1
|
Randall RS, Jourdain C, Nowicka A, Kaduchová K, Kubová M, Ayoub MA, Schubert V, Tatout C, Colas I, Kalyanikrishna, Desset S, Mermet S, Boulaflous-Stevens A, Kubalová I, Mandáková T, Heckmann S, Lysak MA, Panatta M, Santoro R, Schubert D, Pecinka A, Routh D, Baroux C. Image analysis workflows to reveal the spatial organization of cell nuclei and chromosomes. Nucleus 2022; 13:277-299. [PMID: 36447428 PMCID: PMC9754023 DOI: 10.1080/19491034.2022.2144013] [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] [Indexed: 12/03/2022] Open
Abstract
Nucleus, chromatin, and chromosome organization studies heavily rely on fluorescence microscopy imaging to elucidate the distribution and abundance of structural and regulatory components. Three-dimensional (3D) image stacks are a source of quantitative data on signal intensity level and distribution and on the type and shape of distribution patterns in space. Their analysis can lead to novel insights that are otherwise missed in qualitative-only analyses. Quantitative image analysis requires specific software and workflows for image rendering, processing, segmentation, setting measurement points and reference frames and exporting target data before further numerical processing and plotting. These tasks often call for the development of customized computational scripts and require an expertise that is not broadly available to the community of experimental biologists. Yet, the increasing accessibility of high- and super-resolution imaging methods fuels the demand for user-friendly image analysis workflows. Here, we provide a compendium of strategies developed by participants of a training school from the COST action INDEPTH to analyze the spatial distribution of nuclear and chromosomal signals from 3D image stacks, acquired by diffraction-limited confocal microscopy and super-resolution microscopy methods (SIM and STED). While the examples make use of one specific commercial software package, the workflows can easily be adapted to concurrent commercial and open-source software. The aim is to encourage biologists lacking custom-script-based expertise to venture into quantitative image analysis and to better exploit the discovery potential of their images.Abbreviations: 3D FISH: three-dimensional fluorescence in situ hybridization; 3D: three-dimensional; ASY1: ASYNAPTIC 1; CC: chromocenters; CO: Crossover; DAPI: 4',6-diamidino-2-phenylindole; DMC1: DNA MEIOTIC RECOMBINASE 1; DSB: Double-Strand Break; FISH: fluorescence in situ hybridization; GFP: GREEN FLUORESCENT PROTEIN; HEI10: HUMAN ENHANCER OF INVASION 10; NCO: Non-Crossover; NE: Nuclear Envelope; Oligo-FISH: oligonucleotide fluorescence in situ hybridization; RNPII: RNA Polymerase II; SC: Synaptonemal Complex; SIM: structured illumination microscopy; ZMM (ZIP: MSH4: MSH5 and MER3 proteins); ZYP1: ZIPPER-LIKE PROTEIN 1.
Collapse
Affiliation(s)
- Ricardo S Randall
- Department of Plant and Microbial Biology, Zürich-Basel Plant Science Center, University of Zürich, Zürich, Switzerland
| | | | - Anna Nowicka
- Centre of the Region Haná for Biotechnological and Agricultural Research (CRH), Institute of Experimental Botany, v. v. i. (IEB), Olomouc, Czech Republic
| | - Kateřina Kaduchová
- Centre of the Region Haná for Biotechnological and Agricultural Research (CRH), Institute of Experimental Botany, v. v. i. (IEB), Olomouc, Czech Republic
| | - Michaela Kubová
- Central European Institute of Technology (CEITEC) and Department of Experimental Biology, Masaryk University, Brno, Czech Republic
| | - Mohammad A. Ayoub
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, D-06466Seeland, Germany
| | - Veit Schubert
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, D-06466Seeland, Germany
| | - Christophe Tatout
- Institut Génétique, Reproduction et Développement (GReD), Université Clermont Auvergne, CNRS, INSERM, 63001Clermont-Ferrand, France
| | - Isabelle Colas
- The James Hutton Institute, Errol Road, Invergowrie, DD2 5DA, Scotland UK
| | | | - Sophie Desset
- Institut Génétique, Reproduction et Développement (GReD), Université Clermont Auvergne, CNRS, INSERM, 63001Clermont-Ferrand, France
| | - Sarah Mermet
- Institut Génétique, Reproduction et Développement (GReD), Université Clermont Auvergne, CNRS, INSERM, 63001Clermont-Ferrand, France
| | - Aurélia Boulaflous-Stevens
- Institut Génétique, Reproduction et Développement (GReD), Université Clermont Auvergne, CNRS, INSERM, 63001Clermont-Ferrand, France
| | - Ivona Kubalová
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, D-06466Seeland, Germany
| | - Terezie Mandáková
- Central European Institute of Technology (CEITEC) and Department of Experimental Biology, Masaryk University, Brno, Czech Republic
| | - Stefan Heckmann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, D-06466Seeland, Germany
| | - Martin A. Lysak
- Central European Institute of Technology (CEITEC) and National Centre for Biomolecular Research, Masaryk University, Brno, Czech Republic
| | - Martina Panatta
- Department of Molecular Mechanisms of Disease, DMMD, University of Zürich, Zürich, Switzerland
| | - Raffaella Santoro
- Department of Molecular Mechanisms of Disease, DMMD, University of Zürich, Zürich, Switzerland
| | | | - Ales Pecinka
- Centre of the Region Haná for Biotechnological and Agricultural Research (CRH), Institute of Experimental Botany, v. v. i. (IEB), Olomouc, Czech Republic
| | - Devin Routh
- Service and Support for Science IT (S3IT), Universität Zürich, Zürich, Switzerland
| | - Célia Baroux
- Department of Plant and Microbial Biology, Zürich-Basel Plant Science Center, University of Zürich, Zürich, Switzerland,CONTACT Célia Baroux Department of Plant and Microbial Biology, Zürich-Basel Plant Science Center, University of Zürich, Zürich, Switzerland
| |
Collapse
|
2
|
Johann to Berens P, Schivre G, Theune M, Peter J, Sall SO, Mutterer J, Barneche F, Bourbousse C, Molinier J. Advanced Image Analysis Methods for Automated Segmentation of Subnuclear Chromatin Domains. EPIGENOMES 2022; 6:epigenomes6040034. [PMID: 36278680 PMCID: PMC9624336 DOI: 10.3390/epigenomes6040034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 09/19/2022] [Accepted: 10/01/2022] [Indexed: 11/07/2022] Open
Abstract
The combination of ever-increasing microscopy resolution with cytogenetical tools allows for detailed analyses of nuclear functional partitioning. However, the need for reliable qualitative and quantitative methodologies to detect and interpret chromatin sub-nuclear organization dynamics is crucial to decipher the underlying molecular processes. Having access to properly automated tools for accurate and fast recognition of complex nuclear structures remains an important issue. Cognitive biases associated with human-based curation or decisions for object segmentation tend to introduce variability and noise into image analysis. Here, we report the development of two complementary segmentation methods, one semi-automated (iCRAQ) and one based on deep learning (Nucl.Eye.D), and their evaluation using a collection of A. thaliana nuclei with contrasted or poorly defined chromatin compartmentalization. Both methods allow for fast, robust and sensitive detection as well as for quantification of subtle nucleus features. Based on these developments, we highlight advantages of semi-automated and deep learning-based analyses applied to plant cytogenetics.
