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Jan M, Spangaro A, Lenartowicz M, Mattiazzi Usaj M. From pixels to insights: Machine learning and deep learning for bioimage analysis. Bioessays 2024; 46:e2300114. [PMID: 38058114 DOI: 10.1002/bies.202300114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 10/25/2023] [Accepted: 11/13/2023] [Indexed: 12/08/2023]
Abstract
Bioimage analysis plays a critical role in extracting information from biological images, enabling deeper insights into cellular structures and processes. The integration of machine learning and deep learning techniques has revolutionized the field, enabling the automated, reproducible, and accurate analysis of biological images. Here, we provide an overview of the history and principles of machine learning and deep learning in the context of bioimage analysis. We discuss the essential steps of the bioimage analysis workflow, emphasizing how machine learning and deep learning have improved preprocessing, segmentation, feature extraction, object tracking, and classification. We provide examples that showcase the application of machine learning and deep learning in bioimage analysis. We examine user-friendly software and tools that enable biologists to leverage these techniques without extensive computational expertise. This review is a resource for researchers seeking to incorporate machine learning and deep learning in their bioimage analysis workflows and enhance their research in this rapidly evolving field.
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Affiliation(s)
- Mahta Jan
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Canada
| | - Allie Spangaro
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Canada
| | - Michelle Lenartowicz
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Canada
| | - Mojca Mattiazzi Usaj
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Canada
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Gritti N, Power RM, Graves A, Huisken J. Image restoration of degraded time-lapse microscopy data mediated by near-infrared imaging. Nat Methods 2024; 21:311-321. [PMID: 38177507 PMCID: PMC10864180 DOI: 10.1038/s41592-023-02127-z] [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: 11/10/2022] [Accepted: 11/10/2023] [Indexed: 01/06/2024]
Abstract
Time-lapse fluorescence microscopy is key to unraveling biological development and function; however, living systems, by their nature, permit only limited interrogation and contain untapped information that can only be captured by more invasive methods. Deep-tissue live imaging presents a particular challenge owing to the spectral range of live-cell imaging probes/fluorescent proteins, which offer only modest optical penetration into scattering tissues. Herein, we employ convolutional neural networks to augment live-imaging data with deep-tissue images taken on fixed samples. We demonstrate that convolutional neural networks may be used to restore deep-tissue contrast in GFP-based time-lapse imaging using paired final-state datasets acquired using near-infrared dyes, an approach termed InfraRed-mediated Image Restoration (IR2). Notably, the networks are remarkably robust over a wide range of developmental times. We employ IR2 to enhance the information content of green fluorescent protein time-lapse images of zebrafish and Drosophila embryo/larval development and demonstrate its quantitative potential in increasing the fidelity of cell tracking/lineaging in developing pescoids. Thus, IR2 is poised to extend live imaging to depths otherwise inaccessible.
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Affiliation(s)
- Nicola Gritti
- Morgridge Institute for Research, Madison, WI, USA
- Mesoscopic Imaging Facility, European Molecular Biology Laboratory Barcelona, Barcelona, Spain
| | - Rory M Power
- Morgridge Institute for Research, Madison, WI, USA
- EMBL Imaging Center, European Molecular Biology Laboratory Heidelberg, Heidelberg, Germany
| | | | - Jan Huisken
- Morgridge Institute for Research, Madison, WI, USA.
- Department of Integrative Biology, University of Wisconsin Madison, Madison, WI, USA.
- Department of Biology and Psychology, Georg-August-University Göttingen, Göttingen, Germany.
- Cluster of Excellence 'Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells' (MBExC), University of Göttingen, Göttingen, Germany.
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Wadkin LE, Makarenko I, Parker NG, Shukurov A, Figueiredo FC, Lako M. Human Stem Cells for Ophthalmology: Recent Advances in Diagnostic Image Analysis and Computational Modelling. CURRENT STEM CELL REPORTS 2023; 9:57-66. [PMID: 38145008 PMCID: PMC10739444 DOI: 10.1007/s40778-023-00229-0] [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: 11/07/2023] [Indexed: 12/26/2023]
Abstract
Purpose of Review To explore the advances and future research directions in image analysis and computational modelling of human stem cells (hSCs) for ophthalmological applications. Recent Findings hSCs hold great potential in ocular regenerative medicine due to their application in cell-based therapies and in disease modelling and drug discovery using state-of-the-art 2D and 3D organoid models. However, a deeper characterisation of their complex, multi-scale properties is required to optimise their translation to clinical practice. Image analysis combined with computational modelling is a powerful tool to explore mechanisms of hSC behaviour and aid clinical diagnosis and therapy. Summary Many computational models draw on a variety of techniques, often blending continuum and discrete approaches, and have been used to describe cell differentiation and self-organisation. Machine learning tools are having a significant impact in model development and improving image classification processes for clinical diagnosis and treatment and will be the focus of much future research.
