1
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Katoh TA, Fukai YT, Ishibashi T. Optical microscopic imaging, manipulation, and analysis methods for morphogenesis research. Microscopy (Oxf) 2024; 73:226-242. [PMID: 38102756 PMCID: PMC11154147 DOI: 10.1093/jmicro/dfad059] [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: 06/30/2023] [Revised: 11/20/2023] [Accepted: 03/22/2024] [Indexed: 12/17/2023] Open
Abstract
Morphogenesis is a developmental process of organisms being shaped through complex and cooperative cellular movements. To understand the interplay between genetic programs and the resulting multicellular morphogenesis, it is essential to characterize the morphologies and dynamics at the single-cell level and to understand how physical forces serve as both signaling components and driving forces of tissue deformations. In recent years, advances in microscopy techniques have led to improvements in imaging speed, resolution and depth. Concurrently, the development of various software packages has supported large-scale, analyses of challenging images at the single-cell resolution. While these tools have enhanced our ability to examine dynamics of cells and mechanical processes during morphogenesis, their effective integration requires specialized expertise. With this background, this review provides a practical overview of those techniques. First, we introduce microscopic techniques for multicellular imaging and image analysis software tools with a focus on cell segmentation and tracking. Second, we provide an overview of cutting-edge techniques for mechanical manipulation of cells and tissues. Finally, we introduce recent findings on morphogenetic mechanisms and mechanosensations that have been achieved by effectively combining microscopy, image analysis tools and mechanical manipulation techniques.
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Affiliation(s)
- Takanobu A Katoh
- Department of Cell Biology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Yohsuke T Fukai
- Nonequilibrium Physics of Living Matter RIKEN Hakubi Research Team, RIKEN Center for Biosystems Dynamics Research, 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Tomoki Ishibashi
- Laboratory for Physical Biology, RIKEN Center for Biosystems Dynamics Research, 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
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2
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Xiao J, Turner JJ, Kõivomägi M, Skotheim JM. Whi5 hypo- and hyper-phosphorylation dynamics control cell-cycle entry and progression. Curr Biol 2024; 34:2434-2447.e5. [PMID: 38749424 DOI: 10.1016/j.cub.2024.04.052] [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: 09/13/2023] [Revised: 03/18/2024] [Accepted: 04/23/2024] [Indexed: 05/28/2024]
Abstract
Progression through the cell cycle depends on the phosphorylation of key substrates by cyclin-dependent kinases. In budding yeast, these substrates include the transcriptional inhibitor Whi5 that regulates G1/S transition. In early G1 phase, Whi5 is hypo-phosphorylated and inhibits the Swi4/Swi6 (SBF) complex that promotes transcription of the cyclins CLN1 and CLN2. In late G1, Whi5 is rapidly hyper-phosphorylated by Cln1 and Cln2 in complex with the cyclin-dependent kinase Cdk1. This hyper-phosphorylation inactivates Whi5 and excludes it from the nucleus. Here, we set out to determine the molecular mechanisms responsible for Whi5's multi-site phosphorylation and how they regulate the cell cycle. To do this, we first identified the 19 Whi5 sites that are appreciably phosphorylated and then determined which of these sites are responsible for G1 hypo-phosphorylation. Mutation of 7 sites removed G1 hypo-phosphorylation, increased cell size, and delayed the G1/S transition. Moreover, the rapidity of Whi5 hyper-phosphorylation in late G1 depends on "priming" sites that dock the Cks1 subunit of Cln1,2-Cdk1 complexes. Hyper-phosphorylation is crucial for Whi5 nuclear export, normal cell size, full expression of SBF target genes, and timely progression through both the G1/S transition and S/G2/M phases. Thus, our work shows how Whi5 phosphorylation regulates the G1/S transition and how it is required for timely progression through S/G2/M phases and not only G1 as previously thought.
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Affiliation(s)
- Jordan Xiao
- Department of Biology, Stanford University, 327 Campus Dr., Stanford, CA 94305, USA
| | - Jonathan J Turner
- Department of Biology, Stanford University, 327 Campus Dr., Stanford, CA 94305, USA
| | - Mardo Kõivomägi
- Department of Biology, Stanford University, 327 Campus Dr., Stanford, CA 94305, USA; Laboratory of Biochemistry and Molecular Biology, National Cancer Institute, National Institutes of Health, 37 Convent Dr., Bethesda, MD 20892, USA.
| | - Jan M Skotheim
- Department of Biology, Stanford University, 327 Campus Dr., Stanford, CA 94305, USA; Chan Zuckerberg Biohub, 499 Illinois St., San Francisco, CA 94158, USA.
