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Pylvänäinen JW, Grobe H, Jacquemet G. Practical considerations for data exploration in quantitative cell biology. J Cell Sci 2025; 138:jcs263801. [PMID: 40190255 PMCID: PMC12045597 DOI: 10.1242/jcs.263801] [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] [Subscribe] [Scholar Register] [Indexed: 05/03/2025] Open
Abstract
Data exploration is an essential step in quantitative cell biology, bridging raw data and scientific insights. Unlike polished, published figures, effective data exploration requires a flexible, hands-on approach that reveals trends, identifies outliers and refines hypotheses. This Opinion offers simple, practical advice for building a structured data exploration workflow, drawing on the authors' personal experience in analyzing bioimage datasets. In addition, the increasing availability of generative artificial intelligence and large language models makes coding and improving data workflows easier than ever before. By embracing these practices, researchers can streamline their workflows, produce more reliable conclusions and foster a collaborative, transparent approach to data analysis in cell biology.
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Affiliation(s)
- Joanna W. Pylvänäinen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, FI-20520 Turku, Finland
- InFLAMES Research Flagship Center, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
| | - Hanna Grobe
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, FI-20520 Turku, Finland
- InFLAMES Research Flagship Center, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
| | - Guillaume Jacquemet
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, FI-20520 Turku, Finland
- InFLAMES Research Flagship Center, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
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Wang J, Zhang Y, Chen H, Wu Y, Liu J, Che H, Zhang Y, Zhu X. Motor-Cargo Structured Nanotractors for Augmented NIR Phototherapy via Gas-Boosted Tumor Penetration and Respiration-Impaired Mitochondrial Dysfunction. Adv Healthc Mater 2024:e2402063. [PMID: 39380347 DOI: 10.1002/adhm.202402063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 09/26/2024] [Indexed: 10/10/2024]
Abstract
Tumor microenvironment, characterized by dense extracellular matrix and severe hypoxia, has caused pronounced resistance to photodynamic therapy (PDT). Herein, it has designed an artificial nitric oxide (NO) nanotractor with a unique "motor-cargo" structure, where a photoswitching upconversion nanoparticle (UCNP) core serves as the optical engine to harvest NIR light and asymmetrically coated mesoporous silica (SiO2) shell acts as a cargo unit to load nitric oxide (NO) fuel molecule (RBS, Roussin's black salt) and PDT photosensitizer (ZnPc, zinc phthalocyanine). Upon illumination by 980 nm light, the UCNP emits blue light to excite RBS salt and release NO gas. On one hand, NO is used as the driving force to propel the particle with a high speed of ≈194 µm s-1 that generates significant rupture stress (over 0.95 kPa) on cell membrane to promote cellular endocytosis and intratumoral penetration. On the other hand, NO enables to alleviate tumor hypoxia by inhibiting cellular respiration as an oxygen conserver. When the excitation is subsequently switched to 808 nm light, the UCNP emits red light, triggering ZnPc to produce large amount of reactive oxygen species for PDT treatment. This study explores Janus-typed nanostructures for cell-particle interaction and gas-assisted phototherapy, opening avenues for versatile bioapplications.
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Affiliation(s)
- Jing Wang
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China
| | - Yi Zhang
- Department of Medical Biochemistry and Molecular Biology, School of Medicine, Jinan University, Guangzhou, 510632, China
| | - Huadong Chen
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China
| | - Yihan Wu
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China
| | - Jinliang Liu
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China
| | - Hailong Che
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China
| | - Yong Zhang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China
| | - Xiaohui Zhu
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China
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3
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Imreh G, Hu J, Le Guyader S. Improving light microscopy training routines with evidence-based education. J Microsc 2024; 294:295-307. [PMID: 37534621 DOI: 10.1111/jmi.13216] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 07/28/2023] [Accepted: 07/31/2023] [Indexed: 08/04/2023]
Abstract
The low reproducibility of scientific data published in articles has recently become a cause of concern in many scientific fields. Data involving light microscopy is no exception. The low awareness of researchers of the technologies they use in their research has been identified as one of the main causes of the problem. Potential solutions have hinted at the need to improve technological and methodological education within research. Despite the pivotal role of microscopy core facilities in the education of researchers being well documented, facility staff (FS) often learn their trade on the job, without receiving themselves any structured education about the technology they teach others to use. Additionally, despite endorsing an important role at the highest level of education, most FS never receive any training in pedagogy, the field of research on teaching and learning methods. In this article, we argue that the low level of awareness that researchers have of microscopy stems from a knowledge gap formed between them and microscopy FS during training routines. On the one hand, FS consider that their teaching task is to explain what is needed to produce reliable data. On the other, despite understanding what is being taught, researchers fail to learn the most challenging aspects of microscopy, those involving their judgement and reasoning. We suggest that the misunderstanding between FS and researchers is due to FS not being educated in pedagogy and thus often confusing understanding and learning. To bridge this knowledge gap and improve the quality of the microscopy education available to researchers, we propose a paradigm shift where training staff at technological core facilities be acknowledged as full-fledged teachers and offered structured education not only in the technology they teach but also in pedagogy. We then suggest that training routines at facilities be upgraded to follow the principles of the Constructive Alignment pedagogical method. We give an example of how this can be applied to existing microscopy training routines. We also describe a model to define where the responsibility of FS in training researchers begins and ends. This involves a major structural change where university staff involved in teaching research technologies themselves receive appropriate education. For this to be achieved, we advocate that funding agencies, universities, microscopy and core facility organisations mobilise resources of time and funding. Such changes may involve funding the creation and development of 'Train-the-trainer' type of courses and giving incentives for FS to upgrade their technological and pedagogical knowledge, for example by including them in career paths. We believe that this paradigm shift is necessary to improve the level of microscopy education and ultimately the reproducibility of published data.
