1
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Refahi Y, Zoghlami A, Viné T, Terryn C, Paës G. Plant cell wall enzymatic deconstruction: Bridging the gap between micro and nano scales. BIORESOURCE TECHNOLOGY 2024; 414:131551. [PMID: 39370009 DOI: 10.1016/j.biortech.2024.131551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2024] [Revised: 09/27/2024] [Accepted: 09/29/2024] [Indexed: 10/08/2024]
Abstract
Understanding lignocellulosic biomass resistance to enzymatic deconstruction is crucial for its sustainable conversion into bioproducts. Despite scientific advances, quantitative morphological analysis of plant deconstruction at cell and tissue scales remains under-explored. In this study, an original pipeline is devised, involving four-dimensional (space + time) fluorescence confocal imaging, and a novel computational tool, to track and quantify deconstruction at cell and tissue scales. By applying this pipeline to poplar wood, dynamics of cellular parameters was computed and cellulose conversion during enzymatic deconstruction was measured. Results showed that enzymatic deconstruction predominantly impacts cell wall volume rather than surface area. Additionally, a negative correlation was observed between pre-hydrolysis compactness measures and volumetric cell wall deconstruction rate, whose strength was modulated by enzymatic activity. Results also revealed a strong positive correlation between average volumetric cell wall deconstruction rate and cellulose conversion rate. These findings link key deconstruction parameters across nano and micro scales.
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Affiliation(s)
- Yassin Refahi
- Université de Reims-Champagne-Ardenne, INRAE, FARE, UMR A 614, Reims 51100, France
| | - Aya Zoghlami
- Université de Reims-Champagne-Ardenne, INRAE, FARE, UMR A 614, Reims 51100, France
| | - Thibaut Viné
- Université de Reims-Champagne-Ardenne, INRAE, FARE, UMR A 614, Reims 51100, France
| | - Christine Terryn
- Platform of Cellular and Tissular Imaging (PICT), Université de Reims Champagne Ardenne, 51100 Reims, France
| | - Gabriel Paës
- Université de Reims-Champagne-Ardenne, INRAE, FARE, UMR A 614, Reims 51100, France
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2
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Hockenberry MA, Daugird TA, Legant WR. Cell dynamics revealed by microscopy advances. Curr Opin Cell Biol 2024; 90:102418. [PMID: 39159598 PMCID: PMC11392612 DOI: 10.1016/j.ceb.2024.102418] [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: 05/01/2024] [Revised: 07/23/2024] [Accepted: 07/24/2024] [Indexed: 08/21/2024]
Abstract
Cell biology emerges from spatiotemporally coordinated molecular processes. Recent advances in live-cell microscopy, fueled by a surge in optical, molecular, and computational technologies, have enabled dynamic observations from single molecules to whole organisms. Despite technological leaps, there is still an untapped opportunity to fully leverage their capabilities toward biological insight. We highlight how single-molecule imaging has transformed our understanding of biological processes, with a focus on chromatin organization and transcription in the nucleus. We describe how this was enabled by the close integration of new imaging techniques with analysis tools and discuss the challenges to make a comparable impact at larger scales from organelles to organisms. By highlighting recent successful examples, we describe an outlook of ever-increasing data and the need for seamless integration between dataset visualization and quantification to realize the full potential warranted by advances in new imaging technologies.
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Affiliation(s)
- Max A Hockenberry
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, Chapel Hill, NC, USA
| | - Timothy A Daugird
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Wesley R Legant
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Joint Department of Biomedical Engineering, North Carolina State University, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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3
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Li C, Rai MR, Cai Y, Ghashghaei HT, Greenbaum A. Intelligent Beam Optimization for Light-Sheet Fluorescence Microscopy through Deep Learning. INTELLIGENT COMPUTING (WASHINGTON, D.C.) 2024; 3:0095. [PMID: 39099879 PMCID: PMC11298055 DOI: 10.34133/icomputing.0095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 04/22/2024] [Indexed: 08/06/2024]
Abstract
Light-sheet fluorescence microscopy (LSFM) provides the benefit of optical sectioning coupled with rapid acquisition times, enabling high-resolution 3-dimensional imaging of large tissue-cleared samples. Inherent to LSFM, the quality of the imaging heavily relies on the characteristics of the illumination beam, which only illuminates a thin section of the sample. Therefore, substantial efforts are dedicated to identifying slender, nondiffracting beam profiles that yield uniform and high-contrast images. An ongoing debate concerns the identification of optimal illumination beams for different samples: Gaussian, Bessel, Airy patterns, and/or others. However, comparisons among different beam profiles are challenging as their optimization objectives are often different. Given that our large imaging datasets (approximately 0.5 TB of images per sample) are already analyzed using deep learning models, we envisioned a different approach to the problem by designing an illumination beam tailored to boost the performance of the deep learning model. We hypothesized that integrating the physical LSFM illumination model (after passing it through a variable phase mask) into the training of a cell detection network would achieve this goal. Here, we report that joint optimization continuously updates the phase mask and results in improved image quality for better cell detection. The efficacy of our method is demonstrated through both simulations and experiments that reveal substantial enhancements in imaging quality compared to the traditional Gaussian light sheet. We discuss how designing microscopy systems through a computational approach provides novel insights for advancing optical design that relies on deep learning models for the analysis of imaging datasets.
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Affiliation(s)
- Chen Li
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
| | - Mani Ratnam Rai
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
| | - Yuheng Cai
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
| | - H. Troy Ghashghaei
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
- Department of Molecular Biomedical Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Alon Greenbaum
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA
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4
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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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5
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Wu Y, Brust-Mascher I, Gareau MG, De Loera JA, Reardon C. PrestoCell: A persistence-based clustering approach for rapid and robust segmentation of cellular morphology in three-dimensional data. PLoS One 2024; 19:e0299006. [PMID: 38422108 PMCID: PMC10903871 DOI: 10.1371/journal.pone.0299006] [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: 10/20/2023] [Accepted: 02/04/2024] [Indexed: 03/02/2024] Open
Abstract
Light microscopy methods have continued to advance allowing for unprecedented analysis of various cell types in tissues including the brain. Although the functional state of some cell types such as microglia can be determined by morphometric analysis, techniques to perform robust, quick, and accurate measurements have not kept pace with the amount of imaging data that can now be generated. Most of these image segmentation tools are further burdened by an inability to assess structures in three-dimensions. Despite the rise of machine learning techniques, the nature of some biological structures prevents the training of several current day implementations. Here we present PrestoCell, a novel use of persistence-based clustering to segment cells in light microscopy images, as a customized Python-based tool that leverages the free multidimensional image viewer Napari. In evaluating and comparing PrestoCell to several existing tools, including 3DMorph, Omipose, and Imaris, we demonstrate that PrestoCell produces image segmentations that rival these solutions. In particular, our use of cell nuclei information resulted in the ability to correctly segment individual cells that were interacting with one another to increase accuracy. These benefits are in addition to the simplified graphically based user refinement of cell masks that does not require expensive commercial software licenses. We further demonstrate that PrestoCell can complete image segmentation in large samples from light sheet microscopy, allowing quantitative analysis of these large datasets. As an open-source program that leverages freely available visualization software, with minimum computer requirements, we believe that PrestoCell can significantly increase the ability of users without data or computer science expertise to perform complex image analysis.
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Affiliation(s)
- Yue Wu
- Department of Computer Science, UC Davis, Davis, California, United States of America
| | - Ingrid Brust-Mascher
- Department of Anatomy, Physiology and Cell Biology, School of Veterinary Medicine, UC Davis, Davis, California, United States of America
| | - Melanie G. Gareau
- Department of Anatomy, Physiology and Cell Biology, School of Veterinary Medicine, UC Davis, Davis, California, United States of America
| | - Jesus A. De Loera
- Department of Mathematics, UC Davis, Davis, California, United States of America
| | - Colin Reardon
- Department of Anatomy, Physiology and Cell Biology, School of Veterinary Medicine, UC Davis, Davis, California, United States of America
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6
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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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7
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De Simone A. Quantitative Live Imaging of Zebrafish Scale Regeneration: From Adult Fish to Signaling Patterns and Tissue Flows. Methods Mol Biol 2024; 2707:185-204. [PMID: 37668913 DOI: 10.1007/978-1-0716-3401-1_12] [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] [Indexed: 09/06/2023]
Abstract
In regeneration, a damaged body part grows back to its original form. Understanding the mechanisms and physical principles underlying this process has been limited by the difficulties of visualizing cell signals and behaviors in regeneration. Zebrafish scales are emerging as a model system to investigate morphogenesis during vertebrate regeneration using quantitative live imaging. Scales are millimeter-sized dermal bone disks forming a skeletal armor on the body of the fish. The scale bone is deposited by an adjacent monolayer of osteoblasts that, after scale loss, regenerates in about 2 weeks. This intriguing regenerative process is accessible to live confocal microscopy, quantifications, and mathematical modeling. Here, I describe methods to image scale regeneration live, tissue-wide and at sub-cellular resolution. Furthermore, I describe methods to process the resulting images and quantify cell, tissue, and signal dynamics.
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Affiliation(s)
- Alessandro De Simone
- Department of Cell Biology, Duke University Medical Center, Durham, NC, USA.
- Duke Regeneration Center, Duke University, Durham, NC, USA.
- Department of Genetics and Evolution, University of Geneva, Geneva, Switzerland.
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8
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Sun G, Liu S, Shi C, Liu X, Guo Q. 3DCNAS: A universal method for predicting the location of fluorescent organelles in living cells in three-dimensional space. Exp Cell Res 2023; 433:113807. [PMID: 37852350 DOI: 10.1016/j.yexcr.2023.113807] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 10/09/2023] [Accepted: 10/09/2023] [Indexed: 10/20/2023]
Abstract
Cellular biology research relies on microscopic imaging techniques for studying the complex structures and dynamic processes within cells. Fluorescence microscopy provides high sensitivity and subcellular resolution but has limitations such as photobleaching and sample preparation challenges. Transmission light microscopy offers a label-free alternative but lacks contrast for detailed interpretation. Deep learning methods have shown promise in analyzing cell images and extracting meaningful information. However, accurately learning and simulating diverse subcellular structures remain challenging. In this study, we propose a method named three-dimensional cell neural architecture search (3DCNAS) to predict subcellular structures of fluorescence using unlabeled transmitted light microscope images. By leveraging the automated search capability of differentiable neural architecture search (NAS), our method partially mitigates the issues of overfitting and underfitting caused by the distinct details of various subcellular structures. Furthermore, we apply our method to analyze cell dynamics in genome-edited human induced pluripotent stem cells during mitotic events. This allows us to study the functional roles of organelles and their involvement in cellular processes, contributing to a comprehensive understanding of cell biology and offering insights into disease pathogenesis.
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Affiliation(s)
- Guocheng Sun
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China
| | - Shitou Liu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China
| | - Chaojing Shi
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China
| | - Xi Liu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China
| | - Qianjin Guo
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China.
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9
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Becker CJ, Cigliola V, Gillotay P, Rich A, De Simone A, Han Y, Di Talia S, Poss KD. In toto imaging of glial JNK signaling during larval zebrafish spinal cord regeneration. Development 2023; 150:dev202076. [PMID: 37997694 PMCID: PMC10753585 DOI: 10.1242/dev.202076] [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: 06/12/2023] [Accepted: 11/09/2023] [Indexed: 11/25/2023]
Abstract
Identification of signaling events that contribute to innate spinal cord regeneration in zebrafish can uncover new targets for modulating injury responses of the mammalian central nervous system. Using a chemical screen, we identify JNK signaling as a necessary regulator of glial cell cycling and tissue bridging during spinal cord regeneration in larval zebrafish. With a kinase translocation reporter, we visualize and quantify JNK signaling dynamics at single-cell resolution in glial cell populations in developing larvae and during injury-induced regeneration. Glial JNK signaling is patterned in time and space during development and regeneration, decreasing globally as the tissue matures and increasing in the rostral cord stump upon transection injury. Thus, dynamic and regional regulation of JNK signaling help to direct glial cell behaviors during innate spinal cord regeneration.
