1
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Bhavna R, Sonawane M. A deep learning framework for quantitative analysis of actin microridges. NPJ Syst Biol Appl 2023; 9:21. [PMID: 37268613 DOI: 10.1038/s41540-023-00276-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 05/03/2023] [Indexed: 06/04/2023] Open
Abstract
Microridges are evolutionarily conserved actin-rich protrusions present on the apical surface of squamous epithelial cells. In zebrafish epidermal cells, microridges form self-evolving patterns due to the underlying actomyosin network dynamics. However, their morphological and dynamic characteristics have remained poorly understood owing to a lack of computational methods. We achieved ~95% pixel-level accuracy with a deep learning microridge segmentation strategy enabling quantitative insights into their bio-physical-mechanical characteristics. From the segmented images, we estimated an effective microridge persistence length of ~6.1 μm. We discovered the presence of mechanical fluctuations and found relatively greater stresses stored within patterns of yolk than flank, indicating distinct regulation of their actomyosin networks. Furthermore, spontaneous formations and positional fluctuations of actin clusters within microridges were associated with pattern rearrangements over short length/time-scales. Our framework allows large-scale spatiotemporal analysis of microridges during epithelial development and probing of their responses to chemical and genetic perturbations to unravel the underlying patterning mechanisms.
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Affiliation(s)
- Rajasekaran Bhavna
- Department of Biological Sciences, Tata Institute of Fundamental Research, Colaba, Mumbai, 400005, India.
- Department of Data Science and Engineering, Indian Institute of Science Education and Research, Bhopal, Madhya Pradesh, 462066, India.
| | - Mahendra Sonawane
- Department of Biological Sciences, Tata Institute of Fundamental Research, Colaba, Mumbai, 400005, India
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2
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Toh K, Saunders D, Verd B, Steventon B. Zebrafish neuromesodermal progenitors undergo a critical state transition in vivo. iScience 2022; 25:105216. [PMID: 36274939 PMCID: PMC9579027 DOI: 10.1016/j.isci.2022.105216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 08/05/2022] [Accepted: 09/22/2022] [Indexed: 11/30/2022] Open
Abstract
The transition state model of cell differentiation proposes that a transient window of gene expression stochasticity precedes entry into a differentiated state. Here, we assess this theoretical model in zebrafish neuromesodermal progenitors (NMps) in vivo during late somitogenesis stages. We observed an increase in gene expression variability at the 24 somite stage (24ss) before their differentiation into spinal cord and paraxial mesoderm. Analysis of a published 18ss scRNA-seq dataset showed that the NMp population is noisier than its derivatives. By building in silico composite gene expression maps from image data, we assigned an 'NM index' to in silico NMps based on the expression of neural and mesodermal markers and demonstrated that cell population heterogeneity peaked at 24ss. Further examination revealed cells with gene expression profiles incongruent with their prospective fate. Taken together, our work supports the transition state model within an endogenous cell fate decision making event.
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Affiliation(s)
- Kane Toh
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK
| | - Dillan Saunders
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK
| | - Berta Verd
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK
- Department of Zoology, University of Oxford, Oxford OX1 3SZ, UK
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3
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Keenan SE, Avdeeva M, Yang L, Alber DS, Wieschaus EF, Shvartsman SY. Dynamics of Drosophila endoderm specification. Proc Natl Acad Sci U S A 2022; 119:e2112892119. [PMID: 35412853 PMCID: PMC9169638 DOI: 10.1073/pnas.2112892119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 02/06/2022] [Indexed: 11/18/2022] Open
Abstract
During early Drosophila embryogenesis, a network of gene regulatory interactions orchestrates terminal patterning, playing a critical role in the subsequent formation of the gut. We utilized CRISPR gene editing at endogenous loci to create live reporters of transcription and light-sheet microscopy to monitor the individual components of the posterior gut patterning network across 90 min prior to gastrulation. We developed a computational approach for fusing imaging datasets of the individual components into a common multivariable trajectory. Data fusion revealed low intrinsic dimensionality of posterior patterning and cell fate specification in wild-type embryos. The simple structure that we uncovered allowed us to construct a model of interactions within the posterior patterning regulatory network and make testable predictions about its dynamics at the protein level. The presented data fusion strategy is a step toward establishing a unified framework that would explore how stochastic spatiotemporal signals give rise to highly reproducible morphogenetic outcomes.
