1
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Fu Y, Liu R, Zhao Y, Xie Y, Ren H, Wu Y, Zhang B, Chen X, Guo Y, Yao Y, Jiang W, Han R. Veliparib exerts protective effects in intracerebral hemorrhage mice by inhibiting the inflammatory response and accelerating hematoma resolution. Brain Res 2024; 1838:148988. [PMID: 38729332 DOI: 10.1016/j.brainres.2024.148988] [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: 01/22/2024] [Revised: 03/29/2024] [Accepted: 05/05/2024] [Indexed: 05/12/2024]
Abstract
Poly (ADP-ribose) polymerase (PARP) inhibitors have potent anti-inflammatory effects, including the suppression of brain microglial activation. Veliparib, a well-known PARP1/2 inhibitor, exhibits particularly high brain penetration, but its effects on stroke outcome is unknown. Here, the effects of veliparib on the short-term outcome of intracerebral hemorrhage (ICH), the most lethal type of stroke, were investigated. Collagenase-induced mice ICH model was applied, and the T2-weighted magnetic resonance imaging was performed to evaluate lesion volume. Motor function and hematoma volume were also measured. We further performed immunofluorescence, enzyme linked immunosorbent assay, flow cytometry, and blood-brain barrier assessment to explore the potential mechanisms. Our results demonstrated veliparib reduced the ICH lesion volume dose-dependently and at a dosage of 5 mg/kg, veliparib significantly improved mouse motor function and promoted hematoma resolution at days 3 and 7 post-ICH. Veliparib inhibited glial activation and downregulated the production of pro-inflammatory cytokines. Veliparib significantly decreased microglia counts and inhibited peripheral immune cell infiltration into the brain on day 3 after ICH. Veliparib improved blood-brain barrier integrity at day 3 after ICH. These findings demonstrate that veliparib improves ICH outcome by inhibiting inflammatory responses and may represent a promising novel therapy for ICH.
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Affiliation(s)
- Yiwei Fu
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China
| | - Rongrong Liu
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China
| | - Yuexin Zhao
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China
| | - Yuhan Xie
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China; Department of Neurology, Nankai Hospital, Tianjin Medical University, Tianjin, China
| | - Honglei Ren
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China
| | - Yu Wu
- Department of Neurology, Nankai Hospital, Tianjin Medical University, Tianjin, China
| | - Bohao Zhang
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xiuju Chen
- Department of Neurology, Nankai Hospital, Tianjin Medical University, Tianjin, China
| | - Ying Guo
- Department of Otorhinolaryngology, Tianjin Medical University General Hospital, Tianjin, China
| | - Yang Yao
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China.
| | - Wei Jiang
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China.
| | - Ranran Han
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China.
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2
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Niessink T, Janssen M, Giesen T, Efdé MN, Comarniceanu AC, Otto C, Jansen TL. Diagnostic Accuracy of Raman Spectroscopy Integrated With Polarized Light Microscopy for Calcium Pyrophosphate-Associated Arthritis. Arthritis Care Res (Hoboken) 2024; 76:1333-1341. [PMID: 38622108 DOI: 10.1002/acr.25350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 03/14/2024] [Accepted: 04/11/2024] [Indexed: 04/17/2024]
Abstract
OBJECTIVE We studied the performance of integrated Raman polarized light microscopy (iRPolM) for the identification of calcium pyrophosphate (CPP)-associated arthritis (CPPD). METHODS This is a diagnostic accuracy study including 400 consecutive synovial fluid samples from a single hospital in the Netherlands. Accuracy measures were calculated against polarized light microscopy (PLM) and the 2023 American College of Rheumatology (ACR)/EULAR criteria set for CPPD. RESULTS The interrater reliability between iRPolM and the 2023 ACR/EULAR criteria set for CPPD was strong (κ = 0.88). The diagnostic performance of iRPolM compared to the 2023 ACR/EULAR criteria set was sensitivity 86.0% (95% confidence interval [CI] 73.3-94.2), specificity 99.1% (95% CI 97.5-99.8), positive likelihood ratio 100.33 (95% CI 32.3-311.3), negative likelihood ratio 0.14 (95% CI 0.07-0.28), positive predictive value 93.5% (95% CI 82.2-97.8), negative predictive value 98.0% (95% CI 82.2-97.8), and accuracy 97.5% (95% CI 95.5-98.8). We allowed rheumatologists to rate the certainty of their microscopic identification of CPP and found a large correspondence between iRPolM and a certain identification (κ = 0.87), whereas only 10% of the uncertain CPP identifications could be confirmed with iRPolM. We identified several novel particle types in synovial fluid analysis, including calcium carbonate crystals, deposited carotenoids, microplastics, and three types of Maltese cross birefringent objects. CONCLUSION iRPolM can easily identify CPP crystals with a strong diagnostic performance. PLM alone is not specific enough to reliably resolve complicated cases, and the implementation of Raman spectroscopy in rheumatology practice can be of benefit to patients with suspected CPPD.
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Affiliation(s)
- Tom Niessink
- Personalized Diagnostics and Therapeutics, University of Twente, Enschede, the Netherlands
- Department of Rheumatology, VieCuri Medical Centre, Venlo, the Netherlands
| | - Matthijs Janssen
- Department of Rheumatology, VieCuri Medical Centre, Venlo, the Netherlands
| | - Tanja Giesen
- Department of Rheumatology, VieCuri Medical Centre, Venlo, the Netherlands
| | - Monique N Efdé
- Department of Rheumatology, VieCuri Medical Centre, Venlo, the Netherlands
| | | | - Cees Otto
- Personalized Diagnostics and Therapeutics, University of Twente, Enschede, the Netherlands
| | - Tim L Jansen
- Department of Rheumatology, VieCuri Medical Centre, Venlo, the Netherlands
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3
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Cimini BA. Creating and troubleshooting microscopy analysis workflows: Common challenges and common solutions. J Microsc 2024; 295:93-101. [PMID: 38532662 PMCID: PMC11245365 DOI: 10.1111/jmi.13288] [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: 07/07/2023] [Revised: 02/29/2024] [Accepted: 03/04/2024] [Indexed: 03/28/2024]
Abstract
As microscopy diversifies and becomes ever more complex, the problem of quantification of microscopy images has emerged as a major roadblock for many researchers. All researchers must face certain challenges in turning microscopy images into answers, independent of their scientific question and the images they have generated. Challenges may arise at many stages throughout the analysis process, including handling of the image files, image pre-processing, object finding, or measurement, and statistical analysis. While the exact solution required for each obstacle will be problem-specific, by keeping analysis in mind, optimizing data quality, understanding tools and tradeoffs, breaking workflows and data sets into chunks, talking to experts, and thoroughly documenting what has been done, analysts at any experience level can learn to overcome these challenges and create better and easier image analyses.
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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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Tasma Z, Rees TA, Guo S, Tan S, O'Carroll SJ, Faull RLM, Curtis MA, Christensen SL, Hay DL, Walker CS. Pharmacology of PACAP and VIP receptors in the spinal cord highlights the importance of the PAC 1 receptor. Br J Pharmacol 2024; 181:2655-2675. [PMID: 38616050 DOI: 10.1111/bph.16376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 12/18/2023] [Accepted: 01/20/2024] [Indexed: 04/16/2024] Open
Abstract
BACKGROUND AND PURPOSE The spinal cord is a key structure involved in the transmission and modulation of pain. Pituitary adenylate cyclase-activating peptide (PACAP) and vasoactive intestinal peptide (VIP), are expressed in the spinal cord. These peptides activate G protein-coupled receptors (PAC1, VPAC1 and VPAC2) that could provide targets for the development of novel pain treatments. However, it is not clear which of these receptors are expressed within the spinal cord and how these receptors signal. EXPERIMENTAL APPROACH Dissociated rat spinal cord cultures were used to examine agonist and antagonist receptor pharmacology. Signalling profiles were determined for five signalling pathways. The expression of different PACAP and VIP receptors was then investigated in mouse, rat and human spinal cords using immunoblotting and immunofluorescence. KEY RESULTS PACAP, but not VIP, potently stimulated cAMP, IP1 accumulation and ERK and cAMP response element-binding protein (CREB) but not Akt phosphorylation in spinal cord cultures. Signalling was antagonised by M65 and PACAP6-38. PACAP-27 was more effectively antagonised than either PACAP-38 or VIP. The patterns of PAC1 and VPAC2 receptor-like immunoreactivity appeared to be distinct in the spinal cord. CONCLUSIONS AND IMPLICATIONS The pharmacological profile in the spinal cord suggested that a PAC1 receptor is the major functional receptor subtype present and thus likely mediates the nociceptive effects of the PACAP family of peptides in the spinal cord. However, the potential expression of both PAC1 and VPAC2 receptors in the spinal cord highlights that these receptors may play differential roles and are both possible therapeutic targets.
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MESH Headings
- Animals
- Spinal Cord/metabolism
- Spinal Cord/drug effects
- Receptors, Pituitary Adenylate Cyclase-Activating Polypeptide, Type I/metabolism
- Receptors, Pituitary Adenylate Cyclase-Activating Polypeptide, Type I/agonists
- Humans
- Pituitary Adenylate Cyclase-Activating Polypeptide/pharmacology
- Pituitary Adenylate Cyclase-Activating Polypeptide/metabolism
- Vasoactive Intestinal Peptide/metabolism
- Vasoactive Intestinal Peptide/pharmacology
- Mice
- Rats
- Signal Transduction/drug effects
- Receptors, Vasoactive Intestinal Peptide/metabolism
- Receptors, Vasoactive Intestinal Peptide/antagonists & inhibitors
- Cells, Cultured
- Rats, Sprague-Dawley
- Male
- Mice, Inbred C57BL
- Cyclic AMP/metabolism
- Receptors, Vasoactive Intestinal Peptide, Type II/metabolism
- Receptors, Vasoactive Intestinal Peptide, Type II/agonists
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Affiliation(s)
- Zoe Tasma
- School of Biological Sciences, The University of Auckland, Auckland, New Zealand
| | - Tayla A Rees
- School of Biological Sciences, The University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand
| | - Song Guo
- Department of Neurology, Danish Headache Center, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
- Department of Odontology, Panum Institute, Faculty of Health, University of Copenhagen, Copenhagen, Denmark
| | - Sheryl Tan
- Department of Anatomy and Medical Imaging and Centre for Brain Research, Faculty of Medical and Health Science, The University of Auckland, Auckland, New Zealand
| | - Simon J O'Carroll
- Department of Anatomy and Medical Imaging and Centre for Brain Research, Faculty of Medical and Health Science, The University of Auckland, Auckland, New Zealand
| | - Richard L M Faull
- Department of Anatomy and Medical Imaging and Centre for Brain Research, Faculty of Medical and Health Science, The University of Auckland, Auckland, New Zealand
| | - Maurice A Curtis
- Department of Anatomy and Medical Imaging and Centre for Brain Research, Faculty of Medical and Health Science, The University of Auckland, Auckland, New Zealand
| | - Sarah L Christensen
- Department of Neurology, Danish Headache Center, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Debbie L Hay
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand
- Department of Pharmacology and Toxicology, The University of Otago, Dunedin, New Zealand
| | - Christopher S Walker
- School of Biological Sciences, The University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand
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5
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Culley S, Caballero AC, Burden JJ, Uhlmann V. Made to measure: An introduction to quantifying microscopy data in the life sciences. J Microsc 2024; 295:61-82. [PMID: 37269048 DOI: 10.1111/jmi.13208] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 05/25/2023] [Accepted: 05/26/2023] [Indexed: 06/04/2023]
Abstract
Images are at the core of most modern biological experiments and are used as a major source of quantitative information. Numerous algorithms are available to process images and make them more amenable to be measured. Yet the nature of the quantitative output that is useful for a given biological experiment is uniquely dependent upon the question being investigated. Here, we discuss the 3 main types of information that can be extracted from microscopy data: intensity, morphology, and object counts or categorical labels. For each, we describe where they come from, how they can be measured, and what may affect the relevance of these measurements in downstream data analysis. Acknowledging that what makes a measurement 'good' is ultimately down to the biological question being investigated, this review aims at providing readers with a toolkit to challenge how they quantify their own data and be critical of conclusions drawn from quantitative bioimage analysis experiments.