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Affiliation(s)
- Clayton J. Becker
- Duke Regeneration Center and Department of Cell Biology, Duke University Medical Center, Durham, NC 27710, USA
| | - Valentina Cigliola
- Duke Regeneration Center and Department of Cell Biology, Duke University Medical Center, Durham, NC 27710, USA
- Université Côte d’Azur, Inserm, CNRS, Institut de Biologie Valrose, 06100 Nice, France
| | - Pierre Gillotay
- Duke Regeneration Center and Department of Cell Biology, Duke University Medical Center, Durham, NC 27710, USA
| | - Ashley Rich
- Duke Regeneration Center and Department of Cell Biology, Duke University Medical Center, Durham, NC 27710, USA
| | - Alessandro De Simone
- Department of Genetics and Evolution, University of Geneva, 1211 Geneva, Switzerland
| | - Yanchao Han
- Department of Cardiovascular Surgery of the First Affiliated Hospital & Institute for Cardiovascular Science, Suzhou Medical College, Soochow University, Suzhou, 215006 Jiangsu, China
| | - Stefano Di Talia
- Duke Regeneration Center and Department of Cell Biology, Duke University Medical Center, Durham, NC 27710, USA
| | - Kenneth D. Poss
- Duke Regeneration Center and Department of Cell Biology, Duke University Medical Center, Durham, NC 27710, USA
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10
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Morishita Y, Lee SW, Suzuki T, Yokoyama H, Kamei Y, Tamura K, Kawasumi-Kita A. An archetype and scaling of developmental tissue dynamics across species. Nat Commun 2023; 14:8199. [PMID: 38081837 PMCID: PMC10713982 DOI: 10.1038/s41467-023-43902-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 11/23/2023] [Indexed: 12/18/2023] Open
Abstract
Morphometric studies have revealed the existence of simple geometric relationships among various animal shapes. However, we have little knowledge of the mathematical principles behind the morphogenetic dynamics that form the organ/body shapes of different species. Here, we address this issue by focusing on limb morphogenesis in Gallus gallus domesticus (chicken) and Xenopus laevis (African clawed frog). To compare the deformation dynamics between tissues with different sizes/shapes as well as their developmental rates, we introduce a species-specific rescaled spatial coordinate and a common clock necessary for cross-species synchronization of developmental times. We find that tissue dynamics are well conserved across species under this spacetime coordinate system, at least from the early stages of development through the phase when basic digit patterning is established. For this developmental period, we also reveal that the tissue dynamics of both species are mapped with each other through a time-variant linear transformation in real physical space, from which hypotheses on a species-independent archetype of tissue dynamics and morphogenetic scaling are proposed.
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Affiliation(s)
- Yoshihiro Morishita
- Laboratory for Developmental Morphogeometry, RIKEN Center for Biosystems Dynamics Research, Kobe, 650-0047, Japan.
- Precursory Research for Embryonic Science and Technology (PRESTO) Program, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan.
| | - Sang-Woo Lee
- Laboratory for Developmental Morphogeometry, RIKEN Center for Biosystems Dynamics Research, Kobe, 650-0047, Japan
| | - Takayuki Suzuki
- Department of Biology, Graduate School of Science, Osaka Metropolitan University, Osaka, 558-8585, Japan
| | - Hitoshi Yokoyama
- Department of Biochemistry and Molecular Biology, Faculty of Agriculture and Life Science, Hirosaki University, Aomori, 036-8561, Japan
| | - Yasuhiro Kamei
- Optics and Bioimaging Facility, Trans-Scale Biology Center, National Institute for Basic Biology, Myodaiji, Okazaki, Aichi, 444-8585, Japan
| | - Koji Tamura
- Department of Ecological Developmental Adaptability Life Sciences, Graduate School of Life Sciences, Tohoku University, Sendai, 980-8578, Japan
| | - Aiko Kawasumi-Kita
- Laboratory for Developmental Morphogeometry, RIKEN Center for Biosystems Dynamics Research, Kobe, 650-0047, Japan
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11
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Natesan G, Hamilton T, Deeds EJ, Shah PK. Novel metrics reveal new structure and unappreciated heterogeneity in Caenorhabditis elegans development. PLoS Comput Biol 2023; 19:e1011733. [PMID: 38113280 PMCID: PMC10763962 DOI: 10.1371/journal.pcbi.1011733] [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: 06/07/2023] [Revised: 01/03/2024] [Accepted: 12/04/2023] [Indexed: 12/21/2023] Open
Abstract
High throughput experimental approaches are increasingly allowing for the quantitative description of cellular and organismal phenotypes. Distilling these large volumes of complex data into meaningful measures that can drive biological insight remains a central challenge. In the quantitative study of development, for instance, one can resolve phenotypic measures for single cells onto their lineage history, enabling joint consideration of heritable signals and cell fate decisions. Most attempts to analyze this type of data, however, discard much of the information content contained within lineage trees. In this work we introduce a generalized metric, which we term the branch edit distance, that allows us to compare any two embryos based on phenotypic measurements in individual cells. This approach aligns those phenotypic measurements to the underlying lineage tree, providing a flexible and intuitive framework for quantitative comparisons between, for instance, Wild-Type (WT) and mutant developmental programs. We apply this novel metric to data on cell-cycle timing from over 1300 WT and RNAi-treated Caenorhabditis elegans embryos. Our new metric revealed surprising heterogeneity within this data set, including subtle batch effects in WT embryos and dramatic variability in RNAi-induced developmental phenotypes, all of which had been missed in previous analyses. Further investigation of these results suggests a novel, quantitative link between pathways that govern cell fate decisions and pathways that pattern cell cycle timing in the early embryo. Our work demonstrates that the branch edit distance we propose, and similar metrics like it, have the potential to revolutionize our quantitative understanding of organismal phenotype.
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Affiliation(s)
- Gunalan Natesan
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, California, United States of America
| | - Timothy Hamilton
- Department of Integrative Biology and Physiology, University of California, Los Angeles, California, United States of America
| | - Eric J. Deeds
- Department of Integrative Biology and Physiology, University of California, Los Angeles, California, United States of America
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, California, United States of America
| | - Pavak K. Shah
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, California, United States of America
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, California, United States of America
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12
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Li C, Rai MR, Cai Y, Ghashghaei HT, Greenbaum A. Enhancing Light-Sheet Fluorescence Microscopy Illumination Beams through Deep Design Optimization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.29.569329. [PMID: 38077074 PMCID: PMC10705487 DOI: 10.1101/2023.11.29.569329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Light sheet fluorescence microscopy (LSFM) provides the benefit of optical sectioning coupled with rapid acquisition times for imaging of tissue-cleared specimen. This allows for high-resolution 3D imaging of large tissue volumes. Inherently to LSFM, the quality of the imaging heavily relies on the characteristics of the illumination beam, with the notion that the illumination beam only illuminates a thin section that is being imaged. Therefore, substantial efforts are dedicated to identifying slender, non-diffracting beam profiles that can yield uniform and high-contrast images. An ongoing debate concerns the employment of the most optimal illumination beam; Gaussian, Bessel, Airy patterns and/or others. Comparisons among different beam profiles is challenging as their optimization objective is often different. Given that our large imaging datasets (~0.5TB images per sample) is already analyzed using deep learning models, we envisioned a different approach to this problem by hypothesizing that we can tailor the illumination beam to boost the deep learning models performance. We achieve this by integrating the physical LSFM illumination model after passing through a variable phase mask into the training of a cell detection network. Here we report that the joint optimization continuously updates the phase mask, improving the image quality for better cell detection. Our method's efficacy is demonstrated through both simulations and experiments, revealing substantial enhancements in imaging quality compared to traditional Gaussian light sheet. We offer valuable insights for designing microscopy systems through a computational approach that exhibits significant potential for advancing optics design that relies on deep learning models for analysis of imaging datasets.
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Affiliation(s)
- Chen Li
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
| | - Mani Ratnam Rai
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
| | - Yuheng Cai
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
| | - H. Troy Ghashghaei
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
- Department of Molecular Biomedical Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Alon Greenbaum
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA
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13
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Chou TC, You L, Beerens C, Feller KJ, Storteboom J, Chien MP. Instant processing of large-scale image data with FACT, a real-time cell segmentation and tracking algorithm. CELL REPORTS METHODS 2023; 3:100636. [PMID: 37963463 PMCID: PMC10694492 DOI: 10.1016/j.crmeth.2023.100636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/25/2023] [Accepted: 10/16/2023] [Indexed: 11/16/2023]
Abstract
Quantifying cellular characteristics from a large heterogeneous population is essential to identify rare, disease-driving cells. A recent development in the combination of high-throughput screening microscopy with single-cell profiling provides an unprecedented opportunity to decipher disease-driving phenotypes. Accurately and instantly processing large amounts of image data, however, remains a technical challenge when an analysis output is required minutes after data acquisition. Here, we present fast and accurate real-time cell tracking (FACT). FACT can segment ∼20,000 cells in an average of 2.5 s (1.9-93.5 times faster than the state of the art). It can export quantifiable features minutes after data acquisition (independent of the number of acquired image frames) with an average of 90%-96% precision. We apply FACT to identify directionally migrating glioblastoma cells with 96% precision and irregular cell lineages from a 24 h movie with an average F1 score of 0.91.
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Affiliation(s)
- Ting-Chun Chou
- Department of Molecular Genetics, Erasmus University Medical Center, 3015 GD Rotterdam, the Netherlands; Erasmus MC Cancer Institute, 3015 GD Rotterdam, the Netherlands; Oncode Institute, 3521 AL Utrecht, the Netherlands
| | - Li You
- Department of Molecular Genetics, Erasmus University Medical Center, 3015 GD Rotterdam, the Netherlands; Erasmus MC Cancer Institute, 3015 GD Rotterdam, the Netherlands; Oncode Institute, 3521 AL Utrecht, the Netherlands
| | - Cecile Beerens
- Department of Molecular Genetics, Erasmus University Medical Center, 3015 GD Rotterdam, the Netherlands; Erasmus MC Cancer Institute, 3015 GD Rotterdam, the Netherlands; Oncode Institute, 3521 AL Utrecht, the Netherlands
| | - Kate J Feller
- Department of Molecular Genetics, Erasmus University Medical Center, 3015 GD Rotterdam, the Netherlands; Erasmus MC Cancer Institute, 3015 GD Rotterdam, the Netherlands; Oncode Institute, 3521 AL Utrecht, the Netherlands
| | - Jelle Storteboom
- Department of Molecular Genetics, Erasmus University Medical Center, 3015 GD Rotterdam, the Netherlands; Erasmus MC Cancer Institute, 3015 GD Rotterdam, the Netherlands; Oncode Institute, 3521 AL Utrecht, the Netherlands
| | - Miao-Ping Chien
- Department of Molecular Genetics, Erasmus University Medical Center, 3015 GD Rotterdam, the Netherlands; Erasmus MC Cancer Institute, 3015 GD Rotterdam, the Netherlands; Oncode Institute, 3521 AL Utrecht, the Netherlands.