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Affiliation(s)
- L. E. Wadkin
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, UK
| | - I. Makarenko
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, UK
| | - N. G. Parker
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, UK
| | - A. Shukurov
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, UK
| | - F. C. Figueiredo
- Department of Ophthalmology, Royal Victoria Infirmary, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - M. Lako
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
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Thompson MK, Ceccarelli A, Ish-Horowicz D, Davis I. Dynamically regulated transcription factors are encoded by highly unstable mRNAs in the Drosophila larval brain. RNA (NEW YORK, N.Y.) 2023; 29:1020-1032. [PMID: 37041032 DOI: 10.1261/rna.079552.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 03/14/2023] [Indexed: 06/18/2023]
Abstract
The level of each RNA species depends on the balance between its rates of production and decay. Although previous studies have measured RNA decay across the genome in tissue culture and single-celled organisms, few experiments have been performed in intact complex tissues and organs. It is therefore unclear whether the determinants of RNA decay found in cultured cells are preserved in an intact tissue, and whether they differ between neighboring cell types and are regulated during development. To address these questions, we measured RNA synthesis and decay rates genome wide via metabolic labeling of whole cultured Drosophila larval brains using 4-thiouridine. Our analysis revealed that decay rates span a range of more than 100-fold, and that RNA stability is linked to gene function, with mRNAs encoding transcription factors being much less stable than mRNAs involved in core metabolic functions. Surprisingly, among transcription factor mRNAs there was a clear demarcation between more widely used transcription factors and those that are expressed only transiently during development. mRNAs encoding transient transcription factors are among the least stable in the brain. These mRNAs are characterized by epigenetic silencing in most cell types, as shown by their enrichment with the histone modification H3K27me3. Our data suggest the presence of an mRNA destabilizing mechanism targeted to these transiently expressed transcription factors to allow their levels to be regulated rapidly with high precision. Our study also demonstrates a general method for measuring mRNA transcription and decay rates in intact organs or tissues, offering insights into the role of mRNA stability in the regulation of complex developmental programs.
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Affiliation(s)
- Mary Kay Thompson
- Department of Biochemistry, University of Oxford, Oxford OX1 3QU, United Kingdom
| | - Arianna Ceccarelli
- Mathematical Institute, University of Oxford, Oxford OX1 3LB, United Kingdom
| | - David Ish-Horowicz
- Department of Biochemistry, University of Oxford, Oxford OX1 3QU, United Kingdom
| | - Ilan Davis
- Department of Biochemistry, University of Oxford, Oxford OX1 3QU, United Kingdom
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Malik H, Idris AS, Toha SF, Mohd Idris I, Daud MF, Azmi NL. A review of open-source image analysis tools for mammalian cell culture: algorithms, features and implementations. PeerJ Comput Sci 2023; 9:e1364. [PMID: 37346656 PMCID: PMC10280419 DOI: 10.7717/peerj-cs.1364] [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] [Received: 11/11/2022] [Accepted: 04/04/2023] [Indexed: 06/23/2023]
Abstract
Cell culture is undeniably important for multiple scientific applications, including pharmaceuticals, transplants, and cosmetics. However, cell culture involves multiple manual steps, such as regularly analyzing cell images for their health and morphology. Computer scientists have developed algorithms to automate cell imaging analysis, but they are not widely adopted by biologists, especially those lacking an interactive platform. To address the issue, we compile and review existing open-source cell image processing tools that provide interactive interfaces for management and prediction tasks. We highlight the prediction tools that can detect, segment, and track different mammalian cell morphologies across various image modalities and present a comparison of algorithms and unique features of these tools, whether they work locally or in the cloud. This would guide non-experts to determine which is best suited for their purposes and, developers to acknowledge what is worth further expansion. In addition, we provide a general discussion on potential implementations of the tools for a more extensive scope, which guides the reader to not restrict them to prediction tasks only. Finally, we conclude the article by stating new considerations for the development of interactive cell imaging tools and suggesting new directions for future research.
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Affiliation(s)
- Hafizi Malik
- Healthcare Engineering and Rehabilitation Research, Department of Mechatronics Engineering, International Islamic University Malaysia, Gombak, Selangor, Malaysia
| | - Ahmad Syahrin Idris
- Department of Electrical and Electronic Engineering, University of Southampton Malaysia, Iskandar Puteri, Johor, Malaysia
| | - Siti Fauziah Toha
- Healthcare Engineering and Rehabilitation Research, Department of Mechatronics Engineering, International Islamic University Malaysia, Gombak, Selangor, Malaysia
| | - Izyan Mohd Idris
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Shah Alam, Selangor, Malaysia
| | - Muhammad Fauzi Daud
- Institute of Medical Science Technology, Universiti Kuala Lumpur, Kajang, Selangor, Malaysia
| | - Nur Liyana Azmi
- Healthcare Engineering and Rehabilitation Research, Department of Mechatronics Engineering, International Islamic University Malaysia, Gombak, Selangor, Malaysia
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Translational organoid technology – the convergence of chemical, mechanical, and computational biology. Trends Biotechnol 2022; 40:1121-1135. [DOI: 10.1016/j.tibtech.2022.03.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 03/04/2022] [Accepted: 03/09/2022] [Indexed: 01/08/2023]
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Retinal Organoid Technology: Where Are We Now? Int J Mol Sci 2021; 22:ijms221910244. [PMID: 34638582 PMCID: PMC8549701 DOI: 10.3390/ijms221910244] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 09/17/2021] [Accepted: 09/20/2021] [Indexed: 12/25/2022] Open
Abstract
It is difficult to regenerate mammalian retinal cells once the adult retina is damaged, and current clinical approaches to retinal damages are very limited. The introduction of the retinal organoid technique empowers researchers to study the molecular mechanisms controlling retinal development, explore the pathogenesis of retinal diseases, develop novel treatment options, and pursue cell/tissue transplantation under a certain genetic background. Here, we revisit the historical background of retinal organoid technology, categorize current methods of organoid induction, and outline the obstacles and potential solutions to next-generation retinal organoids. Meanwhile, we recapitulate recent research progress in cell/tissue transplantation to treat retinal diseases, and discuss the pros and cons of transplanting single-cell suspension versus retinal organoid sheet for cell therapies.
