a public resource serving the neuroscience community that provides a persistent centralized repository for brain microscopy data. BIL currently holds thousands of brain datasets and provides an integrated analysis ecosystem, allowing for exploration, visualization, and data access without the need to download, thus encouraging scientific discovery and data reuse.
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11
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Adams JM, Sawe C, Rogers S, Reid J, Dasari R, Engelke MF. Characterization of the disease-causing mechanism of KIF3B mutations from ciliopathy patients. Front Mol Biosci 2024; 11:1327963. [PMID: 38665936 PMCID: PMC11043552 DOI: 10.3389/fmolb.2024.1327963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 03/04/2024] [Indexed: 04/28/2024] Open
Abstract
The heterodimeric kinesin-2 motor (KIF3A/KIF3B with accessory protein KAP3) drives intraflagellar transport, essential for ciliogenesis and ciliary function. Three point mutations in the KIF3B subunit have recently been linked to disease in humans (E250Q and L523P) and Bengal cats (A334T) (Cogné et al., Am. J. Hum. Genet., 2020, 106, 893-904). Patients display retinal atrophy and, in some cases, other ciliopathy phenotypes. However, the molecular mechanism leading to disease is currently unknown. Here, we used Kif3a -/- ;Kif3b -/- (knockout) 3T3 cells, which cannot make cilia, to characterize these mutations. While reexpression of KIF3B(E250Q) and KIF3B(L523P) did not rescue ciliogenesis, reexpression of wildtype or KIF3B(A334T) restored ciliogenesis to wildtype levels. Fluorescent tagging revealed that the E250Q mutant decorated microtubules and thus is a rigor mutation. The L523P mutation, in the alpha-helical stalk domain, surprisingly did not affect formation of the KIF3A/KIF3B/KAP3 complex but instead impaired motility along microtubules. Lastly, expression of the A334T motor was reduced in comparison to all other motors, and this motor displayed an impaired ability to disperse the Golgi complex when artificially linked to this high-load cargo. In summary, this work uses cell-based assays to elucidate the molecular effects of disease-causing mutations in the KIF3B subunit on the kinesin-2 holoenzyme.
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Affiliation(s)
| | | | | | | | | | - Martin F. Engelke
- School of Biological Sciences, Cell Physiology, Illinois State University, Normal, IL, United States
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12
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Childs A, Chand D, Pereira J, Santra S, Rajaraman S. BacteSign: Building a Findable, Accessible, Interoperable, and Reusable (FAIR) Database for Universal Bacterial Identification. BIOSENSORS 2024; 14:176. [PMID: 38667169 PMCID: PMC11047924 DOI: 10.3390/bios14040176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 03/15/2024] [Accepted: 03/15/2024] [Indexed: 04/28/2024]
Abstract
With the increasing incidence of diverse global bacterial outbreaks, it is important to build an immutable decentralized database that can capture regional changes in bacterial resistance with time. Herein, we investigate the use of a rapid 3D printed µbiochamber with a laser-ablated interdigitated electrode developed for biofilm analysis of Pseudomonas aeruginosa, Acinetobacter baumannii and Bacillus subtilis using electrochemical biological impedance spectroscopy (EBIS) across a 48 h spectrum, along with novel ladder-based minimum inhibitory concentration (MIC) stencil tests against oxytetracycline, kanamycin, penicillin G and streptomycin. Furthermore, in this investigation, a search query database has been built demonstrating the deterministic nature of the bacterial strains with real and imaginary impedance, phase, and capacitance, showing increased bacterial specification selectivity in the 9772.37 Hz range.
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Affiliation(s)
- Andre Childs
- Department of Materials Science and Engineering, University of Central Florida, Orlando, FL 32816, USA
- NanoScience Technology Center, University of Central Florida, Orlando, FL 32826, USA
| | - David Chand
- Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL 32816, USA
| | - Jorge Pereira
- NanoScience Technology Center, University of Central Florida, Orlando, FL 32826, USA
- Department of Chemistry, University of Central Florida, Orlando, FL 32816, USA
| | - Swadeshmukul Santra
- NanoScience Technology Center, University of Central Florida, Orlando, FL 32826, USA
- Department of Chemistry, University of Central Florida, Orlando, FL 32816, USA
- Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL 32827, USA
| | - Swaminathan Rajaraman
- Department of Materials Science and Engineering, University of Central Florida, Orlando, FL 32816, USA
- NanoScience Technology Center, University of Central Florida, Orlando, FL 32826, USA
- Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL 32827, USA
- Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA
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13
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Fasawe AS, Adams JM, Engelke MF. KIF3A tail domain phosphorylation is not required for ciliogenesis in mouse embryonic fibroblasts. iScience 2024; 27:109149. [PMID: 38405607 PMCID: PMC10884758 DOI: 10.1016/j.isci.2024.109149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 11/21/2023] [Accepted: 02/01/2024] [Indexed: 02/27/2024] Open
Abstract
Primary cilia are essential signaling organelles that protrude from most cells in the body. Heterodimeric kinesin-2 (KIF3A/KIF3B/KAP3) powers several intracellular transport processes, including intraflagellar transport (IFT), essential for ciliogenesis. A long-standing question is how a motor protein is differentially regulated for specific cargos. Since phosphorylation of the KIF3A tail domain was suggested to regulate the activity of kinesin-2 for ciliogenesis, similarly as for the cytosolic cargo N-Cadherin, we set out to map the phosphosites involved in this regulation. Using well-characterized Kif3a-/-; Kif3b-/- mouse embryonic fibroblasts, we performed ciliogenesis rescue assays with a library of phosphomimetic mutants comprising all predicted phosphosites in the KIF3A tail domain. In contrast to previous reports, we found that KIF3A tail domain phosphorylation is dispensable for ciliogenesis in mammals. Thus, mammalian kinesin-2 is differently regulated for IFT than currently thought, consistent with the idea of differential regulation for ciliary and cytosolic cargo.