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Affiliation(s)
- Gabriela Imreh
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
| | - Jianjiang Hu
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
| | - Sylvie Le Guyader
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
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4
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Sytwu K, Rangel DaCosta L, Scott MC. Generalization Across Experimental Parameters in Neural Network Analysis of High-Resolution Transmission Electron Microscopy Datasets. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2024; 30:85-95. [PMID: 38285915 DOI: 10.1093/micmic/ozae001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 12/19/2023] [Accepted: 01/06/2024] [Indexed: 01/31/2024]
Abstract
Neural networks are promising tools for high-throughput and accurate transmission electron microscopy (TEM) analysis of nanomaterials, but are known to generalize poorly on data that is "out-of-distribution" from their training data. Given the limited set of image features typically seen in high-resolution TEM imaging, it is unclear which images are considered out-of-distribution from others. Here, we investigate how the choice of metadata features in the training dataset influences neural network performance, focusing on the example task of nanoparticle segmentation. We train and validate neural networks across curated, experimentally collected high-resolution TEM image datasets of nanoparticles under various imaging and material parameters, including magnification, dosage, nanoparticle diameter, and nanoparticle material. Overall, we find that our neural networks are not robust across microscope parameters, but do generalize across certain sample parameters. Additionally, data preprocessing can have unintended consequences on neural network generalization. Our results highlight the need to understand how dataset features affect deployment of data-driven algorithms.
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Affiliation(s)
- Katherine Sytwu
- Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
| | - Luis Rangel DaCosta
- Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
- Materials Science and Engineering, University of California Berkeley, 2607 Hearst Ave, Berkeley, CA 94720, USA
| | - Mary C Scott
- Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
- Materials Science and Engineering, University of California Berkeley, 2607 Hearst Ave, Berkeley, CA 94720, USA
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5
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Jan M, Spangaro A, Lenartowicz M, Mattiazzi Usaj M. From pixels to insights: Machine learning and deep learning for bioimage analysis. Bioessays 2024; 46:e2300114. [PMID: 38058114 DOI: 10.1002/bies.202300114] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 10/25/2023] [Accepted: 11/13/2023] [Indexed: 12/08/2023]
Abstract
Bioimage analysis plays a critical role in extracting information from biological images, enabling deeper insights into cellular structures and processes. The integration of machine learning and deep learning techniques has revolutionized the field, enabling the automated, reproducible, and accurate analysis of biological images. Here, we provide an overview of the history and principles of machine learning and deep learning in the context of bioimage analysis. We discuss the essential steps of the bioimage analysis workflow, emphasizing how machine learning and deep learning have improved preprocessing, segmentation, feature extraction, object tracking, and classification. We provide examples that showcase the application of machine learning and deep learning in bioimage analysis. We examine user-friendly software and tools that enable biologists to leverage these techniques without extensive computational expertise. This review is a resource for researchers seeking to incorporate machine learning and deep learning in their bioimage analysis workflows and enhance their research in this rapidly evolving field.
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Affiliation(s)
- Mahta Jan
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Canada
| | - Allie Spangaro
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Canada
| | - Michelle Lenartowicz
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Canada
| | - Mojca Mattiazzi Usaj
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Canada
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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: 22] [Impact Index Per Article: 22.0] [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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7
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Lee RM, Eisenman LR, Khuon S, Aaron JS, Chew TL. Believing is seeing - the deceptive influence of bias in quantitative microscopy. J Cell Sci 2024; 137:jcs261567. [PMID: 38197776 DOI: 10.1242/jcs.261567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2024] Open
Abstract
The visual allure of microscopy makes it an intuitively powerful research tool. Intuition, however, can easily obscure or distort the reality of the information contained in an image. Common cognitive biases, combined with institutional pressures that reward positive research results, can quickly skew a microscopy project towards upholding, rather than rigorously challenging, a hypothesis. The impact of these biases on a variety of research topics is well known. What might be less appreciated are the many forms in which bias can permeate a microscopy experiment. Even well-intentioned researchers are susceptible to bias, which must therefore be actively recognized to be mitigated. Importantly, although image quantification has increasingly become an expectation, ostensibly to confront subtle biases, it is not a guarantee against bias and cannot alone shield an experiment from cognitive distortions. Here, we provide illustrative examples of the insidiously pervasive nature of bias in microscopy experiments - from initial experimental design to image acquisition, analysis and data interpretation. We then provide suggestions that can serve as guard rails against bias.
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Affiliation(s)
- Rachel M Lee
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Leanna R Eisenman
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Satya Khuon
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Jesse S Aaron
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Teng-Leong Chew
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
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8
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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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Bianchini RM, Kurz EU. The analysis of protein recruitment to laser microirradiation-induced DNA damage in live cells: Best practices for data analysis. DNA Repair (Amst) 2023; 129:103545. [PMID: 37524003 DOI: 10.1016/j.dnarep.2023.103545] [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] [Received: 05/05/2023] [Revised: 07/20/2023] [Accepted: 07/24/2023] [Indexed: 08/02/2023]
Abstract
Laser microirradiation coupled with live-cell fluorescence microscopy is a powerful technique that has been used widely in studying the recruitment and retention of proteins at sites of DNA damage. Results obtained from this technique can be found in published works by both seasoned and infrequent users of microscopy. However, like many other microscopy-based techniques, the presentation of data from laser microirradiation experiments is inconsistent; papers report a wide assortment of analytic techniques, not all of which result in accurate and/or appropriate representation of the data. In addition to the varied methods of analysis, experimental and analytical details are commonly under-reported. Consequently, publications reporting data from laser microirradiation coupled with fluorescence microscopy experiments need to be carefully and critically assessed by readers. Here, we undertake a systematic investigation of commonly reported corrections used in the analysis of laser microirradiation data. We validate the critical need to correct data for photobleaching and we identify key experimental parameters that must be accounted for when presenting data from laser microirradiation experiments. Furthermore, we propose a straightforward, four-step analytical protocol that can readily be applied across platforms and that aims to improve the quality of data reporting in the DNA damage field.