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14
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Zhang Y, Li X, Gao S, Liao Y, Luo Y, Liu M, Bian Y, Xiong H, Yue Y, He A. Genetic reporter for live tracing fluid flow forces during cell fate segregation in mouse blastocyst development. Cell Stem Cell 2023; 30:1110-1123.e9. [PMID: 37541214 DOI: 10.1016/j.stem.2023.07.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 06/02/2023] [Accepted: 07/10/2023] [Indexed: 08/06/2023]
Abstract
Mechanical forces are known to be important in mammalian blastocyst formation; however, due to limited tools, specific force inputs and how they relay to first cell fate control of inner cell mass (ICM) and/or trophectoderm (TE) remain elusive. Combining in toto live imaging and various perturbation experiments, we demonstrate and measure fluid flow forces existing in the mouse blastocyst cavity and identify Klf2(Krüppel-like factor 2) as a fluid force reporter with force-responsive enhancers. Long-term live imaging and lineage reconstructions reveal that blastomeres subject to higher fluid flow forces adopt ICM cell fates. These are reinforced by internal ferrofluid-induced flow force assays. We also utilize ex vivo fluid flow force mimicking and pharmacological perturbations to confirm mechanosensing specificity. Together, we report a genetically encoded reporter for continuously monitoring fluid flow forces and cell fate decisions and provide a live imaging framework to infer force information enriched lineage landscape during development. VIDEO ABSTRACT.
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Affiliation(s)
- Youdong Zhang
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Xin Li
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Shu Gao
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Yuanhui Liao
- School of Software and Microelectronics, Peking University, Beijing 100871, China
| | - Yingjie Luo
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Min Liu
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Yunkun Bian
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Haiqing Xiong
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Yanzhu Yue
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China; Department of Cell Fate and Diseases, Jilin Provincial Key Laboratory of Women's Reproductive Health, the First Hospital of Jilin University, Changchun, Jilin 130061, China.
| | - Aibin He
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China.
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15
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Kondow A, Ohnuma K, Taniguchi A, Sakamoto J, Asashima M, Kato K, Kamei Y, Nonaka S. Automated contour extraction for light-sheet microscopy images of zebrafish embryos based on object edge detection algorithm. Dev Growth Differ 2023; 65:311-320. [PMID: 37350158 DOI: 10.1111/dgd.12871] [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: 11/07/2022] [Revised: 06/01/2023] [Accepted: 06/16/2023] [Indexed: 06/24/2023]
Abstract
Embryo contour extraction is the initial step in the quantitative analysis of embryo morphology, and it is essential for understanding the developmental process. Recent developments in light-sheet microscopy have enabled the in toto time-lapse imaging of embryos, including zebrafish. However, embryo contour extraction from images generated via light-sheet microscopy is challenging owing to the large amount of data and the variable sizes, shapes, and textures of objects. In this report, we provide a workflow for extracting the contours of zebrafish blastula and gastrula without contour labeling of an embryo. This workflow is based on the edge detection method using a change point detection approach. We assessed the performance of the edge detection method and compared it with widely used edge detection and segmentation methods. The results showed that the edge detection accuracy of the proposed method was superior to those of the Sobel, Laplacian of Gaussian, adaptive threshold, Multi Otsu, and k-means clustering-based methods, and the noise robustness of the proposed method was superior to those of the Multi Otsu and k-means clustering-based methods. The proposed workflow was shown to be useful for automating small-scale contour extractions of zebrafish embryos that cannot be specifically labeled owing to constraints, such as the availability of microscopic channels. This workflow may offer an option for contour extraction when deep learning-based approaches or existing non-deep learning-based methods cannot be applied.
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Affiliation(s)
- Akiko Kondow
- Advanced Comprehensive Research Organization, Teikyo University, Tokyo, Japan
| | - Kiyoshi Ohnuma
- Department of Bioengineering, Nagaoka University of Technology, Niigata, Japan
- Department of Science of Technology Innovation, Nagaoka University of Technology, Niigata, Japan
| | - Atsushi Taniguchi
- Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University, Hokkaido, Japan
| | - Joe Sakamoto
- Optics and Imaging Facility, Trans-Scale Biology Center, National Institute for Basic Biology, Aichi, Japan
| | - Makoto Asashima
- Advanced Comprehensive Research Organization, Teikyo University, Tokyo, Japan
| | - Kagayaki Kato
- Bioimage Informatics Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Aichi, Japan
- Laboratory for Biological Diversity, National Institute for Basic Biology, National Institutes of Natural Sciences, Aichi, Japan
| | - Yasuhiro Kamei
- Optics and Imaging Facility, Trans-Scale Biology Center, National Institute for Basic Biology, Aichi, Japan
- Department of Basic Biology, School of Life Science, the Graduate University for Advanced Studies (SOKENDAI), Aichi, Japan
| | - Shigenori Nonaka
- Department of Basic Biology, School of Life Science, the Graduate University for Advanced Studies (SOKENDAI), Aichi, Japan
- Laboratory for Spatiotemporal Regulations, National Institute for Basic Biology, Aichi, Japan
- Spatiotemporal Regulations Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Aichi, Japan
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16
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Soelistyo CJ, Ulicna K, Lowe AR. Machine learning enhanced cell tracking. FRONTIERS IN BIOINFORMATICS 2023; 3:1228989. [PMID: 37521315 PMCID: PMC10380934 DOI: 10.3389/fbinf.2023.1228989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 07/03/2023] [Indexed: 08/01/2023] Open
Abstract
Quantifying cell biology in space and time requires computational methods to detect cells, measure their properties, and assemble these into meaningful trajectories. In this aspect, machine learning (ML) is having a transformational effect on bioimage analysis, now enabling robust cell detection in multidimensional image data. However, the task of cell tracking, or constructing accurate multi-generational lineages from imaging data, remains an open challenge. Most cell tracking algorithms are largely based on our prior knowledge of cell behaviors, and as such, are difficult to generalize to new and unseen cell types or datasets. Here, we propose that ML provides the framework to learn aspects of cell behavior using cell tracking as the task to be learned. We suggest that advances in representation learning, cell tracking datasets, metrics, and methods for constructing and evaluating tracking solutions can all form part of an end-to-end ML-enhanced pipeline. These developments will lead the way to new computational methods that can be used to understand complex, time-evolving biological systems.
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Affiliation(s)
- Christopher J. Soelistyo
- Department of Structural and Molecular Biology, University College London, London, United Kingdom
- Institute for the Physics of Living Systems, London, United Kingdom
| | - Kristina Ulicna
- Department of Structural and Molecular Biology, University College London, London, United Kingdom
- Institute for the Physics of Living Systems, London, United Kingdom
| | - Alan R. Lowe
- Department of Structural and Molecular Biology, University College London, London, United Kingdom
- Institute for the Physics of Living Systems, London, United Kingdom
- Alan Turing Institute, London, United Kingdom
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17
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Natesan G, Hamilton T, Deeds EJ, Shah PK. Novel metrics reveal new structure and unappreciated heterogeneity in C. elegans development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.12.540617. [PMID: 37292606 PMCID: PMC10245744 DOI: 10.1101/2023.05.12.540617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
High throughput experimental approaches are increasingly allowing for the quantitative description of cellular and organismal phenotypes. Distilling these large volumes of complex data into meaningful measures that can drive biological insight remains a central challenge. In the quantitative study of development, for instance, one can resolve phenotypic measures for single cells onto their lineage history, enabling joint consideration of heritable signals and cell fate decisions. Most attempts to analyze this type of data, however, discard much of the information content contained within lineage trees. In this work we introduce a generalized metric, which we term the branch distance, that allows us to compare any two embryos based on phenotypic measurements in individual cells. This approach aligns those phenotypic measurements to the underlying lineage tree, providing a flexible and intuitive framework for quantitative comparisons between, for instance, Wild-Type (WT) and mutant developmental programs. We apply this novel metric to data on cell-cycle timing from over 1300 WT and RNAi-treated Caenorhabditis elegans embryos. Our new metric revealed surprising heterogeneity within this data set, including subtle batch effects in WT embryos and dramatic variability in RNAi-induced developmental phenotypes, all of which had been missed in previous analyses. Further investigation of these results suggests a novel, quantitative link between pathways that govern cell fate decisions and pathways that pattern cell cycle timing in the early embryo. Our work demonstrates that the branch distance we propose, and similar metrics like it, have the potential to revolutionize our quantitative understanding of organismal phenotype.
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Affiliation(s)
- Gunalan Natesan
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA
| | - Timothy Hamilton
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA
| | - Eric J. Deeds
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, CA
| | - Pavak K. Shah
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, CA
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18
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Dominguez MH, Krup AL, Muncie JM, Bruneau BG. Graded mesoderm assembly governs cell fate and morphogenesis of the early mammalian heart. Cell 2023; 186:479-496.e23. [PMID: 36736300 PMCID: PMC10091855 DOI: 10.1016/j.cell.2023.01.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 11/07/2022] [Accepted: 01/03/2023] [Indexed: 02/05/2023]
Abstract
Using four-dimensional whole-embryo light sheet imaging with improved and accessible computational tools, we longitudinally reconstruct early murine cardiac development at single-cell resolution. Nascent mesoderm progenitors form opposing density and motility gradients, converting the temporal birth sequence of gastrulation into a spatial anterolateral-to-posteromedial arrangement. Migrating precardiac mesoderm does not strictly preserve cellular neighbor relationships, and spatial patterns only become solidified as the cardiac crescent emerges. Progenitors undergo a mesenchymal-to-epithelial transition, with a first heart field (FHF) ridge apposing a motile juxta-cardiac field (JCF). Anchored along the ridge, the FHF epithelium rotates the JCF forward to form the initial heart tube, along with push-pull morphodynamics of the second heart field. In Mesp1 mutants that fail to make a cardiac crescent, mesoderm remains highly motile but directionally incoherent, resulting in density gradient inversion. Our practicable live embryo imaging approach defines spatial origins and behaviors of cardiac progenitors and identifies their unanticipated morphological transitions.
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Affiliation(s)
- Martin H Dominguez
- Gladstone Institutes, San Francisco, CA, USA; Department of Medicine, Division of Cardiology, University of California, San Francisco, San Francisco, CA, USA; Cardiovascular Institute, University of Pennsylvania, Philadelphia, PA, USA.
| | - Alexis Leigh Krup
- Gladstone Institutes, San Francisco, CA, USA; Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | | | - Benoit G Bruneau
- Gladstone Institutes, San Francisco, CA, USA; Roddenberry Center for Stem Cell Biology and Medicine at Gladstone, San Francisco, CA, USA; Department of Pediatrics, Cardiovascular Research Institute, Institute for Human Genetics, and Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94158, USA.
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19
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Chiang HJ, Koo DES, Kitano M, Burkitt S, Unruh JR, Zavaleta C, Trinh LA, Fraser SE, Cutrale F. HyU: Hybrid Unmixing for longitudinal in vivo imaging of low signal-to-noise fluorescence. Nat Methods 2023; 20:248-258. [PMID: 36658278 PMCID: PMC9911352 DOI: 10.1038/s41592-022-01751-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 12/13/2022] [Indexed: 01/21/2023]
Abstract
The expansion of fluorescence bioimaging toward more complex systems and geometries requires analytical tools capable of spanning widely varying timescales and length scales, cleanly separating multiple fluorescent labels and distinguishing these labels from background autofluorescence. Here we meet these challenging objectives for multispectral fluorescence microscopy, combining hyperspectral phasors and linear unmixing to create Hybrid Unmixing (HyU). HyU is efficient and robust, capable of quantitative signal separation even at low illumination levels. In dynamic imaging of developing zebrafish embryos and in mouse tissue, HyU was able to cleanly and efficiently unmix multiple fluorescent labels, even in demanding volumetric timelapse imaging settings. HyU permits high dynamic range imaging, allowing simultaneous imaging of bright exogenous labels and dim endogenous labels. This enables coincident studies of tagged components, cellular behaviors and cellular metabolism within the same specimen, providing more accurate insights into the orchestrated complexity of biological systems.
