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Moretti B, Rodriguez Alvarez SN, Grecco HE. Nfinder: automatic inference of cell neighborhood in 2D and 3D using nuclear markers. BMC Bioinformatics 2023; 24:230. [PMID: 37270479 DOI: 10.1186/s12859-023-05284-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 04/12/2023] [Indexed: 06/05/2023] Open
Abstract
BACKGROUND In tissues and organisms, the coordination of neighboring cells is essential to maintain their properties and functions. Therefore, knowing which cells are adjacent is crucial to understand biological processes that involve physical interactions among them, e.g. cell migration and proliferation. In addition, some signaling pathways, such as Notch or extrinsic apoptosis, are highly dependent on cell-cell communication. While this is straightforward to obtain from membrane images, nuclei labelling is much more ubiquitous for technical reasons. However, there are no automatic and robust methods to find neighboring cells based only on nuclear markers. RESULTS In this work, we describe Nfinder, a method to assess the cell's local neighborhood from images with nuclei labeling. To achieve this goal, we approximate the cell-cell interaction graph by the Delaunay triangulation of nuclei centroids. Then, links are filtered by automatic thresholding in cell-cell distance (pairwise interaction) and the maximum angle that a pair of cells subtends with shared neighbors (non-pairwise interaction). We systematically characterized the detection performance by applying Nfinder to publicly available datasets from Drosophila melanogaster, Tribolium castaneum, Arabidopsis thaliana and C. elegans. In each case, the result of the algorithm was compared to a cell neighbor graph generated by manually annotating the original dataset. On average, our method detected 95% of true neighbors, with only 6% of false discoveries. Remarkably, our findings indicate that taking into account non-pairwise interactions might increase the Positive Predictive Value up to + 11.5%. CONCLUSION Nfinder is the first robust and automatic method for estimating neighboring cells in 2D and 3D based only on nuclear markers and without any free parameters. Using this tool, we found that taking non-pairwise interactions into account improves the detection performance significantly. We believe that using our method might improve the effectiveness of other workflows to study cell-cell interactions from microscopy images. Finally, we also provide a reference implementation in Python and an easy-to-use napari plugin.
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Affiliation(s)
- Bruno Moretti
- Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física, Buenos Aires, Argentina.
- CONICET - Universidad de Buenos Aires, Instituto de Física de Buenos Aires (IFIBA), Buenos Aires, Argentina.
- Department of Molecular and Cell Biology, University of California at Berkeley, Berkeley, USA.
- Chan Zuckerberg Biohub-San Francisco, San Francisco, USA.
| | - Santiago N Rodriguez Alvarez
- Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física, Buenos Aires, Argentina
- Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
| | - Hernán E Grecco
- Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física, Buenos Aires, Argentina.
- CONICET - Universidad de Buenos Aires, Instituto de Física de Buenos Aires (IFIBA), Buenos Aires, Argentina.
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Gómez-Gálvez P, Anbari S, Escudero LM, Buceta J. Mechanics and self-organization in tissue development. Semin Cell Dev Biol 2021; 120:147-159. [PMID: 34417092 DOI: 10.1016/j.semcdb.2021.07.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 06/25/2021] [Accepted: 07/01/2021] [Indexed: 01/01/2023]
Abstract
Self-organization is an all-important feature of living systems that provides the means to achieve specialization and functionality at distinct spatio-temporal scales. Herein, we review this concept by addressing the packing organization of cells, the sorting/compartmentalization phenomenon of cell populations, and the propagation of organizing cues at the tissue level through traveling waves. We elaborate on how different theoretical models and tools from Topology, Physics, and Dynamical Systems have improved the understanding of self-organization by shedding light on the role played by mechanics as a driver of morphogenesis. Altogether, by providing a historical perspective, we show how ideas and hypotheses in the field have been revisited, developed, and/or rejected and what are the open questions that need to be tackled by future research.
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Affiliation(s)
- Pedro Gómez-Gálvez
- Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocio/CSIC/Universidad de Sevilla and Departamento de Biologia Celular, Universidad de Sevilla, 41013 Seville, Spain; Biomedical Network Research Centre on Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain
| | - Samira Anbari
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Luis M Escudero
- Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocio/CSIC/Universidad de Sevilla and Departamento de Biologia Celular, Universidad de Sevilla, 41013 Seville, Spain; Biomedical Network Research Centre on Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain
| | - Javier Buceta
- Institute for Integrative Systems Biology (I2SysBio), CSIC-UV, Paterna, 46980 Valencia, Spain.