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Affiliation(s)
- Siân Culley
- Randall Centre for Cell and Molecular Biophysics, King's College London, London, UK
| | | | | | - Virginie Uhlmann
- European Bioinformatics Institute (EMBL-EBI), EMBL, Cambridge, UK
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6
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Renaud O, Aulner N, Salles A, Halidi N, Brunstein M, Mallet A, Aumayr K, Terjung S, Levy D, Lippens S, Verbavatz JM, Heuser T, Santarella-Mellwig R, Tinevez JY, Woller T, Botzki A, Cawthorne C, Munck S. Staying on track - Keeping things running in a high-end scientific imaging core facility. J Microsc 2024; 294:276-294. [PMID: 38656474 DOI: 10.1111/jmi.13304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 03/19/2024] [Accepted: 04/08/2024] [Indexed: 04/26/2024]
Abstract
Modern life science research is a collaborative effort. Few research groups can single-handedly support the necessary equipment, expertise and personnel needed for the ever-expanding portfolio of technologies that are required across multiple disciplines in today's life science endeavours. Thus, research institutes are increasingly setting up scientific core facilities to provide access and specialised support for cutting-edge technologies. Maintaining the momentum needed to carry out leading research while ensuring high-quality daily operations is an ongoing challenge, regardless of the resources allocated to establish such facilities. Here, we outline and discuss the range of activities required to keep things running once a scientific imaging core facility has been established. These include managing a wide range of equipment and users, handling repairs and service contracts, planning for equipment upgrades, renewals, or decommissioning, and continuously upskilling while balancing innovation and consolidation.
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Affiliation(s)
- Oliver Renaud
- Cell and Tissue Imaging Platform (PICT-IBiSA, France-BioImaging), Institut Curie, Université PSL, Sorbonne Université, CNRS, Inserm, Paris, France
| | - Nathalie Aulner
- Centre de Ressources et Recherches Technologiques (UTechS-PBI, C2RT), Institut Pasteur, Université Paris Cité, Photonic Bio-Imaging, Paris, France
| | - Audrey Salles
- Centre de Ressources et Recherches Technologiques (UTechS-PBI, C2RT), Institut Pasteur, Université Paris Cité, Photonic Bio-Imaging, Paris, France
| | - Nadia Halidi
- Advanced Light Microscopy Unit, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Maia Brunstein
- Bioimaging Core Facility, Centre de Ressources et Recherches Technologiques (C2RT), Institut Pasteur, Université Paris Cité, Inserm, Institut de l'Audition, Paris, France
| | - Adeline Mallet
- Centre de Ressources et Recherches Technologiques (UBI, C2RT), Institut Pasteur, Université Paris Cité, Ultrastructural BioImaging, Paris, France
| | - Karin Aumayr
- BioOptics Facility, Research Institute of Molecular Pathology (IMP) Campus-Vienna-Biocenter 1, Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Dr. Bohr-Gasse 3, Vienna, Austria
- Gregor Mendel Institute of Molecular Plant Biology, Austrian Academy of Sciences (GMI), Dr. Bohr-Gasse 3, Vienna, Austria
| | - Stefan Terjung
- Advanced Light Microscopy Facility, EMBL Heidelberg, Heidelberg, Germany
| | - Daniel Levy
- Cell and Tissue Imaging Platform (PICT-IBiSA, France-BioImaging), Institut Curie, Université PSL, Sorbonne Université, CNRS, Inserm, Paris, France
| | | | - Jean-Marc Verbavatz
- Institut Jacques Monod (Imagoseine), Université Paris Cité, CNRS, Paris, France
| | - Thomas Heuser
- Vienna Biocenter Core Facilities GmbH (VBCF), Wien, Austria
| | | | - Jean-Yves Tinevez
- Image Analysis Hub, Institut Pasteur, Université de Paris Cité, Paris, France
| | - Tatiana Woller
- VIB Technology Training, Data Core, VIB BioImaging Core, VIB, Ghent, Belgium
- Neuroscience Department, KU Leuven, Leuven, Belgium
| | | | - Christopher Cawthorne
- Department of Imaging and Pathology, Nuclear Medicine and Molecular Imaging, KU Leuven, Leuven, Belgium
| | - Sebastian Munck
- Neuroscience Department, KU Leuven, Leuven, Belgium
- VIB BioImaging Core, VIB, Leuven, Belgium
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7
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Schmidt C, Boissonnet T, Dohle J, Bernhardt K, Ferrando-May E, Wernet T, Nitschke R, Kunis S, Weidtkamp-Peters S. A practical guide to bioimaging research data management in core facilities. J Microsc 2024; 294:350-371. [PMID: 38752662 DOI: 10.1111/jmi.13317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 05/21/2024]
Abstract
Bioimage data are generated in diverse research fields throughout the life and biomedical sciences. Its potential for advancing scientific progress via modern, data-driven discovery approaches reaches beyond disciplinary borders. To fully exploit this potential, it is necessary to make bioimaging data, in general, multidimensional microscopy images and image series, FAIR, that is, findable, accessible, interoperable and reusable. These FAIR principles for research data management are now widely accepted in the scientific community and have been adopted by funding agencies, policymakers and publishers. To remain competitive and at the forefront of research, implementing the FAIR principles into daily routines is an essential but challenging task for researchers and research infrastructures. Imaging core facilities, well-established providers of access to imaging equipment and expertise, are in an excellent position to lead this transformation in bioimaging research data management. They are positioned at the intersection of research groups, IT infrastructure providers, the institution´s administration, and microscope vendors. In the frame of German BioImaging - Society for Microscopy and Image Analysis (GerBI-GMB), cross-institutional working groups and third-party funded projects were initiated in recent years to advance the bioimaging community's capability and capacity for FAIR bioimage data management. Here, we provide an imaging-core-facility-centric perspective outlining the experience and current strategies in Germany to facilitate the practical adoption of the FAIR principles closely aligned with the international bioimaging community. We highlight which tools and services are ready to be implemented and what the future directions for FAIR bioimage data have to offer.
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Affiliation(s)
- Christian Schmidt
- Enabling Technology Department, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tom Boissonnet
- Center for Advanced Imaging, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Julia Dohle
- Center of Cellular Nanoanalytics, Integrated Bioimaging Facility iBiOs, University of Osnabrück, Osnabrück, Germany
| | - Karen Bernhardt
- Center of Cellular Nanoanalytics, Integrated Bioimaging Facility iBiOs, University of Osnabrück, Osnabrück, Germany
| | - Elisa Ferrando-May
- Enabling Technology Department, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Tobias Wernet
- Life Imaging Center, University of Freiburg, Freiburg, Germany
| | - Roland Nitschke
- Life Imaging Center, University of Freiburg, Freiburg, Germany
- CIBSS and BIOSS - Centres for Biological Signalling Studies, University of Freiburg, Freiburg, Germany
| | - Susanne Kunis
- Center of Cellular Nanoanalytics, Integrated Bioimaging Facility iBiOs, University of Osnabrück, Osnabrück, Germany
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8
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Kasprzycka W, Szumigraj W, Wachulak P, Trafny EA. New approaches for low phototoxicity imaging of living cells and tissues. Bioessays 2024; 46:e2300122. [PMID: 38514402 DOI: 10.1002/bies.202300122] [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: 07/04/2023] [Revised: 03/06/2024] [Accepted: 03/07/2024] [Indexed: 03/23/2024]
Abstract
Fluorescence microscopy is a powerful tool used in scientific and medical research, but it is inextricably linked to phototoxicity. Neglecting phototoxicity can lead to erroneous or inconclusive results. Recently, several reports have addressed this issue, but it is still underestimated by many researchers, even though it can lead to cell death. Phototoxicity can be reduced by appropriate microscopic techniques and carefully designed experiments. This review focuses on recent strategies to reduce phototoxicity in microscopic imaging of living cells and tissues. We describe digital image processing and new hardware solutions. We point out new modifications of microscopy methods and hope that this review will interest microscopy hardware engineers. Our aim is to underscore the challenges and potential solutions integral to the design of microscopy systems. Simultaneously, we intend to engage biologists, offering insight into the latest technological advancements in imaging that can enhance their understanding and practice.
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Affiliation(s)
- Wiktoria Kasprzycka
- Biomedical Engineering Centre, Institute of Optoelectronics, Military University of Technology, Kaliskiego, Warsaw, Poland
| | - Wiktoria Szumigraj
- Biomedical Engineering Centre, Institute of Optoelectronics, Military University of Technology, Kaliskiego, Warsaw, Poland
| | - Przemysław Wachulak
- Laser Technology Division, Institute of Optoelectronics, Military University of Technology, Kaliskiego, Warsaw, Poland
| | - Elżbieta Anna Trafny
- Biomedical Engineering Centre, Institute of Optoelectronics, Military University of Technology, Kaliskiego, Warsaw, Poland
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9
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Cimini BA. Creating and troubleshooting microscopy analysis workflows: common challenges and common solutions. ARXIV 2024:arXiv:2403.04520v1. [PMID: 38495561 PMCID: PMC10942474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
As microscopy diversifies and becomes ever-more complex, the problem of quantification of microscopy images has emerged as a major roadblock for many researchers. All researchers must face certainchallenges in turning microscopy images into answers, independent of their scientific question and the images they've generated. Challenges may arise at many stages throughout the analysis process, including handling of the image files, image pre-processing, object finding, or measurement, and statistical analysis. While the exact solution required for each obstacle will be problem-specific, by understanding tools and tradeoffs, optimizing data quality, breaking workflows and data sets into chunks, talking to experts, and thoroughly documenting what has been done, analysts at any experience level can learn to overcome these challenges and create better and easier image analyses.
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Affiliation(s)
- Beth A Cimini
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
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10
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Galkin M, Priss A, Kyriukha Y, Shvadchak V. Navigating α-Synuclein Aggregation Inhibition: Methods, Mechanisms, and Molecular Targets. CHEM REC 2024; 24:e202300282. [PMID: 37919046 DOI: 10.1002/tcr.202300282] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 10/08/2023] [Indexed: 11/04/2023]
Abstract
Parkinson's disease is a yet incurable, age-related neurodegenerative disorder characterized by the aggregation of small neuronal protein α-synuclein into amyloid fibrils. Inhibition of this process is a prospective strategy for developing a disease-modifying treatment. We overview here small molecule, peptide, and protein inhibitors of α-synuclein fibrillization reported to date. Special attention was paid to the specificity of inhibitors and critical analysis of their action mechanisms. Namely, the importance of oxidation of polyphenols and cross-linking of α-synuclein into inhibitory dimers was highlighted. We also compared strategies of targeting monomeric, oligomeric, and fibrillar α-synuclein species, thoroughly discussed the strong and weak sides of different approaches to testing the inhibitors.