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Affiliation(s)
- Hsiao Ju Chiang
- Translational Imaging Center, University of Southern California, Los Angeles, CA, USA
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Daniel E S Koo
- Translational Imaging Center, University of Southern California, Los Angeles, CA, USA
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Masahiro Kitano
- Translational Imaging Center, University of Southern California, Los Angeles, CA, USA
- Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Sean Burkitt
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Jay R Unruh
- Stowers Institute for Medical Research, Kansas City, MO, USA
| | - Cristina Zavaleta
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Le A Trinh
- Translational Imaging Center, University of Southern California, Los Angeles, CA, USA
- Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Scott E Fraser
- Translational Imaging Center, University of Southern California, Los Angeles, CA, USA
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
- Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Francesco Cutrale
- Translational Imaging Center, University of Southern California, Los Angeles, CA, USA.
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA.
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20
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Park SA, Sipka T, Krivá Z, Lutfalla G, Nguyen-Chi M, Mikula K. Segmentation-based tracking of macrophages in 2D+time microscopy movies inside a living animal. Comput Biol Med 2023; 153:106499. [PMID: 36599208 DOI: 10.1016/j.compbiomed.2022.106499] [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: 08/25/2022] [Revised: 12/19/2022] [Accepted: 12/27/2022] [Indexed: 12/31/2022]
Abstract
The automated segmentation and tracking of macrophages during their migration are challenging tasks due to their dynamically changing shapes and motions. This paper proposes a new algorithm to achieve automatic cell tracking in time-lapse microscopy macrophage data. First, we design a segmentation method employing space-time filtering, local Otsu's thresholding, and the SUBSURF (subjective surface segmentation) method. Next, the partial trajectories for cells overlapping in the temporal direction are extracted in the segmented images. Finally, the extracted trajectories are linked by considering their direction of movement. The segmented images and the obtained trajectories from the proposed method are compared with those of the semi-automatic segmentation and manual tracking. The proposed tracking achieved 97.4% of accuracy for macrophage data under challenging situations, feeble fluorescent intensity, irregular shapes, and motion of macrophages. We expect that the automatically extracted trajectories of macrophages can provide pieces of evidence of how macrophages migrate depending on their polarization modes in the situation, such as during wound healing.
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Affiliation(s)
- Seol Ah Park
- Department of Mathematics and Descriptive Geometry, Slovak University of Technology in Bratislava, Radlinskeho 11, Bratislava, 810 05, Slovakia.
| | - Tamara Sipka
- LPHI Laboratory of Pathogen Host Interaction, CNRS, Univ. Montpellier, Place E.Bataillon-Building 24, 34095, Montpellier Cedex 05, France.
| | - Zuzana Krivá
- Department of Mathematics and Descriptive Geometry, Slovak University of Technology in Bratislava, Radlinskeho 11, Bratislava, 810 05, Slovakia.
| | - Georges Lutfalla
- LPHI Laboratory of Pathogen Host Interaction, CNRS, Univ. Montpellier, Place E.Bataillon-Building 24, 34095, Montpellier Cedex 05, France.
| | - Mai Nguyen-Chi
- LPHI Laboratory of Pathogen Host Interaction, CNRS, Univ. Montpellier, Place E.Bataillon-Building 24, 34095, Montpellier Cedex 05, France.
| | - Karol Mikula
- Department of Mathematics and Descriptive Geometry, Slovak University of Technology in Bratislava, Radlinskeho 11, Bratislava, 810 05, Slovakia.
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21
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Malin-Mayor C, Hirsch P, Guignard L, McDole K, Wan Y, Lemon WC, Kainmueller D, Keller PJ, Preibisch S, Funke J. Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations. Nat Biotechnol 2023; 41:44-49. [PMID: 36065022 PMCID: PMC7614077 DOI: 10.1038/s41587-022-01427-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 07/12/2022] [Indexed: 01/19/2023]
Abstract
We present a method to automatically identify and track nuclei in time-lapse microscopy recordings of entire developing embryos. The method combines deep learning and global optimization. On a mouse dataset, it reconstructs 75.8% of cell lineages spanning 1 h, as compared to 31.8% for the competing method. Our approach improves understanding of where and when cell fate decisions are made in developing embryos, tissues, and organs.
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Affiliation(s)
| | - Peter Hirsch
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Faculty of Mathematics and Natural Sciences, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Leo Guignard
- HHMI Janelia, Ashburn, VA, USA
- CNRS, UTLN, LIS 7020, Turing Centre for Living Systems, Aix Marseille University, Marseille, France
| | - Katie McDole
- HHMI Janelia, Ashburn, VA, USA
- MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Yinan Wan
- HHMI Janelia, Ashburn, VA, USA
- Biozentrum, University of Basel, Basel, Switzerland
| | | | - Dagmar Kainmueller
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Faculty of Mathematics and Natural Sciences, Humboldt-Universität zu Berlin, Berlin, Germany
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22
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Cuny AP, Ponti A, Kündig T, Rudolf F, Stelling J. Cell region fingerprints enable highly precise single-cell tracking and lineage reconstruction. Nat Methods 2022; 19:1276-1285. [PMID: 36138173 DOI: 10.1038/s41592-022-01603-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 08/02/2022] [Indexed: 11/09/2022]
Abstract
Experimental studies of cell growth, inheritance and their associated processes by microscopy require accurate single-cell observations of sufficient duration to reconstruct the genealogy. However, cell tracking-assigning identical cells on consecutive images to a track-is often challenging, resulting in laborious manual verification. Here, we propose fingerprints to identify problematic assignments rapidly. A fingerprint distance compares the structural information contained in the low frequencies of a Fourier transform to measure the similarity between cells in two consecutive images. We show that fingerprints are broadly applicable across cell types and image modalities, provided the image has sufficient structural information. Our tracker (TracX) uses fingerprints to reject unlikely assignments, thereby increasing tracking performance on published and newly generated long-term data sets. For Saccharomyces cerevisiae, we propose a comprehensive model for cell size control at the single-cell and population level centered on the Whi5 regulator, demonstrating how precise tracking can help uncover previously undescribed single-cell biology.
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Affiliation(s)
- Andreas P Cuny
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Aaron Ponti
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Tomas Kündig
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Fabian Rudolf
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Jörg Stelling
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland. .,Swiss Institute of Bioinformatics, Basel, Switzerland.
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23
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Guan G, Zhao Z, Tang C. Delineating the mechanisms and design principles of Caenorhabditis elegans embryogenesis using in toto high-resolution imaging data and computational modeling. Comput Struct Biotechnol J 2022; 20:5500-5515. [PMID: 36284714 PMCID: PMC9562942 DOI: 10.1016/j.csbj.2022.08.024] [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: 04/26/2022] [Revised: 08/10/2022] [Accepted: 08/11/2022] [Indexed: 11/19/2022] Open
Abstract
The nematode (roundworm) Caenorhabditis elegans is one of the most popular animal models for the study of developmental biology, as its invariant development and transparent body enable in toto cellular-resolution fluorescence microscopy imaging of developmental processes at 1-min intervals. This has led to the development of various computational tools for the systematic and automated analysis of imaging data to delineate the molecular and cellular processes throughout the embryogenesis of C. elegans, such as those associated with cell lineage, cell migration, cell morphology, and gene activity. In this review, we first introduce C. elegans embryogenesis and the development of techniques for tracking cell lineage and reconstructing cell morphology during this process. We then contrast the developmental modes of C. elegans and the customized technologies used for studying them with the ones of other animal models, highlighting its advantage for studying embryogenesis with exceptional spatial and temporal resolution. This is followed by an examination of the physical models that have been devised-based on accurate determinations of developmental processes afforded by analyses of imaging data-to interpret the early embryonic development of C. elegans from subcellular to intercellular levels of multiple cells, which focus on two key processes: cell polarization and morphogenesis. We subsequently discuss how quantitative data-based theoretical modeling has improved our understanding of the mechanisms of C. elegans embryogenesis. We conclude by summarizing the challenges associated with the acquisition of C. elegans embryogenesis data, the construction of algorithms to analyze them, and the theoretical interpretation.
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Affiliation(s)
- Guoye Guan
- Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Zhongying Zhao
- Department of Biology, Hong Kong Baptist University, Hong Kong 999077, China
- State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong 999077, China
| | - Chao Tang
- Center for Quantitative Biology, Peking University, Beijing 100871, China
- Peking–Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
- School of Physics, Peking University, Beijing 100871, China
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24
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You L, Su PR, Betjes M, Rad RG, Chou TC, Beerens C, van Oosten E, Leufkens F, Gasecka P, Muraro M, van Tol R, van Steenderen D, Farooq S, Hardillo JAU, de Jong RB, Brinks D, Chien MP. Linking the genotypes and phenotypes of cancer cells in heterogenous populations via real-time optical tagging and image analysis. Nat Biomed Eng 2022; 6:667-675. [PMID: 35301448 DOI: 10.1038/s41551-022-00853-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 11/23/2021] [Indexed: 02/07/2023]
Abstract
Linking single-cell genomic or transcriptomic profiles to functional cellular characteristics, in particular time-varying phenotypic changes, could help unravel molecular mechanisms driving the growth of tumour-cell subpopulations. Here we show that a custom-built optical microscope with an ultrawide field of view, fast automated image analysis and a dye activatable by visible light enables the screening and selective photolabelling of cells of interest in large heterogeneous cell populations on the basis of specific functional cellular dynamics, such as fast migration, morphological variation, small-molecule uptake or cell division. Combining such functional single-cell selection with single-cell RNA sequencing allowed us to (1) functionally annotate the transcriptomic profiles of fast-migrating and spindle-shaped MCF10A cells, of fast-migrating MDA-MB-231 cells and of patient-derived head-and-neck squamous carcinoma cells, and (2) identify critical genes and pathways driving aggressive migration and mesenchymal-like morphology in these cells. Functional single-cell selection upstream of single-cell sequencing does not depend on molecular biomarkers, allows for the enrichment of sparse subpopulations of cells, and can facilitate the identification and understanding of the molecular mechanisms underlying functional phenotypes.
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Affiliation(s)
- Li You
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands.,Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Pin-Rui Su
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands.,Erasmus MC Cancer Institute, Rotterdam, The Netherlands.,Department of Chemistry, National Taiwan University, Taipei, Taiwan
| | - Max Betjes
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands.,Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Reza Ghadiri Rad
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands.,Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Ting-Chun Chou
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands.,Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Cecile Beerens
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands.,Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Eva van Oosten
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands.,Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Felix Leufkens
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands.,Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Paulina Gasecka
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands.,Erasmus MC Cancer Institute, Rotterdam, The Netherlands.,Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Mauro Muraro
- Single Cell Discoveries, Utrecht, The Netherlands
| | - Ruud van Tol
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Debby van Steenderen
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands.,Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Shazia Farooq
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands.,Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Jose Angelito U Hardillo
- Department of Otorhinolaryngology, Head and Neck Surgery, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Robert Baatenburg de Jong
- Department of Otorhinolaryngology, Head and Neck Surgery, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Daan Brinks
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands. .,Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands.
| | - Miao-Ping Chien
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands. .,Erasmus MC Cancer Institute, Rotterdam, The Netherlands. .,Oncode Institute, Utrecht, The Netherlands.
