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Stepanova D, Byrne HM, Maini PK, Alarcón T. Computational modeling of angiogenesis: The importance of cell rearrangements during vascular growth. WIREs Mech Dis 2024; 16:e1634. [PMID: 38084799 DOI: 10.1002/wsbm.1634] [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: 07/04/2023] [Revised: 11/10/2023] [Accepted: 11/13/2023] [Indexed: 03/16/2024]
Abstract
Angiogenesis is the process wherein endothelial cells (ECs) form sprouts that elongate from the pre-existing vasculature to create new vascular networks. In addition to its essential role in normal development, angiogenesis plays a vital role in pathologies such as cancer, diabetes and atherosclerosis. Mathematical and computational modeling has contributed to unraveling its complexity. Many existing theoretical models of angiogenic sprouting are based on the "snail-trail" hypothesis. This framework assumes that leading ECs positioned at sprout tips migrate toward low-oxygen regions while other ECs in the sprout passively follow the leaders' trails and proliferate to maintain sprout integrity. However, experimental results indicate that, contrary to the snail-trail assumption, ECs exchange positions within developing vessels, and the elongation of sprouts is primarily driven by directed migration of ECs. The functional role of cell rearrangements remains unclear. This review of the theoretical modeling of angiogenesis is the first to focus on the phenomenon of cell mixing during early sprouting. We start by describing the biological processes that occur during early angiogenesis, such as phenotype specification, cell rearrangements and cell interactions with the microenvironment. Next, we provide an overview of various theoretical approaches that have been employed to model angiogenesis, with particular emphasis on recent in silico models that account for the phenomenon of cell mixing. Finally, we discuss when cell mixing should be incorporated into theoretical models and what essential modeling components such models should include in order to investigate its functional role. This article is categorized under: Cardiovascular Diseases > Computational Models Cancer > Computational Models.
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Affiliation(s)
- Daria Stepanova
- Laboratorio Subterráneo de Canfranc, Canfranc-Estación, Huesca, Spain
| | - Helen M Byrne
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Philip K Maini
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK
| | - Tomás Alarcón
- Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
- Centre de Recerca Matemàtica, Bellaterra, Barcelona, Spain
- Departament de Matemàtiques, Universitat Autònoma de Barcelona, Bellaterra, Spain
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2
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Fischer SC, Schardt S, Lilao-Garzón J, Muñoz-Descalzo S. The salt-and-pepper pattern in mouse blastocysts is compatible with signaling beyond the nearest neighbors. iScience 2023; 26:108106. [PMID: 37915595 PMCID: PMC10616410 DOI: 10.1016/j.isci.2023.108106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 08/03/2023] [Accepted: 09/26/2023] [Indexed: 11/03/2023] Open
Abstract
Embryos develop in a concerted sequence of spatiotemporal arrangements of cells. In the preimplantation mouse embryo, the distribution of the cells in the inner cell mass evolves from a salt-and-pepper pattern to spatial segregation of two distinct cell types. The exact properties of the salt-and-pepper pattern have not been analyzed so far. We investigate the spatiotemporal distribution of NANOG- and GATA6-expressing cells in the ICM of the mouse blastocysts with quantitative three-dimensional single-cell-based neighborhood analyses. A combination of spatial statistics and agent-based modeling reveals that the cell fate distribution follows a local clustering pattern. Using ordinary differential equations modeling, we show that this pattern can be established by a distance-based signaling mechanism enabling cells to integrate information from the whole inner cell mass into their cell fate decision. Our work highlights the importance of longer-range signaling to ensure coordinated decisions in groups of cells to successfully build embryos.
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Affiliation(s)
- Sabine C. Fischer
- Julius-Maximilians-Universität Würzburg, Faculty of Biology, Center for Computational and Theoretical Biology, Klara-Oppenheimer-Weg 32, Campus Hubland Nord, 97074 Würzburg, Germany
| | - Simon Schardt
- Julius-Maximilians-Universität Würzburg, Faculty of Biology, Center for Computational and Theoretical Biology, Klara-Oppenheimer-Weg 32, Campus Hubland Nord, 97074 Würzburg, Germany
| | - Joaquín Lilao-Garzón
- Instituto Universitario de Investigaciones Biomédicas y Sanitarias (IUIBS), Universidad Las Palmas de Gran Canaria (ULPGC), Paseo Blas Cabrera Felipe "Físico" 17, Las Palmas de Gran Canaria 35016, Spain
| | - Silvia Muñoz-Descalzo
- Instituto Universitario de Investigaciones Biomédicas y Sanitarias (IUIBS), Universidad Las Palmas de Gran Canaria (ULPGC), Paseo Blas Cabrera Felipe "Físico" 17, Las Palmas de Gran Canaria 35016, Spain
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3
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Hamis S, Somervuo P, Ågren JA, Tadele DS, Kesseli J, Scott JG, Nykter M, Gerlee P, Finkelshtein D, Ovaskainen O. Spatial cumulant models enable spatially informed treatment strategies and analysis of local interactions in cancer systems. J Math Biol 2023; 86:68. [PMID: 37017776 PMCID: PMC10076412 DOI: 10.1007/s00285-023-01903-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 01/13/2023] [Accepted: 03/09/2023] [Indexed: 04/06/2023]
Abstract
Theoretical and applied cancer studies that use individual-based models (IBMs) have been limited by the lack of a mathematical formulation that enables rigorous analysis of these models. However, spatial cumulant models (SCMs), which have arisen from theoretical ecology, describe population dynamics generated by a specific family of IBMs, namely spatio-temporal point processes (STPPs). SCMs are spatially resolved population models formulated by a system of differential equations that approximate the dynamics of two STPP-generated summary statistics: first-order spatial cumulants (densities), and second-order spatial cumulants (spatial covariances). We exemplify how SCMs can be used in mathematical oncology by modelling theoretical cancer cell populations comprising interacting growth factor-producing and non-producing cells. To formulate model equations, we use computational tools that enable the generation of STPPs, SCMs and mean-field population models (MFPMs) from user-defined model descriptions (Cornell et al. Nat Commun 10:4716, 2019). To calculate and compare STPP, SCM and MFPM-generated summary statistics, we develop an application-agnostic computational pipeline. Our results demonstrate that SCMs can capture STPP-generated population density dynamics, even when MFPMs fail to do so. From both MFPM and SCM equations, we derive treatment-induced death rates required to achieve non-growing cell populations. When testing these treatment strategies in STPP-generated cell populations, our results demonstrate that SCM-informed strategies outperform MFPM-informed strategies in terms of inhibiting population growths. We thus demonstrate that SCMs provide a new framework in which to study cell-cell interactions, and can be used to describe and perturb STPP-generated cell population dynamics. We, therefore, argue that SCMs can be used to increase IBMs' applicability in cancer research.