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Affiliation(s)
- Maksym Galkin
- Institute of Organic Chemistry and Biochemistry, Academy of Sciences of the Czech Republic, Prague, Czech Republic
| | - Anastasiia Priss
- Institute of Organic Chemistry and Biochemistry, Academy of Sciences of the Czech Republic, Prague, Czech Republic
| | - Yevhenii Kyriukha
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, Saint Louis, Missouri, 63110, United States
| | - Volodymyr Shvadchak
- Department of Biochemistry and Biotechnology, Vasyl Stefanyk Precarpathian National University, Ivano-Frankivsk, Ukraine
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11
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Vitali C, Peters RJB, Janssen HG, Undas AK, Munniks S, Ruggeri FS, Nielen MWF. Quantitative image analysis of microplastics in bottled water using artificial intelligence. Talanta 2024; 266:124965. [PMID: 37487270 DOI: 10.1016/j.talanta.2023.124965] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 07/04/2023] [Accepted: 07/18/2023] [Indexed: 07/26/2023]
Abstract
The ubiquitous occurrence of microplastics (MPs) in the environment and the use of plastics in packaging materials result in the presence of MPs in the food chain and exposure of consumers. Yet, no fully validated analytical method is available for microplastic (MP) quantification, thereby preventing the reliable estimation of the level of exposure and, ultimately, the assessment of the food safety risks associated with MP contamination. In this study, a novel approach is presented that exploits interactive artificial intelligence tools to enable automation of MP analysis. An integrated method for the analysis of MPs in bottled water based on Nile Red staining and fluorescent microscopy was developed and validated, featuring a partial interrogation of the filter and a fully automated image processing workflow based on a Random Forest classifier, thereby boosting the analysis speed. The image analysis provided particle count, size and size distribution of the MPs. From these data, a rough estimation of the mass of the individual MPs, and consequently of the MP mass concentration in the sample, could be obtained as well. Critical materials, method performance characteristics, and final applicability were studied in detail. The method showed to be highly sensitive in sizing MPs down to 10 μm, with a particle count limit of detection and quantification of 28 and 85 items/500 mL, respectively. Linearity of mass concentration determined between 10 ppb and 1.5 ppm showed a regression coefficient (R2) of 0.99. Method precision was demonstrated by a repeatability of 9-16% RSD (n = 7) and within-laboratory reproducibility of 15-27% RSD (n = 21). Accuracy based on recovery was 92 ± 15% and 98 ± 23% at a level of 0.1 and 1.0 ppm, respectively. The quantitative performance characteristics thus obtained complied with regulatory requirements. Finally, the method was successfully applied to the analysis of twenty commercial samples of bottled water, with and without gas and flavor additives, yielding results ranging from values below the limit of detection to 7237 (95% CI [6456, 8088]) items/500 mL.
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Affiliation(s)
- Clementina Vitali
- Wageningen Food Safety Research, Wageningen University & Research, Akkermaalsbos 2, 6708 WB, Wageningen, the Netherlands; Wageningen University, Laboratory of Organic Chemistry, Stippeneng 4, 6708 WE, Wageningen, the Netherlands.
| | - Ruud J B Peters
- Wageningen Food Safety Research, Wageningen University & Research, Akkermaalsbos 2, 6708 WB, Wageningen, the Netherlands
| | - Hans-Gerd Janssen
- Wageningen University, Laboratory of Organic Chemistry, Stippeneng 4, 6708 WE, Wageningen, the Netherlands; Unilever Foods Innovation Centre - Hive, Bronland 14, 6708 WH, Wageningen, the Netherlands
| | - Anna K Undas
- Wageningen Food Safety Research, Wageningen University & Research, Akkermaalsbos 2, 6708 WB, Wageningen, the Netherlands
| | - Sandra Munniks
- Wageningen Food Safety Research, Wageningen University & Research, Akkermaalsbos 2, 6708 WB, Wageningen, the Netherlands
| | - Francesco Simone Ruggeri
- Wageningen University, Laboratory of Organic Chemistry, Stippeneng 4, 6708 WE, Wageningen, the Netherlands; Wageningen University, Physical Chemistry and Soft Matter, Stippeneng 4, 6708 WE, Wageningen, the Netherlands.
| | - Michel W F Nielen
- Wageningen Food Safety Research, Wageningen University & Research, Akkermaalsbos 2, 6708 WB, Wageningen, the Netherlands; Wageningen University, Laboratory of Organic Chemistry, Stippeneng 4, 6708 WE, Wageningen, the Netherlands
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12
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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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13
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Niessink T, Giesen T, Efdé M, Comarniceanu A, Janssen M, Otto C, Jansen TL. Test characteristics of Raman spectroscopy integrated with polarized light microscopy for the diagnosis of acute gouty arthritis. Joint Bone Spine 2023; 90:105611. [PMID: 37442334 DOI: 10.1016/j.jbspin.2023.105611] [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: 03/21/2023] [Revised: 06/26/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023]
Abstract
OBJECTIVES We studied the performance of Raman spectroscopy integrated with polarized light microscopy (iRPolM) as a next-generation technique for synovial fluid analysis in gout. METHODS This is a prospective study, including consecutive synovial fluid samples drawn from any peripheral swollen joint. Diagnostic accuracy was compared to the 2015 ACR/EULAR Gout classification criteria as a reference test and to polarized light microscopy (PLM) analysis by a rheumatologist. Synovial fluid was analysed with iRPolM after unblinding the PLM results. RESULTS Two hundred unselected consecutive patient samples were included in this study. Validation against clinical criteria: 67 patients were classified as gout according to 2015 ACR/EULAR classification criteria. Compared to the 2015 ACR/EULAR gout classification criteria, iRPolM had a sensitivity of 77.6% (95% CI: 65.8-86.9), specificity of 97.7% (95% CI: 93.5-99.5), positive predictive value (PPV) of 94.5% (95% CI: 84.9-98.2), negative predictive value (NPV) of 89.7% (95% CI: 84.7-93.1), an accuracy of 91.0% (95% CI: 86.2-94.6), a positive likelihood ratio of 34.4 (95% CI: 11.16-106.10) and a negative likelihood ratio of 0.23 (95% CI: 0.15-0.36). Validation against PLM: 55 samples were positive for MSU according to PLM. The interrater agreement between PLM and iRPolM was near perfect (к=0.90). The sensitivity of iRPolM to identify MSU in PLM-positive samples was 91.2% (95% CI: 80.7-97.1), the specificity was 97.6% (95% CI: 93.0-99.5), the PPV was 94.6% (95% CI: 85.0-98.2), NPV was 96.0% (95% CI: 91.2-98.2) and the accuracy was 95.6% (95% CI: 91.4-98.2). The positive likelihood ratio was 37.4 (95% CI: 12.20-114.71), and the negative likelihood ratio was 0.09 (95% CI: 0.04-0.21). CONCLUSION iRPolM is a promising next-generation diagnostic tool for rheumatology by diagnosing gout with high specificity, increased objectivity, and a sensitivity comparable to PLM.
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Affiliation(s)
- Tom Niessink
- Medical Cell BioPhysics Group, TechMed Centre, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, The Netherlands; Department of Rheumatology, VieCuri Medical Centre, Tegelseweg 210, 5912 BL, Venlo, The Netherlands.
| | - Tanja Giesen
- Department of Rheumatology, VieCuri Medical Centre, Tegelseweg 210, 5912 BL, Venlo, The Netherlands
| | - Monique Efdé
- Department of Rheumatology, VieCuri Medical Centre, Tegelseweg 210, 5912 BL, Venlo, The Netherlands
| | - Antoaneta Comarniceanu
- Department of Rheumatology, VieCuri Medical Centre, Tegelseweg 210, 5912 BL, Venlo, The Netherlands
| | - Matthijs Janssen
- Department of Rheumatology, VieCuri Medical Centre, Tegelseweg 210, 5912 BL, Venlo, The Netherlands
| | - Cees Otto
- Medical Cell BioPhysics Group, TechMed Centre, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, The Netherlands
| | - Tim L Jansen
- Department of Rheumatology, VieCuri Medical Centre, Tegelseweg 210, 5912 BL, Venlo, The Netherlands
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14
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Delage E, Guilbert T, Yates F. Successful 3D imaging of cleared biological samples with light sheet fluorescence microscopy. J Cell Biol 2023; 222:e202307143. [PMID: 37847528 PMCID: PMC10583220 DOI: 10.1083/jcb.202307143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 09/22/2023] [Accepted: 09/22/2023] [Indexed: 10/18/2023] Open
Abstract
In parallel with the development of tissue-clearing methods, over the last decade, light sheet fluorescence microscopy has contributed to major advances in various fields, such as cell and developmental biology and neuroscience. While biologists are increasingly integrating three-dimensional imaging into their research projects, their experience with the technique is not always up to their expectations. In response to a survey of specific challenges associated with sample clearing and labeling, image acquisition, and data analysis, we have critically assessed the recent literature to characterize the difficulties inherent to light sheet fluorescence microscopy applied to cleared biological samples and to propose solutions to overcome them. This review aims to provide biologists interested in light sheet fluorescence microscopy with a primer for the development of their imaging pipeline, from sample preparation to image analysis. Importantly, we believe that issues could be avoided with better anticipation of image analysis requirements, which should be kept in mind while optimizing sample preparation and acquisition parameters.
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Affiliation(s)
- Elise Delage
- CellTechs Laboratory, SupBiotech, Villejuif, France
- Service d’Etude des Prions et des Infections Atypiques, Institut François Jacob, Commissariat à l’Energie Atomique et aux Energies Alternatives, Université Paris Saclay, Fontenay-aux-Roses, France
| | - Thomas Guilbert
- Institut Cochin, Institut national de la santé et de la recherche médicale (U1016), Centre National de la Recherche Scientifique (UMR 8104), Université de Paris (UMR-S1016), Paris, France
| | - Frank Yates
- CellTechs Laboratory, SupBiotech, Villejuif, France
- Service d’Etude des Prions et des Infections Atypiques, Institut François Jacob, Commissariat à l’Energie Atomique et aux Energies Alternatives, Université Paris Saclay, Fontenay-aux-Roses, France
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15
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Frei AL, McGuigan A, Sinha RRAK, Glaire MA, Jabbar F, Gneo L, Tomasevic T, Harkin A, Iveson TJ, Saunders M, Oein K, Maka N, Pezella F, Campo L, Hay J, Edwards J, Sansom OJ, Kelly C, Tomlinson I, Kildal W, Kerr RS, Kerr DJ, Danielsen HE, Domingo E, Church DN, Koelzer VH. Accounting for intensity variation in image analysis of large-scale multiplexed clinical trial datasets. J Pathol Clin Res 2023; 9:449-463. [PMID: 37697694 PMCID: PMC10556275 DOI: 10.1002/cjp2.342] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 08/14/2023] [Accepted: 08/20/2023] [Indexed: 09/13/2023]
Abstract
Multiplex immunofluorescence (mIF) imaging can provide comprehensive quantitative and spatial information for multiple immune markers for tumour immunoprofiling. However, application at scale to clinical trial samples sourced from multiple institutions is challenging due to pre-analytical heterogeneity. This study reports an analytical approach to the largest multi-parameter immunoprofiling study of clinical trial samples to date. We analysed 12,592 tissue microarray (TMA) spots from 3,545 colorectal cancers sourced from more than 240 institutions in two clinical trials (QUASAR 2 and SCOT) stained for CD4, CD8, CD20, CD68, FoxP3, pan-cytokeratin, and DAPI by mIF. TMA slides were multi-spectrally imaged and analysed by cell-based and pixel-based marker analysis. We developed an adaptive thresholding method to account for inter- and intra-slide intensity variation in TMA analysis. Applying this method effectively ameliorated inter- and intra-slide intensity variation improving the image analysis results compared with methods using a single global threshold. Correlation of CD8 data derived by our mIF analysis approach with single-plex chromogenic immunohistochemistry CD8 data derived from subsequent sections indicates the validity of our method (Spearman's rank correlation coefficients ρ between 0.63 and 0.66, p ≪ 0.01) as compared with the current gold standard analysis approach. Evaluation of correlation between cell-based and pixel-based analysis results confirms equivalency (ρ > 0.8, p ≪ 0.01, except for CD20 in the epithelial region) of both analytical approaches. These data suggest that our adaptive thresholding approach can enable analysis of mIF-stained clinical trial TMA datasets by digital pathology at scale for precision immunoprofiling.