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25
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Wang C, Wang Y, Yu G. Efficient Global MOT Under Minimum-Cost Circulation Framework. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022; 44:1888-1904. [PMID: 32966213 PMCID: PMC8966209 DOI: 10.1109/tpami.2020.3026257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
We developed a minimum-cost circulation framework for solving the global data association problem, which plays a key role in the tracking-by-detection paradigm of multi-object tracking (MOT). The global data association problem was extensively studied under the minimum-cost flow framework, which is theoretically attractive as being flexible and globally solvable. However, the high computational burden has been a long-standing obstacle to its wide adoption in practice. While enjoying the same theoretical advantages and maintaining the same optimal solution as the minimum-cost flow framework, our new framework has a better theoretical complexity bound and leads to orders of practical efficiency improvement. This new framework is motivated by the observation that minimum-cost flow only partially models the data association problem and it must be accompanied by an additional and time-consuming searching scheme to determine the optimal object number. By employing a minimum-cost circulation framework, we eliminate the searching step and naturally integrate the number of objects into the optimization problem. By exploring the special property of the associated graph, that is, an overwhelming majority of the vertices are with unit capacity, we designed an implementation of the framework and proved it has the best theoretical computational complexity so far for the global data association problem. We evaluated our method with 40 experiments on five MOT benchmark datasets. Our method was always the most efficient in every single experiment and averagely 53 to 1,192 times faster than the three state-of-the-art methods. When our method served as a sub-module for global data association methods utilizing higher-order constraints, similar running time improvement was attained. We further illustrated through several case studies how the improved computational efficiency enables more sophisticated tracking models and yields better tracking accuracy. We made the source code publicly available on GitHub with both Python and MATLAB interfaces.
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26
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Arbelle A, Cohen S, Raviv TR. Dual-Task ConvLSTM-UNet for Instance Segmentation of Weakly Annotated Microscopy Videos. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; PP:1948-1960. [PMID: 35180079 DOI: 10.1109/tmi.2022.3152927] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Convolutional Neural Networks (CNNs) are considered state of the art segmentation methods for biomedical images in general and microscopy sequences of living cells, in particular. The success of the CNNs is attributed to their ability to capture the structural properties of the data, which enables accommodating complex spatial structures of the cells, low contrast, and unclear boundaries. However, in their standard form CNNs do not exploit the temporal information available in time-lapse sequences, which can be crucial to separating touching and partially overlapping cell instances. In this work, we exploit cell dynamics using a novel CNN architecture which allows multi-scale spatio-temporal feature extraction. Specifically, a novel recurrent neural network (RNN) architecture is proposed based on the integration of a Convolutional Long Short Term Memory (ConvLSTM) network with the U-Net. The proposed ConvLSTM-UNet network is constructed as a dual-task network to enable training with weakly annotated data, in the form of approximate cell centers, termed markers, when the complete cells' outlines are not available. We further use the fast marching method to facilitate the partitioning of clustered cells into individual connected components. Finally, we suggest an adaptation of the method for 3D microscopy sequences without drastically increasing the computational load. The method was evaluated on the Cell Segmentation Benchmark and was ranked among the top three methods on six submitted datasets. Exploiting the proposed built-in marker estimator we also present state-of-the-art cell detection results for an additional, publicly available, weekly annotated dataset. The source code is available at https://gitlab.com/shaked0/lstmUnet.
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Li C, Rai MR, Ghashghaei HT, Greenbaum A. Illumination angle correction during image acquisition in light-sheet fluorescence microscopy using deep learning. BIOMEDICAL OPTICS EXPRESS 2022; 13:888-901. [PMID: 35284156 PMCID: PMC8884226 DOI: 10.1364/boe.447392] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 01/05/2022] [Accepted: 01/06/2022] [Indexed: 05/07/2023]
Abstract
Light-sheet fluorescence microscopy (LSFM) is a high-speed imaging technique that provides optical sectioning with reduced photodamage. LSFM is routinely used in life sciences for live cell imaging and for capturing large volumes of cleared tissues. LSFM has a unique configuration, in which the illumination and detection paths are separated and perpendicular to each other. As such, the image quality, especially at high resolution, largely depends on the degree of overlap between the detection focal plane and the illuminating beam. However, spatial heterogeneity within the sample, curved specimen boundaries, and mismatch of refractive index between tissues and immersion media can refract the well-aligned illumination beam. This refraction can cause extensive blur and non-uniform image quality over the imaged field-of-view. To address these issues, we tested a deep learning-based approach to estimate the angular error of the illumination beam relative to the detection focal plane. The illumination beam was then corrected using a pair of galvo scanners, and the correction significantly improved the image quality across the entire field-of-view. The angular estimation was based on calculating the defocus level on a pixel level within the image using two defocused images. Overall, our study provides a framework that can correct the angle of the light-sheet and improve the overall image quality in high-resolution LSFM 3D image acquisition.
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Affiliation(s)
- Chen Li
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
| | - Mani Ratnam Rai
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
| | - H. Troy Ghashghaei
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
- Department of Molecular Biomedical Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Alon Greenbaum
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA
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28
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Belmonte-Mateos C, Pujades C. From Cell States to Cell Fates: How Cell Proliferation and Neuronal Differentiation Are Coordinated During Embryonic Development. Front Neurosci 2022; 15:781160. [PMID: 35046768 PMCID: PMC8761814 DOI: 10.3389/fnins.2021.781160] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/29/2021] [Indexed: 12/24/2022] Open
Abstract
The central nervous system (CNS) exhibits an extraordinary diversity of neurons, with the right cell types and proportions at the appropriate sites. Thus, to produce brains with specific size and cell composition, the rates of proliferation and differentiation must be tightly coordinated and balanced during development. Early on, proliferation dominates; later on, the growth rate almost ceases as more cells differentiate and exit the cell cycle. Generation of cell diversity and morphogenesis takes place concomitantly. In the vertebrate brain, this results in dramatic changes in the position of progenitor cells and their neuronal derivatives, whereas in the spinal cord morphogenetic changes are not so important because the structure mainly grows by increasing its volume. Morphogenesis is under control of specific genetic programs that coordinately unfold over time; however, little is known about how they operate and impact in the pools of progenitor cells in the CNS. Thus, the spatiotemporal coordination of these processes is fundamental for generating functional neuronal networks. Some key aims in developmental neurobiology are to determine how cell diversity arises from pluripotent progenitor cells, and how the progenitor potential changes upon time. In this review, we will share our view on how the advance of new technologies provides novel data that challenge some of the current hypothesis. We will cover some of the latest studies on cell lineage tracing and clonal analyses addressing the role of distinct progenitor cell division modes in balancing the rate of proliferation and differentiation during brain morphogenesis. We will discuss different hypothesis proposed to explain how progenitor cell diversity is generated and how they challenged prevailing concepts and raised new questions.
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Affiliation(s)
| | - Cristina Pujades
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
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29
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Li X, Yue Y, Zhang Y, Liao Y, Wang Q, Bian Y, Na J, He A. Continuous live imaging reveals a subtle pathological alteration with cell behaviors in congenital heart malformation. FUNDAMENTAL RESEARCH 2022; 2:14-22. [PMID: 38933910 PMCID: PMC11197809 DOI: 10.1016/j.fmre.2021.11.025] [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: 09/06/2021] [Revised: 10/26/2021] [Accepted: 11/03/2021] [Indexed: 11/17/2022] Open
Abstract
To form fully functional four-chambered structure, mammalian heart development undergoes a transient finger-shaped trabeculae, crucial for efficient contraction and exchange for gas and nutrient. Although its developmental origin and direct relevance to congenital heart disease has been studied extensively, the time-resolved cellular mechanism underlying hypotrabeculation remains elusive. Here, we employed in toto live imaging and reconstructed the holistic cell lineages and cellular behavior landscape of control and hypotrabeculed hearts of mouse embryos from E9.5 for up to 24 h. Compared to control, hypotrabeculation in ErbB2 mutants arose mainly through dual mechanisms: both reduced proliferation of trabecular cardiomyocytes from early cell fate segregation and markedly impaired oriented cell division and migration. Further examination of mosaic mutant hearts confirmed alterations in cellular behaviors in a cell autonomous manner. Thus, our work offers a framework for continuous live imaging and digital cell lineage analysis to better understand subtle pathological alterations in congenital heart disease.
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Affiliation(s)
- Xin Li
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Yanzhu Yue
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Youdong Zhang
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Yuanhui Liao
- School of Software and Microelectronics, Peking University, Beijing 100871, China
| | - Qianhao Wang
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Yunkun Bian
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Jie Na
- Centre for Stem Cell Biology and Regenerative Medicine, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Aibin He
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
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30
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linus: Conveniently explore, share, and present large-scale biological trajectory data in a web browser. PLoS Comput Biol 2021; 17:e1009503. [PMID: 34723958 PMCID: PMC8584757 DOI: 10.1371/journal.pcbi.1009503] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 11/11/2021] [Accepted: 09/30/2021] [Indexed: 11/26/2022] Open
Abstract
In biology, we are often confronted with information-rich, large-scale trajectory data, but exploring and communicating patterns in such data can be a cumbersome task. Ideally, the data should be wrapped with an interactive visualisation in one concise packet that makes it straightforward to create and test hypotheses collaboratively. To address these challenges, we have developed a tool, linus, which makes the process of exploring and sharing 3D trajectories as easy as browsing a website. We provide a python script that reads trajectory data, enriches them with additional features such as edge bundling or custom axes, and generates an interactive web-based visualisation that can be shared online. linus facilitates the collaborative discovery of patterns in complex trajectory data. Many of the processes that we study in biology are dynamic or interconnected. We can represent most of them as trajectories, being it connections between neurons in a brain or species in an ecosystem or motion traces of animals, cells or molecules. Modern experiments allow researchers to generate such trajectory data at unprecedented scales: think the parallel tracking of thousands of cells in a developing embryo over hours or days. However, visualising large-scale trajectory data is a challenge: the typical static visualisations result in excessive overplotting and often resemble the infamous hairballs. Simplification and interactivity are crucial strategies to deal with this problem. We present the lightweight tool linus that enables researchers to explore and share their trajectory data in an engaging way in web browsers from almost any device.
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31
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Wu X, Kong K, Xiao W, Liu F. Attractive internuclear force drives the collective behavior of nuclear arrays in Drosophila embryos. PLoS Comput Biol 2021; 17:e1009605. [PMID: 34797833 PMCID: PMC8641897 DOI: 10.1371/journal.pcbi.1009605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 12/03/2021] [Accepted: 10/31/2021] [Indexed: 11/25/2022] Open
Abstract
The collective behavior of the nuclear array in Drosophila embryos during nuclear cycle (NC) 11 to NC14 is crucial in controlling cell size, establishing developmental patterns, and coordinating morphogenesis. After live imaging on Drosophila embryos with light sheet microscopy, we extract the nuclear trajectory, speed, and internuclear distance with an automatic nuclear tracing method. We find that the nuclear speed shows a period of standing waves along the anterior-posterior (AP) axis after each metaphase as the nuclei collectively migrate towards the embryo poles and partially move back. And the maximum nuclear speed dampens by 28-45% in the second half of the standing wave. Moreover, the nuclear density is 22-42% lower in the pole region than the middle of the embryo during the interphase of NC12-14. To find mechanical rules controlling the collective motion and packing patterns of the nuclear array, we use a deep neural network (DNN) to learn the underlying force field from data. We apply the learned spatiotemporal attractive force field in the simulations with a particle-based model. And the simulations recapitulate nearly all the observed characteristic collective behaviors of nuclear arrays in Drosophila embryos.