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Affiliation(s)
- Ryan M Bianchini
- Robson DNA Science Centre, Arnie Charbonneau Cancer Institute, and Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB T2N 4N1, Canada
| | - Ebba U Kurz
- Robson DNA Science Centre, Arnie Charbonneau Cancer Institute, and Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB T2N 4N1, Canada.
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10
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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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11
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Helmbrecht H, Lin TJ, Janakiraman S, Decker K, Nance E. Prevalence and practices of immunofluorescent cell image processing: a systematic review. Front Cell Neurosci 2023; 17:1188858. [PMID: 37545881 PMCID: PMC10400723 DOI: 10.3389/fncel.2023.1188858] [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: 03/17/2023] [Accepted: 07/06/2023] [Indexed: 08/08/2023] Open
Abstract
Background We performed a systematic review that identified at least 9,000 scientific papers on PubMed that include immunofluorescent images of cells from the central nervous system (CNS). These CNS papers contain tens of thousands of immunofluorescent neural images supporting the findings of over 50,000 associated researchers. While many existing reviews discuss different aspects of immunofluorescent microscopy, such as image acquisition and staining protocols, few papers discuss immunofluorescent imaging from an image-processing perspective. We analyzed the literature to determine the image processing methods that were commonly published alongside the associated CNS cell, microscopy technique, and animal model, and highlight gaps in image processing documentation and reporting in the CNS research field. Methods We completed a comprehensive search of PubMed publications using Medical Subject Headings (MeSH) terms and other general search terms for CNS cells and common fluorescent microscopy techniques. Publications were found on PubMed using a combination of column description terms and row description terms. We manually tagged the comma-separated values file (CSV) metadata of each publication with the following categories: animal or cell model, quantified features, threshold techniques, segmentation techniques, and image processing software. Results Of the almost 9,000 immunofluorescent imaging papers identified in our search, only 856 explicitly include image processing information. Moreover, hundreds of the 856 papers are missing thresholding, segmentation, and morphological feature details necessary for explainable, unbiased, and reproducible results. In our assessment of the literature, we visualized current image processing practices, compiled the image processing options from the top twelve software programs, and designed a road map to enhance image processing. We determined that thresholding and segmentation methods were often left out of publications and underreported or underutilized for quantifying CNS cell research. Discussion Less than 10% of papers with immunofluorescent images include image processing in their methods. A few authors are implementing advanced methods in image analysis to quantify over 40 different CNS cell features, which can provide quantitative insights in CNS cell features that will advance CNS research. However, our review puts forward that image analysis methods will remain limited in rigor and reproducibility without more rigorous and detailed reporting of image processing methods. Conclusion Image processing is a critical part of CNS research that must be improved to increase scientific insight, explainability, reproducibility, and rigor.
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Affiliation(s)
- Hawley Helmbrecht
- Department of Chemical Engineering, University of Washington, Seattle, WA, United States
| | - Teng-Jui Lin
- Department of Chemical Engineering, University of Washington, Seattle, WA, United States
| | - Sanjana Janakiraman
- Paul G. Allen School of Computer Science & Engineering, Seattle, WA, United States
| | - Kaleb Decker
- Department of Chemical Engineering, University of Washington, Seattle, WA, United States
| | - Elizabeth Nance
- Department of Chemical Engineering, University of Washington, Seattle, WA, United States
- Department of Bioengineering, University of Washington, Seattle, WA, United States
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12
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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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13
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Gorman C, Punzo D, Octaviano I, Pieper S, Longabaugh WJR, Clunie DA, Kikinis R, Fedorov AY, Herrmann MD. Interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology. Nat Commun 2023; 14:1572. [PMID: 36949078 PMCID: PMC10033920 DOI: 10.1038/s41467-023-37224-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/08/2023] [Indexed: 03/24/2023] Open
Abstract
The exchange of large and complex slide microscopy imaging data in biomedical research and pathology practice is impeded by a lack of data standardization and interoperability, which is detrimental to the reproducibility of scientific findings and clinical integration of technological innovations. We introduce Slim, an open-source, web-based slide microscopy viewer that implements the internationally accepted Digital Imaging and Communications in Medicine (DICOM) standard to achieve interoperability with a multitude of existing medical imaging systems. We showcase the capabilities of Slim as the slide microscopy viewer of the NCI Imaging Data Commons and demonstrate how the viewer enables interactive visualization of traditional brightfield microscopy and highly-multiplexed immunofluorescence microscopy images from The Cancer Genome Atlas and Human Tissue Atlas Network, respectively, using standard DICOMweb services. We further show how Slim enables the collection of standardized image annotations for the development or validation of machine learning models and the visual interpretation of model inference results in the form of segmentation masks, spatial heat maps, or image-derived measurements.
