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      From observing to predicting single-cell structure and function with high-throughput/high-content microscopy

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          Abstract

          In the past 15 years, cell-based microscopy has evolved its focus from observing cell function to aiming to predict it. In particular—powered by breakthroughs in computer vision, large-scale image analysis and machine learning—high-throughput and high-content microscopy imaging have enabled to uniquely harness single-cell information to systematically discover and annotate genes and regulatory pathways, uncover systems-level interactions and causal links between cellular processes, and begin to clarify and predict causal cellular behaviour and decision making. Here we review these developments, discuss emerging trends in the field, and describe how single-cell ‘omics and single-cell microscopy are imminently in an intersecting trajectory. The marriage of these two fields will make possible an unprecedented understanding of cell and tissue behaviour and function.

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          A global genetic interaction network maps a wiring diagram of cellular function.

          We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell.
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            Reconstruction of zebrafish early embryonic development by scanned light sheet microscopy.

            A long-standing goal of biology is to map the behavior of all cells during vertebrate embryogenesis. We developed digital scanned laser light sheet fluorescence microscopy and recorded nuclei localization and movement in entire wild-type and mutant zebrafish embryos over the first 24 hours of development. Multiview in vivo imaging at 1.5 billion voxels per minute provides "digital embryos," that is, comprehensive databases of cell positions, divisions, and migratory tracks. Our analysis of global cell division patterns reveals a maternally defined initial morphodynamic symmetry break, which identifies the embryonic body axis. We further derive a model of germ layer formation and show that the mesendoderm forms from one-third of the embryo's cells in a single event. Our digital embryos, with 55 million nucleus entries, are provided as a resource.
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              Causal protein-signaling networks derived from multiparameter single-cell data.

              Machine learning was applied for the automated derivation of causal influences in cellular signaling networks. This derivation relied on the simultaneous measurement of multiple phosphorylated protein and phospholipid components in thousands of individual primary human immune system cells. Perturbing these cells with molecular interventions drove the ordering of connections between pathway components, wherein Bayesian network computational methods automatically elucidated most of the traditionally reported signaling relationships and predicted novel interpathway network causalities, which we verified experimentally. Reconstruction of network models from physiologically relevant primary single cells might be applied to understanding native-state tissue signaling biology, complex drug actions, and dysfunctional signaling in diseased cells.
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                Author and article information

                Journal
                Essays Biochem
                Essays Biochem
                ppebio
                BSE
                Essays in Biochemistry
                Portland Press Ltd.
                0071-1365
                1744-1358
                26 June 2019
                03 July 2019
                : 63
                : 2 , Single Cell Biology
                : 197-208
                Affiliations
                [1 ]École polytechnique, Université Paris-Saclay, 91128 Palaiseau Cedex, France
                [2 ]School of Cellular & Molecular Medicine, University of Bristol, BS8 1TD Bristol, UK
                Author notes
                Correspondence: Rafael E. Carazo Salas ( rafael.carazosalas@ 123456bristol.ac.uk ) and Anatole Chessel ( anatole.chessel@ 123456polytechnique.edu )
                Author information
                http://orcid.org/0000-0002-5943-3981
                Article
                10.1042/EBC20180044
                6610450
                31243141
                d340552d-3956-4ce5-af1d-e059ba6c7061
                © 2019 The Author(s).

                This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY-NC-ND).

                History
                : 11 March 2019
                : 24 May 2019
                : 24 May 2019
                Page count
                Pages: 12
                Categories
                Review Articles
                Review Article
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                causal cell behaviour,gene regulatory networks,genome-wide screening,high-content microscopy,high-throughput microscopy,machine learning

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