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Affiliation(s)
- Ayoola S. Fasawe
- School of Biological Sciences, Cell Physiology, Illinois State University, Normal, IL 61790, USA
| | - Jessica M. Adams
- School of Biological Sciences, Cell Physiology, Illinois State University, Normal, IL 61790, USA
| | - Martin F. Engelke
- School of Biological Sciences, Cell Physiology, Illinois State University, Normal, IL 61790, USA
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14
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Korir PK, Iudin A, Somasundharam S, Weyand S, Salih O, Hartley M, Sarkans U, Patwardhan A, Kleywegt GJ. Ten recommendations for organising bioimaging data for archival. F1000Res 2024; 12:ELIXIR-1391. [PMID: 38486614 PMCID: PMC10938051 DOI: 10.12688/f1000research.129720.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/12/2024] [Indexed: 03/17/2024] Open
Abstract
Organised data is easy to use but the rapid developments in the field of bioimaging, with improvements in instrumentation, detectors, software and experimental techniques, have resulted in an explosion of the volumes of data being generated, making well-organised data an elusive goal. This guide offers a handful of recommendations for bioimage depositors, analysts and microscope and software developers, whose implementation would contribute towards better organised data in preparation for archival. Based on our experience archiving large image datasets in EMPIAR, the BioImage Archive and BioStudies, we propose a number of strategies that we believe would improve the usability (clarity, orderliness, learnability, navigability, self-documentation, coherence and consistency of identifiers, accessibility, succinctness) of future data depositions more useful to the bioimaging community (data authors and analysts, researchers, clinicians, funders, collaborators, industry partners, hardware/software producers, journals, archive developers as well as interested but non-specialist users of bioimaging data). The recommendations that may also find use in other data-intensive disciplines. To facilitate the process of analysing data organisation, we present bandbox, a Python package that provides users with an assessment of their data by flagging potential issues, such as redundant directories or invalid characters in file or folder names, that should be addressed before archival. We offer these recommendations as a starting point and hope to engender more substantial conversations across and between the various data-rich communities.
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Affiliation(s)
- Paul K. Korir
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Andrii Iudin
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | | | - Simone Weyand
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Osman Salih
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Matthew Hartley
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Ugis Sarkans
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Ardan Patwardhan
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
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15
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Vierdag WMAM, Saka SK. A perspective on FAIR quality control in multiplexed imaging data processing. FRONTIERS IN BIOINFORMATICS 2024; 4:1336257. [PMID: 38405548 PMCID: PMC10885342 DOI: 10.3389/fbinf.2024.1336257] [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: 11/10/2023] [Accepted: 01/26/2024] [Indexed: 02/27/2024] Open
Abstract
Multiplexed imaging approaches are getting increasingly adopted for imaging of large tissue areas, yielding big imaging datasets both in terms of the number of samples and the size of image data per sample. The processing and analysis of these datasets is complex owing to frequent technical artifacts and heterogeneous profiles from a high number of stained targets To streamline the analysis of multiplexed images, automated pipelines making use of state-of-the-art algorithms have been developed. In these pipelines, the output quality of one processing step is typically dependent on the output of the previous step and errors from each step, even when they appear minor, can propagate and confound the results. Thus, rigorous quality control (QC) at each of these different steps of the image processing pipeline is of paramount importance both for the proper analysis and interpretation of the analysis results and for ensuring the reusability of the data. Ideally, QC should become an integral and easily retrievable part of the imaging datasets and the analysis process. Yet, limitations of the currently available frameworks make integration of interactive QC difficult for large multiplexed imaging data. Given the increasing size and complexity of multiplexed imaging datasets, we present the different challenges for integrating QC in image analysis pipelines as well as suggest possible solutions that build on top of recent advances in bioimage analysis.
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Affiliation(s)
| | - Sinem K. Saka
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
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16
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Wong SS, Wainman A, Saurya S, Raff JW. Regulation of centrosome size by the cell-cycle oscillator in Drosophila embryos. EMBO J 2024; 43:414-436. [PMID: 38233576 PMCID: PMC10898259 DOI: 10.1038/s44318-023-00022-z] [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: 09/05/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 01/19/2024] Open
Abstract
Mitotic centrosomes assemble when centrioles recruit large amounts of pericentriolar material (PCM) around themselves. In early C. elegans embryos, mitotic centrosome size appears to be set by the limiting amount of a key component. In Drosophila syncytial embryos, thousands of mitotic centrosomes are assembled as the embryo proceeds through 13 rounds of rapid nuclear division, driven by a core cell cycle oscillator. These divisions slow during nuclear cycles 11-13, and we find that centrosomes respond by reciprocally decreasing their growth rate, but increasing their growth period-so that they grow to a relatively consistent size at each cycle. At the start of each cycle, moderate CCO activity initially promotes centrosome growth, in part by stimulating Polo/PLK1 recruitment to centrosomes. Later in each cycle, high CCO activity inhibits centrosome growth by suppressing the centrosomal recruitment and/or maintenance of centrosome proteins. Thus, in fly embryos, mitotic centrosome size appears to be regulated predominantly by the core cell cycle oscillator, rather than by the depletion of a limiting component.
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Affiliation(s)
- Siu-Shing Wong
- Sir William Dunn School of Pathology, University of Oxford, Oxford, OX1 3RE, UK
| | - Alan Wainman
- Sir William Dunn School of Pathology, University of Oxford, Oxford, OX1 3RE, UK
| | - Saroj Saurya
- Sir William Dunn School of Pathology, University of Oxford, Oxford, OX1 3RE, UK
| | - Jordan W Raff
- Sir William Dunn School of Pathology, University of Oxford, Oxford, OX1 3RE, UK.