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Affiliation(s)
- Sara Hamis
- Tampere Institute for Advanced Study, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
- Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland.
| | - Panu Somervuo
- Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
| | - J Arvid Ågren
- Department of Evolutionary Biology, Uppsala University, Uppsala, Sweden
- Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA
| | - Dagim Shiferaw Tadele
- Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA
- Department for Medical Genetics, Oslo University Hospital, Ullevål, Oslo, Norway
| | - Juha Kesseli
- Prostate Cancer Research Center, Faculty of Medicine and Health Technology, Tampere University and Tays Cancer Centre, Tampere, Finland
| | - Jacob G Scott
- Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA
- Case Western Reserve School of Medicine, Cleveland, OH, USA
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, OH, USA
| | - Matti Nykter
- Prostate Cancer Research Center, Faculty of Medicine and Health Technology, Tampere University and Tays Cancer Centre, Tampere, Finland
- Foundation for the Finnish Cancer Institute, Helsinki, Finland
| | - Philip Gerlee
- Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
- Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden
| | - Dmitri Finkelshtein
- Department of Mathematics, Faculty of Science and Engineering, Swansea University, Swansea, UK
| | - Otso Ovaskainen
- Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland
- Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
- Department of Biology, Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, Trondheim, Norway
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Pillai M, Hojel E, Jolly MK, Goyal Y. Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools. NATURE COMPUTATIONAL SCIENCE 2023; 3:301-313. [PMID: 38177938 DOI: 10.1038/s43588-023-00427-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 03/03/2023] [Indexed: 01/06/2024]
Abstract
Individual cells within an otherwise genetically homogenous population constantly undergo fluctuations in their molecular state, giving rise to non-genetic heterogeneity. Such diversity is being increasingly implicated in cancer therapy resistance and metastasis. Identifying the origins of non-genetic heterogeneity is therefore crucial for making clinical breakthroughs. We discuss with examples how dynamical models and computational tools have provided critical multiscale insights into the nature and consequences of non-genetic heterogeneity in cancer. We demonstrate how mechanistic modeling has been pivotal in establishing key concepts underlying non-genetic diversity at various biological scales, from population dynamics to gene regulatory networks. We discuss advances in single-cell longitudinal profiling techniques to reveal patterns of non-genetic heterogeneity, highlighting the ongoing efforts and challenges in statistical frameworks to robustly interpret such multimodal datasets. Moving forward, we stress the need for data-driven statistical and mechanistically motivated dynamical frameworks to come together to develop predictive cancer models and inform therapeutic strategies.
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Affiliation(s)
- Maalavika Pillai
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India
| | - Emilia Hojel
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Department of Biomedical Engineering, Northwestern University McCormick School of Engineering, Evanston, IL, USA
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India.
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA.
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
- Department of Biomedical Engineering, Northwestern University McCormick School of Engineering, Evanston, IL, USA.
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Montenegro-Rojas I, Yañez G, Skog E, Guerrero-Calvo O, Andaur-Lobos M, Dolfi L, Cellerino A, Cerda M, Concha ML, Bertocchi C, Rojas NO, Ravasio A, Rudge TJ. A computational framework for testing hypotheses of the minimal mechanical requirements for cell aggregation using early annual killifish embryogenesis as a model. Front Cell Dev Biol 2023; 11:959611. [PMID: 37020464 PMCID: PMC10067630 DOI: 10.3389/fcell.2023.959611] [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: 06/01/2022] [Accepted: 02/08/2023] [Indexed: 04/07/2023] Open
Abstract
Introduction: Deciphering the biological and physical requirements for the outset of multicellularity is limited to few experimental models. The early embryonic development of annual killifish represents an almost unique opportunity to investigate de novo cellular aggregation in a vertebrate model. As an adaptation to seasonal drought, annual killifish employs a unique developmental pattern in which embryogenesis occurs only after undifferentiated embryonic cells have completed epiboly and dispersed in low density on the egg surface. Therefore, the first stage of embryogenesis requires the congregation of embryonic cells at one pole of the egg to form a single aggregate that later gives rise to the embryo proper. This unique process presents an opportunity to dissect the self-organizing principles involved in early organization of embryonic stem cells. Indeed, the physical and biological processes required to form the aggregate of embryonic cells are currently unknown. Methods: Here, we developed an in silico, agent-based biophysical model that allows testing how cell-specific and environmental properties could determine the aggregation dynamics of early Killifish embryogenesis. In a forward engineering approach, we then proceeded to test two hypotheses for cell aggregation (cell-autonomous and a simple taxis model) as a proof of concept of modeling feasibility. In a first approach (cell autonomous system), we considered how intrinsic biophysical properties of the cells such as motility, polarity, density, and the interplay between cell adhesion and contact inhibition of locomotion drive cell aggregation into self-organized clusters. Second, we included guidance of cell migration through a simple taxis mechanism to resemble the activity of an organizing center found in several developmental models. Results: Our numerical simulations showed that random migration combined with low cell-cell adhesion is sufficient to maintain cells in dispersion and that aggregation can indeed arise spontaneously under a limited set of conditions, but, without environmental guidance, the dynamics and resulting structures do not recapitulate in vivo observations. Discussion: Thus, an environmental guidance cue seems to be required for correct execution of early aggregation in early killifish development. However, the nature of this cue (e.g., chemical or mechanical) can only be determined experimentally. Our model provides a predictive tool that could be used to better characterize the process and, importantly, to design informed experimental strategies.