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Affiliation(s)
- Chris Gorman
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | | | | | | | | | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrey Y Fedorov
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Markus D Herrmann
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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14
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Larsen DD, Gaudreault N, Gibbs HC. Reporting reproducible imaging protocols. STAR Protoc 2023; 4:102040. [PMID: 36861824 PMCID: PMC9996438 DOI: 10.1016/j.xpro.2022.102040] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 11/21/2022] [Accepted: 12/29/2022] [Indexed: 03/03/2023] Open
Abstract
A reproducible imaging protocol should include four main detailed sections. The first should describe the sample preparation and include details about the tissue and/or cell culture preparation, the staining procedure, the optical grade of the coverslip, and the type of mounting media used to mount the sample. The second section should describe the configuration and components of the microscope and include the type of stand, stage, illumination, and detector, as well as the emission (EM) and excitation (EX) filters, objective, and immersion medium specifications. Specialized microscopes may have other important components in the optical path to include. The third section should describe the settings used to acquire an image like the exposure and/or dwell time, final magnification and optical resolution, the pixel and field of view (FOV) sizes, time intervals for any time lapse, total power at the objective (i.e., directed at your sample) and number of planes and step size used to collect a 3-dimensional image, and order of operations used in multi-dimensional image acquisitions. The final section should include details about the image analysis workflow such as the image processing steps, segmentation and measurement methods used to extract information from the image, data size, and necessary computing hardware and networking requirements if data sets are >1 GB, as well as citations and versions for the software and code used to perform any of these steps. Every effort should be made to make an example dataset with accurate metadata available online. Finally, specifics about the type of replicates included in the experiment and details about the statistical analysis conducted are also necessary.
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Affiliation(s)
- DeLaine D Larsen
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
| | | | - Holly C Gibbs
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA; Microscopy and Imaging Center, Texas A&M University, College Station, TX, USA.
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15
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Wallmeier K, Würthwein T, Lemberger N, Brinkmann M, Hellwig T, Fallnich C. Frequency modulation stimulated Raman scattering scheme for real-time background correction with a single light source. BIOMEDICAL OPTICS EXPRESS 2023; 14:315-325. [PMID: 36698676 PMCID: PMC9841997 DOI: 10.1364/boe.476513] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 11/17/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
A frequency modulation (FM) scheme for stimulated Raman scattering (SRS) is presented with a single fiber-based light source. Pulse-to-pulse wavelength-switching allows real-time subtraction of parasitic signals leaving only the resonant SRS signal with a noise reduction of up to 30 % compared to digital subtraction schemes, leading effectively to a contrast improvement by a factor of up to 8.3. The wide tuning range of the light source from 1500 cm-1 to 3000 cm-1 and the possibility to separately adjust the resonant and the nonresonant wavenumber for every specimen allow to investigate a variety of samples with high contrast and high signal-to-noise ratio, e. g., for medical diagnostics.
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Affiliation(s)
- Kristin Wallmeier
- University of Münster
, Institute of Applied Physics, Corrensstraße 2, 48149 Münster, Germany
| | - Thomas Würthwein
- University of Münster
, Institute of Applied Physics, Corrensstraße 2, 48149 Münster, Germany
| | - Nick Lemberger
- University of Münster
, Institute of Applied Physics, Corrensstraße 2, 48149 Münster, Germany
| | | | - Tim Hellwig
- Refined Laser Systems GmbH, Mendelstraße 11, 48149 Münster, Germany
| | - Carsten Fallnich
- University of Münster
, Institute of Applied Physics, Corrensstraße 2, 48149 Münster, Germany
- University of Münster, Cells in Motion Interfaculty Centre, Münster, Germany
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16
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Practical considerations for quantitative light sheet fluorescence microscopy. Nat Methods 2022; 19:1538-1549. [PMID: 36266466 DOI: 10.1038/s41592-022-01632-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 08/31/2022] [Indexed: 12/25/2022]
Abstract
Fluorescence microscopy has evolved from a purely observational tool to a platform for quantitative, hypothesis-driven research. As such, the demand for faster and less phototoxic imaging modalities has spurred a rapid growth in light sheet fluorescence microscopy (LSFM). By restricting the excitation to a thin plane, LSFM reduces the overall light dose to a specimen while simultaneously improving image contrast. However, the defining characteristics of light sheet microscopes subsequently warrant unique considerations in their use for quantitative experiments. In this Perspective, we outline many of the pitfalls in LSFM that can compromise analysis and confound interpretation. Moreover, we offer guidance in addressing these caveats when possible. In doing so, we hope to provide a useful resource for life scientists seeking to adopt LSFM to quantitatively address complex biological hypotheses.
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17
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Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy. Nat Biotechnol 2022; 40:606-617. [PMID: 34782739 DOI: 10.1038/s41587-021-01092-2] [Citation(s) in RCA: 143] [Impact Index Per Article: 47.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 09/10/2021] [Indexed: 02/07/2023]
Abstract
A main determinant of the spatial resolution of live-cell super-resolution (SR) microscopes is the maximum photon flux that can be collected. To further increase the effective resolution for a given photon flux, we take advantage of a priori knowledge about the sparsity and continuity of biological structures to develop a deconvolution algorithm that increases the resolution of SR microscopes nearly twofold. Our method, sparse structured illumination microscopy (Sparse-SIM), achieves ~60-nm resolution at a frame rate of up to 564 Hz, allowing it to resolve intricate structures, including small vesicular fusion pores, ring-shaped nuclear pores formed by nucleoporins and relative movements of inner and outer mitochondrial membranes in live cells. Sparse deconvolution can also be used to increase the three-dimensional resolution of spinning-disc confocal-based SIM, even at low signal-to-noise ratios, which allows four-color, three-dimensional live-cell SR imaging at ~90-nm resolution. Overall, sparse deconvolution will be useful to increase the spatiotemporal resolution of live-cell fluorescence microscopy.
