1
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Cavanagh H, Kempe D, Mazalo JK, Biro M, Endres RG. T cell morphodynamics reveal periodic shape oscillations in three-dimensional migration. J R Soc Interface 2022; 19:20220081. [PMID: 35537475 PMCID: PMC9090490 DOI: 10.1098/rsif.2022.0081] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
T cells use sophisticated shape dynamics (morphodynamics) to migrate towards and neutralize infected and cancerous cells. However, there is limited quantitative understanding of the migration process in three-dimensional extracellular matrices (ECMs) and across timescales. Here, we leveraged recent advances in lattice light-sheet microscopy to quantitatively explore the three-dimensional morphodynamics of migrating T cells at high spatio-temporal resolution. We first developed a new shape descriptor based on spherical harmonics, incorporating key polarization information of the uropod. We found that the shape space of T cells is low-dimensional. At the behavioural level, run-and-stop migration modes emerge at approximately 150 s, and we mapped the morphodynamic composition of each mode using multiscale wavelet analysis, finding 'stereotyped' motifs. Focusing on the run mode, we found morphodynamics oscillating periodically (every approx. 100 s) that can be broken down into a biphasic process: front-widening with retraction of the uropod, followed by a rearward surface motion and forward extension, where intercalation with the ECM in both of these steps likely facilitates forward motion. Further application of these methods may enable the comparison of T cell migration across different conditions (e.g. differentiation, activation, tissues and drug treatments) and improve the precision of immunotherapeutic development.
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Affiliation(s)
- Henry Cavanagh
- Imperial College London, Centre for Integrative Systems Biology and Bioinformatics, London SW7 2BU, UK
| | - Daryan Kempe
- EMBL Australia, Single Molecule Science Node, School of Medical Sciences, The University of New South Wales, Sydney, NSW 2052, Australia
| | - Jessica K Mazalo
- EMBL Australia, Single Molecule Science Node, School of Medical Sciences, The University of New South Wales, Sydney, NSW 2052, Australia
| | - Maté Biro
- EMBL Australia, Single Molecule Science Node, School of Medical Sciences, The University of New South Wales, Sydney, NSW 2052, Australia
| | - Robert G Endres
- Imperial College London, Centre for Integrative Systems Biology and Bioinformatics, London SW7 2BU, UK
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2
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Artificial intelligence unifies knowledge and actions in drug repositioning. Emerg Top Life Sci 2021; 5:803-813. [PMID: 34881780 DOI: 10.1042/etls20210223] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 11/08/2021] [Accepted: 11/09/2021] [Indexed: 11/17/2022]
Abstract
Drug repositioning aims to reuse existing drugs, shelved drugs, or drug candidates that failed clinical trials for other medical indications. Its attraction is sprung from the reduction in risk associated with safety testing of new medications and the time to get a known drug into the clinics. Artificial Intelligence (AI) has been recently pursued to speed up drug repositioning and discovery. The essence of AI in drug repositioning is to unify the knowledge and actions, i.e. incorporating real-world and experimental data to map out the best way forward to identify effective therapeutics against a disease. In this review, we share positive expectations for the evolution of AI and drug repositioning and summarize the role of AI in several methods of drug repositioning.
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3
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German Y, Vulliard L, Kamnev A, Pfajfer L, Huemer J, Mautner AK, Rubio A, Kalinichenko A, Boztug K, Ferrand A, Menche J, Dupré L. Morphological profiling of human T and NK lymphocytes by high-content cell imaging. Cell Rep 2021; 36:109318. [PMID: 34233185 DOI: 10.1016/j.celrep.2021.109318] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 02/25/2021] [Accepted: 06/07/2021] [Indexed: 01/21/2023] Open
Abstract
The immunological synapse is a complex structure that decodes stimulatory signals into adapted lymphocyte responses. It is a unique window to monitor lymphocyte activity because of development of systematic quantitative approaches. Here we demonstrate the applicability of high-content imaging to human T and natural killer (NK) cells and develop a pipeline for unbiased analysis of high-definition morphological profiles. Our approach reveals how distinct facets of actin cytoskeleton remodeling shape immunological synapse architecture and affect lytic granule positioning. Morphological profiling of CD8+ T cells from immunodeficient individuals allows discrimination of the roles of the ARP2/3 subunit ARPC1B and the ARP2/3 activator Wiskott-Aldrich syndrome protein (WASP) in immunological synapse assembly. Single-cell analysis further identifies uncoupling of lytic granules and F-actin radial distribution in ARPC1B-deficient lymphocytes. Our study provides a foundation for development of morphological profiling as a scalable approach to monitor primary lymphocyte responsiveness and to identify complex aspects of lymphocyte micro-architecture.
