1
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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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2
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Marconato L, Palla G, Yamauchi KA, Virshup I, Heidari E, Treis T, Vierdag WM, Toth M, Stockhaus S, Shrestha RB, Rombaut B, Pollaris L, Lehner L, Vöhringer H, Kats I, Saeys Y, Saka SK, Huber W, Gerstung M, Moore J, Theis FJ, Stegle O. SpatialData: an open and universal data framework for spatial omics. Nat Methods 2024:10.1038/s41592-024-02212-x. [PMID: 38509327 DOI: 10.1038/s41592-024-02212-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 02/14/2024] [Indexed: 03/22/2024]
Abstract
Spatially resolved omics technologies are transforming our understanding of biological tissues. However, the handling of uni- and multimodal spatial omics datasets remains a challenge owing to large data volumes, heterogeneity of data types and the lack of flexible, spatially aware data structures. Here we introduce SpatialData, a framework that establishes a unified and extensible multiplatform file-format, lazy representation of larger-than-memory data, transformations and alignment to common coordinate systems. SpatialData facilitates spatial annotations and cross-modal aggregation and analysis, the utility of which is illustrated in the context of multiple vignettes, including integrative analysis on a multimodal Xenium and Visium breast cancer study.
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Affiliation(s)
- Luca Marconato
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
- Division of Computational Genomics and System Genetics, German Cancer Research Center, Heidelberg, Germany
- Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences, Heidelberg, Germany
| | - Giovanni Palla
- Institute of Computational Biology, Helmholtz, Center Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Kevin A Yamauchi
- Department of Biosystems, Science and Engineering, ETH Zürich, Basel, Switzerland
- Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Isaac Virshup
- Institute of Computational Biology, Helmholtz, Center Munich, Munich, Germany
| | - Elyas Heidari
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
- Division of Computational Genomics and System Genetics, German Cancer Research Center, Heidelberg, Germany
- Division of Artificial Intelligence in Oncology, German Cancer Research Center, Heidelberg, Germany
| | - Tim Treis
- Division of Computational Genomics and System Genetics, German Cancer Research Center, Heidelberg, Germany
- Institute of Computational Biology, Helmholtz, Center Munich, Munich, Germany
| | | | - Marcella Toth
- Institute of Computational Biology, Helmholtz, Center Munich, Munich, Germany
| | - Sonja Stockhaus
- Institute of Computational Biology, Helmholtz, Center Munich, Munich, Germany
- TUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Rahul B Shrestha
- Institute of Computational Biology, Helmholtz, Center Munich, Munich, Germany
| | - Benjamin Rombaut
- Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
- VIB Center for AI and Computational Biology, Ghent, Belgium
| | - Lotte Pollaris
- Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
- VIB Center for AI and Computational Biology, Ghent, Belgium
| | - Laurens Lehner
- Institute of Computational Biology, Helmholtz, Center Munich, Munich, Germany
- TUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Harald Vöhringer
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
- Molecular Medicine Partnership Unit, Heidelberg, Germany
- Department of Medicine V, Hematology, Oncology, and Rheumatology, University of Heidelberg, Heidelberg, Germany
| | - Ilia Kats
- Division of Computational Genomics and System Genetics, German Cancer Research Center, Heidelberg, Germany
| | - Yvan Saeys
- Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
- VIB Center for AI and Computational Biology, Ghent, Belgium
| | - Sinem K Saka
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Wolfgang Huber
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Moritz Gerstung
- Division of Artificial Intelligence in Oncology, German Cancer Research Center, Heidelberg, Germany
| | - Josh Moore
- German BioImaging - Gesellschaft für Mikroskopie und Bildanalyse e.V, Konstanz, Germany.
- Open Microscopy Environment Consortium, Munich, Germany.
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz, Center Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
- Department of Mathematics, Technical University of Munich, Munich, Germany.
- Cellular Genetics Programme, Wellcome Sanger Institute, Cambridge, UK.
| | - Oliver Stegle
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.
- Division of Computational Genomics and System Genetics, German Cancer Research Center, Heidelberg, Germany.
- Cellular Genetics Programme, Wellcome Sanger Institute, Cambridge, UK.