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17
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Schmied C, Nelson MS, Avilov S, Bakker GJ, Bertocchi C, Bischof J, Boehm U, Brocher J, Carvalho MT, Chiritescu C, Christopher J, Cimini BA, Conde-Sousa E, Ebner M, Ecker R, Eliceiri K, Fernandez-Rodriguez J, Gaudreault N, Gelman L, Grunwald D, Gu T, Halidi N, Hammer M, Hartley M, Held M, Jug F, Kapoor V, Koksoy AA, Lacoste J, Le Dévédec S, Le Guyader S, Liu P, Martins GG, Mathur A, Miura K, Montero Llopis P, Nitschke R, North A, Parslow AC, Payne-Dwyer A, Plantard L, Ali R, Schroth-Diez B, Schütz L, Scott RT, Seitz A, Selchow O, Sharma VP, Spitaler M, Srinivasan S, Strambio-De-Castillia C, Taatjes D, Tischer C, Jambor HK. Community-developed checklists for publishing images and image analyses. Nat Methods 2024; 21:170-181. [PMID: 37710020 PMCID: PMC10922596 DOI: 10.1038/s41592-023-01987-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 07/26/2023] [Indexed: 09/16/2023]
Abstract
Images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. However, for scientists wishing to publish obtained images and image-analysis results, there are currently no unified guidelines for best practices. Consequently, microscopy images and image data in publications may be unclear or difficult to interpret. Here, we present community-developed checklists for preparing light microscopy images and describing image analyses for publications. These checklists offer authors, readers and publishers key recommendations for image formatting and annotation, color selection, data availability and reporting image-analysis workflows. The goal of our guidelines is to increase the clarity and reproducibility of image figures and thereby to heighten the quality and explanatory power of microscopy data.
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Affiliation(s)
- Christopher Schmied
- Fondazione Human Technopole, Milano, Italy.
- Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP), Berlin, Germany.
| | - Michael S Nelson
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Sergiy Avilov
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Gert-Jan Bakker
- Medical BioSciences Department, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Cristina Bertocchi
- Laboratory for Molecular Mechanics of Cell Adhesions, Pontificia Universidad Católica de Chile Santiago, Santiago de Chile, Chile
- Graduate School of Engineering Science, Osaka University, Osaka, Japan
| | | | | | - Jan Brocher
- Scientific Image Processing and Analysis, BioVoxxel, Ludwigshafen, Germany
| | - Mariana T Carvalho
- Nanophotonics and BioImaging Facility at INL, International Iberian Nanotechnology Laboratory, Braga, Portugal
| | | | - Jana Christopher
- Biochemistry Center Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Beth A Cimini
- Imaging Platform, Broad Institute, Cambridge, MA, USA
| | - Eduardo Conde-Sousa
- i3S, Instituto de Investigação e Inovação Em Saúde and INEB, Instituto de Engenharia Biomédica, Universidade do Porto, Porto, Portugal
| | - Michael Ebner
- Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP), Berlin, Germany
| | - Rupert Ecker
- Translational Research Institute, Queensland University of Technology, Woolloongabba, Queensland, Australia
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia
- TissueGnostics GmbH, Vienna, Austria
| | - Kevin Eliceiri
- Department of Medical Physics and Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Julia Fernandez-Rodriguez
- Centre for Cellular Imaging Core Facility, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | | | - Laurent Gelman
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - David Grunwald
- RNA Therapeutics Institute, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | | | - Nadia Halidi
- Advanced Light Microscopy Unit, Centre for Genomic Regulation, Barcelona, Spain
| | - Mathias Hammer
- RNA Therapeutics Institute, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Matthew Hartley
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute, Hinxton, UK
| | - Marie Held
- Centre for Cell Imaging, the University of Liverpool, Liverpool, UK
| | | | - Varun Kapoor
- Department of AI Research, Kapoor Labs, Paris, France
| | | | | | - Sylvia Le Dévédec
- Division of Drug Discovery and Safety, Cell Observatory, Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands
| | | | - Penghuan Liu
- Key Laboratory for Modern Measurement Technology and Instruments of Zhejiang Province, College of Optical and Electronic Technology, China Jiliang University, Hangzhou, China
| | - Gabriel G Martins
- Advanced Imaging Facility, Instituto Gulbenkian de Ciência, Oeiras, Portugal
| | | | - Kota Miura
- Bioimage Analysis and Research, Heidelberg, Germany
| | | | - Roland Nitschke
- Life Imaging Center, Signalling Research Centres CIBSS and BIOSS, University of Freiburg, Freiburg, Germany
| | - Alison North
- Bio-Imaging Resource Center, the Rockefeller University, New York, NY, USA
| | - Adam C Parslow
- Baker Institute Microscopy Platform, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Alex Payne-Dwyer
- School of Physics, Engineering and Technology, University of York, Heslington, UK
| | - Laure Plantard
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Rizwan Ali
- King Abdullah International Medical Research Center (KAIMRC), Medical Research Core Facility and Platforms (MRCFP), King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Britta Schroth-Diez
- Light Microscopy Facility, Max Planck Institute of Molecular Cell Biology and Genetics Dresden, Dresden, Germany
| | | | - Ryan T Scott
- Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA, USA
| | - Arne Seitz
- BioImaging and Optics Platform, Faculty of Life Sciences (SV), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Olaf Selchow
- Microscopy and BioImaging Consulting, Image Processing and Large Data Handling, Gera, Germany
| | - Ved P Sharma
- Bio-Imaging Resource Center, the Rockefeller University, New York, NY, USA
| | | | - Sathya Srinivasan
- Imaging and Morphology Support Core, Oregon National Primate Research Center, OHSU West Campus, Beaverton, OR, USA
| | | | - Douglas Taatjes
- Department of Pathology and Laboratory Medicine, Microscopy Imaging Center, Center for Biomedical Shared Resources, University of Vermont, Burlington, VT, USA
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18
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Thakur M, Buniello A, Brooksbank C, Gurwitz KT, Hall M, Hartley M, Hulcoop DG, Leach AR, Marques D, Martin M, Mithani A, McDonagh EM, Mutasa-Gottgens E, Ochoa D, Perez-Riverol Y, Stephenson J, Varadi M, Velankar S, Vizcaino JA, Witham R, McEntyre J. EMBL's European Bioinformatics Institute (EMBL-EBI) in 2023. Nucleic Acids Res 2024; 52:D10-D17. [PMID: 38015445 PMCID: PMC10767983 DOI: 10.1093/nar/gkad1088] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 10/23/2023] [Accepted: 10/30/2023] [Indexed: 11/29/2023] Open
Abstract
The European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) is one of the world's leading sources of public biomolecular data. Based at the Wellcome Genome Campus in Hinxton, UK, EMBL-EBI is one of six sites of the European Molecular Biology Laboratory (EMBL), Europe's only intergovernmental life sciences organisation. This overview summarises the latest developments in the services provided by EMBL-EBI data resources to scientific communities globally. These developments aim to ensure EMBL-EBI resources meet the current and future needs of these scientific communities, accelerating the impact of open biological data for all.