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Affiliation(s)
- Shannon E. Keenan
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08540
- The Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540
| | - Maria Avdeeva
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010
| | - Liu Yang
- The Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540
| | - Daniel S. Alber
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08540
- The Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540
| | - Eric F. Wieschaus
- The Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540
- Department of Molecular Biology, Princeton University, Princeton, NJ 08540
| | - Stanislav Y. Shvartsman
- The Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010
- Department of Molecular Biology, Princeton University, Princeton, NJ 08540
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4
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Fuentes R, Letelier J, Tajer B, Valdivia LE, Mullins MC. Fishing forward and reverse: Advances in zebrafish phenomics. Mech Dev 2018; 154:296-308. [PMID: 30130581 PMCID: PMC6289646 DOI: 10.1016/j.mod.2018.08.007] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 08/06/2018] [Accepted: 08/17/2018] [Indexed: 12/15/2022]
Abstract
Understanding how the genome instructs the phenotypic characteristics of an organism is one of the major scientific endeavors of our time. Advances in genetics have progressively deciphered the inheritance, identity and biological relevance of genetically encoded information, contributing to the rise of several, complementary omic disciplines. One of them is phenomics, an emergent area of biology dedicated to the systematic multi-scale analysis of phenotypic traits. This discipline provides valuable gene function information to the rapidly evolving field of genetics. Current molecular tools enable genome-wide analyses that link gene sequence to function in multi-cellular organisms, illuminating the genome-phenome relationship. Among vertebrates, zebrafish has emerged as an outstanding model organism for high-throughput phenotyping and modeling of human disorders. Advances in both systematic mutagenesis and phenotypic analyses of embryonic and post-embryonic stages in zebrafish have revealed the function of a valuable collection of genes and the general structure of several complex traits. In this review, we summarize multiple large-scale genetic efforts addressing parental, embryonic, and adult phenotyping in the zebrafish. The genetic and quantitative tools available in the zebrafish model, coupled with the broad spectrum of phenotypes that can be assayed, make it a powerful model for phenomics, well suited for the dissection of genotype-phenotype associations in development, physiology, health and disease.
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Affiliation(s)
- Ricardo Fuentes
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joaquín Letelier
- Centro Andaluz de Biología del Desarrollo (CSIC/UPO/JA), Seville, Spain; Center for Integrative Biology, Facultad de Ciencias, Universidad Mayor, Santiago, Chile
| | - Benjamin Tajer
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Leonardo E Valdivia
- Center for Integrative Biology, Facultad de Ciencias, Universidad Mayor, Santiago, Chile.
| | - Mary C Mullins
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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5
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Villoutreix P, Andén J, Lim B, Lu H, Kevrekidis IG, Singer A, Shvartsman SY. Synthesizing developmental trajectories. PLoS Comput Biol 2017; 13:e1005742. [PMID: 28922353 PMCID: PMC5619836 DOI: 10.1371/journal.pcbi.1005742] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Revised: 09/28/2017] [Accepted: 08/25/2017] [Indexed: 11/18/2022] Open
Abstract
Dynamical processes in biology are studied using an ever-increasing number of techniques, each of which brings out unique features of the system. One of the current challenges is to develop systematic approaches for fusing heterogeneous datasets into an integrated view of multivariable dynamics. We demonstrate that heterogeneous data fusion can be successfully implemented within a semi-supervised learning framework that exploits the intrinsic geometry of high-dimensional datasets. We illustrate our approach using a dataset from studies of pattern formation in Drosophila. The result is a continuous trajectory that reveals the joint dynamics of gene expression, subcellular protein localization, protein phosphorylation, and tissue morphogenesis. Our approach can be readily adapted to other imaging modalities and forms a starting point for further steps of data analytics and modeling of biological dynamics. A wide range of problems in biology require analysis of multivariable dynamics in space and time. As a rule, the multiscale nature and complexity of real systems precludes simultaneous monitoring of all the relevant variables, and multivariable dynamics must be synthesized from partial views provided by different experimental techniques. We present a formal framework for accomplishing this task in the context of imaging studies of pattern formation in developing tissues.