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3
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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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4
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Bialy N, Alber F, Andrews B, Angelo M, Beliveau B, Bintu L, Boettiger A, Boehm U, Brown CM, Maina MB, Chambers JJ, Cimini BA, Eliceiri K, Errington R, Faklaris O, Gaudreault N, Germain RN, Goscinski W, Grunwald D, Halter M, Hanein D, Hickey JW, Lacoste J, Laude A, Lundberg E, Ma J, Malacrida L, Moore J, Nelson G, Neumann EK, Nitschke R, Onami S, Pimentel JA, Plant AL, Radtke AJ, Sabata B, Schapiro D, Schöneberg J, Spraggins JM, Sudar D, Adrien Maria Vierdag WM, Volkmann N, Wählby C, Wang SS, Yaniv Z, Strambio-De-Castillia C. Harmonizing the Generation and Pre-publication Stewardship of FAIR Image data. ARXIV 2024:arXiv:2401.13022v4. [PMID: 38351940 PMCID: PMC10862930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured image data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated. There are two broad sets of requirements to enable image data sharing in the life sciences. One set of requirements is articulated in the companion White Paper entitled "Enabling Global Image Data Sharing in the Life Sciences," which is published in parallel and addresses the need to build the cyberinfrastructure for sharing the digital array data (arXiv:2401.13023 [q-bio.OT], https://doi.org/10.48550/arXiv.2401.13023). In this White Paper, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse image data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made considerable progress toward generating community standard practices for imaging Quality Control (QC) and metadata. We then provide a clear set of recommendations for amplifying this work. The driving goal is to address remaining challenges, and democratize access to common practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.
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Affiliation(s)
- Nikki Bialy
- Morgridge Institute for Research, Madison, USA
| | | | | | | | | | | | | | | | | | | | | | - Beth A Cimini
- Broad Institute of MIT and Harvard, Imaging Platform, Cambridge, USA
| | - Kevin Eliceiri
- Morgridge Institute for Research, Madison, USA
- University of Wisconsin-Madison, Madison, USA
| | | | | | | | - Ronald N Germain
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, USA
| | | | | | - Michael Halter
- National Institute of Standards and Technology, Gaithersburg, USA
| | | | | | | | - Alex Laude
- Newcastle University, Newcastle upon Tyne, UK
| | - Emma Lundberg
- Stanford University, Palo Alto, USA
- SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jian Ma
- Carnegie Mellon University, Pittsburgh, USA
| | - Leonel Malacrida
- Institut Pasteur de Montevideo, & Universidad de la República, Montevideo, Uruguay
| | - Josh Moore
- German BioImaging-Gesellschaft für Mikroskopie und Bildanalyse e.V., Constance, Germany
| | - Glyn Nelson
- Newcastle University, Newcastle upon Tyne, UK
| | | | | | - Shuichi Onami
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | | | - Anne L Plant
- National Institute of Standards and Technology, Gaithersburg, USA
| | - Andrea J Radtke
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, USA
| | | | | | | | | | - Damir Sudar
- Quantitative Imaging Systems LLC, Portland, USA
| | | | | | | | | | - Ziv Yaniv
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, USA
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5
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Weisbart E, Kumar A, Arevalo J, Carpenter AE, Cimini BA, Singh S. Cell Painting Gallery: an open resource for image-based profiling. ARXIV 2024:arXiv:2402.02203v1. [PMID: 38351939 PMCID: PMC10862924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Affiliation(s)
- Erin Weisbart
- Broad Institute of MIT and Harvard, Cambridge MA, USA; Department: Imaging Platform
| | - Ankur Kumar
- Broad Institute of MIT and Harvard, Cambridge MA, USA; Department: Imaging Platform
| | - John Arevalo
- Broad Institute of MIT and Harvard, Cambridge MA, USA; Department: Imaging Platform
| | - Anne E. Carpenter
- Broad Institute of MIT and Harvard, Cambridge MA, USA; Department: Imaging Platform
| | - Beth A. Cimini
- Broad Institute of MIT and Harvard, Cambridge MA, USA; Department: Imaging Platform
| | - Shantanu Singh
- Broad Institute of MIT and Harvard, Cambridge MA, USA; Department: Imaging Platform
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6
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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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7
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Ivanov IE, Hirata-Miyasaki E, Chandler T, Kovilakam RC, Liu Z, Liu C, Leonetti MD, Huang B, Mehta SB. Mantis: high-throughput 4D imaging and analysis of the molecular and physical architecture of cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.19.572435. [PMID: 38187521 PMCID: PMC10769231 DOI: 10.1101/2023.12.19.572435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
High-throughput dynamic imaging of cells and organelles is important for parsing complex cellular responses. We report a high-throughput 4D microscope, named Mantis, that combines two complementary, gentle, live-imaging technologies: remote-refocus label-free microscopy and oblique light-sheet fluorescence microscopy. We also report open-source software for automated acquisition, registration, and reconstruction, and virtual staining software for single-cell segmentation and phenotyping. Mantis enabled high-content correlative imaging of molecular components and the physical architecture of 20 cell lines every 15 minutes over 7.5 hours, and also detailed measurements of the impacts of viral infection on the architecture of host cells and host proteins. The Mantis platform can enable high-throughput profiling of intracellular dynamics, long-term imaging and analysis of cellular responses to stress, and live cell optical screens to dissect gene regulatory networks.