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3
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Ramakanth S, Kennedy T, Yalcinkaya B, Neupane S, Tadic N, Buchler NE, Argüello-Miranda O. Deep learning-driven imaging of cell division and cell growth across an entire eukaryotic life cycle. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.25.591211. [PMID: 38712227 PMCID: PMC11071524 DOI: 10.1101/2024.04.25.591211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
The life cycle of biomedical and agriculturally relevant eukaryotic microorganisms involves complex transitions between proliferative and non-proliferative states such as dormancy, mating, meiosis, and cell division. New drugs, pesticides, and vaccines can be created by targeting specific life cycle stages of parasites and pathogens. However, defining the structure of a microbial life cycle often relies on partial observations that are theoretically assembled in an ideal life cycle path. To create a more quantitative approach to studying complete eukaryotic life cycles, we generated a deep learning-driven imaging framework to track microorganisms across sexually reproducing generations. Our approach combines microfluidic culturing, life cycle stage-specific segmentation of microscopy images using convolutional neural networks, and a novel cell tracking algorithm, FIEST, based on enhancing the overlap of single cell masks in consecutive images through deep learning video frame interpolation. As proof of principle, we used this approach to quantitatively image and compare cell growth and cell cycle regulation across the sexual life cycle of Saccharomyces cerevisiae . We developed a fluorescent reporter system based on a fluorescently labeled Whi5 protein, the yeast analog of mammalian Rb, and a new High-Cdk1 activity sensor, LiCHI, designed to report during DNA replication, mitosis, meiotic homologous recombination, meiosis I, and meiosis II. We found that cell growth preceded the exit from non-proliferative states such as mitotic G1, pre-meiotic G1, and the G0 spore state during germination. A decrease in the total cell concentration of Whi5 characterized the exit from non-proliferative states, which is consistent with a Whi5 dilution model. The nuclear accumulation of Whi5 was developmentally regulated, being at its highest during meiotic exit and spore formation. The temporal coordination of cell division and growth was not significantly different across three sexually reproducing generations. Our framework could be used to quantitatively characterize other single-cell eukaryotic life cycles that remain incompletely described. An off-the-shelf user interface Yeastvision provides free access to our image processing and single-cell tracking algorithms.
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4
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Quinsgaard EMB, Korsnes MS, Korsnes R, Moestue SA. Single-cell tracking as a tool for studying EMT-phenotypes. Exp Cell Res 2024; 437:113993. [PMID: 38485079 DOI: 10.1016/j.yexcr.2024.113993] [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: 10/02/2023] [Revised: 02/28/2024] [Accepted: 03/06/2024] [Indexed: 03/24/2024]
Abstract
This article demonstrates that label-free single-cell video tracking is a useful approach for in vitro studies of Epithelial-Mesenchymal Transition (EMT). EMT is a highly heterogeneous process, involved in wound healing, embryogenesis and cancer. The process promotes metastasis, and increased understanding can aid development of novel therapeutic strategies. The role of EMT-associated biomarkers depends on biological context, making it challenging to compare and interpret data from different studies. We demonstrate single-cell video tracking for comprehensive phenotype analysis. In this study we performed single-cell video tracking on 72-h long recordings. We quantified several behaviours at a single-cell level during induced EMT in MDA-MB-468 cells. This revealed notable variations in migration speed, with different dose-response patterns and varying distributions of speed. By registering cell morphologies during the recording, we determined preferred paths of morphological transitions. We also found a clear association between migration speed and cell morphology. We found elevated rates of cell death, diminished proliferation, and an increase in mitotic failures followed by re-fusion of sister-cells. The method allows tracking of phenotypes in cell lineages, which can be particularly useful in epigenetic studies. Sister-cells were found to have significant similarities in their speeds and morphologies, illustrating the heritability of these traits.