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Affiliation(s)
- Paul Villoutreix
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Joakim Andén
- Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey, United States of America
| | - Bomyi Lim
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, United States of America
| | - Hang Lu
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- Interdisciplinary Program in Bioengineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Ioannis G. Kevrekidis
- Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey, United States of America
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, United States of America
| | - Amit Singer
- Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey, United States of America
- Department of Mathematics, Princeton University, Princeton, New Jersey, United States of America
| | - Stanislav Y. Shvartsman
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey, United States of America
- * E-mail:
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6
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A cell-based computational model of early embryogenesis coupling mechanical behaviour and gene regulation. Nat Commun 2017; 8:13929. [PMID: 28112150 PMCID: PMC5264012 DOI: 10.1038/ncomms13929] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2016] [Accepted: 11/14/2016] [Indexed: 01/01/2023] Open
Abstract
The study of multicellular development is grounded in two complementary domains: cell biomechanics, which examines how physical forces shape the embryo, and genetic regulation and molecular signalling, which concern how cells determine their states and behaviours. Integrating both sides into a unified framework is crucial to fully understand the self-organized dynamics of morphogenesis. Here we introduce MecaGen, an integrative modelling platform enabling the hypothesis-driven simulation of these dual processes via the coupling between mechanical and chemical variables. Our approach relies upon a minimal 'cell behaviour ontology' comprising mesenchymal and epithelial cells and their associated behaviours. MecaGen enables the specification and control of complex collective movements in 3D space through a biologically relevant gene regulatory network and parameter space exploration. Three case studies investigating pattern formation, epithelial differentiation and tissue tectonics in zebrafish early embryogenesis, the latter with quantitative comparison to live imaging data, demonstrate the validity and usefulness of our framework.
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7
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Villoutreix P, Delile J, Rizzi B, Duloquin L, Savy T, Bourgine P, Doursat R, Peyriéras N. An integrated modelling framework from cells to organism based on a cohort of digital embryos. Sci Rep 2016; 6:37438. [PMID: 27910875 PMCID: PMC5133568 DOI: 10.1038/srep37438] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Accepted: 10/28/2016] [Indexed: 11/17/2022] Open
Abstract
We conducted a quantitative comparison of developing sea urchin embryos based on the analysis of five digital specimens obtained by automatic processing of in toto 3D+ time image data. These measurements served the reconstruction of a prototypical cell lineage tree able to predict the spatiotemporal cellular organisation of a normal sea urchin blastula. The reconstruction was achieved by designing and tuning a multi-level probabilistic model that reproduced embryo-level dynamics from a small number of statistical parameters characterising cell proliferation, cell surface area and cell volume evolution along the cell lineage. Our resulting artificial prototype was embedded in 3D space by biomechanical agent-based modelling and simulation, which allowed a systematic exploration and optimisation of free parameters to fit the experimental data and test biological hypotheses. The spherical monolayered blastula and the spatial arrangement of its different cell types appeared tightly constrained by cell stiffness, cell-adhesion parameters and blastocoel turgor pressure.
