1
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Katoh TA, Fukai YT, Ishibashi T. Optical microscopic imaging, manipulation, and analysis methods for morphogenesis research. Microscopy (Oxf) 2024; 73:226-242. [PMID: 38102756 PMCID: PMC11154147 DOI: 10.1093/jmicro/dfad059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 11/20/2023] [Accepted: 03/22/2024] [Indexed: 12/17/2023] Open
Abstract
Morphogenesis is a developmental process of organisms being shaped through complex and cooperative cellular movements. To understand the interplay between genetic programs and the resulting multicellular morphogenesis, it is essential to characterize the morphologies and dynamics at the single-cell level and to understand how physical forces serve as both signaling components and driving forces of tissue deformations. In recent years, advances in microscopy techniques have led to improvements in imaging speed, resolution and depth. Concurrently, the development of various software packages has supported large-scale, analyses of challenging images at the single-cell resolution. While these tools have enhanced our ability to examine dynamics of cells and mechanical processes during morphogenesis, their effective integration requires specialized expertise. With this background, this review provides a practical overview of those techniques. First, we introduce microscopic techniques for multicellular imaging and image analysis software tools with a focus on cell segmentation and tracking. Second, we provide an overview of cutting-edge techniques for mechanical manipulation of cells and tissues. Finally, we introduce recent findings on morphogenetic mechanisms and mechanosensations that have been achieved by effectively combining microscopy, image analysis tools and mechanical manipulation techniques.
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Affiliation(s)
- Takanobu A Katoh
- Department of Cell Biology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Yohsuke T Fukai
- Nonequilibrium Physics of Living Matter RIKEN Hakubi Research Team, RIKEN Center for Biosystems Dynamics Research, 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Tomoki Ishibashi
- Laboratory for Physical Biology, RIKEN Center for Biosystems Dynamics Research, 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
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2
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Tran M, Askary A, Elowitz MB. Lineage motifs as developmental modules for control of cell type proportions. Dev Cell 2024; 59:812-826.e3. [PMID: 38359830 DOI: 10.1016/j.devcel.2024.01.017] [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: 05/19/2023] [Revised: 10/10/2023] [Accepted: 01/19/2024] [Indexed: 02/17/2024]
Abstract
In multicellular organisms, cell types must be produced and maintained in appropriate proportions. One way this is achieved is through committed progenitor cells or extrinsic interactions that produce specific patterns of descendant cell types on lineage trees. However, cell fate commitment is probabilistic in most contexts, making it difficult to infer these dynamics and understand how they establish overall cell type proportions. Here, we introduce Lineage Motif Analysis (LMA), a method that recursively identifies statistically overrepresented patterns of cell fates on lineage trees as potential signatures of committed progenitor states or extrinsic interactions. Applying LMA to published datasets reveals spatial and temporal organization of cell fate commitment in zebrafish and rat retina and early mouse embryonic development. Comparative analysis of vertebrate species suggests that lineage motifs facilitate adaptive evolutionary variation of retinal cell type proportions. LMA thus provides insight into complex developmental processes by decomposing them into simpler underlying modules.
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Affiliation(s)
- Martin Tran
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Amjad Askary
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA 90095, USA.
| | - Michael B Elowitz
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.
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3
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Waliman M, Johnson RL, Natesan G, Tan S, Santella A, Hong RL, Shah PK. Automated Cell Lineage Reconstruction using Label-Free 4D Microscopy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.20.576449. [PMID: 38328064 PMCID: PMC10849476 DOI: 10.1101/2024.01.20.576449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Here we describe embGAN, a deep learning pipeline that addresses the challenge of automated cell detection and tracking in label-free 3D time lapse imaging. embGAN requires no manual data annotation for training, learns robust detections that exhibits a high degree of scale invariance and generalizes well to images acquired in multiple labs on multiple instruments.