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Affiliation(s)
- Ellen Marie Botne Quinsgaard
- Norwegian University of Science and Technology (NTNU), Department of Clinical and Molecular Medicine, NO-7491 Trondheim, Norway.
| | - Mónica Suárez Korsnes
- Norwegian University of Science and Technology (NTNU), Department of Clinical and Molecular Medicine, NO-7491 Trondheim, Norway; Korsnes Biocomputing (KoBio), Trondheim, Norway
| | | | - Siver Andreas Moestue
- Norwegian University of Science and Technology (NTNU), Department of Clinical and Molecular Medicine, NO-7491 Trondheim, Norway; Department of Pharmacy, Nord University, Bodø, Norway
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5
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Kato S, Hotta K. Automatic enhancement preprocessing for segmentation of low quality cell images. Sci Rep 2024; 14:3619. [PMID: 38351053 PMCID: PMC10864346 DOI: 10.1038/s41598-024-53411-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 01/31/2024] [Indexed: 02/16/2024] Open
Abstract
We present a novel automatic preprocessing and ensemble learning technique for the segmentation of low-quality cell images. Capturing cells subjected to intense light is challenging due to their vulnerability to light-induced cell death. Consequently, microscopic cell images tend to be of low quality and it causes low accuracy for semantic segmentation. This problem can not be satisfactorily solved by classical image preprocessing methods. Therefore, we propose a novel approach of automatic enhancement preprocessing (AEP), which translates an input image into images that are easy to recognize by deep learning. AEP is composed of two deep neural networks, and the penultimate feature maps of the first network are employed as filters to translate an input image with low quality into images that are easily classified by deep learning. Additionally, we propose an automatic weighted ensemble learning (AWEL), which combines the multiple segmentation results. Since the second network predicts segmentation results corresponding to each translated input image, multiple segmentation results can be aggregated by automatically determining suitable weights. Experiments on two types of cell image segmentation confirmed that AEP can translate low-quality cell images into images that are easy to segment and that segmentation accuracy improves using AWEL.
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Affiliation(s)
- Sota Kato
- Department of Electrical, Information, Materials and Materials Engineering, Graduate School of Science and Engineering, Meijo University, Shiogamaguchi, Tempaku-ku, Nagoya, Aichi, 468-8502, Japan.
| | - Kazuhiro Hotta
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Meijo University, Nagoya, Aichi, Japan
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6
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Kato S, Hotta K. Adaptive t-vMF dice loss: An effective expansion of dice loss for medical image segmentation. Comput Biol Med 2024; 168:107695. [PMID: 38061152 DOI: 10.1016/j.compbiomed.2023.107695] [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: 11/25/2022] [Revised: 10/30/2023] [Accepted: 11/06/2023] [Indexed: 01/10/2024]
Abstract
Dice loss is widely used for medical image segmentation, and many improved loss functions have been proposed. However, further Dice loss improvements are still possible. In this study, we reconsidered the use of Dice loss and discovered that Dice loss can be rewritten in the loss function using the cosine similarity through a simple equation transformation. Using this knowledge, we present a novel t-vMF Dice loss based on the t-vMF similarity instead of the cosine similarity. Based on the t-vMF similarity, our proposed Dice loss is formulated in a more compact similarity loss function than the original Dice loss. Furthermore, we present an effective algorithm that automatically determines the parameter κ for the t-vMF similarity using a validation accuracy, called Adaptive t-vMF Dice loss. Using this algorithm, it is possible to apply more compact similarities for easy classes and wider similarities for difficult classes, and we are able to achieve adaptive training based on the accuracy of each class. We evaluated binary segmentation datasets of CVC-ClinicDB and Kvasir-SEG, and multi-class segmentation datasets of Automated Cardiac Diagnosis Challenge and Synapse multi-organ segmentation. Through experiments conducted on four datasets using a five-fold cross-validation, we confirmed that the Dice score coefficient (DSC) was further improved in comparison with the original Dice loss and other loss functions.
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Affiliation(s)
- Sota Kato
- Department of Electrical, Information, Materials and Materials Engineering, Meijo University, Tempaku-ku, Nagoya, 468-8502, Aichi, Japan.
| | - Kazuhiro Hotta
- Department of Electrical and Electronic Engineering, Meijo University, Nagoya, Japan.
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7
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Piñeiro López C, Rodrigues Neves AR, Čavka I, Gros OJ, Köhler S. Segmentation of C. elegans germline nuclei. MICROPUBLICATION BIOLOGY 2023; 2023:10.17912/micropub.biology.001062. [PMID: 38148986 PMCID: PMC10750166 DOI: 10.17912/micropub.biology.001062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/05/2023] [Accepted: 12/08/2023] [Indexed: 12/28/2023]
Abstract
Immunofluorescence microscopy is a widely adopted method for studying meiotic prophase in the nematode model organism, Caenorhabditis elegans . An in-depth examination of specific meiotic processes requires the quantitative analysis of immunofluorescence images, which often involves the segmentation of individual cells or nuclei. Here, we introduce our image analysis pipeline to automate significant portions of this task. This pipeline relies on the powerful deep learning model Cellpose 2.0 to segment cellular structures. To further improve the segmentation accuracy for germline nuclei stained for chromatin or synaptonemal complexes, we retrained the generalist Cellpose model and integrated our data processing pipeline into the easy-to-use Cell-ACDC image analysis software. Our pipeline thus makes deep learning-based segmentation of nuclei in the distal germline of C. elegans accessible for users without coding experience.