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Maigne L, Delsol A, Fois G, Debiton E, Degoul F, Payno H. CPOP: An open source C++ cell POPulation modeler for radiation biology applications. Phys Med 2021; 89:41-50. [PMID: 34343765 DOI: 10.1016/j.ejmp.2021.07.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 07/12/2021] [Accepted: 07/13/2021] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Multicellular tumor spheroids are realistic in-vitro systems used in radiation biology research to study the effect of anticancer drugs or to evaluate the resistance of cancer cells under specific conditions. When combining the modeling of spheroids together with the simulation of radiation using Monte Carlo methods, one could estimate cell and DNA damage to be compared with experimental data. We developed a Cell Population (CPOP) modeler combined to Geant4 simulations in order to tackle how energy depositions are allocated to cells, especially when enhancing radiation outcomes using high-Z nanoparticles. CPOP manages to model large three-dimensional cell populations with independent deformable cells described with their nucleus, cytoplasm and membranes together with force law systems to manage cell-cell interactions. METHODS CPOP is an opensource platform written in C++. It is divided into two main libraries: a "Modeler" library, for cell geometry modeling using meshes, and a Multi Agent System (MAS) library, simulating all agent (cell) interactions among the population. CPOP is fully interfaced with the Geant4 Monte Carlo toolkit and is able to directly launch Geant4 simulations after compilation. We modeled a full and realistic 3D cell population from SK-MEL28 melanoma cell population cultured experimentally. The spheroid diameter of 550 ± 40 µm corresponds to a population of approximately 1000 cells having a diameter of 17.2 ± 2.5 µm and a nucleus diameter of 11.2 ± 2.0 µm. We decided to reproduce cell irradiations performed with a X-RAD 320 Biological Irradiator (Precision XRay Inc., North Branford, CT). RESULTS We simulated the energy spectrum of secondary particles generated in the vicinity of the spheroid and plotted the different energy spectra recovered internally to the spheroid. We evaluated also the impact of AGuIX (Gadolinium) nanoparticles modeled into the spheroid with their corresponding secondary energy spectra. CONCLUSIONS We succeeded into modeling cell populations and combined them with Geant4 simulations. The next step will be to integrate DNA geometrical models into cell nuclei and to use the Geant4-DNA physics and radiolysis modeling capabilities in order to evaluate early strand breaks induced on DNA.
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Affiliation(s)
- L Maigne
- Université Clermont Auvergne, CNRS/IN2P3, LPC, 63000 Clermont-Ferrand, France.
| | - A Delsol
- Université Clermont Auvergne, CNRS/IN2P3, LPC, 63000 Clermont-Ferrand, France
| | - G Fois
- Université Clermont Auvergne, CNRS/IN2P3, LPC, 63000 Clermont-Ferrand, France
| | - E Debiton
- INSERM, 1240, 58 Rue Montalembert, 63 005 Clermont-Ferrand cedex, France; Université Clermont Auvergne, Imagerie Moléculaire et Stratégies Théranostiques, BP 10448, 63000 Clermont-Ferrand, France
| | - F Degoul
- INSERM, 1240, 58 Rue Montalembert, 63 005 Clermont-Ferrand cedex, France; Université Clermont Auvergne, Imagerie Moléculaire et Stratégies Théranostiques, BP 10448, 63000 Clermont-Ferrand, France
| | - H Payno
- Université Clermont Auvergne, CNRS/IN2P3, LPC, 63000 Clermont-Ferrand, France
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Mathias S, Coulier A, Bouchnita A, Hellander A. Impact of Force Function Formulations on the Numerical Simulation of Centre-Based Models. Bull Math Biol 2020; 82:132. [PMID: 33025278 PMCID: PMC7538447 DOI: 10.1007/s11538-020-00810-2] [Citation(s) in RCA: 4] [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: 03/18/2020] [Accepted: 09/21/2020] [Indexed: 12/17/2022]
Abstract
Centre-based or cell-centre models are a framework for the computational study of multicellular systems with widespread use in cancer modelling and computational developmental biology. At the core of these models are the numerical method used to update cell positions and the force functions that encode the pairwise mechanical interactions of cells. For the latter, there are multiple choices that could potentially affect both the biological behaviour captured, and the robustness and efficiency of simulation. For example, available open-source software implementations of centre-based models rely on different force functions for their default behaviour and it is not straightforward for a modeller to know if these are interchangeable. Our study addresses this problem and contributes to the understanding of the potential and limitations of three popular force functions from a numerical perspective. We show empirically that choosing the force parameters such that the relaxation time for two cells after cell division is consistent between different force functions results in good agreement of the population radius of a two-dimensional monolayer relaxing mechanically after intense cell proliferation. Furthermore, we report that numerical stability is not sufficient to prevent unphysical cell trajectories following cell division, and consequently, that too large time steps can cause geometrical differences at the population level.
