1
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Pham DL, Gillette AA, Riendeau J, Wiech K, Guzman EC, Datta R, Skala MC. Perspectives on label-free microscopy of heterogeneous and dynamic biological systems. JOURNAL OF BIOMEDICAL OPTICS 2025; 29:S22702. [PMID: 38434231 PMCID: PMC10903072 DOI: 10.1117/1.jbo.29.s2.s22702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 11/22/2023] [Accepted: 12/14/2023] [Indexed: 03/05/2024]
Abstract
Significance Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging). Aim This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs. Approach Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended. Conclusion Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.
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Affiliation(s)
- Dan L. Pham
- University of Wisconsin—Madison, Department of Biomedical Engineering, Madison, Wisconsin, United States
| | | | | | - Kasia Wiech
- University of Wisconsin—Madison, Department of Biomedical Engineering, Madison, Wisconsin, United States
| | | | - Rupsa Datta
- Morgridge Institute for Research, Madison, Wisconsin, United States
| | - Melissa C. Skala
- University of Wisconsin—Madison, Department of Biomedical Engineering, Madison, Wisconsin, United States
- Morgridge Institute for Research, Madison, Wisconsin, United States
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2
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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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3
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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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4
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Weisbart E, Tromans-Coia C, Diaz-Rohrer B, Stirling DR, Garcia-Fossa F, Senft RA, Hiner MC, de Jesus MB, Eliceiri KW, Cimini BA. CellProfiler plugins - An easy image analysis platform integration for containers and Python tools. J Microsc 2023. [PMID: 37690102 PMCID: PMC10924770 DOI: 10.1111/jmi.13223] [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: 08/10/2023] [Accepted: 09/05/2023] [Indexed: 09/12/2023]
Abstract
CellProfiler is a widely used software for creating reproducible, reusable image analysis workflows without needing to code. In addition to the >90 modules that make up the main CellProfiler program, CellProfiler has a plugins system that allows for the creation of new modules which integrate with other Python tools or tools that are packaged in software containers. The CellProfiler-plugins repository contains a number of these CellProfiler modules, especially modules that are experimental and/or dependency-heavy. Here, we present an upgraded CellProfiler-plugins repository, an example of accessing containerised tools, improved documentation and added citation/reference tools to facilitate the use and contribution of the community.
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Affiliation(s)
- Erin Weisbart
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Callum Tromans-Coia
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Barbara Diaz-Rohrer
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - David R Stirling
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Fernanda Garcia-Fossa
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas, Campinas, São Paulo, Brazil
| | - Rebecca A Senft
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Mark C Hiner
- Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Marcelo B de Jesus
- Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas, Campinas, São Paulo, Brazil
| | - Kevin W Eliceiri
- Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
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5
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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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6
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Silkotch C, Garcia-Milian R, Hersey D. Partnering with health sciences libraries to address challenges in bioimaging data management and sharing. Histochem Cell Biol 2023; 160:193-198. [PMID: 37247072 DOI: 10.1007/s00418-023-02198-1] [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] [Accepted: 04/13/2023] [Indexed: 05/30/2023]
Abstract
Federal mandates, publishing requirements, and an interest in open science have all generated renewed attention on research data management and, in particular, data sharing practices. Due to the size and types of data they produce, bioimaging researchers confront specific challenges in aligning their data with FAIR principles, ensuring that it is findable, accessible, interoperable, and reusable. Although not always recognized by researchers, libraries can, and have been, offering support for data throughout its lifecycle by assisting with data management planning, acquisition, processing and analysis, and sharing and reuse of data. Libraries can educate researchers on best practices for research data management and sharing, facilitate connections to experts by coordinating sessions using peer educators and appropriate vendors, help assess the needs of different researcher groups to identify challenges or gaps, recommend appropriate repositories to make data as accessible as possible, and comply with funder and publisher requirements. As a centralized service within an institution, health sciences libraries have the capability to bridge silos and connect bioimaging researchers with specialized data support across campus and beyond.
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Affiliation(s)
- Christie Silkotch
- David W. Howe Memorial Library, University of Vermont, Burlington, VT, 05405, USA
| | - Rolando Garcia-Milian
- Bioinformatics Support Hub, Harvey Cushing/John Whitney Medical Library, Yale University, New Haven, CT, 06510, USA
| | - Denise Hersey
- Dana Health Sciences Library, University of Vermont, Burlington, VT, 05405, USA.
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7
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Weisbart E, Tromans-Coia C, Diaz-Rohrer B, Stirling DR, Garcia-Fossa F, Senft RA, Hiner MC, de Jesus MB, Eliceiri KW, Cimini BA. CellProfiler plugins -- an easy image analysis platform integration for containers and Python tools. ARXIV 2023:arXiv:2306.01915v2. [PMID: 37645041 PMCID: PMC10462170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
CellProfiler is a widely used software for creating reproducible, reusable image analysis workflows without needing to code. In addition to the >90 modules that make up the main CellProfiler program, CellProfiler has a plugins system that allows for the creation of new modules which integrate with other Python tools or tools that are packaged in software containers. The CellProfiler-plugins repository contains a number of these CellProfiler modules, especially modules that are experimental and/or dependency-heavy. Here, we present an upgraded CellProfiler-plugins repository, an example of accessing containerized tools, improved documentation, and added citation/reference tools to facilitate the use and contribution of the community.