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Affiliation(s)
- Xiaoxuan Wu
- Center for Quantitative Biology, Peking University, Beijing, China
| | - Kakit Kong
- State Key Laboratory of Nuclear Physics and Technology, School of Physics, Peking University, Beijing, China
| | - Wenlei Xiao
- School of Mechanical Engineering and Automation, Beihang University, Beijing, China
| | - Feng Liu
- Center for Quantitative Biology, Peking University, Beijing, China
- State Key Laboratory of Nuclear Physics and Technology, School of Physics, Peking University, Beijing, China
- Key Laboratory of Hebei Province for Molecular Biophysics, Institute of Biophysics, School of Health Science & Biomedical Engineering, Hebei University of Technology, Tianjin, China
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32
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Gibbs HC, Mota SM, Hart NA, Min SW, Vernino AO, Pritchard AL, Sen A, Vitha S, Sarasamma S, McIntosh AL, Yeh AT, Lekven AC, McCreedy DA, Maitland KC, Perez LM. Navigating the Light-Sheet Image Analysis Software Landscape: Concepts for Driving Cohesion From Data Acquisition to Analysis. Front Cell Dev Biol 2021; 9:739079. [PMID: 34858975 PMCID: PMC8631767 DOI: 10.3389/fcell.2021.739079] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 09/16/2021] [Indexed: 11/26/2022] Open
Abstract
From the combined perspective of biologists, microscope instrumentation developers, imaging core facility scientists, and high performance computing experts, we discuss the challenges faced when selecting imaging and analysis tools in the field of light-sheet microscopy. Our goal is to provide a contextual framework of basic computing concepts that cell and developmental biologists can refer to when mapping the peculiarities of different light-sheet data to specific existing computing environments and image analysis pipelines. We provide our perspective on efficient processes for tool selection and review current hardware and software commonly used in light-sheet image analysis, as well as discuss what ideal tools for the future may look like.
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Affiliation(s)
- Holly C. Gibbs
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
- Microscopy and Imaging Center, Texas A&M University, College Station, TX, United States
| | - Sakina M. Mota
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
| | - Nathan A. Hart
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
| | - Sun Won Min
- Department of Biology, Texas A&M University, College Station, TX, United States
| | - Alex O. Vernino
- Department of Biology, Texas A&M University, College Station, TX, United States
| | - Anna L. Pritchard
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
| | - Anindito Sen
- Microscopy and Imaging Center, Texas A&M University, College Station, TX, United States
| | - Stan Vitha
- Microscopy and Imaging Center, Texas A&M University, College Station, TX, United States
| | - Sreeja Sarasamma
- Department of Neurology, Baylor College of Medicine, Houston, TX, United States
| | - Avery L. McIntosh
- Microscopy and Imaging Center, Texas A&M University, College Station, TX, United States
| | - Alvin T. Yeh
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
| | - Arne C. Lekven
- Department of Biology and Biochemistry, University of Houston, Houston, TX, United States
| | - Dylan A. McCreedy
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
- Department of Biology, Texas A&M University, College Station, TX, United States
| | - Kristen C. Maitland
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
- Microscopy and Imaging Center, Texas A&M University, College Station, TX, United States
| | - Lisa M. Perez
- High Performance Research Computing, Texas A&M University, College Station, TX, United States
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Ulicna K, Vallardi G, Charras G, Lowe AR. Automated Deep Lineage Tree Analysis Using a Bayesian Single Cell Tracking Approach. FRONTIERS IN COMPUTER SCIENCE 2021. [DOI: 10.3389/fcomp.2021.734559] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Single-cell methods are beginning to reveal the intrinsic heterogeneity in cell populations, arising from the interplay of deterministic and stochastic processes. However, it remains challenging to quantify single-cell behaviour from time-lapse microscopy data, owing to the difficulty of extracting reliable cell trajectories and lineage information over long time-scales and across several generations. Therefore, we developed a hybrid deep learning and Bayesian cell tracking approach to reconstruct lineage trees from live-cell microscopy data. We implemented a residual U-Net model coupled with a classification CNN to allow accurate instance segmentation of the cell nuclei. To track the cells over time and through cell divisions, we developed a Bayesian cell tracking methodology that uses input features from the images to enable the retrieval of multi-generational lineage information from a corpus of thousands of hours of live-cell imaging data. Using our approach, we extracted 20,000 + fully annotated single-cell trajectories from over 3,500 h of video footage, organised into multi-generational lineage trees spanning up to eight generations and fourth cousin distances. Benchmarking tests, including lineage tree reconstruction assessments, demonstrate that our approach yields high-fidelity results with our data, with minimal requirement for manual curation. To demonstrate the robustness of our minimally supervised cell tracking methodology, we retrieve cell cycle durations and their extended inter- and intra-generational family relationships in 5,000 + fully annotated cell lineages. We observe vanishing cycle duration correlations across ancestral relatives, yet reveal correlated cyclings between cells sharing the same generation in extended lineages. These findings expand the depth and breadth of investigated cell lineage relationships in approximately two orders of magnitude more data than in previous studies of cell cycle heritability, which were reliant on semi-manual lineage data analysis.
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34
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Kohrman AQ, Kim-Yip RP, Posfai E. Imaging developmental cell cycles. Biophys J 2021; 120:4149-4161. [PMID: 33964274 PMCID: PMC8516676 DOI: 10.1016/j.bpj.2021.04.035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/14/2021] [Accepted: 04/30/2021] [Indexed: 01/05/2023] Open
Abstract
The last decade has seen a major expansion in development of live biosensors, the tools needed to genetically encode them into model organisms, and the microscopic techniques used to visualize them. When combined, these offer us powerful tools with which to make fundamental discoveries about complex biological processes. In this review, we summarize the availability of biosensors to visualize an essential cellular process, the cell cycle, and the techniques for single-cell tracking and quantification of these reporters. We also highlight studies investigating the connection of cellular behavior to the cell cycle, particularly through live imaging, and anticipate exciting discoveries with the combination of these technologies in developmental contexts.
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Affiliation(s)
- Abraham Q Kohrman
- Department of Molecular Biology, Princeton University, Princeton, New Jersey
| | - Rebecca P Kim-Yip
- Department of Molecular Biology, Princeton University, Princeton, New Jersey
| | - Eszter Posfai
- Department of Molecular Biology, Princeton University, Princeton, New Jersey.
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35
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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: 11] [Impact Index Per Article: 3.7] [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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36
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Xu B, Shi J, Lu M, Cong J, Wang L, Nener B. An Automated Cell Tracking Approach With Multi-Bernoulli Filtering and Ant Colony Labor Division. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1850-1863. [PMID: 31751247 DOI: 10.1109/tcbb.2019.2954502] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
In this article, we take as inspiration the labor division into scouts and workers in an ant colony and propose a novel approach for automated cell tracking in the framework of multi-Bernoulli random finite sets. To approximate the Bernoulli parameter sets, we first define an existence probability of an ant colony as well as its discrete density distribution. During foraging, the behavior of scouts is modeled as a chaotic movement to produce a set of potential candidates. Afterwards, a group of workers, i.e., a worker ant colony, is recruited for each candidate, which then embark on gathering heuristic information in a self-organized way. Finally, the pheromone field is formed by the corresponding worker ant colony, from which the Bernoulli parameter is derived and the state of the cell is estimated accordingly to be associated with the existing tracks. Performance comparisons with other previous approaches are conducted on both simulated and real cell image sequences and show the superiority of this algorithm.
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Thiels W, Smeets B, Cuvelier M, Caroti F, Jelier R. spheresDT/Mpacts-PiCS: Cell Tracking and Shape Retrieval in Membrane-labeled Embryos. Bioinformatics 2021; 37:4851-4856. [PMID: 34329378 PMCID: PMC8665764 DOI: 10.1093/bioinformatics/btab557] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 07/16/2021] [Accepted: 07/28/2021] [Indexed: 11/13/2022] Open
Abstract
Motivation Uncovering the cellular and mechanical processes that drive embryo formation requires an accurate read out of cell geometries over time. However, automated extraction of 3D cell shapes from time-lapse microscopy remains challenging, especially when only membranes are labeled. Results We present an image analysis framework for automated tracking and three-dimensional cell segmentation in confocal time lapses. A sphere clustering approach allows for local thresholding and application of logical rules to facilitate tracking and unseeded segmentation of variable cell shapes. Next, the segmentation is refined by a discrete element method simulation where cell shapes are constrained by a biomechanical cell shape model. We apply the framework on Caenorhabditis elegans embryos in various stages of early development and analyze the geometry of the 7- and 8-cell stage embryo, looking at volume, contact area and shape over time. Availability and implementation The Python code for the algorithm and for measuring performance, along with all data needed to recreate the results is freely available at 10.5281/zenodo.5108416 and 10.5281/zenodo.4540092. The most recent version of the software is maintained at https://bitbucket.org/pgmsembryogenesis/sdt-pics. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Wim Thiels
- CMPG, KU Leuven, Heverlee, 3001, Belgium
| | | | | | | | - Rob Jelier
- CMPG, KU Leuven, Heverlee, 3001, Belgium
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Betjes MA, Zheng X, Kok RNU, van Zon JS, Tans SJ. Cell Tracking for Organoids: Lessons From Developmental Biology. Front Cell Dev Biol 2021; 9:675013. [PMID: 34150770 PMCID: PMC8209328 DOI: 10.3389/fcell.2021.675013] [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: 03/02/2021] [Accepted: 05/03/2021] [Indexed: 12/20/2022] Open
Abstract
Organoids have emerged as powerful model systems to study organ development and regeneration at the cellular level. Recently developed microscopy techniques that track individual cells through space and time hold great promise to elucidate the organizational principles of organs and organoids. Applied extensively in the past decade to embryo development and 2D cell cultures, cell tracking can reveal the cellular lineage trees, proliferation rates, and their spatial distributions, while fluorescent markers indicate differentiation events and other cellular processes. Here, we review a number of recent studies that exemplify the power of this approach, and illustrate its potential to organoid research. We will discuss promising future routes, and the key technical challenges that need to be overcome to apply cell tracking techniques to organoid biology.
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Affiliation(s)
| | | | | | | | - Sander J Tans
- AMOLF, Amsterdam, Netherlands.,Bionanoscience Department, Kavli Institute of Nanoscience Delft, Delft University of Technology, Delft, Netherlands
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Tian C, Yang C, Spencer SL. EllipTrack: A Global-Local Cell-Tracking Pipeline for 2D Fluorescence Time-Lapse Microscopy. Cell Rep 2021; 32:107984. [PMID: 32755578 DOI: 10.1016/j.celrep.2020.107984] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 05/29/2020] [Accepted: 07/09/2020] [Indexed: 12/12/2022] Open
Abstract
Time-lapse microscopy provides an unprecedented opportunity to monitor single-cell dynamics. However, tracking cells for long periods remains a technical challenge, especially for multi-day, large-scale movies with rapid cell migration, high cell density, and drug treatments that alter cell morphology/behavior. Here, we present EllipTrack, a global-local cell-tracking pipeline optimized for tracking such movies. EllipTrack first implements a global track-linking algorithm to construct tracks that maximize the probability of cell lineages. Tracking mistakes are then corrected with a local track-correction module in which tracks generated by the global algorithm are systematically examined and amended if a more probable alternative can be found. Through benchmarking, we show that EllipTrack outperforms state-of-the-art cell trackers and generates nearly error-free cell lineages for multiple large-scale movies. In addition, EllipTrack can adapt to time- and cell-density-dependent changes in cell migration speeds and requires minimal training datasets. EllipTrack is available at https://github.com/tianchengzhe/EllipTrack.