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Affiliation(s)
- Sonja Mathias
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Adrien Coulier
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Anass Bouchnita
- Department of Information Technology, Uppsala University, Uppsala, Sweden
- Present Address: Ecole Centrale Casablanca, Bouskoura, Morocco
| | - Andreas Hellander
- Department of Information Technology, Uppsala University, Uppsala, Sweden
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Karolak A, Markov DA, McCawley LJ, Rejniak KA. Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues. J R Soc Interface 2019; 15:rsif.2017.0703. [PMID: 29367239 DOI: 10.1098/rsif.2017.0703] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 01/02/2018] [Indexed: 02/06/2023] Open
Abstract
A main goal of mathematical and computational oncology is to develop quantitative tools to determine the most effective therapies for each individual patient. This involves predicting the right drug to be administered at the right time and at the right dose. Such an approach is known as precision medicine. Mathematical modelling can play an invaluable role in the development of such therapeutic strategies, since it allows for relatively fast, efficient and inexpensive simulations of a large number of treatment schedules in order to find the most effective. This review is a survey of mathematical models that explicitly take into account the spatial architecture of three-dimensional tumours and address tumour development, progression and response to treatments. In particular, we discuss models of epithelial acini, multicellular spheroids, normal and tumour spheroids and organoids, and multi-component tissues. Our intent is to showcase how these in silico models can be applied to patient-specific data to assess which therapeutic strategies will be the most efficient. We also present the concept of virtual clinical trials that integrate standard-of-care patient data, medical imaging, organ-on-chip experiments and computational models to determine personalized medical treatment strategies.
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Affiliation(s)
- Aleksandra Karolak
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Dmitry A Markov
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.,Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA
| | - Lisa J McCawley
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.,Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA
| | - Katarzyna A Rejniak
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA .,Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
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A cell-based computational model of early embryogenesis coupling mechanical behaviour and gene regulation. Nat Commun 2017; 8:13929. [PMID: 28112150 PMCID: PMC5264012 DOI: 10.1038/ncomms13929] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2016] [Accepted: 11/14/2016] [Indexed: 01/01/2023] Open
Abstract
The study of multicellular development is grounded in two complementary domains: cell biomechanics, which examines how physical forces shape the embryo, and genetic regulation and molecular signalling, which concern how cells determine their states and behaviours. Integrating both sides into a unified framework is crucial to fully understand the self-organized dynamics of morphogenesis. Here we introduce MecaGen, an integrative modelling platform enabling the hypothesis-driven simulation of these dual processes via the coupling between mechanical and chemical variables. Our approach relies upon a minimal 'cell behaviour ontology' comprising mesenchymal and epithelial cells and their associated behaviours. MecaGen enables the specification and control of complex collective movements in 3D space through a biologically relevant gene regulatory network and parameter space exploration. Three case studies investigating pattern formation, epithelial differentiation and tissue tectonics in zebrafish early embryogenesis, the latter with quantitative comparison to live imaging data, demonstrate the validity and usefulness of our framework.
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Kennedy RC, Ropella GE, Hunt CA. A cell-centered, agent-based framework that enables flexible environment granularities. Theor Biol Med Model 2016; 13:4. [PMID: 26839017 PMCID: PMC4736144 DOI: 10.1186/s12976-016-0030-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2015] [Accepted: 01/20/2016] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Mechanistic explanations of cell-level phenomena typically adopt an observer perspective. Explanations developed from a cell's perspective may offer new insights. Agent-based models lend themselves to model from an individual perspective, and existing agent-based models generally utilize a regular lattice-based environment. A framework which utilizes a cell's perspective in an off-lattice environment could improve the overall understanding of biological phenomena. RESULTS We present an agent-based, discrete event framework, with a demonstrative focus on biomimetic agents. The framework was first developed in 2-dimensions and then extended, with a subset of behaviors, to 3-dimensions. The framework is expected to facilitate studies of more complex biological phenomena through exploitation of a dynamic Delaunay and Voronoi off-lattice environment. We used the framework to model biological cells and to specifically demonstrate basic biological cell behaviors in two- and three-dimensional space. Potential use cases are highlighted, suggesting the utility of the framework in various scenarios. CONCLUSIONS The framework presented in this manuscript expands on existing cell- and agent-centered methods by offering a new perspective in an off-lattice environment. As the demand for biomimetic models grows, the demand for new methods, such as the presented Delaunay and Voronoi framework, is expected to increase.