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Affiliation(s)
- Ignacio Montenegro-Rojas
- Laboratory for Mechanobiology of Transforming Systems, Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences. Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Guillermo Yañez
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences. Pontificia Universidad Católica de Chile, Santiago, Chile
- Interdisciplinary Computing and Complex Biosystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Emily Skog
- Laboratory for Mechanobiology of Transforming Systems, Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences. Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Oscar Guerrero-Calvo
- Laboratory for Mechanobiology of Transforming Systems, Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences. Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Martin Andaur-Lobos
- Laboratory for Mechanobiology of Transforming Systems, Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences. Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Luca Dolfi
- Max Planck Institute for Biology of Ageing, Cologne, Germany
- Center for Anatomy and Cell Biology, Medical University of Vienna, Vienna, Austria
| | - Alessandro Cellerino
- BIO@SNS, Scuola Normale Superiore, Pisa, Italy
- Leibniz Institute on Aging - Fritz Lipmann Institute, Jena, Germany
| | - Mauricio Cerda
- Integrative Biology Program, Institute of Biomedical Sciences, Facultad de Medicina. Universidad de Chile, Santiago, Chile
- Biomedical Neuroscience Institute, Santiago, Chile
- Center for Medical Informatics and Telemedicine, Facultad de Medicina, Universidad de Chile, Santiago, Chile
| | - Miguel L. Concha
- Integrative Biology Program, Institute of Biomedical Sciences, Facultad de Medicina. Universidad de Chile, Santiago, Chile
- Biomedical Neuroscience Institute, Santiago, Chile
- Center for Geroscience, Brain Health and Metabolism, Santiago, Chile
| | - Cristina Bertocchi
- Laboratory for Molecular Mechanics of Cell Adhesion, Department of Physiology Pontificia Universidad Católica de Chile, Santiago, Chile
- Graduate School of Engineering Science, Osaka University, Osaka, Japan
| | - Nicolás O. Rojas
- Laboratory for Mechanobiology of Transforming Systems, Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences. Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Andrea Ravasio
- Laboratory for Mechanobiology of Transforming Systems, Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences. Pontificia Universidad Católica de Chile, Santiago, Chile
- *Correspondence: Timothy J. Rudge, ; Andrea Ravasio,
| | - Timothy J. Rudge
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences. Pontificia Universidad Católica de Chile, Santiago, Chile
- Interdisciplinary Computing and Complex Biosystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
- *Correspondence: Timothy J. Rudge, ; Andrea Ravasio,
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Leo M, Lattuada E, Caprara D, Salvatori L, Vecchione A, Sciortino F, Filetici P, Stoppacciaro A. Treatment of kidney clear cell carcinoma, lung adenocarcinoma and glioblastoma cell lines with hydrogels made of DNA nanostars. Biomater Sci 2022; 10:1304-1316. [DOI: 10.1039/d1bm01643a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Overcoming the systemic administration of chemotherapy to reduce drug toxicity and the application of personalised medicine are two of the major challenges in the treatment of cancer. To this aim,...
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Pereyra M, Drusko A, Krämer F, Strobl F, Stelzer EHK, Matthäus F. QuickPIV: Efficient 3D particle image velocimetry software applied to quantifying cellular migration during embryogenesis. BMC Bioinformatics 2021; 22:579. [PMID: 34863116 PMCID: PMC8642913 DOI: 10.1186/s12859-021-04474-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 10/15/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND The technical development of imaging techniques in life sciences has enabled the three-dimensional recording of living samples at increasing temporal resolutions. Dynamic 3D data sets of developing organisms allow for time-resolved quantitative analyses of morphogenetic changes in three dimensions, but require efficient and automatable analysis pipelines to tackle the resulting Terabytes of image data. Particle image velocimetry (PIV) is a robust and segmentation-free technique that is suitable for quantifying collective cellular migration on data sets with different labeling schemes. This paper presents the implementation of an efficient 3D PIV package using the Julia programming language-quickPIV. Our software is focused on optimizing CPU performance and ensuring the robustness of the PIV analyses on biological data. RESULTS QuickPIV is three times faster than the Python implementation hosted in openPIV, both in 2D and 3D. Our software is also faster than the fastest 2D PIV package in openPIV, written in C++. The accuracy evaluation of our software on synthetic data agrees with the expected accuracies described in the literature. Additionally, by applying quickPIV to three data sets of the embryogenesis of Tribolium castaneum, we obtained vector fields that recapitulate the migration movements of gastrulation, both in nuclear and actin-labeled embryos. We show normalized squared error cross-correlation to be especially accurate in detecting translations in non-segmentable biological image data. CONCLUSIONS The presented software addresses the need for a fast and open-source 3D PIV package in biological research. Currently, quickPIV offers efficient 2D and 3D PIV analyses featuring zero-normalized and normalized squared error cross-correlations, sub-pixel/voxel approximation, and multi-pass. Post-processing options include filtering and averaging of the resulting vector fields, extraction of velocity, divergence and collectiveness maps, simulation of pseudo-trajectories, and unit conversion. In addition, our software includes functions to visualize the 3D vector fields in Paraview.