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Affiliation(s)
- Yolla German
- Toulouse Institute for Infectious and Inflammatory Diseases (INFINITy), INSERM UMR1291, CNRS UMR5051, Toulouse III Paul Sabatier University, Toulouse, France; Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases (LBI-RUD), Vienna, Austria
| | - Loan Vulliard
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria; Department of Structural and Computational Biology, Max Perutz Labs, University of Vienna, Vienna, Austria
| | - Anton Kamnev
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases (LBI-RUD), Vienna, Austria; Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Laurène Pfajfer
- Toulouse Institute for Infectious and Inflammatory Diseases (INFINITy), INSERM UMR1291, CNRS UMR5051, Toulouse III Paul Sabatier University, Toulouse, France; Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases (LBI-RUD), Vienna, Austria
| | - Jakob Huemer
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases (LBI-RUD), Vienna, Austria; St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Anna-Katharina Mautner
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases (LBI-RUD), Vienna, Austria; Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Aude Rubio
- IRSD, Université de Toulouse, INSERM, INRA, ENVT, UPS, 31024 Toulouse, France
| | - Artem Kalinichenko
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases (LBI-RUD), Vienna, Austria; St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Kaan Boztug
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases (LBI-RUD), Vienna, Austria; CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria; St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria; Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria; St. Anna Children's Hospital, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Audrey Ferrand
- IRSD, Université de Toulouse, INSERM, INRA, ENVT, UPS, 31024 Toulouse, France
| | - Jörg Menche
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria; Department of Structural and Computational Biology, Max Perutz Labs, University of Vienna, Vienna, Austria; Faculty of Mathematics, University of Vienna, Vienna, Austria
| | - Loïc Dupré
- Toulouse Institute for Infectious and Inflammatory Diseases (INFINITy), INSERM UMR1291, CNRS UMR5051, Toulouse III Paul Sabatier University, Toulouse, France; Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases (LBI-RUD), Vienna, Austria; Department of Dermatology, Medical University of Vienna, Vienna, Austria.
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4
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Bodor DL, Pönisch W, Endres RG, Paluch EK. Of Cell Shapes and Motion: The Physical Basis of Animal Cell Migration. Dev Cell 2020; 52:550-562. [PMID: 32155438 DOI: 10.1016/j.devcel.2020.02.013] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 02/10/2020] [Accepted: 02/14/2020] [Indexed: 01/31/2023]
Abstract
Motile cells have developed a variety of migration modes relying on diverse traction-force-generation mechanisms. Before the behavior of intracellular components could be easily imaged, cell movements were mostly classified by different types of cellular shape dynamics. Indeed, even though some types of cells move without any significant change in shape, most cell propulsion mechanisms rely on global or local deformations of the cell surface. In this review, focusing mostly on metazoan cells, we discuss how different types of local and global shape changes underlie distinct migration modes. We then discuss mechanical differences between force-generation mechanisms and finish by speculating on how they may have evolved.
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Affiliation(s)
- Dani L Bodor
- MRC Laboratory for Molecular Cell Biology, University College London, London WC1E 6BT, UK; Oncode Institute, Hubrecht Institute-KNAW, Utrecht, the Netherlands
| | - Wolfram Pönisch
- MRC Laboratory for Molecular Cell Biology, University College London, London WC1E 6BT, UK; Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge CB2 3DY, UK
| | - Robert G Endres
- Department of Life Sciences and Centre for Integrative Systems Biology and Bioinformatics, Imperial College, London SW7 2AZ, UK
| | - Ewa K Paluch
- MRC Laboratory for Molecular Cell Biology, University College London, London WC1E 6BT, UK; Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge CB2 3DY, UK.