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Affiliation(s)
- Anja L Frei
- Department of Pathology and Molecular PathologyUniversity Hospital Zurich, University of ZurichZurichSwitzerland
- Life Science Zurich Graduate School, PhD Program in BiomedicineUniversity of ZurichZurichSwitzerland
| | | | | | - Mark A Glaire
- Nuffield Department of MedicineUniversity of OxfordOxfordUK
| | - Faiz Jabbar
- Nuffield Department of MedicineUniversity of OxfordOxfordUK
| | - Luciana Gneo
- Nuffield Department of MedicineUniversity of OxfordOxfordUK
| | | | - Andrea Harkin
- Cancer Research UK Glasgow Clinical Trials UnitUniversity of GlasgowGlasgowUK
| | - Tim J Iveson
- Southampton University Hospital NHS Foundation TrustSouthamptonUK
| | | | - Karin Oein
- Glasgow Tissue Research FacilityUniversity of Glasgow, Queen Elizabeth University HospitalGlasgowUK
| | - Noori Maka
- Glasgow Tissue Research FacilityUniversity of Glasgow, Queen Elizabeth University HospitalGlasgowUK
| | - Francesco Pezella
- Nuffield Division of Clinical Laboratory SciencesUniversity of OxfordOxfordUK
| | | | - Jennifer Hay
- Glasgow Tissue Research FacilityUniversity of Glasgow, Queen Elizabeth University HospitalGlasgowUK
| | | | - Owen J Sansom
- School of Cancer SciencesUniversity of GlasgowGlasgowUK
- Cancer Research UK Beatson InstituteGlasgowUK
- Cancer Research UK Scotland CentreEdinburgh and GlasgowUK
| | - Caroline Kelly
- Cancer Research UK Glasgow Clinical Trials UnitUniversity of GlasgowGlasgowUK
| | | | - Wanja Kildal
- Institute for Cancer Genetics and InformaticsOslo University HospitalOsloNorway
| | | | - David J Kerr
- Nuffield Division of Clinical Laboratory SciencesUniversity of OxfordOxfordUK
| | - Håvard E Danielsen
- Nuffield Division of Clinical Laboratory SciencesUniversity of OxfordOxfordUK
- Institute for Cancer Genetics and InformaticsOslo University HospitalOsloNorway
- Department of InformaticsUniversity of OsloOsloNorway
| | - Enric Domingo
- Department of OncologyUniversity of OxfordOxfordUK
- Cancer Research UK Scotland CentreEdinburgh and GlasgowUK
| | | | - David N Church
- Nuffield Department of MedicineUniversity of OxfordOxfordUK
- Oxford NIHR Comprehensive Biomedical Research CentreOxford University Hospitals NHS Foundation TrustOxfordUK
| | - Viktor H Koelzer
- Department of Pathology and Molecular PathologyUniversity Hospital Zurich, University of ZurichZurichSwitzerland
- Nuffield Department of MedicineUniversity of OxfordOxfordUK
- Department of OncologyUniversity of OxfordOxfordUK
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16
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Monfort T, Azzollini S, Brogard J, Clémençon M, Slembrouck-Brec A, Forster V, Picaud S, Goureau O, Reichman S, Thouvenin O, Grieve K. Dynamic full-field optical coherence tomography module adapted to commercial microscopes allows longitudinal in vitro cell culture study. Commun Biol 2023; 6:992. [PMID: 37770552 PMCID: PMC10539404 DOI: 10.1038/s42003-023-05378-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 09/20/2023] [Indexed: 09/30/2023] Open
Abstract
Dynamic full-field optical coherence tomography (D-FFOCT) has recently emerged as a label-free imaging tool, capable of resolving cell types and organelles within 3D live samples, whilst monitoring their activity at tens of milliseconds resolution. Here, a D-FFOCT module design is presented which can be coupled to a commercial microscope with a stage top incubator, allowing non-invasive label-free longitudinal imaging over periods of minutes to weeks on the same sample. Long term volumetric imaging on human induced pluripotent stem cell-derived retinal organoids is demonstrated, highlighting tissue and cell organization processes such as rosette formation and mitosis as well as cell shape and motility. Imaging on retinal explants highlights single 3D cone and rod structures. An optimal workflow for data acquisition, postprocessing and saving is demonstrated, resulting in a time gain factor of 10 compared to prior state of the art. Finally, a method to increase D-FFOCT signal-to-noise ratio is demonstrated, allowing rapid organoid screening.
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Affiliation(s)
- Tual Monfort
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
- CHNO des Quinze-Vingts, INSERM-DGOS CIC 1423, 28 rue de Charenton, F-75012, Paris, France
- Paris Eye Imaging Group, Quinze-Vingts National Eye Hospital, INSERM-DGOS, CIC 1423, 28 rue de Charenton, Paris, 75012, France
| | - Salvatore Azzollini
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Jérémy Brogard
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Marilou Clémençon
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Amélie Slembrouck-Brec
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Valerie Forster
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Serge Picaud
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Olivier Goureau
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Sacha Reichman
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Olivier Thouvenin
- Institut Langevin, ESPCI Paris, Université PSL, CNRS, 75005, Paris, France
| | - Kate Grieve
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France.
- CHNO des Quinze-Vingts, INSERM-DGOS CIC 1423, 28 rue de Charenton, F-75012, Paris, France.
- Paris Eye Imaging Group, Quinze-Vingts National Eye Hospital, INSERM-DGOS, CIC 1423, 28 rue de Charenton, Paris, 75012, France.
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17
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Mandracchia B, Liu W, Hua X, Forghani P, Lee S, Hou J, Nie S, Xu C, Jia S. Optimal sparsity allows reliable system-aware restoration of fluorescence microscopy images. SCIENCE ADVANCES 2023; 9:eadg9245. [PMID: 37647399 PMCID: PMC10468132 DOI: 10.1126/sciadv.adg9245] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 07/31/2023] [Indexed: 09/01/2023]
Abstract
Fluorescence microscopy is one of the most indispensable and informative driving forces for biological research, but the extent of observable biological phenomena is essentially determined by the content and quality of the acquired images. To address the different noise sources that can degrade these images, we introduce an algorithm for multiscale image restoration through optimally sparse representation (MIRO). MIRO is a deterministic framework that models the acquisition process and uses pixelwise noise correction to improve image quality. Our study demonstrates that this approach yields a remarkable restoration of the fluorescence signal for a wide range of microscopy systems, regardless of the detector used (e.g., electron-multiplying charge-coupled device, scientific complementary metal-oxide semiconductor, or photomultiplier tube). MIRO improves current imaging capabilities, enabling fast, low-light optical microscopy, accurate image analysis, and robust machine intelligence when integrated with deep neural networks. This expands the range of biological knowledge that can be obtained from fluorescence microscopy.
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Affiliation(s)
- Biagio Mandracchia
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Scientific-Technical Central Units, Instituto de Salud Carlos III (ISCIII), Majadahonda, Spain
- ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Wenhao Liu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Xuanwen Hua
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Parvin Forghani
- Department of Pediatrics, School of Medicine, Emory University, Atlanta, GA, USA
| | - Soojung Lee
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Jessica Hou
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - Shuyi Nie
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
- Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA
| | - Chunhui Xu
- Department of Pediatrics, School of Medicine, Emory University, Atlanta, GA, USA
- Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA
| | - Shu Jia
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA
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18
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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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19
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Wagner EL, Im JS, Sala S, Nakahata MI, Imbery TE, Li S, Chen D, Nimchuk K, Noy Y, Archer DW, Xu W, Hashisaki G, Avraham KB, Oakes PW, Shin JB. Repair of noise-induced damage to stereocilia F-actin cores is facilitated by XIRP2 and its novel mechanosensor domain. eLife 2023; 12:e72681. [PMID: 37294664 PMCID: PMC10259482 DOI: 10.7554/elife.72681] [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: 08/01/2021] [Accepted: 05/17/2023] [Indexed: 06/11/2023] Open
Abstract
Prolonged exposure to loud noise has been shown to affect inner ear sensory hair cells in a variety of deleterious manners, including damaging the stereocilia core. The damaged sites can be visualized as 'gaps' in phalloidin staining of F-actin, and the enrichment of monomeric actin at these sites, along with an actin nucleator and crosslinker, suggests that localized remodeling occurs to repair the broken filaments. Herein, we show that gaps in mouse auditory hair cells are largely repaired within 1 week of traumatic noise exposure through the incorporation of newly synthesized actin. We provide evidence that Xin actin binding repeat containing 2 (XIRP2) is required for the repair process and facilitates the enrichment of monomeric γ-actin at gaps. Recruitment of XIRP2 to stereocilia gaps and stress fiber strain sites in fibroblasts is force-dependent, mediated by a novel mechanosensor domain located in the C-terminus of XIRP2. Our study describes a novel process by which hair cells can recover from sublethal hair bundle damage and which may contribute to recovery from temporary hearing threshold shifts and the prevention of age-related hearing loss.
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Affiliation(s)
- Elizabeth L Wagner
- Department of Neuroscience, University of VirginiaCharlottesvilleUnited States
- Department of Biochemistry & Molecular Genetics, University of VirginiaCharlottesvilleUnited States
| | - Jun-Sub Im
- Department of Neuroscience, University of VirginiaCharlottesvilleUnited States
| | - Stefano Sala
- Department of Cell & Molecular Physiology, Stritch School of Medicine, Loyola University ChicagoChicagoUnited States
| | - Maura I Nakahata
- Department of Neuroscience, University of VirginiaCharlottesvilleUnited States
| | - Terence E Imbery
- Department of Neuroscience, University of VirginiaCharlottesvilleUnited States
- Department of Otolaryngology-Head & Neck Surgery, University of VirginiaCharlottesvilleUnited States
| | - Sihan Li
- Department of Neuroscience, University of VirginiaCharlottesvilleUnited States
- Department of Biochemistry & Molecular Genetics, University of VirginiaCharlottesvilleUnited States
| | - Daniel Chen
- Department of Neuroscience, University of VirginiaCharlottesvilleUnited States
| | - Katherine Nimchuk
- Department of Neuroscience, University of VirginiaCharlottesvilleUnited States
| | - Yael Noy
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv UniversityTel AvivIsrael
| | - David W Archer
- Department of Neuroscience, University of VirginiaCharlottesvilleUnited States
| | - Wenhao Xu
- Genetically Engineered Murine Model (GEMM) Core, University of VirginiaCharlottesvilleUnited States
| | - George Hashisaki
- Department of Otolaryngology-Head & Neck Surgery, University of VirginiaCharlottesvilleUnited States
| | - Karen B Avraham
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv UniversityTel AvivIsrael
| | - Patrick W Oakes
- Department of Cell & Molecular Physiology, Stritch School of Medicine, Loyola University ChicagoChicagoUnited States
| | - Jung-Bum Shin
- Department of Neuroscience, University of VirginiaCharlottesvilleUnited States
- Department of Biochemistry & Molecular Genetics, University of VirginiaCharlottesvilleUnited States
- Department of Otolaryngology-Head & Neck Surgery, University of VirginiaCharlottesvilleUnited States
- Department of Cell Biology, University of VirginiaCharlottesvilleUnited States
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20
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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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21
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Jones RA, Renshaw MJ, Barry DJ, Smith JC. Automated staging of zebrafish embryos using machine learning. Wellcome Open Res 2023; 7:275. [PMID: 37614774 PMCID: PMC10442596 DOI: 10.12688/wellcomeopenres.18313.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/19/2023] [Indexed: 11/25/2023] Open
Abstract
The zebrafish ( Danio rerio), is an important biomedical model organism used in many disciplines, including development, disease modeling and toxicology, to better understand vertebrate biology. The phenomenon of developmental delay in zebrafish embryos has been widely reported as part of a mutant or treatment-induced phenotype, and accurate characterization of such delays is imperative. Despite this, the only way at present to identify and quantify these delays is through manual observation, which is both time-consuming and subjective. Machine learning approaches in biology are rapidly becoming part of the toolkit used by researchers to address complex questions. In this work, we introduce a machine learning-based classifier that has been trained to detect temporal developmental differences across groups of zebrafish embryos. Our classifier is capable of rapidly analyzing thousands of images, allowing comparisons of developmental temporal rates to be assessed across and between experimental groups of embryos. Finally, as our classifier uses images obtained from a standard live-imaging widefield microscope and camera set-up, we envisage it will be readily accessible to the zebrafish community, and prove to be a valuable resource.
