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Ibbini Z, Truebano M, Spicer JI, McCoy JCS, Tills O. Dev-ResNet: automated developmental event detection using deep learning. J Exp Biol 2024; 227:jeb247046. [PMID: 38806151 PMCID: PMC11152166 DOI: 10.1242/jeb.247046] [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: 11/24/2023] [Accepted: 04/22/2024] [Indexed: 05/30/2024]
Abstract
Delineating developmental events is central to experimental research using early life stages, permitting widespread identification of changes in event timing between species and environments. Yet, identifying developmental events is incredibly challenging, limiting the scale, reproducibility and throughput of using early life stages in experimental biology. We introduce Dev-ResNet, a small and efficient 3D convolutional neural network capable of detecting developmental events characterised by both spatial and temporal features, such as the onset of cardiac function and radula activity. We demonstrate the efficacy of Dev-ResNet using 10 diverse functional events throughout the embryonic development of the great pond snail, Lymnaea stagnalis. Dev-ResNet was highly effective in detecting the onset of all events, including the identification of thermally induced decoupling of event timings. Dev-ResNet has broad applicability given the ubiquity of bioimaging in developmental biology, and the transferability of deep learning, and so we provide comprehensive scripts and documentation for applying Dev-ResNet to different biological systems.
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Affiliation(s)
- Ziad Ibbini
- Marine Biology and Ecology Research Centre, School of Biological and Marine Sciences, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK
| | - Manuela Truebano
- Marine Biology and Ecology Research Centre, School of Biological and Marine Sciences, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK
| | - John I. Spicer
- Marine Biology and Ecology Research Centre, School of Biological and Marine Sciences, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK
| | - Jamie C. S. McCoy
- Marine Biology and Ecology Research Centre, School of Biological and Marine Sciences, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK
| | - Oliver Tills
- Marine Biology and Ecology Research Centre, School of Biological and Marine Sciences, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK
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Jones RA, Renshaw MJ, Barry DJ. Automated staging of zebrafish embryos with deep learning. Life Sci Alliance 2024; 7:e202302351. [PMID: 37884343 PMCID: PMC10602791 DOI: 10.26508/lsa.202302351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 10/14/2023] [Accepted: 10/18/2023] [Indexed: 10/28/2023] Open
Abstract
The zebrafish (Danio rerio) is an important biomedical model organism used in many disciplines. The phenomenon of developmental delay in zebrafish embryos has been widely reported as part of a mutant or treatment-induced phenotype. However, the detection and quantification of these delays is often achieved through manual observation, which is both time-consuming and subjective. We present KimmelNet, a deep learning model trained to predict embryo age (hours post fertilisation) from 2D brightfield images. KimmelNet's predictions agree closely with established staging methods and can detect developmental delays between populations with high confidence using as few as 100 images. Moreover, KimmelNet generalises to previously unseen data, with transfer learning enhancing its performance. With the ability to analyse tens of thousands of standard brightfield microscopy images on a timescale of minutes, we envisage that KimmelNet will be a valuable resource for the developmental biology community. Furthermore, the approach we have used could easily be adapted to generate models for other organisms.
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Affiliation(s)
- Rebecca A Jones
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
- https://ror.org/04tnbqb63 Developmental Biology Laboratory, The Francis Crick Institute, London, UK
| | - Matthew J Renshaw
- https://ror.org/04tnbqb63 Crick Advanced Light Microscopy (CALM), The Francis Crick Institute, London, UK
| | - David J Barry
- https://ror.org/04tnbqb63 Crick Advanced Light Microscopy (CALM), The Francis Crick Institute, London, UK
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Groves I, Holmshaw J, Furley D, Manning E, Chinnaiya K, Towers M, Evans BD, Placzek M, Fletcher AG. Accurate staging of chick embryonic tissues via deep learning of salient features. Development 2023; 150:dev202068. [PMID: 37830145 PMCID: PMC10690058 DOI: 10.1242/dev.202068] [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: 06/07/2023] [Accepted: 10/05/2023] [Indexed: 10/14/2023]
Abstract
Recent work shows that the developmental potential of progenitor cells in the HH10 chick brain changes rapidly, accompanied by subtle changes in morphology. This demands increased temporal resolution for studies of the brain at this stage, necessitating precise and unbiased staging. Here, we investigated whether we could train a deep convolutional neural network to sub-stage HH10 chick brains using a small dataset of 151 expertly labelled images. By augmenting our images with biologically informed transformations and data-driven preprocessing steps, we successfully trained a classifier to sub-stage HH10 brains to 87.1% test accuracy. To determine whether our classifier could be generally applied, we re-trained it using images (269) of randomised control and experimental chick wings, and obtained similarly high test accuracy (86.1%). Saliency analyses revealed that biologically relevant features are used for classification. Our strategy enables training of image classifiers for various applications in developmental biology with limited microscopy data.