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Affiliation(s)
- Cristina Piñeiro López
- Cell Biology and Biophysics, European Molecular Biology Laboratory, Heidelberg, Baden-Württemberg, Germany
| | - Ana Rita Rodrigues Neves
- Cell Biology and Biophysics, European Molecular Biology Laboratory, Heidelberg, Baden-Württemberg, Germany
- Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences, Heidelberg, Germany
| | - Ivana Čavka
- Cell Biology and Biophysics, European Molecular Biology Laboratory, Heidelberg, Baden-Württemberg, Germany
- Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences, Heidelberg, Germany
| | - Oane Jan Gros
- Cell Biology and Biophysics, European Molecular Biology Laboratory, Heidelberg, Baden-Württemberg, Germany
| | - Simone Köhler
- Cell Biology and Biophysics, European Molecular Biology Laboratory, Heidelberg, Baden-Württemberg, Germany
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8
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Pylvänäinen JW, Gómez-de-Mariscal E, Henriques R, Jacquemet G. Live-cell imaging in the deep learning era. Curr Opin Cell Biol 2023; 85:102271. [PMID: 37897927 DOI: 10.1016/j.ceb.2023.102271] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/29/2023] [Accepted: 10/02/2023] [Indexed: 10/30/2023]
Abstract
Live imaging is a powerful tool, enabling scientists to observe living organisms in real time. In particular, when combined with fluorescence microscopy, live imaging allows the monitoring of cellular components with high sensitivity and specificity. Yet, due to critical challenges (i.e., drift, phototoxicity, dataset size), implementing live imaging and analyzing the resulting datasets is rarely straightforward. Over the past years, the development of bioimage analysis tools, including deep learning, is changing how we perform live imaging. Here we briefly cover important computational methods aiding live imaging and carrying out key tasks such as drift correction, denoising, super-resolution imaging, artificial labeling, tracking, and time series analysis. We also cover recent advances in self-driving microscopy.
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Affiliation(s)
- Joanna W Pylvänäinen
- Faculty of Science and Engineering, Cell Biology, Åbo Akademi, University, 20520 Turku, Finland
| | | | - Ricardo Henriques
- Instituto Gulbenkian de Ciência, Oeiras 2780-156, Portugal; University College London, London WC1E 6BT, United Kingdom
| | - Guillaume Jacquemet
- Faculty of Science and Engineering, Cell Biology, Åbo Akademi, University, 20520 Turku, Finland; Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku and Åbo Akademi University, 20520 Turku, Finland; Turku Bioimaging, University of Turku and Åbo Akademi University, FI- 20520 Turku, Finland.
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9
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Chai B, Efstathiou C, Yue H, Draviam VM. Opportunities and challenges for deep learning in cell dynamics research. Trends Cell Biol 2023:S0962-8924(23)00228-3. [PMID: 38030542 DOI: 10.1016/j.tcb.2023.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/30/2023] [Accepted: 10/13/2023] [Indexed: 12/01/2023]
Abstract
The growth of artificial intelligence (AI) has led to an increase in the adoption of computer vision and deep learning (DL) techniques for the evaluation of microscopy images and movies. This adoption has not only addressed hurdles in quantitative analysis of dynamic cell biological processes but has also started to support advances in drug development, precision medicine, and genome-phenome mapping. We survey existing AI-based techniques and tools, as well as open-source datasets, with a specific focus on the computational tasks of segmentation, classification, and tracking of cellular and subcellular structures and dynamics. We summarise long-standing challenges in microscopy video analysis from a computational perspective and review emerging research frontiers and innovative applications for DL-guided automation in cell dynamics research.
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Affiliation(s)
- Binghao Chai
- School of Biological and Behavioural Sciences, Queen Mary University of London (QMUL), London E1 4NS, UK
| | - Christoforos Efstathiou
- School of Biological and Behavioural Sciences, Queen Mary University of London (QMUL), London E1 4NS, UK
| | - Haoran Yue
- School of Biological and Behavioural Sciences, Queen Mary University of London (QMUL), London E1 4NS, UK
| | - Viji M Draviam
- School of Biological and Behavioural Sciences, Queen Mary University of London (QMUL), London E1 4NS, UK; The Alan Turing Institute, London NW1 2DB, UK.