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Affiliation(s)
- Marc Pereyra
- Frankfurt Institute for Advanced Studies (FIAS) and Goethe Universität Frankfurt am Main, Ruth-Moufang-Straße 1, 60438 Frankfurt am Main, Germany
| | - Armin Drusko
- Heidelberg University Hospital, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany
| | - Franziska Krämer
- Buchmann Institute for Molecular Life Sciences (BMLS), Max-von-Laue Straße 15, 60438 Frankfurt am Main, Germany
| | - Frederic Strobl
- Buchmann Institute for Molecular Life Sciences (BMLS), Max-von-Laue Straße 15, 60438 Frankfurt am Main, Germany
| | - Ernst H. K. Stelzer
- Buchmann Institute for Molecular Life Sciences (BMLS), Max-von-Laue Straße 15, 60438 Frankfurt am Main, Germany
| | - Franziska Matthäus
- Frankfurt Institute for Advanced Studies (FIAS) and Goethe Universität Frankfurt am Main, Ruth-Moufang-Straße 1, 60438 Frankfurt am Main, Germany
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Hof L, Moreth T, Koch M, Liebisch T, Kurtz M, Tarnick J, Lissek SM, Verstegen MMA, van der Laan LJW, Huch M, Matthäus F, Stelzer EHK, Pampaloni F. Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis. BMC Biol 2021; 19:37. [PMID: 33627108 PMCID: PMC7903752 DOI: 10.1186/s12915-021-00958-w] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 01/12/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Organoids are morphologically heterogeneous three-dimensional cell culture systems and serve as an ideal model for understanding the principles of collective cell behaviour in mammalian organs during development, homeostasis, regeneration, and pathogenesis. To investigate the underlying cell organisation principles of organoids, we imaged hundreds of pancreas and cholangiocarcinoma organoids in parallel using light sheet and bright-field microscopy for up to 7 days. RESULTS We quantified organoid behaviour at single-cell (microscale), individual-organoid (mesoscale), and entire-culture (macroscale) levels. At single-cell resolution, we monitored formation, monolayer polarisation, and degeneration and identified diverse behaviours, including lumen expansion and decline (size oscillation), migration, rotation, and multi-organoid fusion. Detailed individual organoid quantifications lead to a mechanical 3D agent-based model. A derived scaling law and simulations support the hypotheses that size oscillations depend on organoid properties and cell division dynamics, which is confirmed by bright-field microscopy analysis of entire cultures. CONCLUSION Our multiscale analysis provides a systematic picture of the diversity of cell organisation in organoids by identifying and quantifying the core regulatory principles of organoid morphogenesis.
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Affiliation(s)
- Lotta Hof
- Physical Biology Group, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany
| | - Till Moreth
- Physical Biology Group, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany
| | - Michael Koch
- Physical Biology Group, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany
| | - Tim Liebisch
- Frankfurt Institute for Advanced Studies and Faculty of Biological Sciences, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany
| | - Marina Kurtz
- Department of Physics, Goethe Universität Frankfurt am Main, Frankfurt am Main, Germany
| | - Julia Tarnick
- Deanery of Biomedical Science, University of Edinburgh, Edinburgh, UK
| | - Susanna M Lissek
- Experimental Medicine and Therapy Research, University of Regensburg, Regensburg, Germany
| | - Monique M A Verstegen
- Department of Surgery, Erasmus MC - University Medical Center, Rotterdam, The Netherlands
| | - Luc J W van der Laan
- Department of Surgery, Erasmus MC - University Medical Center, Rotterdam, The Netherlands
| | - Meritxell Huch
- The Wellcome Trust/CRUK Gurdon Institute, University of Cambridge, Cambridge, UK
- Present address: Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Franziska Matthäus
- Frankfurt Institute for Advanced Studies and Faculty of Biological Sciences, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany
| | - Ernst H K Stelzer
- Physical Biology Group, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany
| | - Francesco Pampaloni
- Physical Biology Group, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany.
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Liebisch T, Drusko A, Mathew B, Stelzer EHK, Fischer SC, Matthäus F. Cell fate clusters in ICM organoids arise from cell fate heredity and division: a modelling approach. Sci Rep 2020; 10:22405. [PMID: 33376253 PMCID: PMC7772343 DOI: 10.1038/s41598-020-80141-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 12/17/2020] [Indexed: 01/13/2023] Open
Abstract
During the mammalian preimplantation phase, cells undergo two subsequent cell fate decisions. During the first decision, the trophectoderm and the inner cell mass are formed. Subsequently, the inner cell mass segregates into the epiblast and the primitive endoderm. Inner cell mass organoids represent an experimental model system, mimicking the second cell fate decision. It has been shown that cells of the same fate tend to cluster stronger than expected for random cell fate decisions. Three major processes are hypothesised to contribute to the cell fate arrangements: (1) chemical signalling; (2) cell sorting; and (3) cell proliferation. In order to quantify the influence of cell proliferation on the observed cell lineage type clustering, we developed an agent-based model accounting for mechanical cell-cell interaction, i.e. adhesion and repulsion, cell division, stochastic cell fate decision and cell fate heredity. The model supports the hypothesis that initial cell fate acquisition is a stochastically driven process, taking place in the early development of inner cell mass organoids. Further, we show that the observed neighbourhood structures can emerge solely due to cell fate heredity during cell division.