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Affiliation(s)
- Paul Villoutreix
- BioEmergences Laboratory USR3695, CNRS, Université Paris-Saclay, 91198 Gif-sur-Yvette Cedex, France.,Complex Systems Institute Paris Île-de-France (ISC-PIF) UPS3611, CNRS, 113 rue Nationale, 75013 Paris, France
| | - Julien Delile
- BioEmergences Laboratory USR3695, CNRS, Université Paris-Saclay, 91198 Gif-sur-Yvette Cedex, France.,Complex Systems Institute Paris Île-de-France (ISC-PIF) UPS3611, CNRS, 113 rue Nationale, 75013 Paris, France
| | - Barbara Rizzi
- BioEmergences Laboratory USR3695, CNRS, Université Paris-Saclay, 91198 Gif-sur-Yvette Cedex, France.,Complex Systems Institute Paris Île-de-France (ISC-PIF) UPS3611, CNRS, 113 rue Nationale, 75013 Paris, France
| | - Louise Duloquin
- BioEmergences Laboratory USR3695, CNRS, Université Paris-Saclay, 91198 Gif-sur-Yvette Cedex, France.,Complex Systems Institute Paris Île-de-France (ISC-PIF) UPS3611, CNRS, 113 rue Nationale, 75013 Paris, France
| | - Thierry Savy
- BioEmergences Laboratory USR3695, CNRS, Université Paris-Saclay, 91198 Gif-sur-Yvette Cedex, France.,Complex Systems Institute Paris Île-de-France (ISC-PIF) UPS3611, CNRS, 113 rue Nationale, 75013 Paris, France
| | - Paul Bourgine
- BioEmergences Laboratory USR3695, CNRS, Université Paris-Saclay, 91198 Gif-sur-Yvette Cedex, France.,Complex Systems Institute Paris Île-de-France (ISC-PIF) UPS3611, CNRS, 113 rue Nationale, 75013 Paris, France
| | - René Doursat
- BioEmergences Laboratory USR3695, CNRS, Université Paris-Saclay, 91198 Gif-sur-Yvette Cedex, France.,Complex Systems Institute Paris Île-de-France (ISC-PIF) UPS3611, CNRS, 113 rue Nationale, 75013 Paris, France
| | - Nadine Peyriéras
- BioEmergences Laboratory USR3695, CNRS, Université Paris-Saclay, 91198 Gif-sur-Yvette Cedex, France.,Complex Systems Institute Paris Île-de-France (ISC-PIF) UPS3611, CNRS, 113 rue Nationale, 75013 Paris, France
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8
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Shaped 3D singular spectrum analysis for quantifying gene expression, with application to the early zebrafish embryo. BIOMED RESEARCH INTERNATIONAL 2015; 2015:986436. [PMID: 26495320 PMCID: PMC4606214 DOI: 10.1155/2015/986436] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2015] [Accepted: 05/01/2015] [Indexed: 02/08/2023]
Abstract
Recent progress in microscopy technologies, biological markers, and automated processing methods is making possible the development of gene expression atlases at cellular-level resolution over whole embryos. Raw data on gene expression is usually very noisy. This noise comes from both experimental (technical/methodological) and true biological sources (from stochastic biochemical processes). In addition, the cells or nuclei being imaged are irregularly arranged in 3D space. This makes the processing, extraction, and study of expression signals and intrinsic biological noise a serious challenge for 3D data, requiring new computational approaches. Here, we present a new approach for studying gene expression in nuclei located in a thick layer around a spherical surface. The method includes depth equalization on the sphere, flattening, interpolation to a regular grid, pattern extraction by Shaped 3D singular spectrum analysis (SSA), and interpolation back to original nuclear positions. The approach is demonstrated on several examples of gene expression in the zebrafish egg (a model system in vertebrate development). The method is tested on several different data geometries (e.g., nuclear positions) and different forms of gene expression patterns. Fully 3D datasets for developmental gene expression are becoming increasingly available; we discuss the prospects of applying 3D-SSA to data processing and analysis in this growing field.