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Affiliation(s)
- Matthew Waliman
- Department of Electrical and Computer Engineering, University of California, Los Angeles, California, United States of America
| | - Ryan L Johnson
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, California, United State of America
| | - Gunalan Natesan
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, California, United State of America
| | - Shiqin Tan
- Department of Computational and Systems Biology, University of California, Los Angeles, California, United States of America
| | - Anthony Santella
- Molecular Cytology Core, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Ray L Hong
- Department of Biology, California State University, Northridge, California, United States of America
| | - Pavak K Shah
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, California, United State of America
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, California, United States of America
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4
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Logotheti S, Papadaki E, Zolota V, Logothetis C, Vrahatis AG, Soundararajan R, Tzelepi V. Lineage Plasticity and Stemness Phenotypes in Prostate Cancer: Harnessing the Power of Integrated "Omics" Approaches to Explore Measurable Metrics. Cancers (Basel) 2023; 15:4357. [PMID: 37686633 PMCID: PMC10486655 DOI: 10.3390/cancers15174357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/21/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023] Open
Abstract
Prostate cancer (PCa), the most frequent and second most lethal cancer type in men in developed countries, is a highly heterogeneous disease. PCa heterogeneity, therapy resistance, stemness, and lethal progression have been attributed to lineage plasticity, which refers to the ability of neoplastic cells to undergo phenotypic changes under microenvironmental pressures by switching between developmental cell states. What remains to be elucidated is how to identify measurements of lineage plasticity, how to implement them to inform preclinical and clinical research, and, further, how to classify patients and inform therapeutic strategies in the clinic. Recent research has highlighted the crucial role of next-generation sequencing technologies in identifying potential biomarkers associated with lineage plasticity. Here, we review the genomic, transcriptomic, and epigenetic events that have been described in PCa and highlight those with significance for lineage plasticity. We further focus on their relevance in PCa research and their benefits in PCa patient classification. Finally, we explore ways in which bioinformatic analyses can be used to determine lineage plasticity based on large omics analyses and algorithms that can shed light on upstream and downstream events. Most importantly, an integrated multiomics approach may soon allow for the identification of a lineage plasticity signature, which would revolutionize the molecular classification of PCa patients.
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Affiliation(s)
- Souzana Logotheti
- Department of Pathology, University of Patras, 26504 Patras, Greece; (S.L.); (E.P.); (V.Z.)
| | - Eugenia Papadaki
- Department of Pathology, University of Patras, 26504 Patras, Greece; (S.L.); (E.P.); (V.Z.)
- Department of Informatics, Ionian University, 49100 Corfu, Greece;
| | - Vasiliki Zolota
- Department of Pathology, University of Patras, 26504 Patras, Greece; (S.L.); (E.P.); (V.Z.)
| | - Christopher Logothetis
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | | | - Rama Soundararajan
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Vasiliki Tzelepi
- Department of Pathology, University of Patras, 26504 Patras, Greece; (S.L.); (E.P.); (V.Z.)
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5
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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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6
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Tran M, Askary A, Elowitz MB. Lineage motifs: developmental modules for control of cell type proportions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.06.543925. [PMID: 37333085 PMCID: PMC10274800 DOI: 10.1101/2023.06.06.543925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
In multicellular organisms, cell types must be produced and maintained in appropriate proportions. One way this is achieved is through committed progenitor cells that produce specific sets of descendant cell types. However, cell fate commitment is probabilistic in most contexts, making it difficult to infer progenitor states and understand how they establish overall cell type proportions. Here, we introduce Lineage Motif Analysis (LMA), a method that recursively identifies statistically overrepresented patterns of cell fates on lineage trees as potential signatures of committed progenitor states. Applying LMA to published datasets reveals spatial and temporal organization of cell fate commitment in zebrafish and rat retina and early mouse embryo development. Comparative analysis of vertebrate species suggests that lineage motifs facilitate adaptive evolutionary variation of retinal cell type proportions. LMA thus provides insight into complex developmental processes by decomposing them into simpler underlying modules.
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Affiliation(s)
- Martin Tran
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Amjad Askary
- Department of Molecular, Cell and Developmental Biology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Michael B. Elowitz
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
- Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
- Lead contact
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7
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Li H, Weng W, Zhou B. Perfect duet: Dual recombinases improve genetic resolution. Cell Prolif 2023; 56:e13446. [PMID: 37060165 PMCID: PMC10212704 DOI: 10.1111/cpr.13446] [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: 02/01/2023] [Revised: 02/25/2023] [Accepted: 03/01/2023] [Indexed: 04/16/2023] Open
Abstract
As a powerful genetic tool, site-specific recombinases (SSRs) have been widely used in genomic manipulation to elucidate cell fate plasticity in vivo, advancing research in stem cell and regeneration medicine. However, the low resolution of conventional single-recombinase-mediated lineage tracing strategies, which rely heavily on the specificity of one marker gene, has led to controversial conclusions in many scientific questions. Therefore, different SSRs systems are combined to improve the accuracy of lineage tracing. Here we review the recent advances in dual-recombinase-mediated genetic approaches, including the development of novel genetic recombination technologies and their applications in cell differentiation, proliferation, and genetic manipulation. In comparison with the single-recombinase system, we also discuss the advantages of dual-genetic strategies in solving scientific issues as well as their technical limitations.