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5
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Alizadeh E, Castle J, Quirk A, Taylor CDL, Xu W, Prasad A. Cellular morphological features are predictive markers of cancer cell state. Comput Biol Med 2020; 126:104044. [PMID: 33049477 DOI: 10.1016/j.compbiomed.2020.104044] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 10/04/2020] [Accepted: 10/06/2020] [Indexed: 11/24/2022]
Abstract
Even genetically identical cells have heterogeneous properties because of stochasticity in gene or protein expression. Single cell techniques that assay heterogeneous properties would be valuable for basic science and diseases like cancer, where accurate estimates of tumor properties is critical for accurate diagnosis and grading. Cell morphology is an emergent outcome of many cellular processes, potentially carrying information about cell properties at the single cell level. Here we study whether morphological parameters are sufficient for classification of single cells, using a set of 15 cell lines, representing three processes: (i) the transformation of normal cells using specific genetic mutations; (ii) metastasis in breast cancer and (iii) metastasis in osteosarcomas. Cellular morphology is defined as quantitative measures of the shape of the cell and the structure of the actin. We use a toolbox that calculates quantitative morphological parameters of cell images and apply it to hundreds of images of cells belonging to different cell lines. Using a combination of dimensional reduction and machine learning, we test whether these different processes have specific morphological signatures and whether single cells can be classified based on morphology alone. Using morphological parameters we could accurately classify cells as belonging to the correct class with high accuracy. Morphological signatures could distinguish between cells that were different only because of a different mutation on a parental line. Furthermore, both oncogenesis and metastasis appear to be characterized by stereotypical morphology changes. Thus, cellular morphology is a signature of cell phenotype, or state, at the single cell level.
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Affiliation(s)
| | | | - Analia Quirk
- Department of Chemical and Biological Engineering, USA; School of Biomedical Engineering, Colorado State University, Fort Collins, CO, 80523, USA
| | - Cameron D L Taylor
- Department of Chemical and Biological Engineering, USA; School of Biomedical Engineering, Colorado State University, Fort Collins, CO, 80523, USA
| | - Wenlong Xu
- Department of Chemical and Biological Engineering, USA
| | - Ashok Prasad
- Department of Chemical and Biological Engineering, USA; School of Biomedical Engineering, Colorado State University, Fort Collins, CO, 80523, USA.
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6
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Zmurchok C, Holmes WR. Simple Rho GTPase Dynamics Generate a Complex Regulatory Landscape Associated with Cell Shape. Biophys J 2020; 118:1438-1454. [PMID: 32084329 DOI: 10.1016/j.bpj.2020.01.035] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 01/27/2020] [Accepted: 01/30/2020] [Indexed: 02/08/2023] Open
Abstract
Migratory cells exhibit a variety of morphologically distinct responses to their environments that manifest in their cell shape. Some protrude uniformly to increase substrate contacts, others are broadly contractile, some polarize to facilitate migration, and yet others exhibit mixtures of these responses. Prior studies have identified a discrete collection of shapes that the majority of cells display and demonstrated that activity levels of the cytoskeletal regulators Rac1 and RhoA GTPase regulate those shapes. Here, we use computational modeling to assess whether known GTPase dynamics can give rise to a sufficient diversity of spatial signaling states to explain the observed shapes. Results show that the combination of autoactivation and mutually antagonistic cross talk between GTPases, along with the conservative membrane binding, generates a wide array of distinct homogeneous and polarized regulatory phenotypes that arise for fixed model parameters. From a theoretical perspective, these results demonstrate that simple GTPase dynamics can generate complex multistability in which six distinct stable steady states (three homogeneous and three polarized) coexist for a fixed set of parameters, each of which naturally maps to an observed morphology. From a biological perspective, although we do not explicitly model the cytoskeleton or resulting cell morphologies, these results, along with prior literature linking GTPase activity to cell morphology, support the hypothesis that GTPase signaling dynamics can generate the broad morphological characteristics observed in many migratory cell populations. Further, the observed diversity may be the result of cells populating a complex morphological landscape generated by GTPase regulation rather than being the result of intrinsic cell-cell variation. These results demonstrate that Rho GTPases may have a central role in regulating the broad characteristics of cell shape (e.g., expansive, contractile, polarized, etc.) and that shape heterogeneity may be (at least partly) a reflection of the rich signaling dynamics regulating the cytoskeleton rather than intrinsic cell heterogeneity.
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Affiliation(s)
- Cole Zmurchok
- Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee
| | - William R Holmes
- Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee; Department of Mathematics, Vanderbilt University, Nashville, Tennessee; Quantitative Systems Biology Center, Vanderbilt University, Nashville, Tennessee.