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9
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Asadulina A, Conzelmann M, Williams EA, Panzera A, Jékely G. Object-based representation and analysis of light and electron microscopic volume data using Blender. BMC Bioinformatics 2015. [PMID: 26208945 PMCID: PMC4513682 DOI: 10.1186/s12859-015-0652-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Background Rapid improvements in light and electron microscopy imaging techniques and the development of 3D anatomical atlases necessitate new approaches for the visualization and analysis of image data. Pixel-based representations of raw light microscopy data suffer from limitations in the number of channels that can be visualized simultaneously. Complex electron microscopic reconstructions from large tissue volumes are also challenging to visualize and analyze. Results Here we exploit the advanced visualization capabilities and flexibility of the open-source platform Blender to visualize and analyze anatomical atlases. We use light-microscopy-based gene expression atlases and electron microscopy connectome volume data from larval stages of the marine annelid Platynereis dumerilii. We build object-based larval gene expression atlases in Blender and develop tools for annotation and coexpression analysis. We also represent and analyze connectome data including neuronal reconstructions and underlying synaptic connectivity. Conclusions We demonstrate the power and flexibility of Blender for visualizing and exploring complex anatomical atlases. The resources we have developed for Platynereis will facilitate data sharing and the standardization of anatomical atlases for this species. The flexibility of Blender, particularly its embedded Python application programming interface, means that our methods can be easily extended to other organisms. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0652-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Albina Asadulina
- Max Planck Institute for Developmental Biology, Spemannstrasse 35, 72076, Tübingen, Germany.
| | - Markus Conzelmann
- Max Planck Institute for Developmental Biology, Spemannstrasse 35, 72076, Tübingen, Germany.
| | - Elizabeth A Williams
- Max Planck Institute for Developmental Biology, Spemannstrasse 35, 72076, Tübingen, Germany.
| | - Aurora Panzera
- Max Planck Institute for Developmental Biology, Spemannstrasse 35, 72076, Tübingen, Germany.
| | - Gáspár Jékely
- Max Planck Institute for Developmental Biology, Spemannstrasse 35, 72076, Tübingen, Germany.
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10
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Carneiro K, de Brito JM, Rossi MID. Development by three-dimensional approaches and four-dimensional imaging: to the knowledge frontier and beyond. ACTA ACUST UNITED AC 2015; 105:1-8. [PMID: 25789860 DOI: 10.1002/bdrc.21089] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Many advances have been taken on elucidating embryonic development and tissue homeostasis and repair by the use of experimental strategies that preserve the three-dimensional (3D) organization and allow quantitative analysis of images over time (four-dimensional). Ranging from the understanding about the relationship between blastomeres and the events that take place during gastrulation by the use of time-lapse imaging through 3D cultures that mimic organogenesis, the advances in this area are of critical value. The studies on embryonic development without disrupting the original architecture and the development of 3D organoid cultures pave a new avenue for unprecedented experimental advances that will positively impact the emergence of new treatments applying regenerative principles for both tissue repair and organ transplant.
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Affiliation(s)
- Katia Carneiro
- Biomedical Institute of Sciences, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
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11
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Shaped singular spectrum analysis for quantifying gene expression, with application to the early Drosophila embryo. BIOMED RESEARCH INTERNATIONAL 2015; 2015:689745. [PMID: 25945341 PMCID: PMC4402483 DOI: 10.1155/2015/689745] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2014] [Revised: 09/10/2014] [Accepted: 09/10/2014] [Indexed: 11/17/2022]
Abstract
In recent years, with the development of automated microscopy technologies, the volume and complexity of image data on gene expression have increased tremendously. The only way to analyze quantitatively and comprehensively such biological data is by developing and applying new sophisticated mathematical approaches. Here, we present extensions of 2D singular spectrum analysis (2D-SSA) for application to 2D and 3D datasets of embryo images. These extensions, circular and shaped 2D-SSA, are applied to gene expression in the nuclear layer just under the surface of the Drosophila (fruit fly) embryo. We consider the commonly used cylindrical projection of the ellipsoidal Drosophila embryo. We demonstrate how circular and shaped versions of 2D-SSA help to decompose expression data into identifiable components (such as trend and noise), as well as separating signals from different genes. Detection and improvement of under- and overcorrection in multichannel imaging is addressed, as well as the extraction and analysis of 3D features in 3D gene expression patterns.
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12
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Affiliation(s)
- René Doursat
- School of Biomedical Engineering, Drexel University, Philadelphia, Pennsylvania
- Complex Systems Institute, Paris Ile-de-France (ISC-PIF), CNRS UPS3611, Paris, France
| | - Carlos Sánchez
- Research Group in Biomimetics (GEB), Universidad de Málaga, Campanillas-Málaga, Spain
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