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18
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Reiche MA, Aaron JS, Boehm U, DeSantis MC, Hobson CM, Khuon S, Lee RM, Chew TL. When light meets biology - how the specimen affects quantitative microscopy. J Cell Sci 2022; 135:274812. [PMID: 35319069 DOI: 10.1242/jcs.259656] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Fluorescence microscopy images should not be treated as perfect representations of biology. Many factors within the biospecimen itself can drastically affect quantitative microscopy data. Whereas some sample-specific considerations, such as photobleaching and autofluorescence, are more commonly discussed, a holistic discussion of sample-related issues (which includes less-routine topics such as quenching, scattering and biological anisotropy) is required to appropriately guide life scientists through the subtleties inherent to bioimaging. Here, we consider how the interplay between light and a sample can cause common experimental pitfalls and unanticipated errors when drawing biological conclusions. Although some of these discrepancies can be minimized or controlled for, others require more pragmatic considerations when interpreting image data. Ultimately, the power lies in the hands of the experimenter. The goal of this Review is therefore to survey how biological samples can skew quantification and interpretation of microscopy data. Furthermore, we offer a perspective on how to manage many of these potential pitfalls.
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Affiliation(s)
- Michael A Reiche
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Jesse S Aaron
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Ulrike Boehm
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Michael C DeSantis
- Light Microscopy Facility, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147,USA
| | - Chad M Hobson
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Satya Khuon
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA.,Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Rachel M Lee
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Teng-Leong Chew
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA.,Light Microscopy Facility, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147,USA
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19
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Lu V, Roy IJ, Torres A, Joly JH, Ahsan FM, Graham NA, Teitell MA. Glutamine-dependent signaling controls pluripotent stem cell fate. Dev Cell 2022; 57:610-623.e8. [PMID: 35216682 PMCID: PMC8930616 DOI: 10.1016/j.devcel.2022.02.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 11/01/2021] [Accepted: 01/31/2022] [Indexed: 12/30/2022]
Abstract
Human pluripotent stem cells (hPSCs) can self-renew indefinitely or can be induced to differentiate. We previously showed that exogenous glutamine (Gln) withdrawal biased hPSC differentiation toward ectoderm and away from mesoderm. We revealed that, although all three germ lineages are capable of de novo Gln synthesis, only ectoderm generates sufficient Gln to sustain cell viability and differentiation, and this finding clarifies lineage fate restrictions under Gln withdrawal. Furthermore, we found that Gln acts as a signaling molecule for ectoderm that supersedes lineage-specifying cytokine induction. In contrast, Gln in mesoderm and endoderm is the preferred precursor of α-ketoglutarate without a direct signaling role. Our work raises a question about whether the nutrient environment functions directly in cell differentiation during development. Interestingly, transcriptome analysis of a gastrulation-stage human embryo shows that unique Gln enzyme-encoding gene expression patterns may also distinguish germ lineages in vivo. Together, our study suggests that intracellular Gln may help coordinate differentiation of the three germ layers.
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Affiliation(s)
- Vivian Lu
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
| | - Irena J Roy
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
| | - Alejandro Torres
- Molecular Biology Institute, University of California at Los Angeles, Los Angeles, CA, USA
| | - James H Joly
- Mork Family Department of Chemical Engineering and Materials Science, Los Angeles, CA 90089, USA
| | - Fasih M Ahsan
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
| | - Nicholas A Graham
- Mork Family Department of Chemical Engineering and Materials Science, Los Angeles, CA 90089, USA; Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90089, USA; Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
| | - Michael A Teitell
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA; Molecular Biology Institute, University of California at Los Angeles, Los Angeles, CA, USA; Department of Bioengineering, Department of Pediatrics, California NanoSystems Institute, and Broad Center for Regenerative Medicine and Stem Cell Research, University of California at Los Angeles, Los Angeles, CA, USA; Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.
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20
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Martinez AL, Shannon MJ, Eisman SE, Hegewisch-Solloa E, Asif AN, Ebrahim TAM, Mace EM. Quantifying Human Natural Killer Cell Migration by Imaging and Image Analysis. Methods Mol Biol 2022; 2463:129-151. [PMID: 35344172 PMCID: PMC9159076 DOI: 10.1007/978-1-0716-2160-8_10] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Migration is an important function for natural killer cells. Cell motility has implications in development, tissue infiltration, and cytotoxicity, and measuring the properties of natural killer (NK) cell migration using in vitro assays can be highly informative. Many researchers have an interest in studying properties of NK cell migration in the context of genetic mutation, disease, or in specific tissues and microenvironments. Motility assays can also provide information on the localization of proteins during different phases of cell migration. These assays can be performed on different surfaces for migration or coupled with chemoattractants and/or target cells to test functional outcomes or characterize cell migration speeds and phenotypes. NK cells undergo migration during differentiation in tissue, and these conditions can be modeled by culturing NK cells on a confluent bed of stromal cells on glass and imaging cell migration. Alternatively, fibronectin- or ICAM-1-coated surfaces promote NK cell migration and can be used as substrates. Here, we will describe techniques for the experimental setup and analysis of NK cell motility assays by confocal microscopy or in-incubator imaging using commercially available systems. Finally, we describe open-source software for analyzing cell migration using manual tracking or automated approaches and discuss considerations for the implementation of each of these methods.
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Affiliation(s)
- Amera L Martinez
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA
| | - Michael J Shannon
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA
| | - Shira E Eisman
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA
| | | | - Aneeza N Asif
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA
- Department of Biology, Barnard College, New York, NY, USA
| | - Tasneem A M Ebrahim
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA
- Department of Cell, Developmental, and Regenerative Biology, Icahn School of Medicine at Mt. Sinai, New York, NY, USA
| | - Emily M Mace
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA.