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Affiliation(s)
- Matthew Thakur
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Annalisa Buniello
- Open Targets, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Catherine Brooksbank
- Training Team, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Kim T Gurwitz
- Training Team, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Matthew Hall
- Industry Partnerships, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Matthew Hartley
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - David G Hulcoop
- Open Targets, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Andrew R Leach
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
- Industry Partnerships, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Diana Marques
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Maria Martin
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Aziz Mithani
- Training Team, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Ellen M McDonagh
- Open Targets, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Euphemia Mutasa-Gottgens
- Industry Partnerships, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - David Ochoa
- Open Targets, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Yasset Perez-Riverol
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - James Stephenson
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Mihaly Varadi
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Sameer Velankar
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Juan Antonio Vizcaino
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Rick Witham
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Johanna McEntyre
- Data Services Teams, EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
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19
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Jones RA, Renshaw MJ, Barry DJ. Automated staging of zebrafish embryos with deep learning. Life Sci Alliance 2024; 7:e202302351. [PMID: 37884343 PMCID: PMC10602791 DOI: 10.26508/lsa.202302351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 10/14/2023] [Accepted: 10/18/2023] [Indexed: 10/28/2023] Open
Abstract
The zebrafish (Danio rerio) is an important biomedical model organism used in many disciplines. The phenomenon of developmental delay in zebrafish embryos has been widely reported as part of a mutant or treatment-induced phenotype. However, the detection and quantification of these delays is often achieved through manual observation, which is both time-consuming and subjective. We present KimmelNet, a deep learning model trained to predict embryo age (hours post fertilisation) from 2D brightfield images. KimmelNet's predictions agree closely with established staging methods and can detect developmental delays between populations with high confidence using as few as 100 images. Moreover, KimmelNet generalises to previously unseen data, with transfer learning enhancing its performance. With the ability to analyse tens of thousands of standard brightfield microscopy images on a timescale of minutes, we envisage that KimmelNet will be a valuable resource for the developmental biology community. Furthermore, the approach we have used could easily be adapted to generate models for other organisms.
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Affiliation(s)
- Rebecca A Jones
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
- https://ror.org/04tnbqb63 Developmental Biology Laboratory, The Francis Crick Institute, London, UK
| | - Matthew J Renshaw
- https://ror.org/04tnbqb63 Crick Advanced Light Microscopy (CALM), The Francis Crick Institute, London, UK
| | - David J Barry
- https://ror.org/04tnbqb63 Crick Advanced Light Microscopy (CALM), The Francis Crick Institute, London, UK
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20
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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] [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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21
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Kumar N, Marée R, Geurts P, Muller M. Recent Advances in Bioimage Analysis Methods for Detecting Skeletal Deformities in Biomedical and Aquaculture Fish Species. Biomolecules 2023; 13:1797. [PMID: 38136667 PMCID: PMC10742266 DOI: 10.3390/biom13121797] [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: 10/31/2023] [Revised: 12/05/2023] [Accepted: 12/09/2023] [Indexed: 12/24/2023] Open
Abstract
Detecting skeletal or bone-related deformities in model and aquaculture fish is vital for numerous biomedical studies. In biomedical research, model fish with bone-related disorders are potential indicators of various chemically induced toxins in their environment or poor dietary conditions. In aquaculture, skeletal deformities are affecting fish health, and economic losses are incurred by fish farmers. This survey paper focuses on showcasing the cutting-edge image analysis tools and techniques based on artificial intelligence that are currently applied in the analysis of bone-related deformities in aquaculture and model fish. These methods and tools play a significant role in improving research by automating various aspects of the analysis. This paper also sheds light on some of the hurdles faced when dealing with high-content bioimages and explores potential solutions to overcome these challenges.