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10
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Xiao J, Turner JJ, Kõivomägi M, Skotheim JM. Whi5 hypo- and hyper-phosphorylation dynamics control cell cycle entry and progression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.02.565392. [PMID: 37961465 PMCID: PMC10635099 DOI: 10.1101/2023.11.02.565392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Progression through the cell cycle depends on the phosphorylation of key substrates by cyclin-dependent kinases. In budding yeast, these substrates include the transcriptional inhibitor Whi5 that regulates the G1/S transition. In early G1 phase, Whi5 is hypo-phosphorylated and inhibits the SBF complex that promotes transcription of the cyclins CLN1 and CLN2 . In late-G1, Whi5 is rapidly hyper-phosphorylated by Cln1,2 in complex with the cyclin-dependent kinase Cdk1. This hyper-phosphorylation inactivates Whi5 and excludes it from the nucleus. Here, we set out to determine the molecular mechanisms responsible for Whi5's multi-site phosphorylation and how they regulate the cell cycle. To do this, we first identified the 19 Whi5 sites that are appreciably phosphorylated and then determined which of these sites are responsible for G1 hypo-phosphorylation. Mutation of 7 sites removed G1 hypo-phosphorylation, increased cell size, and delayed the G1/S transition. Moreover, the rapidity of Whi5 hyper-phosphorylation in late G1 depends on 'priming' sites that dock the Cks1 subunit of Cln1,2-Cdk1 complexes. Hyper-phosphorylation is crucial for Whi5 nuclear export, normal cell size, full expression of SBF target genes, and timely progression through both the G1/S transition and S/G2/M phases. Thus, our work shows how Whi5 phosphorylation regulates the G1/S transition and how it is required for timely progression through S/G2/M phases and not only G1 as previously thought.
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11
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Seel A, Padovani F, Mayer M, Finster A, Bureik D, Thoma F, Osman C, Klecker T, Schmoller KM. Regulation with cell size ensures mitochondrial DNA homeostasis during cell growth. Nat Struct Mol Biol 2023; 30:1549-1560. [PMID: 37679564 PMCID: PMC10584693 DOI: 10.1038/s41594-023-01091-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 08/07/2023] [Indexed: 09/09/2023]
Abstract
To maintain stable DNA concentrations, proliferating cells need to coordinate DNA replication with cell growth. For nuclear DNA, eukaryotic cells achieve this by coupling DNA replication to cell-cycle progression, ensuring that DNA is doubled exactly once per cell cycle. By contrast, mitochondrial DNA replication is typically not strictly coupled to the cell cycle, leaving the open question of how cells maintain the correct amount of mitochondrial DNA during cell growth. Here, we show that in budding yeast, mitochondrial DNA copy number increases with cell volume, both in asynchronously cycling populations and during G1 arrest. Our findings suggest that cell-volume-dependent mitochondrial DNA maintenance is achieved through nuclear-encoded limiting factors, including the mitochondrial DNA polymerase Mip1 and the packaging factor Abf2, whose amount increases in proportion to cell volume. By directly linking mitochondrial DNA maintenance to nuclear protein synthesis and thus cell growth, constant mitochondrial DNA concentrations can be robustly maintained without a need for cell-cycle-dependent regulation.
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Affiliation(s)
- Anika Seel
- Institute of Functional Epigenetics, Molecular Targets and Therapeutics Center, Helmholtz Zentrum München, Neuherberg, Germany
| | - Francesco Padovani
- Institute of Functional Epigenetics, Molecular Targets and Therapeutics Center, Helmholtz Zentrum München, Neuherberg, Germany
| | - Moritz Mayer
- Institute of Cell Biology, University of Bayreuth, Bayreuth, Germany
| | - Alissa Finster
- Institute of Functional Epigenetics, Molecular Targets and Therapeutics Center, Helmholtz Zentrum München, Neuherberg, Germany
| | - Daniela Bureik
- Institute of Functional Epigenetics, Molecular Targets and Therapeutics Center, Helmholtz Zentrum München, Neuherberg, Germany
| | - Felix Thoma
- Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany
| | - Christof Osman
- Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany
| | - Till Klecker
- Institute of Cell Biology, University of Bayreuth, Bayreuth, Germany
| | - Kurt M Schmoller
- Institute of Functional Epigenetics, Molecular Targets and Therapeutics Center, Helmholtz Zentrum München, Neuherberg, Germany.