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Affiliation(s)
- Hongxin Li
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of SciencesUniversity of Chinese Academy of SciencesShanghaiChina
| | - Wendong Weng
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of SciencesUniversity of Chinese Academy of SciencesShanghaiChina
| | - Bin Zhou
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of SciencesUniversity of Chinese Academy of SciencesShanghaiChina
- Key Laboratory of Systems Health Science of Zhejiang ProvinceSchool of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of SciencesHangzhouChina
- School of Life Science and TechnologyShanghaiTech UniversityShanghaiChina
- New Cornerstone Science LaboratoryShenzhenChina
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8
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Yamamoto T, Cockburn K, Greco V, Kawaguchi K. Probing the rules of cell coordination in live tissues by interpretable machine learning based on graph neural networks. PLoS Comput Biol 2022; 18:e1010477. [PMID: 36067226 PMCID: PMC9481156 DOI: 10.1371/journal.pcbi.1010477] [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: 04/21/2022] [Revised: 09/16/2022] [Accepted: 08/09/2022] [Indexed: 11/18/2022] Open
Abstract
Robustness in developing and homeostatic tissues is supported by various types of spatiotemporal cell-to-cell interactions. Although live imaging and cell tracking are powerful in providing direct evidence of cell coordination rules, extracting and comparing these rules across many tissues with potentially different length and timescales of coordination requires a versatile framework of analysis. Here we demonstrate that graph neural network (GNN) models are suited for this purpose, by showing how they can be applied to predict cell fate in tissues and utilized to infer the cell interactions governing the multicellular dynamics. Analyzing the live mammalian epidermis data, where spatiotemporal graphs constructed from cell tracks and cell contacts are given as inputs, GNN discovers distinct neighbor cell fate coordination rules that depend on the region of the body. This approach demonstrates how the GNN framework is powerful in inferring general cell interaction rules from live data without prior knowledge of the signaling involved. During development and homeostasis, cells coordinate with each other to grow, deform, and maintain the tissues. Even with the modern high-throughput cell profiling technologies and high-resolution microscopy, it is still challenging to infer how cell coordination affects the dynamics such as cell fate choice, due to the complexity of the problem and the limited methods to perform perturbation experiments. We here propose a versatile framework of analysis utilizing an interpretable machine learning method based on graph neural network (GNN) which infers the cell-to-cell interaction rules from live images of multicellular dynamics. From the spatiotemporal graphs generated from live images of skin stem cells, we identified previously unaddressed neighbor fate coupling as well as rules consistent with past findings. We further found distinct interaction rules in a different skin region of the body, indicating that our method is useful in probing the diverse mechanism behind the robustness and flexibility in multicellular systems. The GNN framework is applicable for interaction rule discovery for general multicellular dynamics as well as in a wide range of systems where modeling by stochastic interacting agents is effective.
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Affiliation(s)
- Takaki Yamamoto
- Nonequilibrium Physics of Living Matter RIKEN Hakubi Research Team, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
- * E-mail: (TY); (KK)
| | - Katie Cockburn
- Department of Genetics, Yale School of Medicine, New Haven, Connecticut, United States of America
- Department of Biochemistry and Rosalind & Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada
| | - Valentina Greco
- Department of Genetics, Yale School of Medicine, New Haven, Connecticut, United States of America
- Departments of Cell Biology and Dermatology, Yale Stem Cell Center, Yale Cancer Center, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Kyogo Kawaguchi
- Nonequilibrium Physics of Living Matter RIKEN Hakubi Research Team, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
- RIKEN Cluster for Pioneering Research, Kobe, Japan
- Universal Biology Institute, The University of Tokyo, Tokyo, Japan
- * E-mail: (TY); (KK)
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Abstract
The Tabula Gallus is a proposed project that aims to create a map of every cell type in the chicken body and chick embryos. Chickens (Gallus gallus) are one of the most recognized model animals that recapitulate the development and physiology of mammals. The Tabula Gallus will generate a compendium of single-cell transcriptome data from Gallus gallus, characterize each cell type, and provide tools for the study of the biology of this species, similar to other ongoing cell atlas projects (Tabula Muris and Tabula Sapiens/Human Cell Atlas for mice and humans, respectively). The Tabula Gallus will potentially become an international collaboration between many researchers. This project will be useful for the basic scientific study of Gallus gallus and other birds (e.g., cell biology, molecular biology, developmental biology, neuroscience, physiology, oncology, virology, behavior, ecology, and evolution). It will eventually be beneficial for a better understanding of human health and diseases.
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10
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Stower MJ, Srinivas S. Advances in live imaging early mouse development: exploring the researcher's interdisciplinary toolkit. Development 2021; 148:dev199433. [PMID: 34897401 PMCID: PMC7615354 DOI: 10.1242/dev.199433] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Live imaging is an important part of the developmental biologist's armoury of methods. In the case of the mouse embryo, recent advances in several disciplines including embryo culture, microscopy hardware and computational analysis have all contributed to our ability to probe dynamic events during early development. Together, these advances have provided us with a versatile and powerful 'toolkit', enabling us not only to image events during mouse embryogenesis, but also to intervene with them. In this short Spotlight article, we summarise advances and challenges in using live imaging specifically for understanding early mouse embryogenesis.
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Affiliation(s)
- Matthew J. Stower
- Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford OX1 3QX, UK
| | - Shankar Srinivas
- Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford OX1 3QX, UK
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