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Affiliation(s)
- Navdeep Kumar
- Department of Computer Science and Electrical Engineering, Montefiore Institute, University of Liège, 4000 Liège, Belgium; (R.M.); (P.G.)
| | - Raphaël Marée
- Department of Computer Science and Electrical Engineering, Montefiore Institute, University of Liège, 4000 Liège, Belgium; (R.M.); (P.G.)
| | - Pierre Geurts
- Department of Computer Science and Electrical Engineering, Montefiore Institute, University of Liège, 4000 Liège, Belgium; (R.M.); (P.G.)
| | - Marc Muller
- Laboratory for Organogenesis and Regeneration (LOR), GIGA Institute, University of Liège, 4000 Liège, Belgium;
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22
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Huttenhower C, Finn RD, McHardy AC. Challenges and opportunities in sharing microbiome data and analyses. Nat Microbiol 2023; 8:1960-1970. [PMID: 37783751 DOI: 10.1038/s41564-023-01484-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 08/28/2023] [Indexed: 10/04/2023]
Abstract
Microbiome data, metadata and analytical workflows have become 'big' in terms of volume and complexity. Although the infrastructure and technologies to share data have been established, the interdisciplinary and multi-omic nature of the field can make resources difficult to identify and use. Following best practices for data deposition requires substantial effort, with sometimes little obvious reward. Gaps remain where microbiome-specific resources for data sharing or reproducibility do not yet exist. We outline available best practices, challenges to their adoption and opportunities in data sharing in microbiome research. We showcase examples of best practices and advocate for their enforcement and incentivization for data sharing. This includes recognition of data curation and sharing endeavours by individuals, institutions, journals and funders. Opportunities for progress include enabling microbiome-specific databases to incorporate future methods for data analysis, integration and reuse.
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Affiliation(s)
- Curtis Huttenhower
- Harvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Departments of Biostatistics and Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Robert D Finn
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Alice Carolyn McHardy
- Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany.
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany.
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23
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Rosenberger FA, Thielert M, Strauss MT, Schweizer L, Ammar C, Mädler SC, Metousis A, Skowronek P, Wahle M, Madden K, Gote-Schniering J, Semenova A, Schiller HB, Rodriguez E, Nordmann TM, Mund A, Mann M. Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome. Nat Methods 2023; 20:1530-1536. [PMID: 37783884 PMCID: PMC10555842 DOI: 10.1038/s41592-023-02007-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 08/15/2023] [Indexed: 10/04/2023]
Abstract
Single-cell proteomics by mass spectrometry is emerging as a powerful and unbiased method for the characterization of biological heterogeneity. So far, it has been limited to cultured cells, whereas an expansion of the method to complex tissues would greatly enhance biological insights. Here we describe single-cell Deep Visual Proteomics (scDVP), a technology that integrates high-content imaging, laser microdissection and multiplexed mass spectrometry. scDVP resolves the context-dependent, spatial proteome of murine hepatocytes at a current depth of 1,700 proteins from a cell slice. Half of the proteome was differentially regulated in a spatial manner, with protein levels changing dramatically in proximity to the central vein. We applied machine learning to proteome classes and images, which subsequently inferred the spatial proteome from imaging data alone. scDVP is applicable to healthy and diseased tissues and complements other spatial proteomics and spatial omics technologies.
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Affiliation(s)
- Florian A Rosenberger
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Marvin Thielert
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Maximilian T Strauss
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lisa Schweizer
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Constantin Ammar
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Sophia C Mädler
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Andreas Metousis
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Patricia Skowronek
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Maria Wahle
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Katherine Madden
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Janine Gote-Schniering
- Comprehensive Pneumology Center (CPC) / Institute of Lung Health and Immunity (LHI), Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Anna Semenova
- Comprehensive Pneumology Center (CPC) / Institute of Lung Health and Immunity (LHI), Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Herbert B Schiller
- Comprehensive Pneumology Center (CPC) / Institute of Lung Health and Immunity (LHI), Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Edwin Rodriguez
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Thierry M Nordmann
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Andreas Mund
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Matthias Mann
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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24
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Sivagurunathan S, Marcotti S, Nelson CJ, Jones ML, Barry DJ, Slater TJA, Eliceiri KW, Cimini BA. Bridging imaging users to imaging analysis - A community survey. J Microsc 2023:10.1111/jmi.13229. [PMID: 37727897 PMCID: PMC10950841 DOI: 10.1111/jmi.13229] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 08/24/2023] [Accepted: 09/13/2023] [Indexed: 09/21/2023]
Abstract
The 'Bridging Imaging Users to Imaging Analysis' survey was conducted in 2022 by the Center for Open Bioimage Analysis (COBA), BioImaging North America (BINA) and the Royal Microscopical Society Data Analysis in Imaging Section (RMS DAIM) to understand the needs of the imaging community. Through multichoice and open-ended questions, the survey inquired about demographics, image analysis experiences, future needs and suggestions on the role of tool developers and users. Participants of the survey were from diverse roles and domains of the life and physical sciences. To our knowledge, this is the first attempt to survey cross-community to bridge knowledge gaps between physical and life sciences imaging. Survey results indicate that respondents' overarching needs are documentation, detailed tutorials on the usage of image analysis tools, user-friendly intuitive software, and better solutions for segmentation, ideally in a format tailored to their specific use cases. The tool creators suggested the users familiarise themselves with the fundamentals of image analysis, provide constant feedback and report the issues faced during image analysis while the users would like more documentation and an emphasis on tool friendliness. Regardless of the computational experience, there is a strong preference for 'written tutorials' to acquire knowledge on image analysis. We also observed that the interest in having 'office hours' to get an expert opinion on their image analysis methods has increased over the years. The results also showed less-than-expected usage of online discussion forums in the imaging community for solving image analysis problems. Surprisingly, we also observed a decreased interest among the survey respondents in deep/machine learning despite the increasing adoption of artificial intelligence in biology. In addition, the community suggests the need for a common repository for the available image analysis tools and their applications. The opinions and suggestions of the community, released here in full, will help the image analysis tool creation and education communities to design and deliver the resources accordingly.