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12
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Ohira M, Rhind N. pomBseen: An automated pipeline for analysis of fission yeast images. PLoS One 2023; 18:e0291391. [PMID: 37699057 PMCID: PMC10497161 DOI: 10.1371/journal.pone.0291391] [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: 01/06/2023] [Accepted: 08/25/2023] [Indexed: 09/14/2023] Open
Abstract
Fission yeast is a model organism widely used for studies of eukaryotic cell biology. As such, it is subject to bright-field and fluorescent microscopy. Manual analysis of such data can be laborious and subjective. Therefore, we have developed pomBseen, an image analysis pipeline for the quantitation of fission yeast micrographs containing a bright-field channel and up to two fluorescent channels. It accepts a wide range of image formats and produces a table with the size and total and nuclear fluorescent intensities of the cells in the image. Benchmarking of the pipeline against manually annotated datasets demonstrates that it reliably segments cells and acquires their image parameters. Written in MATLAB, pomBseen is also available as a standalone application.
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Affiliation(s)
- Makoto Ohira
- Department of Biochemistry and Molecular Biotechnology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
| | - Nicholas Rhind
- Department of Biochemistry and Molecular Biotechnology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
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13
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Corallo D, Dalla Vecchia M, Lazic D, Taschner-Mandl S, Biffi A, Aveic S. The molecular basis of tumor metastasis and current approaches to decode targeted migration-promoting events in pediatric neuroblastoma. Biochem Pharmacol 2023; 215:115696. [PMID: 37481138 DOI: 10.1016/j.bcp.2023.115696] [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: 04/23/2023] [Revised: 07/12/2023] [Accepted: 07/12/2023] [Indexed: 07/24/2023]
Abstract
Cell motility is a crucial biological process that plays a critical role in the development of multicellular organisms and is essential for tissue formation and regeneration. However, uncontrolled cell motility can lead to the development of various diseases, including neoplasms. In this review, we discuss recent advances in the discovery of regulatory mechanisms underlying the metastatic spread of neuroblastoma, a solid pediatric tumor that originates in the embryonic migratory cells of the neural crest. The highly motile phenotype of metastatic neuroblastoma cells requires targeting of intracellular and extracellular processes, that, if affected, would be helpful for the treatment of high-risk patients with neuroblastoma, for whom current therapies remain inadequate. Development of new potentially migration-inhibiting compounds and standardized preclinical approaches for the selection of anti-metastatic drugs in neuroblastoma will also be discussed.
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Affiliation(s)
- Diana Corallo
- Laboratory of Target Discovery and Biology of Neuroblastoma, Istituto di Ricerca Pediatrica (IRP), Fondazione Città della Speranza, 35127 Padova, Italy
| | - Marco Dalla Vecchia
- Laboratory of Target Discovery and Biology of Neuroblastoma, Istituto di Ricerca Pediatrica (IRP), Fondazione Città della Speranza, 35127 Padova, Italy
| | - Daria Lazic
- St. Anna Children's Cancer Research Institute, CCRI, Zimmermannplatz 10, 1090, Vienna, Austria
| | - Sabine Taschner-Mandl
- St. Anna Children's Cancer Research Institute, CCRI, Zimmermannplatz 10, 1090, Vienna, Austria
| | - Alessandra Biffi
- Pediatric Hematology, Oncology and Stem Cell Transplant Division, Woman's and Child Health Department, University of Padova, 35121 Padova, Italy
| | - Sanja Aveic
- Laboratory of Target Discovery and Biology of Neuroblastoma, Istituto di Ricerca Pediatrica (IRP), Fondazione Città della Speranza, 35127 Padova, Italy.
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14
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Al-Refaie N, Padovani F, Binando F, Hornung J, Zhao Q, Towbin BD, Cenik ES, Stroustrup N, Schmoller KM, Cabianca DS. An mTOR/RNA pol I axis shapes chromatin architecture in response to fasting. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.22.550032. [PMID: 37503059 PMCID: PMC10370172 DOI: 10.1101/2023.07.22.550032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Chromatin architecture is a fundamental mediator of genome function. Fasting is a major environmental cue across the animal kingdom. Yet, how it impacts on 3D genome organization is unknown. Here, we show that fasting induces a reversible and large-scale spatial reorganization of chromatin in C. elegans . This fasting-induced 3D genome reorganization requires inhibition of the nutrient-sensing mTOR pathway, a major regulator of ribosome biogenesis. Remarkably, loss of transcription by RNA Pol I, but not RNA Pol II nor Pol III, induces a similar 3D genome reorganization in fed animals, and prevents the restoration of the fed-state architecture upon restoring nutrients to fasted animals. Our work documents the first large-scale chromatin reorganization triggered by fasting and reveals that mTOR and RNA Pol I shape genome architecture in response to nutrients.