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7
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Caldera M, Müller F, Kaltenbrunner I, Licciardello MP, Lardeau CH, Kubicek S, Menche J. Mapping the perturbome network of cellular perturbations. Nat Commun 2019; 10:5140. [PMID: 31723137 PMCID: PMC6853941 DOI: 10.1038/s41467-019-13058-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 10/15/2019] [Indexed: 12/15/2022] Open
Abstract
Drug combinations provide effective treatments for diverse diseases, but also represent a major cause of adverse reactions. Currently there is no systematic understanding of how the complex cellular perturbations induced by different drugs influence each other. Here, we introduce a mathematical framework for classifying any interaction between perturbations with high-dimensional effects into 12 interaction types. We apply our framework to a large-scale imaging screen of cell morphology changes induced by diverse drugs and their combination, resulting in a perturbome network of 242 drugs and 1832 interactions. Our analysis of the chemical and biological features of the drugs reveals distinct molecular fingerprints for each interaction type. We find a direct link between drug similarities on the cell morphology level and the distance of their respective protein targets within the cellular interactome of molecular interactions. The interactome distance is also predictive for different types of drug interactions.
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Affiliation(s)
- Michael Caldera
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090, Vienna, Austria
| | - Felix Müller
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090, Vienna, Austria
| | - Isabel Kaltenbrunner
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090, Vienna, Austria
| | - Marco P Licciardello
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090, Vienna, Austria
- Cancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research, London, UK
| | - Charles-Hugues Lardeau
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090, Vienna, Austria
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Alderley Park, Macclesfield, UK
| | - Stefan Kubicek
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090, Vienna, Austria
| | - Jörg Menche
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, A-1090, Vienna, Austria.
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8
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Hegemann B, Peter M. Local sampling paints a global picture: Local concentration measurements sense direction in complex chemical gradients. Bioessays 2017; 39. [PMID: 28556309 DOI: 10.1002/bies.201600134] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Detecting and interpreting extracellular spatial signals is essential for cellular orientation within complex environments, such as during directed cell migration or growth in multicellular development. Although the molecular understanding of how cells read spatial signals like chemical gradients is still lacking, recent work has revealed that stochastic processes at different temporal and spatial scales are at the core of this gradient sensing process in a wide range of eukaryotes. Fast biochemical reactions like those underlying GTPase activity dynamics form a functional module together with slower cell morphological changes driven by membrane remodelling. This biochemical-morphological module explores the environment by stochastic local concentration sampling to determine the source of the gradient signal, enabling efficient signal detection and interpretation before polarised growth or migration towards the gradient source is initiated. Here we review recent data describing local sampling and propose a model of local fast and slow feedback counteracted by gradient-dependent substrate limitation to be at the core of gradient sensing by local sampling.
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Affiliation(s)
- Björn Hegemann
- Department of Biology, Institute of Biochemistry, ETH Zurich, Zürich, Switzerland
| | - Matthias Peter
- Department of Biology, Institute of Biochemistry, ETH Zurich, Zürich, Switzerland
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9
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Cosette J, Moussy A, Paldi A, Stockholm D. Combination of imaging flow cytometry and time-lapse microscopy for the study of label-free morphology dynamics of hematopoietic cells. Cytometry A 2017; 91:254-260. [DOI: 10.1002/cyto.a.23064] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Revised: 12/06/2016] [Accepted: 01/15/2017] [Indexed: 12/27/2022]
Affiliation(s)
| | - Alice Moussy
- Ecole Pratique des Hautes Etudes, PSL Research University, UMRS_951, Genethon; Evry 91002 France
| | - Andras Paldi
- Ecole Pratique des Hautes Etudes, PSL Research University, UMRS_951, Genethon; Evry 91002 France
| | - Daniel Stockholm
- Ecole Pratique des Hautes Etudes, PSL Research University, UMRS_951, Genethon; Evry 91002 France
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10
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Sero JE, Bakal C. Multiparametric Analysis of Cell Shape Demonstrates that β-PIX Directly Couples YAP Activation to Extracellular Matrix Adhesion. Cell Syst 2017; 4:84-96.e6. [PMID: 28065575 PMCID: PMC5289939 DOI: 10.1016/j.cels.2016.11.015] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2016] [Revised: 09/14/2016] [Accepted: 11/30/2016] [Indexed: 01/19/2023]
Abstract
Mechanical signals from the extracellular matrix (ECM) and cellular geometry regulate the nuclear translocation of transcriptional regulators such as Yes-associated protein (YAP). Elucidating how physical signals control the activity of mechanosensitive proteins poses a technical challenge, because perturbations that affect cell shape may also affect protein localization indirectly. Here, we present an approach that mitigates confounding effects of cell-shape changes, allowing us to identify direct regulators of YAP localization. This method uses single-cell image analysis and statistical models that exploit the naturally occurring heterogeneity of cellular populations. Through systematic depletion of all human kinases, Rho family GTPases, GEFs, and GTPase activating proteins (GAPs), together with targeted chemical perturbations, we found that β-PIX, a Rac1/Ccd42 GEF, and PAK2, a Rac1/Cdc42 effector, drive both YAP activation and cell-ECM adhesion turnover during cell spreading. Our observations suggest that coupling YAP to adhesion dynamics acts as a mechano-timer, allowing cells to rapidly tune gene expression in response to physical signals.