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Affiliation(s)
| | | | | | | | | | | | | | - Beth A Cimini
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
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25
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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: 6] [Impact Index Per Article: 6.0] [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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26
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Hartley M, Iudin A, Padwardhan A, Sarkans U, Yoldaş AK, Kleywegt GJ. Providing open imaging data at scale: An EMBL-EBI perspective. Histochem Cell Biol 2023; 160:211-221. [PMID: 37537341 PMCID: PMC10492673 DOI: 10.1007/s00418-023-02216-2] [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] [Accepted: 05/30/2023] [Indexed: 08/05/2023]
Abstract
Biological imaging is one of the primary tools by which we understand living systems across scales from atoms to organisms. Rapid advances in imaging technology have increased both the spatial and temporal resolutions at which we examine those systems, as well as enabling visualisation of larger tissue volumes. These advances have huge potential but also generate ever increasing amounts of imaging data that must be stored and analysed. Public image repositories provide a critical scientific service through open data provision, supporting reproducibility of scientific results, access to reference imaging datasets and reuse of data for new scientific discovery and acceleration of image analysis methods development. The scale and scope of imaging data provides both challenges and opportunities for open sharing of image data. In this article, we provide a perspective influenced by decades of provision of open data resources for biological information, suggesting areas to focus on and a path towards global interoperability.
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Affiliation(s)
- Matthew Hartley
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.
| | - Andrii Iudin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Ardan Padwardhan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Ugis Sarkans
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Aybüke Küpcü Yoldaş
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Gerard J Kleywegt
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
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27
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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] [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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28
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Ouyang W, Eliceiri KW, Cimini BA. Moving beyond the desktop: prospects for practical bioimage analysis via the web. FRONTIERS IN BIOINFORMATICS 2023; 3:1233748. [PMID: 37560357 PMCID: PMC10409478 DOI: 10.3389/fbinf.2023.1233748] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 07/12/2023] [Indexed: 08/11/2023] Open
Abstract
As biological imaging continues to rapidly advance, it results in increasingly complex image data, necessitating a reevaluation of conventional bioimage analysis methods and their accessibility. This perspective underscores our belief that a transition from desktop-based tools to web-based bioimage analysis could unlock immense opportunities for improved accessibility, enhanced collaboration, and streamlined workflows. We outline the potential benefits, such as reduced local computational demands and solutions to common challenges, including software installation issues and limited reproducibility. Furthermore, we explore the present state of web-based tools, hurdles in implementation, and the significance of collective involvement from the scientific community in driving this transition. In acknowledging the potential roadblocks and complexity of data management, we suggest a combined approach of selective prototyping and large-scale workflow application for optimal usage. Embracing web-based bioimage analysis could pave the way for the life sciences community to accelerate biological research, offering a robust platform for a more collaborative, efficient, and democratized science.
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Affiliation(s)
- Wei Ouyang
- Science for Life Laboratory, Department of Applied Physics, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Kevin W. Eliceiri
- Morgridge Institute for Research and Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI, United States
| | - Beth A. Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, United States
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29
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Nogare DD, Hartley M, Deschamps J, Ellenberg J, Jug F. Using AI in bioimage analysis to elevate the rate of scientific discovery as a community. Nat Methods 2023; 20:973-975. [PMID: 37434004 DOI: 10.1038/s41592-023-01929-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2023]
Affiliation(s)
| | - Matthew Hartley
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | | | - Jan Ellenberg
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
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30
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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] [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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31
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Senft RA, Diaz-Rohrer B, Colarusso P, Swift L, Jamali N, Jambor H, Pengo T, Brideau C, Llopis PM, Uhlmann V, Kirk J, Gonzales KA, Bankhead P, Evans EL, Eliceiri KW, Cimini BA. A biologist's guide to planning and performing quantitative bioimaging experiments. PLoS Biol 2023; 21:e3002167. [PMID: 37368874 PMCID: PMC10298797 DOI: 10.1371/journal.pbio.3002167] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2023] Open
Abstract
Technological advancements in biology and microscopy have empowered a transition from bioimaging as an observational method to a quantitative one. However, as biologists are adopting quantitative bioimaging and these experiments become more complex, researchers need additional expertise to carry out this work in a rigorous and reproducible manner. This Essay provides a navigational guide for experimental biologists to aid understanding of quantitative bioimaging from sample preparation through to image acquisition, image analysis, and data interpretation. We discuss the interconnectedness of these steps, and for each, we provide general recommendations, key questions to consider, and links to high-quality open-access resources for further learning. This synthesis of information will empower biologists to plan and execute rigorous quantitative bioimaging experiments efficiently.
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Affiliation(s)
- Rebecca A. Senft
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Barbara Diaz-Rohrer
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Pina Colarusso
- Live Cell Imaging Laboratory, University of Calgary, Calgary, Alberta, Canada
| | - Lucy Swift
- Live Cell Imaging Laboratory, University of Calgary, Calgary, Alberta, Canada
| | - Nasim Jamali
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Helena Jambor
- National Center for Tumor Diseases, University Cancer Center, NCT-UCC, Universitätsklinikum Carl Gustav Carus an der Technischen Universität Dresden, Dresden, Germany
| | - Thomas Pengo
- Informatics Institute, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
| | - Craig Brideau
- Live Cell Imaging Laboratory, University of Calgary, Calgary, Alberta, Canada
| | - Paula Montero Llopis
- MicRoN Core, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Virginie Uhlmann
- European Bioinformatic Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Jason Kirk
- Optical Imaging & Vital Microscopy Core, Baylor College of Medicine, Houston, Texas, United States of America
| | - Kevin Andrew Gonzales
- Mammalian Cell Biology and Development, Rockefeller University, New York, New York, United States of America
| | - Peter Bankhead
- Edinburgh Pathology, Centre for Genomic and Experimental Medicine, and CRUK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Edward L. Evans
- Morgridge Institute and University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Kevin W. Eliceiri
- Morgridge Institute and University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Beth A. Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
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32
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Iudin A, Hartley M, Kleywegt GJ, Patwardhan A. Public archiving of volume EM data. Methods Cell Biol 2023; 177:389-399. [PMID: 37451775 DOI: 10.1016/bs.mcb.2023.02.002] [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] [Indexed: 03/29/2023]
Abstract
Volume electron microscopy (vEM) techniques produce scientifically important datasets which are time and resource intensive to generate (Peddie et al., 2022). Public archival of such datasets, usually described in the literature, provides many benefits to the data depositors, to those making use of research results based on the datasets, and to the vEM community at large, both now and in the future. In this chapter we discuss these benefits, explain how EMBL-EBI's image data services support archival of both vEM and correlative imaging data, and discuss how future developments will unlock more value from these vEM datasets.