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Affiliation(s)
- Chengzhe Tian
- Department of Biochemistry, University of Colorado Boulder, Boulder, CO 80303, USA; BioFrontiers Institute, University of Colorado Boulder, Boulder, CO 80303, USA.
| | - Chen Yang
- Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, CO 80303, USA; BioFrontiers Institute, University of Colorado Boulder, Boulder, CO 80303, USA
| | - Sabrina L Spencer
- Department of Biochemistry, University of Colorado Boulder, Boulder, CO 80303, USA; BioFrontiers Institute, University of Colorado Boulder, Boulder, CO 80303, USA.
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40
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Liu M, Liu Y, Qian W, Wang Y. DeepSeed Local Graph Matching for Densely Packed Cells Tracking. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1060-1069. [PMID: 31443049 DOI: 10.1109/tcbb.2019.2936851] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The tracking of densely packed plant cells across microscopy image sequences is very challenging, because their appearance change greatly over time. A local graph matching algorithm was proposed to track such cells by exploiting the tight spatial topology of neighboring cells, and then an iterative searching strategy was used to grow the correspondence from a seed cell pair. Thus, the performance of the existing tracking approach heavily relies on the robustness of finding seed cell pair. However, the existing local graph matching algorithm cannot guarantee the correctness of the seed cell pair, especially in unregistered image sequences or image sequences with large time intervals. In this paper, we propose a DeepSeed local graph matching model to find seed cell pair robustly, by combining local graph matching and CNN-based similarity learning, which uses cells' spatial-temporal contextual information and cell pairs' similarity information. The CNN-based similarity learning is designed to learn cells' deep feature and measure cell pairs' similarity. Compared with the existing plant cell matching methods, the experimental results show that the DeepSeed local graph matching method can track most cells in unregistered image sequences. Moreover, the DeepSeed tracking algorithm can accurately track cells across image sequences with large time intervals.
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Abstract
Cell imaging has entered the 'Big Data' era. New technologies in light microscopy and molecular biology have led to an explosion in high-content, dynamic and multidimensional imaging data. Similar to the 'omics' fields two decades ago, our current ability to process, visualize, integrate and mine this new generation of cell imaging data is becoming a critical bottleneck in advancing cell biology. Computation, traditionally used to quantitatively test specific hypotheses, must now also enable iterative hypothesis generation and testing by deciphering hidden biologically meaningful patterns in complex, dynamic or high-dimensional cell image data. Data science is uniquely positioned to aid in this process. In this Perspective, we survey the rapidly expanding new field of data science in cell imaging. Specifically, we highlight how data science tools are used within current image analysis pipelines, propose a computation-first approach to derive new hypotheses from cell image data, identify challenges and describe the next frontiers where we believe data science will make an impact. We also outline steps to ensure broad access to these powerful tools - democratizing infrastructure availability, developing sensitive, robust and usable tools, and promoting interdisciplinary training to both familiarize biologists with data science and expose data scientists to cell imaging.
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Affiliation(s)
- Meghan K Driscoll
- Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Assaf Zaritsky
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
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42
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Winter MR, Morgulis M, Gildor T, Cohen AR, Ben-Tabou de-Leon S. Calcium-vesicles perform active diffusion in the sea urchin embryo during larval biomineralization. PLoS Comput Biol 2021; 17:e1008780. [PMID: 33617532 PMCID: PMC7932551 DOI: 10.1371/journal.pcbi.1008780] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 03/04/2021] [Accepted: 02/08/2021] [Indexed: 11/18/2022] Open
Abstract
Biomineralization is the process by which organisms use minerals to harden their tissues and provide them with physical support. Biomineralizing cells concentrate the mineral in vesicles that they secret into a dedicated compartment where crystallization occurs. The dynamics of vesicle motion and the molecular mechanisms that control it, are not well understood. Sea urchin larval skeletogenesis provides an excellent platform for investigating the kinetics of mineral-bearing vesicles. Here we used lattice light-sheet microscopy to study the three-dimensional (3D) dynamics of calcium-bearing vesicles in the cells of normal sea urchin embryos and of embryos where skeletogenesis is blocked through the inhibition of Vascular Endothelial Growth Factor Receptor (VEGFR). We developed computational tools for displaying 3D-volumetric movies and for automatically quantifying vesicle dynamics. Our findings imply that calcium vesicles perform an active diffusion motion in both, calcifying (skeletogenic) and non-calcifying (ectodermal) cells of the embryo. The diffusion coefficient and vesicle speed are larger in the mesenchymal skeletogenic cells compared to the epithelial ectodermal cells. These differences are possibly due to the distinct mechanical properties of the two tissues, demonstrated by the enhanced f-actin accumulation and myosinII activity in the ectodermal cells compared to the skeletogenic cells. Vesicle motion is not directed toward the biomineralization compartment, but the vesicles slow down when they approach it, and probably bind for mineral deposition. VEGFR inhibition leads to an increase of vesicle volume but hardly changes vesicle kinetics and doesn’t affect f-actin accumulation and myosinII activity. Thus, calcium vesicles perform an active diffusion motion in the cells of the sea urchin embryo, with diffusion length and speed that inversely correlate with the strength of the actomyosin network. Overall, our studies provide an unprecedented view of calcium vesicle 3D-dynamics and point toward cytoskeleton remodeling as an important effector of the motion of mineral-bearing vesicles. Biomineralization is a widespread, fundamental process by which organisms use minerals to harden their tissues. Mineral-bearing vesicles were observed in biomineralizing cells and play an essential role in biomineralization, yet little is known about their three-dimensional (3D) dynamics. Here we quantify 3D-vesicle-dynamics during calcite skeleton formation in sea urchin larvae, using lattice-light-sheet microscopy. We discover that calcium vesicles perform a diffusive motion in both calcifying and non-calcifying cells of the embryo. The diffusion coefficient and vesicle speed are higher in the mesenchymal skeletogenic cells compared to the epithelial ectodermal cells. This difference is possibly due to the higher rigidity of the ectodermal cells as demonstrated by the enhanced signal of f-actin and myosinII activity in these cells compared to the skeletogenic cells. The motion of the vesicles in the skeletogenic cells, is not directed toward the biomineralization compartment but the vesicles slow down near it, possibly to deposit their content. Blocking skeletogenesis through the inhibition of Vascular Endothelial Growth Factor Receptor (VEGFR), increases vesicle volume but doesn’t change the diffusion mode and the cytoskeleton markers in the cells. Our studies reveal the active diffusive motion of mineral bearing vesicles that is apparently defined by the mechanical properties of the cells.
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Affiliation(s)
- Mark R. Winter
- Marine Biology Department, Charney School of Marine Sciences, the University of Haifa, Haifa, Israel
- * E-mail: (MRW); (SBD)
| | - Miri Morgulis
- Marine Biology Department, Charney School of Marine Sciences, the University of Haifa, Haifa, Israel
| | - Tsvia Gildor
- Marine Biology Department, Charney School of Marine Sciences, the University of Haifa, Haifa, Israel
| | - Andrew R. Cohen
- Dept of Electrical Engineering, Drexel University, Pennsylvania, United States of America
| | - Smadar Ben-Tabou de-Leon
- Marine Biology Department, Charney School of Marine Sciences, the University of Haifa, Haifa, Israel
- * E-mail: (MRW); (SBD)
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Fang J, Swain A, Unni R, Zheng Y. Decoding Optical Data with Machine Learning. LASER & PHOTONICS REVIEWS 2021; 15:2000422. [PMID: 34539925 PMCID: PMC8443240 DOI: 10.1002/lpor.202000422] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Indexed: 05/24/2023]
Abstract
Optical spectroscopy and imaging techniques play important roles in many fields such as disease diagnosis, biological study, information technology, optical science, and materials science. Over the past decade, machine learning (ML) has proved promising in decoding complex data, enabling rapid and accurate analysis of optical spectra and images. This review aims to shed light on various ML algorithms for optical data analysis with a focus on their applications in a wide range of fields. The goal of this work is to sketch the validity of ML-based optical data decoding. The review concludes with an outlook on unaddressed problems and opportunities in this emerging subject that interfaces optics, data science and ML.
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Affiliation(s)
- Jie Fang
- Walker Department of Mechanical Engineering and Texas Materials Institute, The University of Texas at Austin, Austin, TX 78712, USA
| | - Anand Swain
- Walker Department of Mechanical Engineering and Texas Materials Institute, The University of Texas at Austin, Austin, TX 78712, USA
| | - Rohit Unni
- Walker Department of Mechanical Engineering and Texas Materials Institute, The University of Texas at Austin, Austin, TX 78712, USA
| | - Yuebing Zheng
- Walker Department of Mechanical Engineering and Texas Materials Institute, The University of Texas at Austin, Austin, TX 78712, USA
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44
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Li L, Ugalde AP, Scheele CLGJ, Dieter SM, Nagel R, Ma J, Pataskar A, Korkmaz G, Elkon R, Chien MP, You L, Su PR, Bleijerveld OB, Altelaar M, Momchev L, Manber Z, Han R, van Breugel PC, Lopes R, ten Dijke P, van Rheenen J, Agami R. A comprehensive enhancer screen identifies TRAM2 as a key and novel mediator of YAP oncogenesis. Genome Biol 2021; 22:54. [PMID: 33514403 PMCID: PMC7845134 DOI: 10.1186/s13059-021-02272-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 01/14/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Frequent activation of the co-transcriptional factor YAP is observed in a large number of solid tumors. Activated YAP associates with enhancer loci via TEAD4-DNA-binding protein and stimulates cancer aggressiveness. Although thousands of YAP/TEAD4 binding-sites are annotated, their functional importance is unknown. Here, we aim at further identification of enhancer elements that are required for YAP functions. RESULTS We first apply genome-wide ChIP profiling of YAP to systematically identify enhancers that are bound by YAP/TEAD4. Next, we implement a genetic approach to uncover functions of YAP/TEAD4-associated enhancers, demonstrate its robustness, and use it to reveal a network of enhancers required for YAP-mediated proliferation. We focus on EnhancerTRAM2, as its target gene TRAM2 shows the strongest expression-correlation with YAP activity in nearly all tumor types. Interestingly, TRAM2 phenocopies the YAP-induced cell proliferation, migration, and invasion phenotypes and correlates with poor patient survival. Mechanistically, we identify FSTL-1 as a major direct client of TRAM2 that is involved in these phenotypes. Thus, TRAM2 is a key novel mediator of YAP-induced oncogenic proliferation and cellular invasiveness. CONCLUSIONS YAP is a transcription co-factor that binds to thousands of enhancer loci and stimulates tumor aggressiveness. Using unbiased functional approaches, we dissect YAP enhancer network and characterize TRAM2 as a novel mediator of cellular proliferation, migration, and invasion. Our findings elucidate how YAP induces cancer aggressiveness and may assist diagnosis of cancer metastasis.