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Affiliation(s)
- Tim Liebisch
- Faculty of Biological Sciences and Frankfurt Institute for Advanced Studies (FIAS), Goethe Universität Frankfurt am Main, Ruth-Moufang-Straße 1, 60438, Frankfurt, Germany.
| | - Armin Drusko
- Faculty of Biological Sciences and Frankfurt Institute for Advanced Studies (FIAS), Goethe Universität Frankfurt am Main, Ruth-Moufang-Straße 1, 60438, Frankfurt, Germany
| | - Biena Mathew
- Faculty of Biological Sciences and Buchmann Institute for Molecular Life Sciences (BMLS), Goethe Universität Frankfurt am Main, Max-von-Laue Str. 15, 60438, Frankfurt, Germany
| | - Ernst H K Stelzer
- Faculty of Biological Sciences and Buchmann Institute for Molecular Life Sciences (BMLS), Goethe Universität Frankfurt am Main, Max-von-Laue Str. 15, 60438, Frankfurt, Germany
| | - Sabine C Fischer
- Center for Computational and Theoretical Biology (CCTB), Julius-Maximilians-Universität Würzburg, Campus Hubland Nord 32, 97074, Würzburg, Germany
| | - Franziska Matthäus
- Faculty of Biological Sciences and Frankfurt Institute for Advanced Studies (FIAS), Goethe Universität Frankfurt am Main, Ruth-Moufang-Straße 1, 60438, Frankfurt, Germany
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10
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Nava-Sedeño JM, Voß-Böhme A, Hatzikirou H, Deutsch A, Peruani F. Modelling collective cell motion: are on- and off-lattice models equivalent? Philos Trans R Soc Lond B Biol Sci 2020; 375:20190378. [PMID: 32713300 DOI: 10.1098/rstb.2019.0378] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Biological processes, such as embryonic development, wound repair and cancer invasion, or bacterial swarming and fruiting body formation, involve collective motion of cells as a coordinated group. Collective cell motion of eukaryotic cells often includes interactions that result in polar alignment of cell velocities, while bacterial patterns typically show features of apolar velocity alignment. For analysing the population-scale effects of these different alignment mechanisms, various on- and off-lattice agent-based models have been introduced. However, discriminating model-specific artefacts from general features of collective cell motion is challenging. In this work, we focus on equivalence criteria at the population level to compare on- and off-lattice models. In particular, we define prototypic off- and on-lattice models of polar and apolar alignment, and show how to obtain an on-lattice from an off-lattice model of velocity alignment. By characterizing the behaviour and dynamical description of collective migration models at the macroscopic level, we suggest the type of phase transitions and possible patterns in the approximative macroscopic partial differential equation descriptions as informative equivalence criteria between on- and off-lattice models. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems'.
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Affiliation(s)
- Josué Manik Nava-Sedeño
- Technische Universität Dresden, Center for Information Services and High Performance Computing, Nöthnitzer Straße 46, 01062 Dresden, Germany
| | - Anja Voß-Böhme
- Technische Universität Dresden, Center for Information Services and High Performance Computing, Nöthnitzer Straße 46, 01062 Dresden, Germany.,Fakultät Informatik/Mathematik, Hochschule für Technik und Wirtschaft, Dresden, Germany
| | - Haralampos Hatzikirou
- Department of Systems Immunology and Braunschweig Integrated Center of Systems Biology, Helmholtz Center for Infection Research, Inhoffenstraße 7, 38124 Braunschweig, Germany
| | - Andreas Deutsch
- Technische Universität Dresden, Center for Information Services and High Performance Computing, Nöthnitzer Straße 46, 01062 Dresden, Germany
| | - Fernando Peruani
- Laboratoire J. A. Dieudonné, Université Côte d'Azur, UMR 7351 CNRS, Parc Valrose, 06108 Nice Cedex 02, France
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11
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Khataee H, Czirok A, Neufeld Z. Multiscale modelling of motility wave propagation in cell migration. Sci Rep 2020; 10:8128. [PMID: 32424155 PMCID: PMC7235313 DOI: 10.1038/s41598-020-63506-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 03/26/2020] [Indexed: 12/16/2022] Open
Abstract
The collective motion of cell monolayers within a tissue is a fundamental biological process that occurs during tissue formation, wound healing, cancerous invasion, and viral infection. Experiments have shown that at the onset of migration, the motility is self-generated as a polarisation wave starting from the leading edge of the monolayer and progressively propagates into the bulk. However, it is unclear how the propagation of this motility wave is influenced by cellular properties. Here, we investigate this question using a computational model based on the Potts model coupled to the dynamics of intracellular polarisation. The model captures the propagation of the polarisation wave and suggests that the cells cortex can regulate the migration modes: strongly contractile cells may depolarise the monolayer, whereas less contractile cells can form swirling movement. Cortical contractility is further found to limit the cells motility, which (i) decelerates the wave speed and the leading edge progression, and (ii) destabilises the leading edge. Together, our model describes how different mechanical properties of cells can contribute to the regulation of collective cell migration.