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Affiliation(s)
- Ian Groves
- School of Mathematics and Statistics, University of Sheffield, Hicks Building, Hounsfield Road, Sheffield S3 7RH, UK
- School of Biosciences, University of Sheffield, Firth Court, Western Bank, Sheffield S10 2TN, UK
| | - Jacob Holmshaw
- School of Mathematics and Statistics, University of Sheffield, Hicks Building, Hounsfield Road, Sheffield S3 7RH, UK
| | - David Furley
- School of Mathematics and Statistics, University of Sheffield, Hicks Building, Hounsfield Road, Sheffield S3 7RH, UK
- School of Biosciences, University of Sheffield, Firth Court, Western Bank, Sheffield S10 2TN, UK
| | - Elizabeth Manning
- School of Biosciences, University of Sheffield, Firth Court, Western Bank, Sheffield S10 2TN, UK
| | - Kavitha Chinnaiya
- School of Biosciences, University of Sheffield, Firth Court, Western Bank, Sheffield S10 2TN, UK
| | - Matthew Towers
- School of Biosciences, University of Sheffield, Firth Court, Western Bank, Sheffield S10 2TN, UK
| | - Benjamin D. Evans
- Department of Informatics, School of Engineering and Informatics, University of Sussex, Falmer, Brighton BN1 9RH, UK
| | - Marysia Placzek
- School of Biosciences, University of Sheffield, Firth Court, Western Bank, Sheffield S10 2TN, UK
| | - Alexander G. Fletcher
- School of Mathematics and Statistics, University of Sheffield, Hicks Building, Hounsfield Road, Sheffield S3 7RH, UK
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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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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] [Grants] [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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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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Munck S, Swoger J, Coll-Lladó M, Gritti N, Vande Velde G. Maximizing content across scales: Moving multimodal microscopy and mesoscopy toward molecular imaging. Curr Opin Chem Biol 2021; 63:188-199. [PMID: 34198170 DOI: 10.1016/j.cbpa.2021.05.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 05/06/2021] [Accepted: 05/16/2021] [Indexed: 10/21/2022]
Abstract
Molecular imaging aims to depict the molecules in living patients. However, because this aim is still far beyond reach, patchworks of different solutions need to be used to tackle this overarching goal. From the vast toolbox of imaging techniques, we focus on those recent advances in optical microscopy that image molecules and cells at the submicron to centimeter scale. Mesoscopic imaging covers the "imaging gap" between techniques such as confocal microscopy and magnetic resonance imagingthat image entire live samples but with limited resolution. Microscopy focuses on the cellular level; mesoscopy visualizes the organization of molecules and cells into tissues and organs. The correlation between these techniques allows us to combine disciplines ranging from whole body imaging to basic research of model systems. We review current developments focused on improving microscopic and mesoscopic imaging technologies and on hardware and software that push the current sensitivity and resolution boundaries.
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Affiliation(s)
- Sebastian Munck
- VIB-KU Leuven Center for Brain & Disease Research, Light Microscopy Expertise Unit & VIB BioImaging Core, O&N4 Herestraat 49 box 602, Leuven, 3000, Belgium; KU Leuven Department of Neurosciences, Leuven Brain Institute, O&N4 Herestraat 49 box 602, Leuven, 3000, Belgium
| | - Jim Swoger
- European Molecular Biology Laboratory (EMBL) Barcelona, Barcelona, 08003, Spain
| | | | - Nicola Gritti
- European Molecular Biology Laboratory (EMBL) Barcelona, Barcelona, 08003, Spain
| | - Greetje Vande Velde
- Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium.
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