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Hallou A, Yevick HG, Dumitrascu B, Uhlmann V. Deep learning for bioimage analysis in developmental biology. Development 2021; 148:dev199616. [PMID: 34490888 PMCID: PMC8451066 DOI: 10.1242/dev.199616] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage data continues to grow, this new analysis paradigm is becoming increasingly ubiquitous. In this Review, we begin by introducing the concepts needed for beginners to understand deep learning. We then review how deep learning has impacted bioimage analysis and explore the open-source resources available to integrate it into a research project. Finally, we discuss the future of deep learning applied to cell and developmental biology. We analyze how state-of-the-art methodologies have the potential to transform our understanding of biological systems through new image-based analysis and modelling that integrate multimodal inputs in space and time.
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Affiliation(s)
- Adrien Hallou
- Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, CB3 0HE, UK
- Wellcome Trust/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, CB2 1QN, UK
- Wellcome Trust/Medical Research Council Stem Cell Institute, University of Cambridge, Cambridge, CB2 1QR, UK
| | - Hannah G. Yevick
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02142, USA
| | - Bianca Dumitrascu
- Computer Laboratory, Cambridge, University of Cambridge, Cambridge, CB3 0FD, UK
| | - Virginie Uhlmann
- European Bioinformatics Institute, European Molecular Biology Laboratory, Cambridge, CB10 1SD, UK
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Wolf S, Wan Y, McDole K. Current approaches to fate mapping and lineage tracing using image data. Development 2021; 148:dev198994. [PMID: 34498046 DOI: 10.1242/dev.198994] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Visualizing, tracking and reconstructing cell lineages in developing embryos has been an ongoing effort for well over a century. Recent advances in light microscopy, labelling strategies and computational methods to analyse complex image datasets have enabled detailed investigations into the fates of cells. Combined with powerful new advances in genomics and single-cell transcriptomics, the field of developmental biology is able to describe the formation of the embryo like never before. In this Review, we discuss some of the different strategies and applications to lineage tracing in live-imaging data and outline software methodologies that can be applied to various cell-tracking challenges.
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Affiliation(s)
- Steffen Wolf
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge CB2 0QH, UK
| | - Yinan Wan
- Biozentrum, University of Basel, Basel, 4056, Switzerland
| | - Katie McDole
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge CB2 0QH, UK
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Brémond Martin C, Simon Chane C, Clouchoux C, Histace A. Recent Trends and Perspectives in Cerebral Organoids Imaging and Analysis. Front Neurosci 2021; 15:629067. [PMID: 34276279 PMCID: PMC8283195 DOI: 10.3389/fnins.2021.629067] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 05/20/2021] [Indexed: 01/04/2023] Open
Abstract
Purpose: Since their first generation in 2013, the use of cerebral organoids has spread exponentially. Today, the amount of generated data is becoming challenging to analyze manually. This review aims to overview the current image acquisition methods and to subsequently identify the needs in image analysis tools for cerebral organoids. Methods: To address this question, we went through all recent articles published on the subject and annotated the protocols, acquisition methods, and algorithms used. Results: Over the investigated period of time, confocal microscopy and bright-field microscopy were the most used acquisition techniques. Cell counting, the most common task, is performed in 20% of the articles and area; around 12% of articles calculate morphological parameters. Image analysis on cerebral organoids is performed in majority using ImageJ software (around 52%) and Matlab language (4%). Treatments remain mostly semi-automatic. We highlight the limitations encountered in image analysis in the cerebral organoid field and suggest possible solutions and implementations to develop. Conclusions: In addition to providing an overview of cerebral organoids cultures and imaging, this work highlights the need to improve the existing image analysis methods for such images and the need for specific analysis tools. These solutions could specifically help to monitor the growth of future standardized cerebral organoids.
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Affiliation(s)
- Clara Brémond Martin
- ETIS Laboratory UMR 8051, CY Cergy Paris Université, ENSEA, CNRS, Cergy, France
- WITSEE, Paris, France
| | - Camille Simon Chane
- ETIS Laboratory UMR 8051, CY Cergy Paris Université, ENSEA, CNRS, Cergy, France
| | | | - Aymeric Histace
- ETIS Laboratory UMR 8051, CY Cergy Paris Université, ENSEA, CNRS, Cergy, France
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