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Affiliation(s)
- Hamid Khataee
- School of Mathematics and Physics, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia.
| | - Andras Czirok
- Department of Biological Physics, Eotvos University, Budapest, 1053, Hungary.,Department of Anatomy and Cell Biology, University of Kansas Medical Center, Kansas City, KS, 66160, USA
| | - Zoltan Neufeld
- School of Mathematics and Physics, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
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12
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Albrecht M, Lucarelli P, Kulms D, Sauter T. Computational models of melanoma. Theor Biol Med Model 2020; 17:8. [PMID: 32410672 PMCID: PMC7222475 DOI: 10.1186/s12976-020-00126-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 04/29/2020] [Indexed: 02/08/2023] Open
Abstract
Genes, proteins, or cells influence each other and consequently create patterns, which can be increasingly better observed by experimental biology and medicine. Thereby, descriptive methods of statistics and bioinformatics sharpen and structure our perception. However, additionally considering the interconnectivity between biological elements promises a deeper and more coherent understanding of melanoma. For instance, integrative network-based tools and well-grounded inductive in silico research reveal disease mechanisms, stratify patients, and support treatment individualization. This review gives an overview of different modeling techniques beyond statistics, shows how different strategies align with the respective medical biology, and identifies possible areas of new computational melanoma research.
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Affiliation(s)
- Marco Albrecht
- Systems Biology Group, Life Science Research Unit, University of Luxembourg, 6, avenue du Swing, Belval, 4367 Luxembourg
| | - Philippe Lucarelli
- Systems Biology Group, Life Science Research Unit, University of Luxembourg, 6, avenue du Swing, Belval, 4367 Luxembourg
| | - Dagmar Kulms
- Experimental Dermatology, Department of Dermatology, Dresden University of Technology, Fetscherstraße 105, Dresden, 01307 Germany
| | - Thomas Sauter
- Systems Biology Group, Life Science Research Unit, University of Luxembourg, 6, avenue du Swing, Belval, 4367 Luxembourg
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13
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Browning AP, Jin W, Plank MJ, Simpson MJ. Identifying density-dependent interactions in collective cell behaviour. J R Soc Interface 2020; 17:20200143. [PMID: 32343933 DOI: 10.1098/rsif.2020.0143] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Scratch assays are routinely used to study collective cell behaviour in vitro. Typical experimental protocols do not vary the initial density of cells, and typical mathematical modelling approaches describe cell motility and proliferation based on assumptions of linear diffusion and logistic growth. Jin et al. (Jin et al. 2016 J. Theor. Biol. 390, 136-145 (doi:10.1016/j.jtbi.2015.10.040)) find that the behaviour of cells in scratch assays is density-dependent, and show that standard modelling approaches cannot simultaneously describe data initiated across a range of initial densities. To address this limitation, we calibrate an individual-based model to scratch assay data across a large range of initial densities. Our model allows proliferation, motility, and a direction bias to depend on interactions between neighbouring cells. By considering a hierarchy of models where we systematically and sequentially remove interactions, we perform model selection analysis to identify the minimum interactions required for the model to simultaneously describe data across all initial densities. The calibrated model is able to match the experimental data across all densities using a single parameter distribution, and captures details about the spatial structure of cells. Our results provide strong evidence to suggest that motility is density-dependent in these experiments. On the other hand, we do not see the effect of crowding on proliferation in these experiments. These results are significant as they are precisely the opposite of the assumptions in standard continuum models, such as the Fisher-Kolmogorov equation and its generalizations.
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Affiliation(s)
- Alexander P Browning
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.,ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Australia
| | - Wang Jin
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.,ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Australia
| | - Michael J Plank
- Biomathematics Research Centre, University of Canterbury, Christchurch, New Zealand.,Te Pūnaha Matatini, a New Zealand Centre of Research Excellence, New Zealand
| | - Matthew J Simpson
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
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14
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Comes MC, Mencattini A, Di Giuseppe D, Filippi J, D’Orazio M, Casti P, Corsi F, Ghibelli L, Di Natale C, Martinelli E. A Camera Sensors-Based System to Study Drug Effects On In Vitro Motility: The Case of PC-3 Prostate Cancer Cells. SENSORS 2020; 20:s20051531. [PMID: 32164292 PMCID: PMC7085768 DOI: 10.3390/s20051531] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 03/03/2020] [Accepted: 03/05/2020] [Indexed: 12/13/2022]
Abstract
Cell motility is the brilliant result of cell status and its interaction with close environments. Its detection is now possible, thanks to the synergy of high-resolution camera sensors, time-lapse microscopy devices, and dedicated software tools for video and data analysis. In this scenario, we formulated a novel paradigm in which we considered the individual cells as a sort of sensitive element of a sensor, which exploits the camera as a transducer returning the movement of the cell as an output signal. In this way, cell movement allows us to retrieve information about the chemical composition of the close environment. To optimally exploit this information, in this work, we introduce a new setting, in which a cell trajectory is divided into sub-tracks, each one characterized by a specific motion kind. Hence, we considered all the sub-tracks of the single-cell trajectory as the signals of a virtual array of cell motility-based sensors. The kinematics of each sub-track is quantified and used for a classification task. To investigate the potential of the proposed approach, we have compared the achieved performances with those obtained by using a single-trajectory paradigm with the scope to evaluate the chemotherapy treatment effects on prostate cancer cells. Novel pattern recognition algorithms have been applied to the descriptors extracted at a sub-track level by implementing features, as well as samples selection (a good teacher learning approach) for model construction. The experimental results have put in evidence that the performances are higher when a further cluster majority role has been considered, by emulating a sort of sensor fusion procedure. All of these results highlighted the high strength of the proposed approach, and straightforwardly prefigure its use in lab-on-chip or organ-on-chip applications, where the cell motility analysis can be massively applied using time-lapse microscopy images.