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Affiliation(s)
- Andrii Iudin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, United Kingdom
| | - Matthew Hartley
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, United Kingdom
| | - Gerard J Kleywegt
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, United Kingdom.
| | - Ardan Patwardhan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, United Kingdom
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33
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Rzepka N, Bogovic JA, Moore JA. Toward scalable reuse of vEM data: OME-Zarr to the rescue. Methods Cell Biol 2023; 177:359-387. [PMID: 37451774 DOI: 10.1016/bs.mcb.2023.01.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
The growing size of EM volumes is a significant barrier to findable, accessible, interoperable, and reusable (FAIR) sharing. Storage, sharing, visualization and processing are challenging for large datasets. Here we discuss a recent development toward the standardized storage of volume electron microscopy (vEM) data which addresses many of the issues that researchers face. The OME-Zarr format splits data into more manageable, performant chunks enabling streaming-based access, and unifies important metadata such as multiresolution pyramid descriptions. The file format is designed for centralized and remote storage (e.g., cloud storage or file system) and is therefore ideal for sharing large data. By coalescing on a common, community-wide format, these benefits will expand as ever more data is made available to the scientific community.
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Affiliation(s)
| | - John A Bogovic
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, United States
| | - Joshua A Moore
- German BioImaging-Society for Microscopy and Image Analysis e.V., Konstanz, Germany
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34
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Iudin A, Korir PK, Somasundharam S, Weyand S, Cattavitello C, Fonseca N, Salih O, Kleywegt GJ, Patwardhan A. EMPIAR: the Electron Microscopy Public Image Archive. Nucleic Acids Res 2023; 51:D1503-D1511. [PMID: 36440762 PMCID: PMC9825465 DOI: 10.1093/nar/gkac1062] [Citation(s) in RCA: 52] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/21/2022] [Accepted: 10/25/2022] [Indexed: 11/29/2022] Open
Abstract
Public archiving in structural biology is well established with the Protein Data Bank (PDB; wwPDB.org) catering for atomic models and the Electron Microscopy Data Bank (EMDB; emdb-empiar.org) for 3D reconstructions from cryo-EM experiments. Even before the recent rapid growth in cryo-EM, there was an expressed community need for a public archive of image data from cryo-EM experiments for validation, software development, testing and training. Concomitantly, the proliferation of 3D imaging techniques for cells, tissues and organisms using volume EM (vEM) and X-ray tomography (XT) led to calls from these communities to publicly archive such data as well. EMPIAR (empiar.org) was developed as a public archive for raw cryo-EM image data and for 3D reconstructions from vEM and XT experiments and now comprises over a thousand entries totalling over 2 petabytes of data. EMPIAR resources include a deposition system, entry pages, facilities to search, visualize and download datasets, and a REST API for programmatic access to entry metadata. The success of EMPIAR also poses significant challenges for the future in dealing with the very fast growth in the volume of data and in enhancing its reusability.
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Affiliation(s)
- Andrii Iudin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Paul K Korir
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Sriram Somasundharam
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Simone Weyand
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Cesare Cattavitello
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Neli Fonseca
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Osman Salih
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Gerard J Kleywegt
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Ardan Patwardhan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
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35
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Shaw F, Minotto A, McTaggart S, Providence A, Harrison P, Paupério J, Rajan J, Burgin J, Cochrane G, Kilias E, Lawniczak M, Davey R. Managing sample metadata for biodiversity: considerations from the Darwin Tree of Life project. Wellcome Open Res 2022. [DOI: 10.12688/wellcomeopenres.18499.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Large-scale reference genome sequencing projects for all of biodiversity are underway and common standards have been in place for some years to enable the understanding and sharing of sequence data. However, the metadata that describes the collection, processing and management of samples, and link to the associated sequencing and genome data, are not yet adequately developed and standardised for these projects. At the time of writing, the Darwin Tree of Life (DToL) Project is over two years into its ten-year ambition to sequence all described eukaryotic species in Britain and Ireland. We have sought consensus from a wide range of scientists across taxonomic domains to determine the minimal set of metadata that we collectively deem as critically important to accompany each sequenced specimen. These metadata are made available throughout the subsequent laboratory processes, and once collected, need to be adequately managed to fulfil the requirements of good data management practice. Due to the size and scale of management required, software tools are needed. These tools need to implement rigorous development pathways and change management procedures to ensure that effective research data management of key project and sample metadata is maintained. Tracking of sample properties through the sequencing process is handled by Lab Information Management Systems (LIMS), so publication of the sequenced data is achieved via technical integration of LIMS and data management tools. Discussions with community members on how metadata standards need to be managed within large-scale programmes is a priority in the planning process. Here we report on the standards we developed with respect to a robust and reusable mechanism of metadata collection, in the hopes that other projects forthcoming or underway will adopt these practices for metadata.