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Tsai HF, Podder S, Chen PY. Microsystem Advances through Integration with Artificial Intelligence. MICROMACHINES 2023; 14:826. [PMID: 37421059 DOI: 10.3390/mi14040826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 07/09/2023]
Abstract
Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.
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Affiliation(s)
- Hsieh-Fu Tsai
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
- Center for Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Soumyajit Podder
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Pin-Yuan Chen
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
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Fukai YT, Kawaguchi K. LapTrack: linear assignment particle tracking with tunable metrics. Bioinformatics 2022; 39:6887138. [PMID: 36495181 PMCID: PMC9825786 DOI: 10.1093/bioinformatics/btac799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/09/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022] Open
Abstract
MOTIVATION Particle tracking is an important step of analysis in a variety of scientific fields and is particularly indispensable for the construction of cellular lineages from live images. Although various supervised machine learning methods have been developed for cell tracking, the diversity of the data still necessitates heuristic methods that require parameter estimations from small amounts of data. For this, solving tracking as a linear assignment problem (LAP) has been widely applied and demonstrated to be efficient. However, there has been no implementation that allows custom connection costs, parallel parameter tuning with ground truth annotations, and the functionality to preserve ground truth connections, limiting the application to datasets with partial annotations. RESULTS We developed LapTrack, a LAP-based tracker which allows including arbitrary cost functions and inputs, parallel parameter tuning and ground-truth track preservation. Analysis of real and artificial datasets demonstrates the advantage of custom metric functions for tracking score improvement from distance-only cases. The tracker can be easily combined with other Python-based tools for particle detection, segmentation and visualization. AVAILABILITY AND IMPLEMENTATION LapTrack is available as a Python package on PyPi, and the notebook examples are shared at https://github.com/yfukai/laptrack. The data and code for this publication are hosted at https://github.com/NoneqPhysLivingMatterLab/laptrack-optimisation. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Kyogo Kawaguchi
- Nonequilibrium Physics of Living Matter RIKEN Hakubi Research Team, RIKEN Center for Biosystems Dynamics Research, Kobe 650-0047, Japan,RIKEN Cluster for Pioneering Research, Kobe 650-0047, Japan,Universal Biology Institute, The University of Tokyo, Tokyo 113-0033, Japan
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Freitag M, Jaklin S, Padovani F, Radzichevici E, Zernia S, Schmoller KM, Stigler J. Single-molecule experiments reveal the elbow as an essential folding guide in SMC coiled-coil arms. Biophys J 2022; 121:4702-4713. [PMID: 36242515 PMCID: PMC9748247 DOI: 10.1016/j.bpj.2022.10.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 09/16/2022] [Accepted: 10/12/2022] [Indexed: 11/19/2022] Open
Abstract
Structural maintenance of chromosome (SMC) complexes form ring-like structures through exceptional elongated coiled-coils (CCs). Recent studies found that variable CC conformations, including open and collapsed forms, which might result from discontinuities in the CC, facilitate the diverse functions of SMCs in DNA organization. However, a detailed description of the SMC CC architecture is still missing. Here, we study the structural composition and mechanical properties of SMC proteins with optical tweezers unfolding experiments using the isolated Psm3 CC as a model system. We find a comparatively unstable protein with three unzipping intermediates, which we could directly assign to CC features by crosslinking experiments and state-of-the-art prediction software. Particularly, the CC elbow is shown to be a flexible, potentially non-structured feature, which divides the CC into sections, induces a pairing shift from one CC strand to the other and could facilitate large-scale conformational changes, most likely via thermal fluctuations of the flanking CC sections. A replacement of the elbow amino acids hinders folding of the consecutive CC region and frequently leads to non-native misalignments, revealing the elbow as a guide for proper folding. Additional in vivo manipulation of the elbow flexibility resulted in impaired cohesin complexes, which directly link the sensitive CC architecture to the biological function of cohesin.
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Affiliation(s)
- Marvin Freitag
- Gene Center Munich, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Sigrun Jaklin
- Gene Center Munich, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Francesco Padovani
- Institute of Functional Epigenetics, Helmholtz Zentrum München, Neuherberg, Germany
| | | | - Sarah Zernia
- Gene Center Munich, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Kurt M Schmoller
- Institute of Functional Epigenetics, Helmholtz Zentrum München, Neuherberg, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Johannes Stigler
- Gene Center Munich, Ludwig-Maximilians-Universität München, Munich, Germany.