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Affiliation(s)
- Maria Colomba Comes
- Dept. Electronic Engineering, University of Rome Tor Vergata, 00133 Roma, Italy; (M.C.C.); (D.D.G.); (J.F.); (M.D.); (P.C.); (C.D.N.); (E.M.)
| | - Arianna Mencattini
- Dept. Electronic Engineering, University of Rome Tor Vergata, 00133 Roma, Italy; (M.C.C.); (D.D.G.); (J.F.); (M.D.); (P.C.); (C.D.N.); (E.M.)
- Correspondence:
| | - Davide Di Giuseppe
- Dept. Electronic Engineering, University of Rome Tor Vergata, 00133 Roma, Italy; (M.C.C.); (D.D.G.); (J.F.); (M.D.); (P.C.); (C.D.N.); (E.M.)
| | - Joanna Filippi
- Dept. Electronic Engineering, University of Rome Tor Vergata, 00133 Roma, Italy; (M.C.C.); (D.D.G.); (J.F.); (M.D.); (P.C.); (C.D.N.); (E.M.)
| | - Michele D’Orazio
- Dept. Electronic Engineering, University of Rome Tor Vergata, 00133 Roma, Italy; (M.C.C.); (D.D.G.); (J.F.); (M.D.); (P.C.); (C.D.N.); (E.M.)
| | - Paola Casti
- Dept. Electronic Engineering, University of Rome Tor Vergata, 00133 Roma, Italy; (M.C.C.); (D.D.G.); (J.F.); (M.D.); (P.C.); (C.D.N.); (E.M.)
| | - Francesca Corsi
- Dept. of Chemical Science and Technologies, University of Rome Tor Vergata, 00133 Roma, Italy;
| | - Lina Ghibelli
- Dept. Biology, University of Rome Tor Vergata, 00133 Roma, Italy;
| | - Corrado Di Natale
- Dept. Electronic Engineering, University of Rome Tor Vergata, 00133 Roma, Italy; (M.C.C.); (D.D.G.); (J.F.); (M.D.); (P.C.); (C.D.N.); (E.M.)
| | - Eugenio Martinelli
- Dept. Electronic Engineering, University of Rome Tor Vergata, 00133 Roma, Italy; (M.C.C.); (D.D.G.); (J.F.); (M.D.); (P.C.); (C.D.N.); (E.M.)
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15
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Liu R, Song K, Hu Z, Cao W, Shuai J, Chen S, Nan H, Zheng Y, Jiang X, Zhang H, Han W, Liao Y, Qu J, Jiao Y, Liu L. Diversity of collective migration patterns of invasive breast cancer cells emerging during microtrack invasion. Phys Rev E 2019; 99:062403. [PMID: 31330694 DOI: 10.1103/physreve.99.062403] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Indexed: 12/15/2022]
Abstract
Understanding the mechanisms underlying the diversity of tumor invasion dynamics, including single-cell migration, multicellular streaming, and the emergence of various collective migration patterns, is a long-standing problem in cancer research. Here we have designed and fabricated a series of microchips containing high-throughput microscale tracks using protein repelling coating technology, which were then covered with a thin Matrigel layer. By varying the geometrical confinement (track width) and microenvironment factors (Matrigel concentration), we have reproduced a diversity of collective migration patterns in the chips, which were also observed in vivo. We have further classified the collective patterns and quantified the emergence probability of each class of patterns as a function of microtrack width and Matrigel concentration to devise a quantitive "collective pattern diagram." To elucidate the mechanisms behind the emergence of various collective patterns, we employed cellular automaton simulations, incorporating the effects of both direct cell-cell interactions and microenvironment factors (e.g., chemical gradient and extracellular matrix degradation). Our simulations suggest that tumor cell phenotype heterogeneity, and the associated dynamic selection of a favorable phenotype via cell-microenivronment interactions, are key to the emergence of the observed collective patterns in vitro.
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Affiliation(s)
- Ruchuan Liu
- Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing 401331, China
| | - Kena Song
- Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing 401331, China
| | - Zhijian Hu
- Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing 401331, China
| | - Wenbin Cao
- Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing 401331, China
| | - Jianwei Shuai
- Department of Physics, Xiamen University, Xiamen 361005, China
| | - Shaohua Chen
- Materials Science and Engineering, Arizona State University, Tempe, Arizona 85287, USA
| | - Hanqing Nan
- Materials Science and Engineering, Arizona State University, Tempe, Arizona 85287, USA
| | - Yu Zheng
- Department of Physics, Arizona State University, Tempe, Arizona 85287, USA
| | - Xuefeng Jiang
- Hygeia International Cancer Hospital, Chongqing 401331, China
| | - Hongfei Zhang
- Hygeia International Cancer Hospital, Chongqing 401331, China
| | - Weijing Han
- Shenzhen Shengyuan Biotechnology Co. Ltd., Shenzhen 518000, China
| | - Yong Liao
- Institute for Viral Hepatitis, Department of Infectious Diseases, Second Affiliated Hospital, Chongqing Medical University, Chongqing 400331, China
| | - Junle Qu
- Key Lab of Optoelectronic Devices and Systems of Ministry of Education/Guangdong Province, College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
| | - Yang Jiao
- Materials Science and Engineering, Arizona State University, Tempe, Arizona 85287, USA.,Department of Physics, Arizona State University, Tempe, Arizona 85287, USA
| | - Liyu Liu
- Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing 401331, China
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16
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Rana S, Samsuzzaman M, Saha A. Tuning the self-organization of confined active particles by the steepness of the trap. SOFT MATTER 2019; 15:8865-8878. [PMID: 31616877 DOI: 10.1039/c9sm01691k] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
We consider the collective dynamics of self-propelling particles in two dimensions. They can align themselves according to the direction of propulsion of their neighbours, together with small rotational fluctuations. They also interact with each other via soft, isotropic, repulsive potentials. The particles are confined in a circular trap. The steepness of the trap is tuneable. The average packing fraction of the particles is low. When the trap is steep, particles flock along its boundary. They form a polar cluster that spreads over the boundary. The cluster is not spatially ordered. We show that when the steepness is decreased beyond a threshold value, the cluster becomes round and compact and eventually spatial order (hexagonal) emerges in addition to the pre-established polar order. We investigate the kinetics of such ordering. We find that while rotating around the centre of the trap along its circular boundary, the cluster needs to roll around its centre of mass to be spatially ordered. We have studied the stability of the order when the trap is suddenly switched off. We find that for the particles with velocity alignment interaction, the decay of the spatial order is much slower than the particles without the alignment interaction.