Collapse
Affiliation(s)
| | - Geoffrey Schivre
- Institut de Biologie de l’Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, Centre National de la Recherche Scientifique, Inserm, Université PSL, 75230 Paris, France
- Université Paris-Saclay, 91190 Orsay, France
| | - Marius Theune
- FB 10 / Molekulare Pflanzenphysiologie, Bioenergetik in Photoautotrophen, Universität Kassel, 34127 Kassel, Germany
| | - Jackson Peter
- Institut de Biologie Moléculaire des Plantes du CNRS, 67000 Strasbourg, France
| | | | - Jérôme Mutterer
- Institut de Biologie Moléculaire des Plantes du CNRS, 67000 Strasbourg, France
| | - Fredy Barneche
- Institut de Biologie de l’Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, Centre National de la Recherche Scientifique, Inserm, Université PSL, 75230 Paris, France
| | - Clara Bourbousse
- Institut de Biologie de l’Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, Centre National de la Recherche Scientifique, Inserm, Université PSL, 75230 Paris, France
- Correspondence: (C.B.); (J.M.)
| | - Jean Molinier
- Institut de Biologie Moléculaire des Plantes du CNRS, 67000 Strasbourg, France
- Correspondence: (C.B.); (J.M.)
| |
Collapse
|
3
|
Mougeot G, Dubos T, Chausse F, Péry E, Graumann K, Tatout C, Evans DE, Desset S. Deep learning -- promises for 3D nuclear imaging: a guide for biologists. J Cell Sci 2022; 135:275041. [PMID: 35420128 PMCID: PMC9016621 DOI: 10.1242/jcs.258986] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
For the past century, the nucleus has been the focus of extensive investigations in cell biology. However, many questions remain about how its shape and size are regulated during development, in different tissues, or during disease and aging. To track these changes, microscopy has long been the tool of choice. Image analysis has revolutionized this field of research by providing computational tools that can be used to translate qualitative images into quantitative parameters. Many tools have been designed to delimit objects in 2D and, eventually, in 3D in order to define their shapes, their number or their position in nuclear space. Today, the field is driven by deep-learning methods, most of which take advantage of convolutional neural networks. These techniques are remarkably adapted to biomedical images when trained using large datasets and powerful computer graphics cards. To promote these innovative and promising methods to cell biologists, this Review summarizes the main concepts and terminologies of deep learning. Special emphasis is placed on the availability of these methods. We highlight why the quality and characteristics of training image datasets are important and where to find them, as well as how to create, store and share image datasets. Finally, we describe deep-learning methods well-suited for 3D analysis of nuclei and classify them according to their level of usability for biologists. Out of more than 150 published methods, we identify fewer than 12 that biologists can use, and we explain why this is the case. Based on this experience, we propose best practices to share deep-learning methods with biologists.
Collapse
Affiliation(s)
- Guillaume Mougeot
- Université Clermont Auvergne, CNRS, Inserm, GReD, F-63000 Clermont-Ferrand, France.,Department of Biological and Molecular Sciences, Faculty of Health and Life Sciences, Oxford Brookes University, Oxford OX3 0BP, UK
| | - Tristan Dubos
- Université Clermont Auvergne, CNRS, Inserm, GReD, F-63000 Clermont-Ferrand, France
| | - Frédéric Chausse
- Université Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut Pascal, F-63000 Clermont-Ferrand, France
| | - Emilie Péry
- Université Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut Pascal, F-63000 Clermont-Ferrand, France
| | - Katja Graumann
- Department of Biological and Molecular Sciences, Faculty of Health and Life Sciences, Oxford Brookes University, Oxford OX3 0BP, UK
| | - Christophe Tatout
- Université Clermont Auvergne, CNRS, Inserm, GReD, F-63000 Clermont-Ferrand, France
| | - David E Evans
- Department of Biological and Molecular Sciences, Faculty of Health and Life Sciences, Oxford Brookes University, Oxford OX3 0BP, UK
| | - Sophie Desset
- Université Clermont Auvergne, CNRS, Inserm, GReD, F-63000 Clermont-Ferrand, France
| |
Collapse
|