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Affiliation(s)
- Li Li
- Division of Oncogenomics, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, The Netherlands
| | - Alejandro P. Ugalde
- Division of Oncogenomics, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, The Netherlands
| | - Colinda L. G. J. Scheele
- Division of Molecular Pathology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Sebastian M. Dieter
- Division of Oncogenomics, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, The Netherlands
| | - Remco Nagel
- Division of Oncogenomics, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, The Netherlands
| | - Jin Ma
- Department of Molecular Cell Biology, Cancer Genomics Centre Netherlands, Leiden University Medical Center, Leiden, The Netherlands
| | - Abhijeet Pataskar
- Division of Oncogenomics, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, The Netherlands
| | - Gozde Korkmaz
- Division of Oncogenomics, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, The Netherlands
| | - Ran Elkon
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Miao-Ping Chien
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Li You
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Pin-Rui Su
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Onno B. Bleijerveld
- Proteomics Facility, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Maarten Altelaar
- Proteomics Facility, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Biomolecular Mass Spectrometry and Proteomics, Bijvt Centre for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, 3584CH Utrecht, The Netherlands
| | - Lyubomir Momchev
- Division of Oncogenomics, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, The Netherlands
| | - Zohar Manber
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ruiqi Han
- Division of Oncogenomics, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, The Netherlands
| | - Pieter C. van Breugel
- Division of Oncogenomics, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, The Netherlands
| | - Rui Lopes
- Division of Oncogenomics, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, The Netherlands
| | - Peter ten Dijke
- Department of Molecular Cell Biology, Cancer Genomics Centre Netherlands, Leiden University Medical Center, Leiden, The Netherlands
| | - Jacco van Rheenen
- Division of Molecular Pathology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Reuven Agami
- Division of Oncogenomics, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, The Netherlands
- Erasmus MC, Rotterdam University, Rotterdam, The Netherlands
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45
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Zhao S, Qian Y, Mu Y. Tracking single cells in zebrafish brain. J Neurosci Methods 2021; 353:109086. [PMID: 33508409 DOI: 10.1016/j.jneumeth.2021.109086] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 01/16/2021] [Accepted: 01/19/2021] [Indexed: 10/22/2022]
Abstract
Changes in cell locations and morphologies shape the brain. Tracking single cells over time is a vital step to study these changes, but densely arranged brain cells impede such observation. Larval zebrafish has become a popular model animal for single-cell tracking, owing to its small, transparent brain and easy genetic manipulation. In this article, we review recent single-cell tracking studies on neurons and non-neuronal cells in the larval zebrafish brain, including soma migration, process refinement, and interactions among cell types. These findings yield new insights regarding how the translocation and morphological changes of individual cells determine brain function.
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Affiliation(s)
- Shan Zhao
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai, 200031, China
| | - Yu Qian
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai, 200031, China
| | - Yu Mu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai, 200031, China.
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Abstract
Bioimage analysis (BIA) has historically helped study how and why cells move; biological experiments evolved in intimate feedback with the most classical image processing techniques because they contribute objectivity and reproducibility to an eminently qualitative science. Cell segmentation, tracking, and morphology descriptors are all discussed here. Using ameboid motility as a case study, these methods help us illustrate how proper quantification can augment biological data, for example, by choosing mathematical representations that amplify initially subtle differences, by statistically uncovering general laws or by integrating physical insight. More recently, the non-invasive nature of quantitative imaging is fertilizing two blooming fields: mechanobiology, where many biophysical measurements remain inaccessible, and microenvironments, where the quest for physiological relevance has exploded data size. From relief to remedy, this trend indicates that BIA is to become a main vector of biological discovery as human visual analysis struggles against ever more complex data.
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Affiliation(s)
- Aleix Boquet-Pujadas
- Institut Pasteur, Bioimage Analysis Unit, 25 rue du Dr. Roux, Paris Cedex 15 75724, France
- Centre National de la Recherche Scientifique, CNRS UMR3691, Paris, France
- Sorbonne Université, Paris 75005, France
| | - Jean-Christophe Olivo-Marin
- Institut Pasteur, Bioimage Analysis Unit, 25 rue du Dr. Roux, Paris Cedex 15 75724, France
- Centre National de la Recherche Scientifique, CNRS UMR3691, Paris, France
| | - Nancy Guillén
- Institut Pasteur, Bioimage Analysis Unit, 25 rue du Dr. Roux, Paris Cedex 15 75724, France
- Centre National de la Recherche Scientifique, CNRS ERL9195, Paris, France
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47
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Control of osteoblast regeneration by a train of Erk activity waves. Nature 2021; 590:129-133. [PMID: 33408418 PMCID: PMC7864885 DOI: 10.1038/s41586-020-03085-8] [Citation(s) in RCA: 92] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 11/19/2020] [Indexed: 12/27/2022]
Abstract
Regeneration is a complex chain of events that restores a tissue to its original size and shape. The tissue-wide coordination of cellular dynamics needed for proper morphogenesis is challenged by the large dimensions of regenerating body parts. Feedback mechanisms in biochemical pathways can provide effective communication across great distances1-5, but how they might regulate growth during tissue regeneration is unresolved6,7. Here, we report that rhythmic traveling waves of Erk activity control the growth of bone in time and space in regenerating zebrafish scales, millimetre-sized discs of protective body armour. We find that Erk activity waves travel as expanding concentric rings, broadcast from a central source, inducing ring-like patterns of osteoblast tissue growth. Using a combination of theoretical and experimental analyses, we show that Erk activity propagates as excitable trigger waves able to traverse the entire scale in approximately two days, with the frequency of wave generation controlling the rate of scale regeneration. Furthermore, periodic induction of synchronous, tissue-wide Erk activation in place of travelling waves impairs tissue growth, indicating that wave-distributed Erk activation is key to regeneration. Our findings reveal trigger waves as a regulatory strategy to coordinate cell behaviour and instruct tissue form during regeneration.
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48
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Ding Y, Gudapati V, Lin R, Fei Y, Sevag Packard RR, Song S, Chang CC, Baek KI, Wang Z, Roustaei M, Kuang D, Jay Kuo CC, Hsiai TK. Saak Transform-Based Machine Learning for Light-Sheet Imaging of Cardiac Trabeculation. IEEE Trans Biomed Eng 2021; 68:225-235. [PMID: 32365015 PMCID: PMC7606319 DOI: 10.1109/tbme.2020.2991754] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
OBJECTIVE Recent advances in light-sheet fluorescence microscopy (LSFM) enable 3-dimensional (3-D) imaging of cardiac architecture and mechanics in toto. However, segmentation of the cardiac trabecular network to quantify cardiac injury remains a challenge. METHODS We hereby employed "subspace approximation with augmented kernels (Saak) transform" for accurate and efficient quantification of the light-sheet image stacks following chemotherapy-treatment. We established a machine learning framework with augmented kernels based on the Karhunen-Loeve Transform (KLT) to preserve linearity and reversibility of rectification. RESULTS The Saak transform-based machine learning enhances computational efficiency and obviates iterative optimization of cost function needed for neural networks, minimizing the number of training datasets for segmentation in our scenario. The integration of forward and inverse Saak transforms can also serve as a light-weight module to filter adversarial perturbations and reconstruct estimated images, salvaging robustness of existing classification methods. The accuracy and robustness of the Saak transform are evident following the tests of dice similarity coefficients and various adversary perturbation algorithms, respectively. The addition of edge detection further allows for quantifying the surface area to volume ratio (SVR) of the myocardium in response to chemotherapy-induced cardiac remodeling. CONCLUSION The combination of Saak transform, random forest, and edge detection augments segmentation efficiency by 20-fold as compared to manual processing. SIGNIFICANCE This new methodology establishes a robust framework for post light-sheet imaging processing, and creating a data-driven machine learning for automated quantification of cardiac ultra-structure.
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Affiliation(s)
- Yichen Ding
- Henry Samueli School of Engineering and David Geffen School of Medicine, University of California, Los Angeles, CA 90095 USA
| | - Varun Gudapati
- Henry Samueli School of Engineering and David Geffen School of Medicine, University of California, Los Angeles, CA 90095 USA
| | - Ruiyuan Lin
- Ming-Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089 USA
| | - Yanan Fei
- Ming-Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089 USA
| | - René R Sevag Packard
- Henry Samueli School of Engineering and David Geffen School of Medicine, University of California, Los Angeles, CA 90095 USA
| | - Sibo Song
- Ming-Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089 USA
| | - Chih-Chiang Chang
- Henry Samueli School of Engineering and David Geffen School of Medicine, University of California, Los Angeles, CA 90095 USA
| | - Kyung In Baek
- Henry Samueli School of Engineering and David Geffen School of Medicine, University of California, Los Angeles, CA 90095 USA
| | - Zhaoqiang Wang
- Henry Samueli School of Engineering and David Geffen School of Medicine, University of California, Los Angeles, CA 90095 USA
| | - Mehrdad Roustaei
- Henry Samueli School of Engineering and David Geffen School of Medicine, University of California, Los Angeles, CA 90095 USA
| | - Dengfeng Kuang
- Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, and Institute of Modern Optics, Nankai University, Tianjin 300350, China
| | - C.-C. Jay Kuo
- Ming-Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089 USA
| | - Tzung K. Hsiai
- Henry Samueli School of Engineering and David Geffen School of Medicine, University of California, Los Angeles, CA 90095 USA
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The art of lineage tracing: From worm to human. Prog Neurobiol 2020; 199:101966. [PMID: 33249090 DOI: 10.1016/j.pneurobio.2020.101966] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 11/03/2020] [Accepted: 11/22/2020] [Indexed: 12/20/2022]
Abstract
Reconstructing the genealogy of every cell that makes up an organism remains a long-standing challenge in developmental biology. Besides its relevance for understanding the mechanisms underlying normal and pathological development, resolving the lineage origin of cell types will be crucial to create these types on-demand. Multiple strategies have been deployed towards the problem of lineage tracing, ranging from direct observation to sophisticated genetic approaches. Here we discuss the achievements and limitations of past and current technology. Finally, we speculate about the future of lineage tracing and how to reach the next milestones in the field.
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50
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Kok RNU, Hebert L, Huelsz-Prince G, Goos YJ, Zheng X, Bozek K, Stephens GJ, Tans SJ, van Zon JS. OrganoidTracker: Efficient cell tracking using machine learning and manual error correction. PLoS One 2020; 15:e0240802. [PMID: 33091031 PMCID: PMC7580893 DOI: 10.1371/journal.pone.0240802] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 10/05/2020] [Indexed: 12/30/2022] Open
Abstract
Time-lapse microscopy is routinely used to follow cells within organoids, allowing direct study of division and differentiation patterns. There is an increasing interest in cell tracking in organoids, which makes it possible to study their growth and homeostasis at the single-cell level. As tracking these cells by hand is prohibitively time consuming, automation using a computer program is required. Unfortunately, organoids have a high cell density and fast cell movement, which makes automated cell tracking difficult. In this work, a semi-automated cell tracker has been developed. To detect the nuclei, we use a machine learning approach based on a convolutional neural network. To form cell trajectories, we link detections at different time points together using a min-cost flow solver. The tracker raises warnings for situations with likely errors. Rapid changes in nucleus volume and position are reported for manual review, as well as cases where nuclei divide, appear and disappear. When the warning system is adjusted such that virtually error-free lineage trees can be obtained, still less than 2% of all detected nuclei positions are marked for manual analysis. This provides an enormous speed boost over manual cell tracking, while still providing tracking data of the same quality as manual tracking.
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Affiliation(s)
| | - Laetitia Hebert
- Okinawa Institute of Science and Technology Graduate University (OIST), Onna-son, Okinawa, Japan
| | | | | | | | - Katarzyna Bozek
- Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
| | - Greg J. Stephens
- Okinawa Institute of Science and Technology Graduate University (OIST), Onna-son, Okinawa, Japan
- Department of Physics and Astronomy, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sander J. Tans
- AMOLF, Amsterdam, The Netherlands
- Bionanoscience Department, Kavli Institute of Nanoscience Delft, Delft University of Technology, Delft, The Netherlands
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