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Affiliation(s)
- Shubhashis Rana
- S. N. Bose National Centre For Basic Sciences, Kolkata, 700098, India.
| | - Md Samsuzzaman
- Department of Physics, Savitribai Phule Pune University, Pune, 411007, India.
| | - Arnab Saha
- Department of Physics, Savitribai Phule Pune University, Pune, 411007, India.
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17
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Harris LA, Beik S, Ozawa PMM, Jimenez L, Weaver AM. Modeling heterogeneous tumor growth dynamics and cell-cell interactions at single-cell and cell-population resolution. CURRENT OPINION IN SYSTEMS BIOLOGY 2019; 17:24-34. [PMID: 32642602 PMCID: PMC7343346 DOI: 10.1016/j.coisb.2019.09.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Cancer is a complex, dynamic disease that despite recent advances remains mostly incurable. Inter- and intratumoral heterogeneity are generally considered major drivers of therapy resistance, metastasis, and treatment failure. Recent advances in high-throughput experimentation have produced a wealth of data on tumor heterogeneity and researchers are increasingly turning to mathematical modeling to aid in the interpretation of these complex datasets. In this mini-review, we discuss three important classes of approaches for modeling cellular dynamics within heterogeneous tumors: agent-based models, population dynamics, and multiscale models. An important new focus, for which we provide an example, is the role of intratumoral cell-cell interactions.
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Affiliation(s)
- Leonard A. Harris
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Samantha Beik
- Cancer Biology Graduate Program, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Patricia M. M. Ozawa
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Lizandra Jimenez
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Alissa M. Weaver
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University School of Medicine, Nashville, TN, USA
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18
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Hross S, Theis FJ, Sixt M, Hasenauer J. Mechanistic description of spatial processes using integrative modelling of noise-corrupted imaging data. J R Soc Interface 2018; 15:20180600. [PMID: 30958238 PMCID: PMC6303801 DOI: 10.1098/rsif.2018.0600] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 11/19/2018] [Indexed: 02/06/2023] Open
Abstract
Spatial patterns are ubiquitous on the subcellular, cellular and tissue level, and can be studied using imaging techniques such as light and fluorescence microscopy. Imaging data provide quantitative information about biological systems; however, mechanisms causing spatial patterning often remain elusive. In recent years, spatio-temporal mathematical modelling has helped to overcome this problem. Yet, outliers and structured noise limit modelling of whole imaging data, and models often consider spatial summary statistics. Here, we introduce an integrated data-driven modelling approach that can cope with measurement artefacts and whole imaging data. Our approach combines mechanistic models of the biological processes with robust statistical models of the measurement process. The parameters of the integrated model are calibrated using a maximum-likelihood approach. We used this integrated modelling approach to study in vivo gradients of the chemokine (C-C motif) ligand 21 (CCL21). CCL21 gradients guide dendritic cells and are important in the adaptive immune response. Using artificial data, we verified that the integrated modelling approach provides reliable parameter estimates in the presence of measurement noise and that bias and variance of these estimates are reduced compared to conventional approaches. The application to experimental data allowed the parametrization and subsequent refinement of the model using additional mechanisms. Among other results, model-based hypothesis testing predicted lymphatic vessel-dependent concentration of heparan sulfate, the binding partner of CCL21. The selected model provided an accurate description of the experimental data and was partially validated using published data. Our findings demonstrate that integrated statistical modelling of whole imaging data is computationally feasible and can provide novel biological insights.
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Affiliation(s)
- Sabrina Hross
- Institute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, München, Germany
- Department of Mathematics, Technische Universität München, München, Germany
| | - Fabian J. Theis
- Institute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, München, Germany
- Department of Mathematics, Technische Universität München, München, Germany
| | - Michael Sixt
- Institute of Science and Technology Austria, Klosterneuburg, Austria
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, München, Germany
- Department of Mathematics, Technische Universität München, München, Germany
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
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19
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Adameyko I. Supracellular contractions propel migration. Science 2018; 362:290-291. [PMID: 30337397 DOI: 10.1126/science.aav3376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Affiliation(s)
- Igor Adameyko
- Department of Physiology and Pharmacology, Karolinska Institutet, 17177 Stockholm, Sweden. .,Center for Brain Research, Medical University of Vienna, 1090 Vienna, Austria
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