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Affiliation(s)
- Erin Weisbart
- Broad Institute of MIT and Harvard, Cambridge MA, USA
| | | | | | | | - Fernanda Garcia-Fossa
- Broad Institute of MIT and Harvard, Cambridge MA, USA
- Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas, Campinas, São Paulo, Brazil
| | | | - Mark C Hiner
- University of Wisconsin-Madison, Madison, WI, USA
| | - Marcelo B de Jesus
- Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas, Campinas, São Paulo, Brazil
| | | | - Beth A Cimini
- Broad Institute of MIT and Harvard, Cambridge MA, USA
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8
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Abstract
In 2018, PLOS Biology announced CellProfiler 3.0, which has become one of the most used pieces of image analysis software in biology. The rapid adoption of this software speaks to the importance of user experience to disseminate new methods of bioimage informatics.
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Affiliation(s)
- Perrine Paul-Gilloteaux
- Nantes Université, CHU Nantes, CNRS, Inserm, BioCore, US16, SFR Bonamy, Nantes, France
- Nantes Université, CNRS, Inserm, l'Institut du Thorax, Nantes, France
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9
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Cimini BA, Eliceiri KW. The Twenty Questions of bioimage object analysis. Nat Methods 2023; 20:976-978. [PMID: 37434006 PMCID: PMC10561713 DOI: 10.1038/s41592-023-01919-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2023]
Abstract
The language used by microscopists who wish to find and measure objects in an image often differs in critical ways from that used by computer scientists who create tools to help them do this, making communication hard across disciplines. This work proposes a set of standardized questions that can guide analyses and shows how it can improve the future of bioimage analysis as a whole by making image analysis workflows and tools more FAIR (findable, accessible, interoperable and reusable).
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Affiliation(s)
- Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Kevin W Eliceiri
- Center for Quantitative Cell Imaging, University of Wisconsin-Madison and Morgridge Institute for Research, Madison, WI, USA
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10
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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. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.05.543701. [PMID: 37333353 PMCID: PMC10274673 DOI: 10.1101/2023.06.05.543701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
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 multi-choice 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 familiarize 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. 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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11
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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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12
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Hung LH, Straw E, Reddy S, Schmitz R, Colburn Z, Yeung KY. Cloud-enabled Biodepot workflow builder integrates image processing using Fiji with reproducible data analysis using Jupyter notebooks. Sci Rep 2022; 12:14920. [PMID: 36056115 PMCID: PMC9440253 DOI: 10.1038/s41598-022-19173-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 08/25/2022] [Indexed: 11/16/2022] Open
Abstract
Modern biomedical image analyses workflows contain multiple computational processing tasks giving rise to problems in reproducibility. In addition, image datasets can span both spatial and temporal dimensions, with additional channels for fluorescence and other data, resulting in datasets that are too large to be processed locally on a laptop. For omics analyses, software containers have been shown to enhance reproducibility, facilitate installation and provide access to scalable computational resources on the cloud. However, most image analyses contain steps that are graphical and interactive, features that are not supported by most omics execution engines. We present the containerized and cloud-enabled Biodepot-workflow-builder platform that supports graphics from software containers and has been extended for image analyses. We demonstrate the potential of our modular approach with multi-step workflows that incorporate the popular and open-source Fiji suite for image processing. One of our examples integrates fully interactive ImageJ macros with Jupyter notebooks. Our second example illustrates how the complicated cloud setup of an computationally intensive process such as stitching 3D digital pathology datasets using BigStitcher can be automated and simplified. In both examples, users can leverage a form-based graphical interface to execute multi-step workflows with a single click, using the provided sample data and preset input parameters. Alternatively, users can interactively modify the image processing steps in the workflow, apply the workflows to their own data, change the input parameters and macros. By providing interactive graphics support to software containers, our modular platform supports reproducible image analysis workflows, simplified access to cloud resources for analysis of large datasets, and integration across different applications such as Jupyter.
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Affiliation(s)
- Ling-Hong Hung
- School of Engineering and Technology, University of Washington Tacoma, Box 358426, Tacoma, 98402, WA, USA
| | - Evan Straw
- Biodepot LLC, Seattle, 98195, WA, USA
- University of Washington, Seattle, 98195, WA, USA
| | - Shishir Reddy
- School of Engineering and Technology, University of Washington Tacoma, Box 358426, Tacoma, 98402, WA, USA
| | - Robert Schmitz
- School of Engineering and Technology, University of Washington Tacoma, Box 358426, Tacoma, 98402, WA, USA
- Biodepot LLC, Seattle, 98195, WA, USA
| | | | - Ka Yee Yeung
- School of Engineering and Technology, University of Washington Tacoma, Box 358426, Tacoma, 98402, WA, USA.
- Biodepot LLC, Seattle, 98195, WA, USA.
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