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21
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Sanchez-Arias JC, Carrier M, Frederiksen SD, Shevtsova O, McKee C, van der Slagt E, Gonçalves de Andrade E, Nguyen HL, Young PA, Tremblay MÈ, Swayne LA. A Systematic, Open-Science Framework for Quantification of Cell-Types in Mouse Brain Sections Using Fluorescence Microscopy. Front Neuroanat 2021; 15:722443. [PMID: 34949993 PMCID: PMC8691181 DOI: 10.3389/fnana.2021.722443] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 10/28/2021] [Indexed: 02/03/2023] Open
Abstract
The ever-expanding availability and evolution of microscopy tools has enabled ground-breaking discoveries in neurobiology, particularly with respect to the analysis of cell-type density and distribution. Widespread implementation of many of the elegant image processing tools available continues to be impeded by the lack of complete workflows that span from experimental design, labeling techniques, and analysis workflows, to statistical methods and data presentation. Additionally, it is important to consider open science principles (e.g., open-source software and tools, user-friendliness, simplicity, and accessibility). In the present methodological article, we provide a compendium of resources and a FIJI-ImageJ-based workflow aimed at improving the quantification of cell density in mouse brain samples using semi-automated open-science-based methods. Our proposed framework spans from principles and best practices of experimental design, histological and immunofluorescence staining, and microscopy imaging to recommendations for statistical analysis and data presentation. To validate our approach, we quantified neuronal density in the mouse barrel cortex using antibodies against pan-neuronal and interneuron markers. This framework is intended to be simple and yet flexible, such that it can be adapted to suit distinct project needs. The guidelines, tips, and proposed methodology outlined here, will support researchers of wide-ranging experience levels and areas of focus in neuroscience research.
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Affiliation(s)
| | - Micaël Carrier
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada.,Axe Neurosciences, Centre de Recherche du CHU de Québec, Université de Laval, Québec City, QC, Canada
| | | | - Olga Shevtsova
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
| | - Chloe McKee
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
| | - Emma van der Slagt
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
| | | | - Hai Lam Nguyen
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
| | - Penelope A Young
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
| | - Marie-Ève Tremblay
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada.,Axe Neurosciences, Centre de Recherche du CHU de Québec, Université de Laval, Québec City, QC, Canada.,Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada.,Department of Molecular Medicine, Université de Laval, Québec City, QC, Canada.,Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Leigh Anne Swayne
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada.,Department of Cellular and Physiological Sciences, University of British Columbia, Vancouver, BC, Canada.,Department of Neurology and Neurosurgery, Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Program, Research Institute of the McGill University Health Centre, Montreal General Hospital, Montreal, QC, Canada
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22
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Ryan J, Pengo T, Rigano A, Llopis PM, Itano MS, Cameron LA, Marqués G, Strambio-De-Castillia C, Sanders MA, Brown CM. MethodsJ2: a software tool to capture metadata and generate comprehensive microscopy methods text. Nat Methods 2021; 18:1414-1416. [PMID: 34654919 PMCID: PMC9488561 DOI: 10.1038/s41592-021-01290-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Joel Ryan
- Advanced BioImaging Facility (ABIF), McGill University, Montreal, Quebec, Canada
- Department of Physiology, McGill University, Montreal, Quebec, Canada
| | - Thomas Pengo
- University of Minnesota Informatics Institute, University of Minnesota, Minneapolis, MN, USA
| | - Alex Rigano
- Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | | | - Michelle S Itano
- Neuroscience Microscopy Core, University of North Carolina, Chapel Hill, NC, USA
- Department of Cell Biology & Physiology, University of North Carolina, Chapel Hill, NC, USA
- Carolina Institute for Developmental Disabilities, University of North Carolina, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC, USA
| | - Lisa A Cameron
- Light Microscopy Core Facility, Duke University, Durham, NC, USA
| | - Guillermo Marqués
- University Imaging Centers, University of Minnesota, Minneapolis, MN, USA
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | | | - Mark A Sanders
- University Imaging Centers, University of Minnesota, Minneapolis, MN, USA
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Claire M Brown
- Advanced BioImaging Facility (ABIF), McGill University, Montreal, Quebec, Canada.
- Department of Physiology, McGill University, Montreal, Quebec, Canada.
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23
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Montero Llopis P, Senft RA, Ross-Elliott TJ, Stephansky R, Keeley DP, Koshar P, Marqués G, Gao YS, Carlson BR, Pengo T, Sanders MA, Cameron LA, Itano MS. Best practices and tools for reporting reproducible fluorescence microscopy methods. Nat Methods 2021; 18:1463-1476. [PMID: 34099930 DOI: 10.1038/s41592-021-01156-w] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 04/15/2021] [Indexed: 02/04/2023]
Abstract
Although fluorescence microscopy is ubiquitous in biomedical research, microscopy methods reporting is inconsistent and perhaps undervalued. We emphasize the importance of appropriate microscopy methods reporting and seek to educate researchers about how microscopy metadata impact data interpretation. We provide comprehensive guidelines and resources to enable accurate reporting for the most common fluorescence light microscopy modalities. We aim to improve microscopy reporting, thus improving the quality, rigor and reproducibility of image-based science.
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Affiliation(s)
| | - Rebecca A Senft
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
| | | | | | - Daniel P Keeley
- Neuroscience Microscopy Core, University of North Carolina, Chapel Hill, NC, USA
| | - Preman Koshar
- Neuroscience Microscopy Core, University of North Carolina, Chapel Hill, NC, USA
| | - Guillermo Marqués
- University Imaging Centers and Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Ya-Sheng Gao
- Duke Light Microscopy Core Facility, Duke University, Durham, NC, USA
| | | | - Thomas Pengo
- University of Minnesota Informatics Institute, University of Minnesota, Minneapolis, MN, USA
| | - Mark A Sanders
- University Imaging Centers and Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Lisa A Cameron
- Duke Light Microscopy Core Facility, Duke University, Durham, NC, USA
| | - Michelle S Itano
- Neuroscience Microscopy Core, University of North Carolina, Chapel Hill, NC, USA
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24
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Hammer M, Huisman M, Rigano A, Boehm U, Chambers JJ, Gaudreault N, North AJ, Pimentel JA, Sudar D, Bajcsy P, Brown CM, Corbett AD, Faklaris O, Lacoste J, Laude A, Nelson G, Nitschke R, Farzam F, Smith CS, Grunwald D, Strambio-De-Castillia C. Towards community-driven metadata standards for light microscopy: tiered specifications extending the OME model. Nat Methods 2021; 18:1427-1440. [PMID: 34862501 PMCID: PMC9271325 DOI: 10.1038/s41592-021-01327-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments.