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Peddie CJ, Genoud C, Kreshuk A, Meechan K, Micheva KD, Narayan K, Pape C, Parton RG, Schieber NL, Schwab Y, Titze B, Verkade P, Aubrey A, Collinson LM. Volume electron microscopy. NATURE REVIEWS. METHODS PRIMERS 2022; 2:51. [PMID: 37409324 PMCID: PMC7614724 DOI: 10.1038/s43586-022-00131-9] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/10/2022] [Indexed: 07/07/2023]
Abstract
Life exists in three dimensions, but until the turn of the century most electron microscopy methods provided only 2D image data. Recently, electron microscopy techniques capable of delving deep into the structure of cells and tissues have emerged, collectively called volume electron microscopy (vEM). Developments in vEM have been dubbed a quiet revolution as the field evolved from established transmission and scanning electron microscopy techniques, so early publications largely focused on the bioscience applications rather than the underlying technological breakthroughs. However, with an explosion in the uptake of vEM across the biosciences and fast-paced advances in volume, resolution, throughput and ease of use, it is timely to introduce the field to new audiences. In this Primer, we introduce the different vEM imaging modalities, the specialized sample processing and image analysis pipelines that accompany each modality and the types of information revealed in the data. We showcase key applications in the biosciences where vEM has helped make breakthrough discoveries and consider limitations and future directions. We aim to show new users how vEM can support discovery science in their own research fields and inspire broader uptake of the technology, finally allowing its full adoption into mainstream biological imaging.
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Affiliation(s)
- Christopher J. Peddie
- Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, UK
| | - Christel Genoud
- Electron Microscopy Facility, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Anna Kreshuk
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Kimberly Meechan
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Present address: Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Kristina D. Micheva
- Department of Molecular and Cellular Physiology, Stanford University, Palo Alto, CA, USA
| | - 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
| | - Constantin Pape
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Robert G. Parton
- The Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Centre for Microscopy and Microanalysis, The University of Queensland, Brisbane, Queensland, Australia
| | - Nicole L. Schieber
- Centre for Microscopy and Microanalysis, The University of Queensland, Brisbane, Queensland, Australia
| | - Yannick Schwab
- Cell Biology and Biophysics Unit/ Electron Microscopy Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
| | | | - Paul Verkade
- School of Biochemistry, University of Bristol, Bristol, UK
| | - Aubrey Aubrey
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Lucy M. Collinson
- Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, UK
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Towards 'end-to-end' analysis and understanding of biological timecourse data. Biochem J 2022; 479:1257-1263. [PMID: 35713413 PMCID: PMC9246344 DOI: 10.1042/bcj20220053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 05/19/2022] [Accepted: 05/20/2022] [Indexed: 11/17/2022]
Abstract
Petabytes of increasingly complex and multidimensional live cell and tissue imaging data are generated every year. These videos hold large promise for understanding biology at a deep and fundamental level, as they capture single-cell and multicellular events occurring over time and space. However, the current modalities for analysis and mining of these data are scattered and user-specific, preventing more unified analyses from being performed over different datasets and obscuring possible scientific insights. Here, we propose a unified pipeline for storage, segmentation, analysis, and statistical parametrization of live cell imaging datasets.
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van Ineveld RL, van Vliet EJ, Wehrens EJ, Alieva M, Rios AC. 3D imaging for driving cancer discovery. EMBO J 2022; 41:e109675. [PMID: 35403737 PMCID: PMC9108604 DOI: 10.15252/embj.2021109675] [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: 09/09/2021] [Revised: 03/09/2022] [Accepted: 03/09/2022] [Indexed: 11/09/2022] Open
Abstract
Our understanding of the cellular composition and architecture of cancer has primarily advanced using 2D models and thin slice samples. This has granted spatial information on fundamental cancer biology and treatment response. However, tissues contain a variety of interconnected cells with different functional states and shapes, and this complex organization is impossible to capture in a single plane. Furthermore, tumours have been shown to be highly heterogenous, requiring large-scale spatial analysis to reliably profile their cellular and structural composition. Volumetric imaging permits the visualization of intact biological samples, thereby revealing the spatio-phenotypic and dynamic traits of cancer. This review focuses on new insights into cancer biology uniquely brought to light by 3D imaging and concomitant progress in cancer modelling and quantitative analysis. 3D imaging has the potential to generate broad knowledge advance from major mechanisms of tumour progression to new strategies for cancer treatment and patient diagnosis. We discuss the expected future contributions of the newest imaging trends towards these goals and the challenges faced for reaching their full application in cancer research.
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Affiliation(s)
- Ravian L van Ineveld
- Princess Máxima Center for Pediatric OncologyUtrechtThe Netherlands
- Oncode InstituteUtrechtThe Netherlands
| | - Esmée J van Vliet
- Princess Máxima Center for Pediatric OncologyUtrechtThe Netherlands
- Oncode InstituteUtrechtThe Netherlands
| | - Ellen J Wehrens
- Princess Máxima Center for Pediatric OncologyUtrechtThe Netherlands
- Oncode InstituteUtrechtThe Netherlands
| | - Maria Alieva
- Princess Máxima Center for Pediatric OncologyUtrechtThe Netherlands
- Oncode InstituteUtrechtThe Netherlands
| | - Anne C Rios
- Princess Máxima Center for Pediatric OncologyUtrechtThe Netherlands
- Oncode InstituteUtrechtThe Netherlands
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39
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Computational Resources for Molecular Biology 2022. J Mol Biol 2022; 434:167625. [PMID: 35569508 DOI: 10.1016/j.jmb.2022.167625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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