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Affiliation(s)
- Julia E Sero
- Chester Beatty Laboratories, Division of Cancer Biology, Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK.
| | - Chris Bakal
- Chester Beatty Laboratories, Division of Cancer Biology, Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
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11
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Sayyid F, Kalvala S. On the importance of modelling the internal spatial dynamics of biological cells. Biosystems 2016; 145:53-66. [PMID: 27262415 DOI: 10.1016/j.biosystems.2016.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2015] [Revised: 05/25/2016] [Accepted: 05/31/2016] [Indexed: 11/16/2022]
Abstract
Spatial effects such as cell shape have very often been considered negligible in models of cellular pathways, and many existing simulation infrastructures do not take such effects into consideration. Recent experimental results are reversing this judgement by showing that very small spatial variations can make a big difference in the fate of a cell. This is particularly the case when considering eukaryotic cells, which have a complex physical structure and many subtle control mechanisms, but bacteria are also interesting for the huge variation in shape both between species and in different phases of their lifecycle. In this work we perform simulations that measure the effect of three common bacterial shapes on the behaviour of model cellular pathways. To perform these experiments we develop ReDi-Cell, a highly scalable GPGPU cell simulation infrastructure for the modelling of cellular pathways in spatially detailed environments. ReDi-Cell is validated against known-good simulations, prior to its use in new work. We then use ReDi-Cell to conduct novel experiments that demonstrate the effect that three common bacterial shapes (Cocci, Bacilli and Spirilli) have on the behaviour of model cellular pathways. Pathway wavefront shape, pathway concentration gradients, and chemical species distribution are measured in the three different shapes. We also quantify the impact of internal cellular clutter on the same pathways. Through this work we show that variations in the shape or configuration of these common cell shapes alter model cell behaviour.
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Affiliation(s)
- Faiz Sayyid
- Department of Computer Science, University of Warwick, Coventry, West Midlands, United Kingdom.
| | - Sara Kalvala
- Department of Computer Science, University of Warwick, Coventry, West Midlands, United Kingdom.
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12
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Abstract
Data visualization is a fundamental aspect of science. In the context of microscopy-based studies, visualization typically involves presentation of the images themselves. However, data visualization is challenging when microscopy experiments entail imaging of millions of cells, and complex cellular phenotypes are quantified in a high-content manner. Most well-established visualization tools are inappropriate for displaying high-content data, which has driven the development of new visualization methodology. In this review, we discuss how data has been visualized in both classical and high-content microscopy studies; as well as the advantages, and disadvantages, of different visualization methods.
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Affiliation(s)
- Heba Z Sailem
- a Department of Engineering Science , University of Oxford , Oxford , UK
| | - Sam Cooper
- b Department of Computational Systems Medicine , Imperial College, South Kensington Campus , London , UK , and.,c Division of Cancer Biology , Chester Beatty Laboratories, Institute of Cancer Research , London , UK
| | - Chris Bakal
- c Division of Cancer Biology , Chester Beatty Laboratories, Institute of Cancer Research , London , UK
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13
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Gordonov S, Hwang MK, Wells A, Gertler FB, Lauffenburger DA, Bathe M. Time series modeling of live-cell shape dynamics for image-based phenotypic profiling. Integr Biol (Camb) 2016; 8:73-90. [PMID: 26658688 PMCID: PMC5058786 DOI: 10.1039/c5ib00283d] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Live-cell imaging can be used to capture spatio-temporal aspects of cellular responses that are not accessible to fixed-cell imaging. As the use of live-cell imaging continues to increase, new computational procedures are needed to characterize and classify the temporal dynamics of individual cells. For this purpose, here we present the general experimental-computational framework SAPHIRE (Stochastic Annotation of Phenotypic Individual-cell Responses) to characterize phenotypic cellular responses from time series imaging datasets. Hidden Markov modeling is used to infer and annotate morphological state and state-switching properties from image-derived cell shape measurements. Time series modeling is performed on each cell individually, making the approach broadly useful for analyzing asynchronous cell populations. Two-color fluorescent cells simultaneously expressing actin and nuclear reporters enabled us to profile temporal changes in cell shape following pharmacological inhibition of cytoskeleton-regulatory signaling pathways. Results are compared with existing approaches conventionally applied to fixed-cell imaging datasets, and indicate that time series modeling captures heterogeneous dynamic cellular responses that can improve drug classification and offer additional important insight into mechanisms of drug action. The software is available at http://saphire-hcs.org.