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Affiliation(s)
- Mathias Hammer
- RNA Therapeutics Institute, UMass Chan Medical School, Worcester, MA, USA
- Department of Biology, Technical University of Darmstadt, Darmstadt, Germany
| | | | - Alessandro Rigano
- Program in Molecular Medicine, UMass Chan Medical School, Worcester, MA, USA
| | - Ulrike Boehm
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - James J Chambers
- Institute for Applied Life Sciences, University of Massachusetts, Amherst, MA, USA
| | | | | | - Jaime A Pimentel
- Laboratorio Nacional de Microscopía Avanzada, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Damir Sudar
- Quantitative Imaging Systems LLC, Portland, OR, USA
| | - Peter Bajcsy
- National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Claire M Brown
- Advanced BioImaging Facility (ABIF), McGill University, Montreal, Quebec, Canada
| | | | - Orestis Faklaris
- MRI, BCM, University of Montpellier, CNRS, INSERM, Montpellier, France
| | | | - Alex Laude
- Bioimaging Unit, Newcastle University, Newcastle upon Tyne, UK
| | - Glyn Nelson
- Bioimaging Unit, Newcastle University, Newcastle upon Tyne, UK
| | - Roland Nitschke
- Life Imaging Center and Signalling Research Centres CIBSS and BIOSS, University of Freiburg, Freiburg, Germany
| | - Farzin Farzam
- RNA Therapeutics Institute, UMass Chan Medical School, Worcester, MA, USA
| | - Carlas S Smith
- Delft Center for Systems and Control and Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
| | - David Grunwald
- RNA Therapeutics Institute, UMass Chan Medical School, Worcester, MA, USA
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25
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Laine RF, Arganda-Carreras I, Henriques R, Jacquemet G. Avoiding a replication crisis in deep-learning-based bioimage analysis. Nat Methods 2021; 18:1136-1144. [PMID: 34608322 PMCID: PMC7611896 DOI: 10.1038/s41592-021-01284-3] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Deep learning algorithms are powerful tools to analyse, restore and transform bioimaging data, increasingly used in life sciences research. These approaches now outperform most other algorithms for a broad range of image analysis tasks. In particular, one of the promises of deep learning is the possibility to provide parameter-free, one-click data analysis achieving expert-level performances in a fraction of the time previously required. However, as with most new and upcoming technologies, the potential for inappropriate use is raising concerns among the biomedical research community. This perspective aims to provide a short overview of key concepts that we believe are important for researchers to consider when using deep learning for their microscopy studies. These comments are based on our own experience gained while optimising various deep learning tools for bioimage analysis and discussions with colleagues from both the developer and user community. In particular, we focus on describing how results obtained using deep learning can be validated and discuss what should, in our views, be considered when choosing a suitable tool. We also suggest what aspects of a deep learning analysis would need to be reported in publications to describe the use of such tools to guarantee that the work can be reproduced. We hope this perspective will foster further discussion between developers, image analysis specialists, users and journal editors to define adequate guidelines and ensure that this transformative technology is used appropriately.
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Affiliation(s)
- Romain F Laine
- MRC-Laboratory for Molecular Cell Biology, University College London, London, UK
- The Francis Crick Institute, London, UK
- Micrographia Bio, Translation and Innovation Hub, London, UK
| | - Ignacio Arganda-Carreras
- Computer Science and Artificial Intelligence Department, University of the Basque Country (UPV/EHU), San Sebastian, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
- Donostia International Physics Center (DIPC), San Sebastian, Spain
| | - Ricardo Henriques
- MRC-Laboratory for Molecular Cell Biology, University College London, London, UK
- The Francis Crick Institute, London, UK
- Instituto Gulbenkian de Ciência, Oeiras, Portugal
| | - Guillaume Jacquemet
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
- Faculty of Science and Engineering, Biosciences, Åbo Akademi University, Turku, Finland.
- Turku Bioimaging, University of Turku and Åbo Akademi University, Turku, Finland.
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Hmeljak J, Agullo-Pascual E. Celebrating FocalPlane and microscopy in Disease Models & Mechanisms. Dis Model Mech 2021; 14:270975. [PMID: 34279567 DOI: 10.1242/dmm.049183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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Heddleston JM, Aaron JS, Khuon S, Chew TL. A guide to accurate reporting in digital image acquisition - can anyone replicate your microscopy data? J Cell Sci 2021; 134:134/6/jcs254144. [PMID: 33785608 DOI: 10.1242/jcs.254144] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Recent technological advances have made microscopy indispensable in life science research. Its ubiquitous use, in turn, underscores the importance of ensuring that microscopy-based experiments are replicable and that the resulting data comparable. While there has been a wealth of review articles, practical guides and conferences devoted to the topic of maintaining standard instrument operating conditions, the paucity of attention dedicated to properly documenting microscopy experiments is undeniable. This lack of emphasis on accurate reporting extends beyond life science researchers themselves, to the review panels and editorial boards of many journals. Such oversight at the final step of communicating a scientific discovery can unfortunately negate the many valiant efforts made to ensure experimental quality control in the name of scientific reproducibility. This Review aims to enumerate the various parameters that should be reported in an imaging experiment by illustrating how their inconsistent application can lead to irreconcilable results.