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Affiliation(s)
- Ivan E. Ivanov
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
| | | | - Talon Chandler
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
| | - Rasmi Cheloor Kovilakam
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, United States
| | - Ziwen Liu
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
| | - Chad Liu
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
| | | | - Bo Huang
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, United States
| | - Shalin B. Mehta
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
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8
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Schwayer C, Brückner DB. Connecting theory and experiment in cell and tissue mechanics. J Cell Sci 2023; 136:jcs261515. [PMID: 38149871 DOI: 10.1242/jcs.261515] [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: 12/28/2023] Open
Abstract
Understanding complex living systems, which are fundamentally constrained by physical phenomena, requires combining experimental data with theoretical physical and mathematical models. To develop such models, collaborations between experimental cell biologists and theoreticians are increasingly important but these two groups often face challenges achieving mutual understanding. To help navigate these challenges, this Perspective discusses different modelling approaches, including bottom-up hypothesis-driven and top-down data-driven models, and highlights their strengths and applications. Using cell mechanics as an example, we explore the integration of specific physical models with experimental data from the molecular, cellular and tissue level up to multiscale input. We also emphasize the importance of constraining model complexity and outline strategies for crosstalk between experimental design and model development. Furthermore, we highlight how physical models can provide conceptual insights and produce unifying and generalizable frameworks for biological phenomena. Overall, this Perspective aims to promote fruitful collaborations that advance our understanding of complex biological systems.
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Affiliation(s)
- Cornelia Schwayer
- Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland
| | - David B Brückner
- Institute of Science and Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria
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9
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Chai B, Efstathiou C, Yue H, Draviam VM. Opportunities and challenges for deep learning in cell dynamics research. Trends Cell Biol 2023:S0962-8924(23)00228-3. [PMID: 38030542 DOI: 10.1016/j.tcb.2023.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/30/2023] [Accepted: 10/13/2023] [Indexed: 12/01/2023]
Abstract
The growth of artificial intelligence (AI) has led to an increase in the adoption of computer vision and deep learning (DL) techniques for the evaluation of microscopy images and movies. This adoption has not only addressed hurdles in quantitative analysis of dynamic cell biological processes but has also started to support advances in drug development, precision medicine, and genome-phenome mapping. We survey existing AI-based techniques and tools, as well as open-source datasets, with a specific focus on the computational tasks of segmentation, classification, and tracking of cellular and subcellular structures and dynamics. We summarise long-standing challenges in microscopy video analysis from a computational perspective and review emerging research frontiers and innovative applications for DL-guided automation in cell dynamics research.
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Affiliation(s)
- Binghao Chai
- School of Biological and Behavioural Sciences, Queen Mary University of London (QMUL), London E1 4NS, UK
| | - Christoforos Efstathiou
- School of Biological and Behavioural Sciences, Queen Mary University of London (QMUL), London E1 4NS, UK
| | - Haoran Yue
- School of Biological and Behavioural Sciences, Queen Mary University of London (QMUL), London E1 4NS, UK
| | - Viji M Draviam
- School of Biological and Behavioural Sciences, Queen Mary University of London (QMUL), London E1 4NS, UK; The Alan Turing Institute, London NW1 2DB, UK.
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10
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Alexandrov T, Saez‐Rodriguez J, Saka SK. Enablers and challenges of spatial omics, a melting pot of technologies. Mol Syst Biol 2023; 19:e10571. [PMID: 37842805 PMCID: PMC10632737 DOI: 10.15252/msb.202110571] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 10/17/2023] Open
Abstract
Spatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot.