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Affiliation(s)
- Rebecca A. Jones
- Developmental Biology Laboratory, The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
- Department of Molecular Biology, Princeton University, Princeton, NJ, 08544, USA
| | - Matthew J. Renshaw
- Crick Advanced Light Microscopy (CALM), The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - David J. Barry
- Crick Advanced Light Microscopy (CALM), The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - James C. Smith
- Developmental Biology Laboratory, The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
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22
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Jones RA, Renshaw MJ, Barry DJ, Smith JC. Automated staging of zebrafish embryos using machine learning. Wellcome Open Res 2023; 7:275. [PMID: 37614774 PMCID: PMC10442596 DOI: 10.12688/wellcomeopenres.18313.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/19/2023] [Indexed: 08/25/2023] Open
Abstract
The zebrafish ( Danio rerio), is an important biomedical model organism used in many disciplines, including development, disease modeling and toxicology, to better understand vertebrate biology. The phenomenon of developmental delay in zebrafish embryos has been widely reported as part of a mutant or treatment-induced phenotype, and accurate characterization of such delays is imperative. Despite this, the only way at present to identify and quantify these delays is through manual observation, which is both time-consuming and subjective. Machine learning approaches in biology are rapidly becoming part of the toolkit used by researchers to address complex questions. In this work, we introduce a machine learning-based classifier that has been trained to detect temporal developmental differences across groups of zebrafish embryos. Our classifier is capable of rapidly analyzing thousands of images, allowing comparisons of developmental temporal rates to be assessed across and between experimental groups of embryos. Finally, as our classifier uses images obtained from a standard live-imaging widefield microscope and camera set-up, we envisage it will be readily accessible to the zebrafish community, and prove to be a valuable resource.
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Affiliation(s)
- Rebecca A. Jones
- Developmental Biology Laboratory, The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
- Department of Molecular Biology, Princeton University, Princeton, NJ, 08544, USA
| | - Matthew J. Renshaw
- Crick Advanced Light Microscopy (CALM), The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - David J. Barry
- Crick Advanced Light Microscopy (CALM), The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - James C. Smith
- Developmental Biology Laboratory, The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
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23
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Tapia GP, Agostinelli LJ, Chenausky SD, Padilla JVS, Navarro VI, Alagh A, Si G, Thompson RH, Balivada S, Khan AM. Glycemic Challenge Is Associated with the Rapid Cellular Activation of the Locus Ceruleus and Nucleus of Solitary Tract: Circumscribed Spatial Analysis of Phosphorylated MAP Kinase Immunoreactivity. J Clin Med 2023; 12:2483. [PMID: 37048567 PMCID: PMC10095283 DOI: 10.3390/jcm12072483] [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: 01/09/2023] [Revised: 02/24/2023] [Accepted: 03/02/2023] [Indexed: 03/31/2023] Open
Abstract
Rodent studies indicate that impaired glucose utilization or hypoglycemia is associated with the cellular activation of neurons in the medulla (Winslow, 1733) (MY), believed to control feeding behavior and glucose counterregulation. However, such activation has been tracked primarily within hours of the challenge, rather than sooner, and has been poorly mapped within standardized brain atlases. Here, we report that, within 15 min of receiving 2-deoxy-d-glucose (2-DG; 250 mg/kg, i.v.), which can trigger glucoprivic feeding behavior, marked elevations were observed in the numbers of rhombic brain (His, 1893) (RB) neuronal cell profiles immunoreactive for the cellular activation marker(s), phosphorylated p44/42 MAP kinases (phospho-ERK1/2), and that some of these profiles were also catecholaminergic. We mapped their distributions within an open-access rat brain atlas and found that 2-DG-treated rats (compared to their saline-treated controls) displayed greater numbers of phospho-ERK1/2+ neurons in the locus ceruleus (Wenzel and Wenzel, 1812) (LC) and the nucleus of solitary tract (>1840) (NTS). Thus, the 2-DG-activation of certain RB neurons is more rapid than perhaps previously realized, engaging neurons that serve multiple functional systems and which are of varying cellular phenotypes. Mapping these populations within standardized brain atlas maps streamlines their targeting and/or comparable mapping in preclinical rodent models of disease.
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Affiliation(s)
- Geronimo P. Tapia
- UTEP Systems Neuroscience Laboratory, Department of Biological Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA
- Ph.D. Program in Bioscience, Department of Biological Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA
| | - Lindsay J. Agostinelli
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Sarah D. Chenausky
- UTEP Systems Neuroscience Laboratory, Department of Biological Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA
- M.S. Program in Biology, Department of Biological Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA
| | - Jessica V. Salcido Padilla
- UTEP Systems Neuroscience Laboratory, Department of Biological Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA
- M.S. Program in Biology, Department of Biological Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA
| | - Vanessa I. Navarro
- UTEP Systems Neuroscience Laboratory, Department of Biological Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA
- Ph.D. Program in Bioscience, Department of Biological Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA
| | - Amy Alagh
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Gabriel Si
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Richard H. Thompson
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
- School of Information, The University of Texas at Austin, Austin, TX 78701, USA
| | - Sivasai Balivada
- UTEP Systems Neuroscience Laboratory, Department of Biological Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA
| | - Arshad M. Khan
- UTEP Systems Neuroscience Laboratory, Department of Biological Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
- Border Biomedical Research Center, The University of Texas at El Paso, El Paso, TX 79968, USA
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24
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Jones RA, Renshaw MJ, Barry DJ, Smith JC. Automated staging of zebrafish embryos using machine learning. Wellcome Open Res 2023. [DOI: 10.12688/wellcomeopenres.18313.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023] Open
Abstract
The zebrafish (Danio rerio), is an important biomedical model organism used in many disciplines, including development, disease modeling and toxicology, to better understand vertebrate biology. The phenomenon of developmental delay in zebrafish embryos has been widely reported as part of a mutant or treatment-induced phenotype, and accurate characterization of such delays is imperative. Despite this, the only way at present to identify and quantify these delays is through manual observation, which is both time-consuming and subjective. Machine learning approaches in biology are rapidly becoming part of the toolkit used by researchers to address complex questions. In this work, we introduce a machine learning-based classifier that has been trained to detect temporal developmental differences across groups of zebrafish embryos. Our classifier is capable of rapidly analyzing thousands of images, allowing comparisons of developmental temporal rates to be assessed across and between experimental groups of embryos. Finally, as our classifier uses images obtained from a standard live-imaging widefield microscope and camera set-up, we envisage it will be readily accessible to the zebrafish community, and prove to be a valuable resource.
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25
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Nikolaus Y, Mujumdar V, Padgaonkar P, Buchanan E, Goldberg A. Cytology-histology correlation of cervical Papanicolaou smears and biopsies performed at a single institution compared to those performed at different institutions. Cytopathology 2023; 34:61-65. [PMID: 36148769 DOI: 10.1111/cyt.13182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 07/10/2022] [Accepted: 09/15/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Cytology-histology correlation (CHC) is the gold standard of quality assurance in cytology laboratories to ensure appropriate patient treatment, and as an educational tool for cytology laboratory personnel. If cervical Pap smears (CPs) and cervical biopsies (CBs) are performed at different institutions, these benefits may be lost. METHODS All CBs performed at our institution from 1 January 2019 to 31 December 2019 with adequate CPs performed in the 6 months prior to the CB were included in this retrospective review. We compared the CHC for CPs and CBs performed at a single institution to the CHC for CPs and CBs performed at different institutions, with a focus on the proportion of overcalls on CPs, as those are the most challenging discrepant CHC to manage clinically. We used the American Society of Cytology guidelines for our discrepancy assessment grid. A Chi-squared test was used to compare the proportions of the populations. The P-value was set at < 0.05. RESULTS Of the 305 CBs in our study population, 69 had a CP performed at our institution and 236 had a CP performed at an outside institution. The CHC for CBs and CPs performed at a single institution showed statistically significantly less disagreement than the CHC for those performed at different institutions (P < 0.05). Further, CBs and CPs performed at a single institution had statistically significantly fewer overcalls than CBs and CPs performed at different institutions (P < 0.05). CONCLUSION This study further supports the use of CHC, and in light of our findings we recommend that a patient's CPs and CBs are performed at the same institution. If performing a CP and CB at the same institution is not feasible, a prospective consultation review of the CP by the institution performing the CB should be strongly considered. Further study, including an evaluation of the reason for the discrepancy in discordant cases may better elucidate the reasons for better CHC agreement when CP and CB are performed at the same institution.
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Affiliation(s)
- Yanina Nikolaus
- Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Vaidehi Mujumdar
- Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Pooja Padgaonkar
- Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Edward Buchanan
- Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Allison Goldberg
- Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
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26
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de Lange EMF, Vlijm R. Super-Resolution Imaging of Peroxisomal Proteins Using STED Nanoscopy. Methods Mol Biol 2023; 2643:65-84. [PMID: 36952178 DOI: 10.1007/978-1-0716-3048-8_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
Peroxisomes are crucial organelles that occur in almost all eukaryotes. Well known are their roles in various metabolic processes, such as hydrogen peroxide detoxification and lipid metabolism. Recent studies indicated that peroxisomes also have several non-metabolic functions, for instance, in stress response, signaling, and cellular ageing. In mammalian cells, the small size of peroxisomes (~200 nm, near the diffraction limit) hinders unveiling peroxisomal structures by conventional light microscopy. However, in the yeast Hansenula polymorpha, they can reach up to 1.5 μm in diameter, depending on the carbon source. To study the localization of peroxisomal proteins in cells in more detail, super-resolution imaging techniques such as stimulated emission depletion (STED) microscopy can be used. STED enables fast (live-cell) imaging well beyond the diffraction limit of light (30-40 nm in cells), without further data processing. Here, we present optimized protocols for the fluorescent labeling of specific peroxisomal proteins in fixed and living cells. Moreover, detailed measurement protocols for successful STED imaging of human and yeast peroxisomes (using antibodies or genetic tags labeled with dyes) are described, extended with suggestions for individual optimizations.