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Affiliation(s)
- John M Heddleston
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Jesse S Aaron
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Satya Khuon
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Teng-Leong Chew
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
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28
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Rigano A, Ehmsen S, Öztürk SU, Ryan J, Balashov A, Hammer M, Kirli K, Boehm U, Brown CM, Bellve K, Chambers JJ, Cosolo A, Coleman RA, Faklaris O, Fogarty KE, Guilbert T, Hamacher AB, Itano MS, Keeley DP, Kunis S, Lacoste J, Laude A, Ma WY, Marcello M, Montero-Llopis P, Nelson G, Nitschke R, Pimentel JA, Weidtkamp-Peters S, Park PJ, Alver BH, Grunwald D, Strambio-De-Castillia C. Micro-Meta App: an interactive tool for collecting microscopy metadata based on community specifications. Nat Methods 2021; 18:1489-1495. [PMID: 34862503 PMCID: PMC8648560 DOI: 10.1038/s41592-021-01315-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 09/30/2021] [Indexed: 12/31/2022]
Abstract
For quality, interpretation, reproducibility and sharing value, microscopy images should be accompanied by detailed descriptions of the conditions that were used to produce them. Micro-Meta App is an intuitive, highly interoperable, open-source software tool that was developed in the context of the 4D Nucleome (4DN) consortium and is designed to facilitate the extraction and collection of relevant microscopy metadata as specified by the recent 4DN-BINA-OME tiered-system of Microscopy Metadata specifications. In addition to substantially lowering the burden of quality assurance, the visual nature of Micro-Meta App makes it particularly suited for training purposes.
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Affiliation(s)
- Alessandro Rigano
- Program in Molecular Medicine, UMass Chan Medical School, Worcester, MA USA
| | - Shannon Ehmsen
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Serkan Utku Öztürk
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Joel Ryan
- grid.14709.3b0000 0004 1936 8649Advanced BioImaging Facility (ABIF), McGill University, Montreal, Quebec Canada
| | - Alexander Balashov
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Mathias Hammer
- RNA Therapeutics Institute, UMass Chan Medical School, Worcester, MA USA
| | - Koray Kirli
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Ulrike Boehm
- grid.443970.dJanelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA USA
| | - Claire M. Brown
- grid.14709.3b0000 0004 1936 8649Advanced BioImaging Facility (ABIF), McGill University, Montreal, Quebec Canada
| | - Karl Bellve
- Program in Molecular Medicine, UMass Chan Medical School, Worcester, MA USA
| | - James J. Chambers
- grid.266683.f0000 0001 2166 5835Institute for Applied Life Sciences, University of Massachusetts, Amherst, MA USA
| | - Andrea Cosolo
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Robert A. Coleman
- grid.251993.50000000121791997Department of Anatomy and Structural Biology, Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine, Bronx, NY USA
| | - Orestis Faklaris
- grid.121334.60000 0001 2097 0141BioCampus Montpellier (BCM), University of Montpellier, CNRS, INSERM, Montpellier, France
| | - Kevin E. Fogarty
- Program in Molecular Medicine, UMass Chan Medical School, Worcester, MA USA
| | - Thomas Guilbert
- grid.508487.60000 0004 7885 7602Institut Cochin, Inserm U1016-CNRS UMR8104-Université de Paris, Paris, France
| | - Anna B. Hamacher
- grid.411327.20000 0001 2176 9917Center for Advanced Imaging, Heinrich-Heine University Duesseldorf, Düsseldorf, Germany
| | - Michelle S. Itano
- grid.10698.360000000122483208UNC Neuroscience Microscopy Core Facility, Department of Cell Biology and Physiology, Carolina Institute for Developmental Disabilities, and UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC USA
| | - Daniel P. Keeley
- grid.10698.360000000122483208UNC Neuroscience Microscopy Core Facility, Department of Cell Biology and Physiology, Carolina Institute for Developmental Disabilities, and UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC USA
| | - Susanne Kunis
- grid.10854.380000 0001 0672 4366Department of Biology/Chemistry and Center for Cellular Nanoanalytics, University Osnabrück, Osnabrück, Germany
| | | | - Alex Laude
- grid.1006.70000 0001 0462 7212Bioimaging Unit, Newcastle University, Newcastle upon Tyne, UK
| | - Willa Y. Ma
- grid.10698.360000000122483208UNC Neuroscience Microscopy Core Facility, Carolina Institute for Developmental Disabilities, and UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC USA
| | - Marco Marcello
- grid.10025.360000 0004 1936 8470Center for Cell Imaging, University of Liverpool, Liverpool, UK
| | - Paula Montero-Llopis
- grid.38142.3c000000041936754XMicroscopy Resources of the North Quad, University of Harvard Medical School, Boston, MA USA
| | - Glyn Nelson
- grid.1006.70000 0001 0462 7212Bioimaging Unit, Newcastle University, Newcastle upon Tyne, UK
| | - Roland Nitschke
- grid.5963.9Life Imaging Center and Signalling Research Centres CIBSS and BIOSS, University of Freiburg, Freiburg, Germany
| | - Jaime A. Pimentel
- grid.9486.30000 0001 2159 0001Laboratorio Nacional de Microscopía Avanzada, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Mexico
| | - Stefanie Weidtkamp-Peters
- grid.411327.20000 0001 2176 9917Center for Advanced Imaging, Heinrich-Heine University Duesseldorf, Düsseldorf, Germany
| | - Peter J. Park
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Burak H. Alver
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - David Grunwald
- RNA Therapeutics Institute, UMass Chan Medical School, Worcester, MA USA
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