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Affiliation(s)
- Simon Gordonov
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- The David H. Koch Institute for Integrative Cancer Research, Cambridge, MA, USA
| | - Mun Kyung Hwang
- The David H. Koch Institute for Integrative Cancer Research, Cambridge, MA, USA
| | - Alan Wells
- Department of Pathology, University of Pittsburgh, and Pittsburgh VA Health System, Pittsburgh, PA, USA
| | - Frank B. Gertler
- The David H. Koch Institute for Integrative Cancer Research, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Douglas A. Lauffenburger
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- The David H. Koch Institute for Integrative Cancer Research, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Mark Bathe
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
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14
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Mancuso JJ, Cheng J, Yin Z, Gilliam JC, Xia X, Li X, Wong STC. Integration of multiscale dendritic spine structure and function data into systems biology models. Front Neuroanat 2014; 8:130. [PMID: 25429262 PMCID: PMC4228840 DOI: 10.3389/fnana.2014.00130] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2014] [Accepted: 10/22/2014] [Indexed: 12/27/2022] Open
Abstract
Comprising 1011 neurons with 1014 synaptic connections the human brain is the ultimate systems biology puzzle. An increasing body of evidence highlights the observation that changes in brain function, both normal and pathological, consistently correlate with dynamic changes in neuronal anatomy. Anatomical changes occur on a full range of scales from the trafficking of individual proteins, to alterations in synaptic morphology both individually and on a systems level, to reductions in long distance connectivity and brain volume. The major sites of contact for synapsing neurons are dendritic spines, which provide an excellent metric for the number and strength of signaling connections between elements of functional neuronal circuits. A comprehensive model of anatomical changes and their functional consequences would be a holy grail for the field of systems neuroscience but its realization appears far on the horizon. Various imaging technologies have advanced to allow for multi-scale visualization of brain plasticity and pathology, but computational analysis of the big data sets involved forms the bottleneck toward the creation of multiscale models of brain structure and function. While a full accounting of techniques and progress toward a comprehensive model of brain anatomy and function is beyond the scope of this or any other single paper, this review serves to highlight the opportunities for analysis of neuronal spine anatomy and function provided by new imaging technologies and the high-throughput application of older technologies while surveying the strengths and weaknesses of currently available computational analytical tools and room for future improvement.
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Affiliation(s)
- James J Mancuso
- Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute Houston, TX, USA ; TT and WF Chao Center for Bioinformatics Research and Imaging for Neurosciences, Houston Methodist Research Institute Houston, TX, USA
| | - Jie Cheng
- Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute Houston, TX, USA ; TT and WF Chao Center for Bioinformatics Research and Imaging for Neurosciences, Houston Methodist Research Institute Houston, TX, USA
| | - Zheng Yin
- Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute Houston, TX, USA ; TT and WF Chao Center for Bioinformatics Research and Imaging for Neurosciences, Houston Methodist Research Institute Houston, TX, USA
| | - Jared C Gilliam
- Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute Houston, TX, USA ; TT and WF Chao Center for Bioinformatics Research and Imaging for Neurosciences, Houston Methodist Research Institute Houston, TX, USA
| | - Xiaofeng Xia
- Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute Houston, TX, USA ; TT and WF Chao Center for Bioinformatics Research and Imaging for Neurosciences, Houston Methodist Research Institute Houston, TX, USA
| | - Xuping Li
- Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute Houston, TX, USA ; TT and WF Chao Center for Bioinformatics Research and Imaging for Neurosciences, Houston Methodist Research Institute Houston, TX, USA
| | - Stephen T C Wong
- Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute Houston, TX, USA ; TT and WF Chao Center for Bioinformatics Research and Imaging for Neurosciences, Houston Methodist Research Institute Houston, TX, USA
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