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Affiliation(s)
- Eline M F de Lange
- Molecular Cell Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands
- Molecular Biophysics, Zernike Institute for Advanced Materials, University of Groningen, Groningen, The Netherlands
| | - Rifka Vlijm
- Molecular Biophysics, Zernike Institute for Advanced Materials, University of Groningen, Groningen, The Netherlands.
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27
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Abstract
In this series of papers on light microscopy imaging, we have covered the fundamentals of microscopy, super-resolution microscopy, and lightsheet microscopy. This last review covers multi-photon microscopy with a brief reference to intravital imaging and Brainbow labeling. Multi-photon microscopy is often referred to as two-photon microscopy. Indeed, using two-photon microscopy is by far the most common way of imaging thick tissues; however, it is theoretically possible to use a higher number of photons, and three-photon microscopy is possible. Therefore, this review is titled "multi-photon microscopy." Another term for describing multi-photon microscopy is "non-linear" microscopy because fluorescence intensity at the focal spot depends upon the average squared intensity rather than the squared average intensity; hence, non-linear optics (NLO) is an alternative name for multi-photon microscopy. It is this non-linear relationship (or third exponential power in the case of three-photon excitation) that determines the axial optical sectioning capability of multi-photon imaging. In this paper, the necessity for two-photon or multi-photon imaging is explained, and the method of optical sectioning by multi-photon microscopy is described. Advice is also given on what fluorescent markers to use and other practical aspects of imaging thick tissues. The technique of Brainbow imaging is discussed. The review concludes with a description of intravital imaging of the mouse. © 2023 Wiley Periodicals LLC.
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28
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Sharma T, Kavita, Mishra BB, Variyar PS. Detection of gamma radiation processed onion during storage using propidium iodide based fluorescence microscopy. Food Chem 2023; 398:133928. [DOI: 10.1016/j.foodchem.2022.133928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 07/11/2022] [Accepted: 08/09/2022] [Indexed: 11/26/2022]
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29
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Tasma Z, Siow A, Harris PWR, Brimble MA, O’Carroll SJ, Hay DL, Walker CS. PAC 1, VPAC 1, and VPAC 2 Receptor Expression in Rat and Human Trigeminal Ganglia: Characterization of PACAP-Responsive Receptor Antibodies. Int J Mol Sci 2022; 23:ijms232213797. [PMID: 36430275 PMCID: PMC9697343 DOI: 10.3390/ijms232213797] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/02/2022] [Accepted: 11/06/2022] [Indexed: 11/11/2022] Open
Abstract
Pituitary adenylate cyclase-activating peptide (PACAP) is a neuropeptide expressed in the trigeminal ganglia (TG). The TG conducts nociceptive signals in the head and may play roles in migraine. PACAP infusion provokes headaches in healthy individuals and migraine-like attacks in patients; however, it is not clear whether targeting this system could be therapeutically efficacious. To effectively target the PACAP system, an understanding of PACAP receptor distribution is required. Therefore, this study aimed to characterize commercially available antibodies and use these to detect PACAP-responsive receptors in the TG. Antibodies were initially validated in receptor transfected cell models and then used to explore receptor expression in rat and human TG. Antibodies were identified that could detect PACAP-responsive receptors, including the first antibody to differentiate between the PAC1n and PAC1s receptor splice variants. PAC1, VPAC1, and VPAC2 receptor-like immunoreactivity were observed in subpopulations of both neuronal and glial-like cells in the TG. In this study, PAC1, VPAC1, and VPAC2 receptors were detected in the TG, suggesting they are all potential targets to treat migraine. These antibodies may be useful tools to help elucidate PACAP-responsive receptor expression in tissues. However, most antibodies exhibited limitations, requiring the use of multiple methodologies and the careful inclusion of controls.
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Affiliation(s)
- Zoe Tasma
- School of Biological Sciences, The University of Auckland, Auckland 1010, New Zealand
| | - Andrew Siow
- School of Biological Sciences, The University of Auckland, Auckland 1010, New Zealand
- School of Chemical Sciences, The University of Auckland, Auckland 1010, New Zealand
| | - Paul W. R. Harris
- School of Biological Sciences, The University of Auckland, Auckland 1010, New Zealand
- School of Chemical Sciences, The University of Auckland, Auckland 1010, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland 1010, New Zealand
| | - Margaret A. Brimble
- School of Biological Sciences, The University of Auckland, Auckland 1010, New Zealand
- School of Chemical Sciences, The University of Auckland, Auckland 1010, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland 1010, New Zealand
| | - Simon J. O’Carroll
- Department of Anatomy and Medical Imaging, and Centre for Brain Research, Faculty of Medical and Health Science, The University of Auckland, Auckland 1023, New Zealand
| | - Debbie L. Hay
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland 1010, New Zealand
- Department of Pharmacology and Toxicology, The University of Otago, Dunedin 9016, New Zealand
| | - Christopher S. Walker
- School of Biological Sciences, The University of Auckland, Auckland 1010, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland 1010, New Zealand
- Correspondence:
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30
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Faklaris O, Bancel-Vallée L, Dauphin A, Monterroso B, Frère P, Geny D, Manoliu T, de Rossi S, Cordelières FP, Schapman D, Nitschke R, Cau J, Guilbert T. Quality assessment in light microscopy for routine use through simple tools and robust metrics. J Biophys Biochem Cytol 2022; 221:213512. [PMID: 36173380 PMCID: PMC9526251 DOI: 10.1083/jcb.202107093] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 04/04/2022] [Accepted: 08/31/2022] [Indexed: 11/22/2022] Open
Abstract
Although there is a need to demonstrate reproducibility in light microscopy acquisitions, the lack of standardized guidelines monitoring microscope health status over time has so far impaired the widespread use of quality control (QC) measurements. As scientists from 10 imaging core facilities who encounter various types of projects, we provide affordable hardware and open source software tools, rigorous protocols, and define reference values to assess QC metrics for the most common fluorescence light microscopy modalities. Seven protocols specify metrics on the microscope resolution, field illumination flatness, chromatic aberrations, illumination power stability, stage drift, positioning repeatability, and spatial-temporal noise of camera sensors. We designed the MetroloJ_QC ImageJ/Fiji Java plugin to incorporate the metrics and automate analysis. Measurements allow us to propose an extensive characterization of the QC procedures that can be used by any seasoned microscope user, from research biologists with a specialized interest in fluorescence light microscopy through to core facility staff, to ensure reproducible and quantifiable microscopy results.
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Affiliation(s)
- Orestis Faklaris
- Montpellier Ressources Imagerie, Biocampus, University of Montpellier, CNRS, INSERM, Montpellier, France
| | - Leslie Bancel-Vallée
- Montpellier Ressources Imagerie, Biocampus, University of Montpellier, CNRS, INSERM, Montpellier, France
| | - Aurélien Dauphin
- Unite Genetique et Biologie du Développement U934, PICT-IBiSA, Institut Curie, INSERM, CNRS, PSL Research University, Paris, France
| | - Baptiste Monterroso
- Prism, Institut de Biologie Valrose, CNRS UMR 7277, INSERM 1091, University of Nice Sophia Antipolis - Parc Valrose, Nice, France
| | - Perrine Frère
- Plate-forme d'Imagerie de Tenon, UMR_S 1155, Hôpital Tenon, Paris, France
| | - David Geny
- Institut de Psychiatrie Et Neurosciences de Paris, INSERM U1266, Paris, France
| | - Tudor Manoliu
- Gustave Roussy, Université Paris-Saclay, Plate-forme Imagerie et Cytométrie, UMS AMMICa. Villejuif, France
| | - Sylvain de Rossi
- Montpellier Ressources Imagerie, Biocampus, University of Montpellier, CNRS, INSERM, Montpellier, France
| | - Fabrice P Cordelières
- University of Bordeaux, CNRS, INSERM, Bordeaux Imaging Center, UMS 3420, US 4, Bordeaux, France
| | - Damien Schapman
- Université of Rouen Normandie, INSERM, Plate-Forme de Recherche en Imagerie Cellulaire de Normandie, Rouen, France
| | - Roland Nitschke
- Life Imaging Center and Signalling Research Centres CIBSS and BIOSS, University Freiburg, Freiburg, Germany
| | - Julien Cau
- Montpellier Ressources Imagerie, Biocampus, University of Montpellier, CNRS, INSERM, Montpellier, France
| | - Thomas Guilbert
- Institut Cochin, INSERM (U1016), CNRS (UMR 8104), Universite de Paris (UMR-S1016), Paris, France
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31
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Courson JA, Langlois KW, Lam FW. Intravital Microscopy to Study Platelet-Leukocyte-Endothelial Interactions in the Mouse Liver. J Vis Exp 2022:10.3791/64239. [PMID: 36282718 PMCID: PMC9915146 DOI: 10.3791/64239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Inflammation and thrombosis are complex processes that occur primarily in the microcirculation. Although standard histology may provide insight into the end pathway for both inflammation and thrombosis, it is not capable of showing the temporal changes that occur throughout the time course of these processes. Intravital microscopy (IVM) is the use of live-animal imaging to gain temporal insight into physiologic processes in vivo. This method is particularly powerful when assessing cellular and protein interactions within the circulation due to the rapid and sequential events that are often necessary for these interactions to occur. While IVM is an extremely powerful imaging methodology capable of viewing complex processes in vivo, there are a number of methodological factors that are important to consider when planning an IVM study. This paper outlines the process of conducting intravital imaging of the liver, identifying important considerations and potential pitfalls that may arise. Thus, this paper describes the use of IVM to study platelet-leukocyte-endothelial interactions in liver sinusoids to study the relative contributions of each in different models of acute liver injury.
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Affiliation(s)
- Justin A Courson
- Center for Translational Research on Inflammatory Diseases, Michael E. DeBakey Veterans Affairs Medical Center; Department of Medicine, Baylor College of Medicine
| | - Kimberly W Langlois
- Center for Translational Research on Inflammatory Diseases, Michael E. DeBakey Veterans Affairs Medical Center; Department of Medicine, Baylor College of Medicine
| | - Fong W Lam
- Center for Translational Research on Inflammatory Diseases, Michael E. DeBakey Veterans Affairs Medical Center; Department of Pediatrics, Baylor College of Medicine;
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32
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Cuny AP, Schlottmann FP, Ewald JC, Pelet S, Schmoller KM. Live cell microscopy: From image to insight. BIOPHYSICS REVIEWS 2022; 3:021302. [PMID: 38505412 PMCID: PMC10903399 DOI: 10.1063/5.0082799] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 03/18/2022] [Indexed: 03/21/2024]
Abstract
Live-cell microscopy is a powerful tool that can reveal cellular behavior as well as the underlying molecular processes. A key advantage of microscopy is that by visualizing biological processes, it can provide direct insights. Nevertheless, live-cell imaging can be technically challenging and prone to artifacts. For a successful experiment, many careful decisions are required at all steps from hardware selection to downstream image analysis. Facing these questions can be particularly intimidating due to the requirement for expertise in multiple disciplines, ranging from optics, biophysics, and programming to cell biology. In this review, we aim to summarize the key points that need to be considered when setting up and analyzing a live-cell imaging experiment. While we put a particular focus on yeast, many of the concepts discussed are applicable also to other organisms. In addition, we discuss reporting and data sharing strategies that we think are critical to improve reproducibility in the field.