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Davies DM, van den Handel K, Bharadwaj S, Lengefeld J. Cellular enlargement - A new hallmark of aging? Front Cell Dev Biol 2022; 10:1036602. [PMID: 36438561 PMCID: PMC9688412 DOI: 10.3389/fcell.2022.1036602] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 10/17/2022] [Indexed: 12/03/2023] Open
Abstract
Years of important research has revealed that cells heavily invest in regulating their size. Nevertheless, it has remained unclear why accurate size control is so important. Our recent study using hematopoietic stem cells (HSCs) in vivo indicates that cellular enlargement is causally associated with aging. Here, we present an overview of these findings and their implications. Furthermore, we performed a broad literature analysis to evaluate the potential of cellular enlargement as a new aging hallmark and to examine its connection to previously described aging hallmarks. Finally, we highlight interesting work presenting a correlation between cell size and age-related diseases. Taken together, we found mounting evidence linking cellular enlargement to aging and age-related diseases. Therefore, we encourage researchers from seemingly unrelated areas to take a fresh look at their data from the perspective of cell size.
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Affiliation(s)
- Daniel M. Davies
- Institute of Biotechnology, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Kim van den Handel
- Institute of Biotechnology, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Soham Bharadwaj
- Institute of Biotechnology, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Jette Lengefeld
- Institute of Biotechnology, HiLIFE, University of Helsinki, Helsinki, Finland
- Center for Hematology and Regenerative Medicine, Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
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Kukhtevich IV, Rivero-Romano M, Rakesh N, Bheda P, Chadha Y, Rosales-Becerra P, Hamperl S, Bureik D, Dornauer S, Dargemont C, Kirmizis A, Schmoller KM, Schneider R. Quantitative RNA imaging in single live cells reveals age-dependent asymmetric inheritance. Cell Rep 2022; 41:111656. [DOI: 10.1016/j.celrep.2022.111656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 08/31/2022] [Accepted: 10/20/2022] [Indexed: 11/18/2022] Open
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Altered expression response upon repeated gene repression in single yeast cells. PLoS Comput Biol 2022; 18:e1010640. [PMID: 36256678 PMCID: PMC9633002 DOI: 10.1371/journal.pcbi.1010640] [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: 07/04/2022] [Revised: 11/03/2022] [Accepted: 10/05/2022] [Indexed: 11/06/2022] Open
Abstract
Cells must continuously adjust to changing environments and, thus, have evolved mechanisms allowing them to respond to repeated stimuli. While faster gene induction upon a repeated stimulus is known as reinduction memory, responses to repeated repression have been less studied so far. Here, we studied gene repression across repeated carbon source shifts in over 1,500 single Saccharomyces cerevisiae cells. By monitoring the expression of a carbon source-responsive gene, galactokinase 1 (Gal1), and fitting a mathematical model to the single-cell data, we observed a faster response upon repeated repressions at the population level. Exploiting our single-cell data and quantitative modeling approach, we discovered that the faster response is mediated by a shortened repression response delay, the estimated time between carbon source shift and Gal1 protein production termination. Interestingly, we can exclude two alternative hypotheses, i) stronger dilution because of e.g., increased proliferation, and ii) a larger fraction of repressing cells upon repeated repressions. Collectively, our study provides a quantitative description of repression kinetics in single cells and allows us to pinpoint potential mechanisms underlying a faster response upon repeated repression. The computational results of our study can serve as the starting point for experimental follow-up studies. Cells have to continuously adjust to their environment and cope with changing temperature, stress conditions, or metabolic resources. Yeast cells exposed to repeated carbon source shifts have shown to be “primed” by their first exposure, exhibiting enhanced gene expression of specific genes later on. However, how cells respond to a repeated repressive stimulus, e.g., withdrawal of metabolic resources, has been so far much less studied. For this, we investigated the expression kinetics of a carbon source-responsive gene across repeated repressions. We measured single-cell expression and used mathematical modeling to evaluate potential causes underlying an observed faster repression response upon a repeated stimulus. Specifically, we investigated whether i) an increased dilution due to e.g., proliferation, ii) an increased fraction of repressing cells, or iii) different kinetic parameters in the repeated repression cause the observed faster response in the second repression. Leveraging quantitative mathematical model comparison, we discovered that the faster response is mediated by a shortened estimated time between carbon source shift and protein production termination at the single-cell level. Our study provides a quantitative description of repression kinetics in single cells and allows us to pinpoint potential mechanisms underlying a faster response upon repeated repression.
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