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Affiliation(s)
- Theodore Alexandrov
- Structural and Computational Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- BioInnovation InstituteCopenhagenDenmark
| | - Julio Saez‐Rodriguez
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Faculty of Medicine and Heidelberg University Hospital, Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Sinem K Saka
- Genome Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
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11
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Larobina M. Thirty Years of the DICOM Standard. Tomography 2023; 9:1829-1838. [PMID: 37888737 PMCID: PMC10610864 DOI: 10.3390/tomography9050145] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 09/21/2023] [Accepted: 09/27/2023] [Indexed: 10/28/2023] Open
Abstract
Digital Imaging and Communications in Medicine (DICOM) is an international standard that defines a format for storing medical images and a protocol to enable and facilitate data communication among medical imaging systems. The DICOM standard has been instrumental in transforming the medical imaging world over the last three decades. Its adoption has been a significant experience for manufacturers, healthcare users, and research scientists. In this review, thirty years after introducing the standard, we discuss the innovation, advantages, and limitations of adopting the DICOM and its possible future directions.
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Affiliation(s)
- Michele Larobina
- Istituto di Biostrutture e Bioimmagini, Consiglio Nazionale delle Ricerche (CNR), I-80145 Napoli, Italy
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12
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Giepmans BNG, Taatjes DJ, Wolstencroft KJ. In focus: data management and data analysis in microscopy. Histochem Cell Biol 2023; 160:165-167. [PMID: 37646975 PMCID: PMC10492866 DOI: 10.1007/s00418-023-02226-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/14/2023] [Indexed: 09/01/2023]
Affiliation(s)
- Ben N G Giepmans
- Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
| | - Douglas J Taatjes
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, 05405, USA
| | - Katherine J Wolstencroft
- The Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands
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Subash P, Gray A, Boswell M, Cohen SL, Garner R, Salehi S, Fisher C, Hobel S, Ghosh S, Halchenko Y, Dichter B, Poldrack RA, Markiewicz C, Hermes D, Delorme A, Makeig S, Behan B, Sparks A, Arnott SR, Wang Z, Magnotti J, Beauchamp MS, Pouratian N, Toga AW, Duncan D. A Comparison of Neuroelectrophysiology Databases. ARXIV 2023:arXiv:2306.15041v2. [PMID: 37426452 PMCID: PMC10327244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration. This paper compares four freely available intracranial neuroelectrophysiology data repositories: Data Archive for the BRAIN Initiative (DABI), Distributed Archives for Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. The aim of this review is to describe archives that provide researchers with tools to store, share, and reanalyze both human and non-human neurophysiology data based on criteria that are of interest to the neuroscientific community. The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are utilized by these archives to make data more accessible to researchers by implementing a common standard. As the necessity for integrating large-scale analysis into data repository platforms continues to grow within the neuroscientific community, this article will highlight the various analytical and customizable tools developed within the chosen archives that may advance the field of neuroinformatics.
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Affiliation(s)
- Priyanka Subash
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles CA 90033
| | - Alex Gray
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles CA 90033
| | - Misque Boswell
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles CA 90033
| | - Samantha L Cohen
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles CA 90033
| | - Rachael Garner
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles CA 90033
| | - Sana Salehi
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles CA 90033
| | - Calvary Fisher
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles CA 90033
| | - Samuel Hobel
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles CA 90033
| | - Satrajit Ghosh
- McGovern Institute for Brain Research, MIT Brain and Cognitive Sciences, 77 Massachusetts Avenue, Cambridge MA 02139
| | - Yaroslav Halchenko
- Department of Psychological & Brain Sciences, Center for Cognitive Neuroscience, Dartmouth Brain Imaging Center, Dartmouth College, 6207 Moore Hall, Hanover NH 03755
| | | | - Russell A Poldrack
- Department of Psychology, Stanford University, 450 Jane Stanford Way, Stanford CA 94305
| | - Chris Markiewicz
- Department of Psychology, Stanford University, 450 Jane Stanford Way, Stanford CA 94305
| | - Dora Hermes
- Mayo Clinic, Department of Physiology & Biomedical Engineering, 200 1st Street SW, Rochester MN 55905
| | - Arnaud Delorme
- Swartz Center of Computational Neuroscience, INC, University of California San Diego, La Jolla CA 92093
| | - Scott Makeig
- Swartz Center of Computational Neuroscience, INC, University of California San Diego, La Jolla CA 92093
| | - Brendan Behan
- Ontario Brain Institute, 1 Richmond Street West, Toronto ON M5H 3W4, Canada
| | | | | | - Zhengjia Wang
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia PA 19104
| | - John Magnotti
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia PA 19104
| | - Michael S Beauchamp
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia PA 19104
| | - Nader Pouratian
- Department of Neurological Surgery, University of Texas Southwestern Medical Center, 5303 Harry Hines Blvd, Dallas TX 75390
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles CA 90033
| | - Dominique Duncan
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles CA 90033
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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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