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Affiliation(s)
| | - Fabian P. Schlottmann
- Interfaculty Institute of Cell Biology, University of Tuebingen, 72076 Tuebingen, Germany
| | - Jennifer C. Ewald
- Interfaculty Institute of Cell Biology, University of Tuebingen, 72076 Tuebingen, Germany
| | - Serge Pelet
- Department of Fundamental Microbiology, University of Lausanne, 1015 Lausanne, Switzerland
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33
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Hobson CM, Aaron JS. Combining multiple fluorescence imaging techniques in biology: when one microscope is not enough. Mol Biol Cell 2022; 33:tp1. [PMID: 35549314 PMCID: PMC9265156 DOI: 10.1091/mbc.e21-10-0506] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 11/16/2021] [Accepted: 11/29/2021] [Indexed: 11/11/2022] Open
Abstract
While fluorescence microscopy has proven to be an exceedingly useful tool in bioscience, it is difficult to offer simultaneous high resolution, fast speed, large volume, and good biocompatibility in a single imaging technique. Thus, when determining the image data required to quantitatively test a complex biological hypothesis, it often becomes evident that multiple imaging techniques are necessary. Recent years have seen an explosion in development of novel fluorescence microscopy techniques, each of which features a unique suite of capabilities. In this Technical Perspective, we highlight recent studies to illustrate the benefits, and often the necessity, of combining multiple fluorescence microscopy modalities. We provide guidance in choosing optimal technique combinations to effectively address a biological question. Ultimately, we aim to promote a more well-rounded approach in designing fluorescence microscopy experiments, leading to more robust quantitative insight.
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Affiliation(s)
- Chad M. Hobson
- Advanced Imaging Center, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147
| | - Jesse S. Aaron
- Advanced Imaging Center, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147
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Three-dimensional structured illumination microscopy data of mitochondria and lysosomes in cardiomyoblasts under normal and galactose-adapted conditions. Sci Data 2022; 9:98. [PMID: 35322035 PMCID: PMC8943179 DOI: 10.1038/s41597-022-01207-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 02/11/2022] [Indexed: 11/23/2022] Open
Abstract
This three-dimensional structured illumination microscopy (3DSIM) dataset was generated to highlight the suitability of 3DSIM to investigate mitochondria-derived vesicles (MDVs) in H9c2 cardiomyoblasts in living or fixed cells. MDVs act as a mitochondria quality control mechanism. The cells were stably expressing the tandem-tag eGFP-mCherry-OMP25-TM (outer mitochondrial membrane) which can be used as a sensor for acidity. A part of the dataset is showing correlative imaging of lysosomes labeled using LysoTracker in fixed and living cells. The cells were cultivated in either normal or glucose-deprived medium containing galactose. The resulting 3DSIM data were of high quality and can be used to undertake a variety of studies. Interestingly, many dynamic tubules derived from mitochondria are visible in the 3DSIM videos under both glucose and galactose-adapted growth conditions. As the raw 3DSIM data, optical parameters, and reconstructed 3DSIM images are provided, the data is especially suitable for use in the development of SIM reconstruction algorithms, bioimage analysis methods, and for biological studies of mitochondria. Measurement(s) | fluorescence microscopy images of mitochondria | Technology Type(s) | three dimensional structured illumination microscopy | Sample Characteristic - Organism | Rattus norvegicus • H9c2 cardiomyoblast cell-line |
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35
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Kempster C, Butler G, Kuznecova E, Taylor KA, Kriek N, Little G, Sowa MA, Sage T, Johnson LJ, Gibbins JM, Pollitt AY. Fully automated platelet differential interference contrast image analysis via deep learning. Sci Rep 2022; 12:4614. [PMID: 35301400 PMCID: PMC8931011 DOI: 10.1038/s41598-022-08613-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 03/08/2022] [Indexed: 11/12/2022] Open
Abstract
Platelets mediate arterial thrombosis, a leading cause of myocardial infarction and stroke. During injury, platelets adhere and spread over exposed subendothelial matrix substrates of the damaged blood vessel wall. The mechanisms which govern platelet activation and their interaction with a range of substrates are therefore regularly investigated using platelet spreading assays. These assays often use differential interference contrast (DIC) microscopy to assess platelet morphology and analysis performed using manual annotation. Here, a convolutional neural network (CNN) allowed fully automated analysis of platelet spreading assays captured by DIC microscopy. The CNN was trained using 120 generalised training images. Increasing the number of training images increases the mean average precision of the CNN. The CNN performance was compared to six manual annotators. Significant variation was observed between annotators, highlighting bias when manual analysis is performed. The CNN effectively analysed platelet morphology when platelets spread over a range of substrates (CRP-XL, vWF and fibrinogen), in the presence and absence of inhibitors (dasatinib, ibrutinib and PRT-060318) and agonist (thrombin), with results consistent in quantifying spread platelet area which is comparable to published literature. The application of a CNN enables, for the first time, automated analysis of platelet spreading assays captured by DIC microscopy.
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Affiliation(s)
- Carly Kempster
- School of Biological Sciences, University of Reading, Reading, UK
| | - George Butler
- School of Biological Sciences, University of Reading, Reading, UK.,The Brady Urological Institute, Johns Hopkins School of Medicine, Baltimore, USA
| | - Elina Kuznecova
- School of Biological Sciences, University of Reading, Reading, UK
| | - Kirk A Taylor
- School of Biological Sciences, University of Reading, Reading, UK
| | - Neline Kriek
- School of Biological Sciences, University of Reading, Reading, UK
| | - Gemma Little
- School of Biological Sciences, University of Reading, Reading, UK
| | - Marcin A Sowa
- School of Biological Sciences, University of Reading, Reading, UK
| | - Tanya Sage
- School of Biological Sciences, University of Reading, Reading, UK
| | - Louise J Johnson
- School of Biological Sciences, University of Reading, Reading, UK
| | | | - Alice Y Pollitt
- School of Biological Sciences, University of Reading, Reading, UK.
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36
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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: 10] [Impact Index Per Article: 5.0] [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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Swift LH, Colarusso P. Fluorescence Microscopy: A Field Guide for Biologists. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2440:3-39. [PMID: 35218530 DOI: 10.1007/978-1-0716-2051-9_1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Optical microscopy is a tool for observing objects, and features within objects, that are not visible to the unaided eye. In the life sciences, fluorescence microscopy has been widely adopted because it allows us to selectively observe molecules, organelles, and cells at multiple levels of organization. Fluorescence microscopy encompasses numerous techniques and applications that share a specialized technical language and concepts that can create barriers for researchers who are new to this area. Our goal is to meet the needs of researchers new to fluorescence microscopy, by introducing the essential concepts and mindset required to navigate and apply this powerful technology to the laboratory.
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Affiliation(s)
- Lucy H Swift
- Department of Physiology and Pharmacology, Live Cell Imaging Laboratory, Snyder Institute for Chronic Diseases, University of Calgary, Calgary, AB, Canada
| | - Pina Colarusso
- Department of Physiology and Pharmacology, Live Cell Imaging Laboratory, Snyder Institute for Chronic Diseases, University of Calgary, Calgary, AB, Canada.
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Özkaya AB, Geyik C. From viability to cell death: Claims with insufficient evidence in high-impact cell culture studies. PLoS One 2022; 17:e0250754. [PMID: 35192623 PMCID: PMC8863264 DOI: 10.1371/journal.pone.0250754] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 01/02/2022] [Indexed: 11/18/2022] Open
Abstract
Background
Reliability of preclinical research is of critical concern. Prior studies have demonstrated the low reproducibility of research results and recommend implementing higher standards to improve overall quality and robustness of research. One understudied aspect of this quality issue is the harmony between the research hypotheses and the experimental design in published work.
Methods and findings
In this study we focused on highly cited cell culture studies and investigated whether commonly asserted cell culture claims such as viability, cytotoxicity, proliferation rate, cell death and apoptosis are backed with sufficient experimental evidence or not. We created an open access database containing 280 claims asserted by 103 different high-impact articles as well as the results of this study. Our findings revealed that only 64% of all claims were sufficiently supported by evidence and there were concerning misinterpretations such as considering the results of tetrazolium salt reduction assays as indicators of cell death or apoptosis.
Conclusions
Our analysis revealed a discordance between experimental findings and the way they were presented and discussed in the manuscripts. To improve quality of pre-clinical research, we require clear nomenclature by which different cell culture claims are distinctively categorized; materials and methods sections to be written more meticulously; and cell culture methods to be selected and utilized more carefully. In this paper we recommend a nomenclature for selected cell culture claims as well as a methodology for collecting evidence to support those claims.
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Affiliation(s)
- Ali Burak Özkaya
- Department of Medical Biochemistry, Faculty of Medicine, İzmir University of Economics, İzmir, Turkey
- * E-mail:
| | - Caner Geyik
- Department of Medical Biochemistry, Faculty of Medicine, İstinye University, İstanbul, Turkey
- ISUMKAM Molecular Cancer Research Center, İstinye University, Istanbul, Turkey
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39
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Mitra-Behura S, Fiolka RP, Daetwyler S. Singularity Containers Improve Reproducibility and Ease of Use in Computational Image Analysis Workflows. FRONTIERS IN BIOINFORMATICS 2022; 1:757291. [PMID: 36303730 PMCID: PMC9581025 DOI: 10.3389/fbinf.2021.757291] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 10/11/2021] [Indexed: 11/13/2022] Open
Abstract
Reproducing computational workflows in image analysis and microscopy can be a daunting task due to different software versions and dependencies. This is especially true for users with little specific knowledge of scientific computation. To overcome these challenges, we introduce Singularity containers as a useful tool to run and share image analysis workflows among many users, even years later after establishing them. Unfortunately, containers are rarely used so far in the image analysis field. To address this lack of use, we provide a detailed step-by-step protocol to package a state-of-the-art segmentation algorithm into a container on a local Windows machine to run the container on a high-performance cluster computer.
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Affiliation(s)
- Shilpita Mitra-Behura
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, United States
| | - Reto Paul Fiolka
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, United States
- Department of Cell Biology, UT Southwestern Medical Center, Dallas, TX, United States
| | - Stephan Daetwyler
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, United States
- Department of Cell Biology, UT Southwestern Medical Center, Dallas, TX, United States
- *Correspondence: Stephan Daetwyler,
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40
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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: 0.5] [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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41
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Serre NBC, Fendrych M. ACORBA: Automated workflow to measure Arabidopsis thaliana root tip angle dynamics. QUANTITATIVE PLANT BIOLOGY 2022; 3:e9. [PMID: 37077987 PMCID: PMC10095971 DOI: 10.1017/qpb.2022.4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 03/07/2022] [Accepted: 03/30/2022] [Indexed: 05/03/2023]
Abstract
The ability of plants to sense and orient their root growth towards gravity is studied in many laboratories. It is known that manual analysis of image data is subjected to human bias. Several semi-automated tools are available for analysing images from flatbed scanners, but there is no solution to automatically measure root bending angle over time for vertical-stage microscopy images. To address these problems, we developed ACORBA, which is an automated software that can measure root bending angle over time from vertical-stage microscope and flatbed scanner images. ACORBA also has a semi-automated mode for camera or stereomicroscope images. It represents a flexible approach based on both traditional image processing and deep machine learning segmentation to measure root angle progression over time. As the software is automated, it limits human interactions and is reproducible. ACORBA will support the plant biologist community by reducing labour and increasing reproducibility of image analysis of root gravitropism.
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Affiliation(s)
- Nelson B C Serre
- Department of Experimental Plant Biology, Faculty of Sciences, Charles University, Prague, Czech Republic
| | - Matyáš Fendrych
- Department of Experimental Plant Biology, Faculty of Sciences, Charles University, Prague, Czech Republic
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42
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Richardson DS, Guan W, Matsumoto K, Pan C, Chung K, Ertürk A, Ueda HR, Lichtman JW. TISSUE CLEARING. NATURE REVIEWS. METHODS PRIMERS 2021; 1:84. [PMID: 35128463 PMCID: PMC8815095 DOI: 10.1038/s43586-021-00080-9] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/29/2021] [Indexed: 12/16/2022]
Abstract
Tissue clearing of gross anatomical samples was first described over a century ago and has only recently found widespread use in the field of microscopy. This renaissance has been driven by the application of modern knowledge of optical physics and chemical engineering to the development of robust and reproducible clearing techniques, the arrival of new microscopes that can image large samples at cellular resolution and computing infrastructure able to store and analyze large data volumes. Many biological relationships between structure and function require investigation in three dimensions and tissue clearing therefore has the potential to enable broad discoveries in the biological sciences. Unfortunately, the current literature is complex and could confuse researchers looking to begin a clearing project. The goal of this Primer is to outline a modular approach to tissue clearing that allows a novice researcher to develop a customized clearing pipeline tailored to their tissue of interest. Further, the Primer outlines the required imaging and computational infrastructure needed to perform tissue clearing at scale, gives an overview of current applications, discusses limitations and provides an outlook on future advances in the field.
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Affiliation(s)
- Douglas S. Richardson
- Harvard Center for Biological Imaging, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Webster Guan
- Department of Chemical Engineering, MIT, Cambridge, MA, USA
| | - Katsuhiko Matsumoto
- Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, Osaka, Japan
| | - Chenchen Pan
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig Maximilians University of Munich, Munich, Germany
- Graduate School of Systemic Neurosciences (GSN), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Kwanghun Chung
- Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Broad Institute of Harvard University and MIT, Cambridge, MA, USA
- Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea
- Nano Biomedical Engineering (Nano BME) Graduate Program, Yonsei-IBS Institute, Yonsei University, Seoul, Republic of Korea
| | - Ali Ertürk
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig Maximilians University of Munich, Munich, Germany
- Graduate School of Systemic Neurosciences (GSN), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Hiroki R. Ueda
- Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, Osaka, Japan
| | - Jeff W. Lichtman
- Harvard Center for Biological Imaging, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
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43
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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: 46] [Impact Index Per Article: 15.3] [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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44
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Brooks PT, Munthe-Fog L, Rieneck K, Banch Clausen F, Rivera OB, Kannik Haastrup E, Fischer-Nielsen A, Svalgaard JD. Application of a deep learning-based image analysis and live-cell imaging system for quantifying adipogenic differentiation kinetics of adipose-derived stem/stromal cells. Adipocyte 2021; 10:621-630. [PMID: 34747303 PMCID: PMC8632106 DOI: 10.1080/21623945.2021.2000696] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Quantitative methods for assessing differentiative potency of adipose-derived stem/stromal cells may lead to improved clinical application of this multipotent stem cell, by advancing our understanding of specific processes such as adipogenic differentiation. Conventional cell staining methods are used to determine the formation of adipose areas during adipogenesis as a qualitative representation of adipogenic potency. Staining methods such as oil-red-O are quantifiable using absorbance measurements, but these assays are time and material consuming. Detection methods for cell characteristics using advanced image analysis by machine learning are emerging. Here, live-cell imaging was combined with a deep learning-based detection tool to quantify the presence of adipose areas and lipid droplet formation during adipogenic differentiation of adipose-derived stem/stromal cells. Different detection masks quantified adipose area and lipid droplet formation at different time points indicating kinetics of adipogenesis and showed differences between individual donors. Whereas CEBPA and PPARG expression seems to precede the increase in adipose area and lipid droplets, it might be able to predict expression of ADIPOQ. The applied method is a proof of concept, demonstrating that deep learning methods can be used to investigate adipogenic differentiation and kinetics in vitro using specific detection masks based on algorithm produced from annotation of image data.
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Affiliation(s)
- Patrick Terrence Brooks
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Lea Munthe-Fog
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Klaus Rieneck
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Frederik Banch Clausen
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Olga Ballesteros Rivera
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Eva Kannik Haastrup
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Anne Fischer-Nielsen
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Jesper Dyrendom Svalgaard
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
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45
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Fletcher G, Anderson KI. What is the structure of our infrastructure? -A Review of UK Light Microscopy Facilities. J Microsc 2021; 285:55-67. [PMID: 34841540 PMCID: PMC9302651 DOI: 10.1111/jmi.13076] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 09/17/2021] [Accepted: 10/29/2021] [Indexed: 11/27/2022]
Abstract
Core Facilities and Technology Platforms are increasingly important components of the science research landscape. However, data on facility operations and staff careers are lacking to inform their development. Here we have surveyed 114 people working in 46 Light Microscopy (LM) facilities within the United Kingdom. Our survey explores issues around Career Progression, Facility Operations, and Funding. The data show that facilities are substantial repositories of equipment and knowledge which adapt to meet the needs of their local environments. Our report highlights the challenges faced by facility managers, institutions, and funders in evaluating facility performance and devising strategies to maximize the return on research funding investment. This article is protected by copyright. All rights reserved.
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46
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Recent advances in the standardization of fluorescence microscopy for quantitative image analysis. Biophys Rev 2021; 14:33-39. [DOI: 10.1007/s12551-021-00871-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 10/22/2021] [Indexed: 12/19/2022] Open
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47
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Vasiukov G, Novitskaya T, Senosain MF, Camai A, Menshikh A, Massion P, Zijlstra A, Novitskiy S. Integrated Cells and Collagen Fibers Spatial Image Analysis. FRONTIERS IN BIOINFORMATICS 2021; 1. [PMID: 35813245 PMCID: PMC9268206 DOI: 10.3389/fbinf.2021.758775] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Modern technologies designed for tissue structure visualization like brightfield microscopy, fluorescent microscopy, mass cytometry imaging (MCI) and mass spectrometry imaging (MSI) provide large amounts of quantitative and spatial information about cells and tissue structures like vessels, bronchioles etc. Many published reports have demonstrated that the structural features of cells and extracellular matrix (ECM) and their interactions strongly predict disease development and progression. Computational image analysis methods in combination with spatial analysis and machine learning can reveal novel structural patterns in normal and diseased tissue. Here, we have developed a Python package designed for integrated analysis of cells and ECM in a spatially dependent manner. The package performs segmentation, labeling and feature analysis of ECM fibers, combines this information with pre-generated single-cell based datasets and realizes cell-cell and cell-fiber spatial analysis. To demonstrate performance and compatibility of our computational tool, we integrated it with a pipeline designed for cell segmentation, classification, and feature analysis in the KNIME analytical platform. For validation, we used a set of mouse mammary gland tumors and human lung adenocarcinoma tissue samples stained for multiple cellular markers and collagen as the main ECM protein. The developed package provides sufficient performance and precision to be used as a novel method to investigate cell-ECM relationships in the tissue, as well as detect structural patterns correlated with specific disease outcomes.
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Affiliation(s)
- Georgii Vasiukov
- Department of Medicine, Division of Allergy, Pulmonary, Critical Care Medicine, Vanderbilt, University Medical Center, Nashville, TN, United States
- *Correspondence: Georgii Vasiukov,
| | - Tatiana Novitskaya
- Department of Pathology, Microbiology, And Immunology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Maria-Fernanda Senosain
- Department of Medicine, Division of Allergy, Pulmonary, Critical Care Medicine, Vanderbilt, University Medical Center, Nashville, TN, United States
| | - Alex Camai
- Department of Medicine, Division of Allergy, Pulmonary, Critical Care Medicine, Vanderbilt, University Medical Center, Nashville, TN, United States
| | - Anna Menshikh
- Department of Medicine, Division of Nephrology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Pierre Massion
- Department of Medicine, Division of Allergy, Pulmonary, Critical Care Medicine, Vanderbilt, University Medical Center, Nashville, TN, United States
| | - Andries Zijlstra
- Department of Pathology, Microbiology, And Immunology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Sergey Novitskiy
- Department of Medicine, Division of Allergy, Pulmonary, Critical Care Medicine, Vanderbilt, University Medical Center, Nashville, TN, United States
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Optimization of Advanced Live-Cell Imaging through Red/Near-Infrared Dye Labeling and Fluorescence Lifetime-Based Strategies. Int J Mol Sci 2021; 22:ijms222011092. [PMID: 34681761 PMCID: PMC8537913 DOI: 10.3390/ijms222011092] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 10/08/2021] [Accepted: 10/09/2021] [Indexed: 12/30/2022] Open
Abstract
Fluorescence microscopy is essential for a detailed understanding of cellular processes; however, live-cell preservation during imaging is a matter of debate. In this study, we proposed a guide to optimize advanced light microscopy approaches by reducing light exposure through fluorescence lifetime (τ) exploitation of red/near-infrared dyes. Firstly, we characterized key instrumental elements which revealed that red/near-infrared laser lines with an 86x (Numerical Aperture (NA) = 1.2, water immersion) objective allowed high transmission of fluorescence signals, low irradiance and super-resolution. As a combination of two technologies, i.e., vacuum tubes (e.g., photomultiplier) and semiconductor microelectronics (e.g., avalanche photodiode), type S, X and R of hybrid detectors (HyD-S, HyD-X and HyD-R) were particularly adapted for red/near-infrared photon counting and τ separation. Secondly, we tested and compared lifetime-based imaging including coarse τ separation for confocal microscopy, fitting and phasor plot analysis for fluorescence lifetime microscopy (FLIM), and lifetimes weighting for enhanced stimulated emission depletion (STED) nanoscopy, in light of red/near-infrared multiplexing. Mainly, we showed that the choice of appropriate imaging approach may depend on fluorochrome number, together with their spectral/lifetime characteristics and STED compatibility. Photon-counting mode and sensitivity of HyDs together with phasor plot analysis of fluorescence lifetimes enabled the flexible and fast imaging of multi-labeled living H28 cells. Therefore, a combination of red/near-infrared dyes labeling with lifetime-based strategies offers new perspectives for live-cell imaging by enhancing sample preservation through acquisition time and light exposure reduction.
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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: 47] [Impact Index Per Article: 15.7] [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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50
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Valli J, Sanderson J. Super-Resolution Fluorescence Microscopy Methods for Assessing Mouse Biology. Curr Protoc 2021; 1:e224. [PMID: 34436832 DOI: 10.1002/cpz1.224] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Super-resolution (diffraction unlimited) microscopy was developed 15 years ago; the developers were awarded the Nobel Prize in Chemistry in recognition of their work in 2014. Super-resolution microscopy is increasingly being applied to diverse scientific fields, from single molecules to cell organelles, viruses, bacteria, plants, and animals, especially the mammalian model organism Mus musculus. In this review, we explain how super-resolution microscopy, along with fluorescence microscopy from which it grew, has aided the renaissance of the light microscope. We cover experiment planning and specimen preparation and explain structured illumination microscopy, super-resolution radial fluctuations, stimulated emission depletion microscopy, single-molecule localization microscopy, and super-resolution imaging by pixel reassignment. The final section of this review discusses the strengths and weaknesses of each super-resolution technique and how to choose the best approach for your research. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC.
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Affiliation(s)
- Jessica Valli
- Edinburgh Super Resolution Imaging Consortium (ESRIC), Institute of Biological Chemistry, Biophysics and Bioengineering, Heriot-Watt University, Edinburgh, United Kingdom
| | - Jeremy Sanderson
- MRC Harwell Institute, Mammalian Genetics Unit, Harwell Campus, Oxfordshire, United Kingdom
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