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Affiliation(s)
- Ryan C Kennedy
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | | | - C Anthony Hunt
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA.
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Christley S, Lee B, Dai X, Nie Q. Integrative multicellular biological modeling: a case study of 3D epidermal development using GPU algorithms. BMC SYSTEMS BIOLOGY 2010; 4:107. [PMID: 20696053 PMCID: PMC2936904 DOI: 10.1186/1752-0509-4-107] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2010] [Accepted: 08/09/2010] [Indexed: 12/18/2022]
Abstract
BACKGROUND Simulation of sophisticated biological models requires considerable computational power. These models typically integrate together numerous biological phenomena such as spatially-explicit heterogeneous cells, cell-cell interactions, cell-environment interactions and intracellular gene networks. The recent advent of programming for graphical processing units (GPU) opens up the possibility of developing more integrative, detailed and predictive biological models while at the same time decreasing the computational cost to simulate those models. RESULTS We construct a 3D model of epidermal development and provide a set of GPU algorithms that executes significantly faster than sequential central processing unit (CPU) code. We provide a parallel implementation of the subcellular element method for individual cells residing in a lattice-free spatial environment. Each cell in our epidermal model includes an internal gene network, which integrates cellular interaction of Notch signaling together with environmental interaction of basement membrane adhesion, to specify cellular state and behaviors such as growth and division. We take a pedagogical approach to describing how modeling methods are efficiently implemented on the GPU including memory layout of data structures and functional decomposition. We discuss various programmatic issues and provide a set of design guidelines for GPU programming that are instructive to avoid common pitfalls as well as to extract performance from the GPU architecture. CONCLUSIONS We demonstrate that GPU algorithms represent a significant technological advance for the simulation of complex biological models. We further demonstrate with our epidermal model that the integration of multiple complex modeling methods for heterogeneous multicellular biological processes is both feasible and computationally tractable using this new technology. We hope that the provided algorithms and source code will be a starting point for modelers to develop their own GPU implementations, and encourage others to implement their modeling methods on the GPU and to make that code available to the wider community.
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Affiliation(s)
- Scott Christley
- Department of Mathematics, University of California, Irvine, CA 92697, USA.
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Beyer T, Meyer-Hermann M. Multiscale modeling of cell mechanics and tissue organization. ACTA ACUST UNITED AC 2009; 28:38-45. [PMID: 19349250 DOI: 10.1109/memb.2009.931790] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Nowadays, experimental biology gathers a large number of molecular and genetic data to understand the processes in living systems. Many of these data are evaluated on the level of cells, resulting in a changed phenotype of cells. Tools are required to translate the information on the cellular scale to the whole tissue, where multiple interacting cell types are involved. Agent-based modeling allows the investigation of properties emerging from the collective behavior of individual units. A typical agent in biology is a single cell that transports information from the intracellular level to larger scales. Mainly, two scales are relevant: changes in the dynamics of the cell, e.g. surface properties, and secreted molecules that can have effects at a distance larger than the cell diameter.
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Affiliation(s)
- Tilo Beyer
- Institute of Molecular and Clinical Immunology Medical Faculty, Ottovon-Guericke-University, Magdeburg, Germany.
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Germinal centres seen through the mathematical eye: B-cell models on the catwalk. Trends Immunol 2009; 30:157-64. [DOI: 10.1016/j.it.2009.01.005] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2008] [Revised: 01/15/2009] [Accepted: 01/16/2009] [Indexed: 11/24/2022]
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Beyer T, Meyer-Hermann M. Cell transmembrane receptors determine tissue pattern stability. PHYSICAL REVIEW LETTERS 2008; 101:148102. [PMID: 18851578 DOI: 10.1103/physrevlett.101.148102] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2007] [Indexed: 05/26/2023]
Abstract
The analysis of biological systems requires mathematical tools that represent their complexity from the molecular scale up to the tissue level. The formation of cell aggregates by chemotaxis is investigated using Delaunay object dynamics. It is found that when cells migrate fast such that the chemokine distribution is far from equilibrium, the details of the chemokine receptor dynamics can induce an internalization driven instability of cell aggregates. The instability occurs in a parameter regime relevant for lymphoid tissue and is similar to ectopic lymphoid structures.
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Affiliation(s)
- Tilo Beyer
- Institute for Molecular and Clinical Immunology, Medical Faculty, Otto-von-Guericke-University, Leipziger Str. 44, 39120 Magdeburg, Germany.
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Beyer T, Meyer-Hermann M. Mechanisms of organogenesis of primary lymphoid follicles. Int Immunol 2008; 20:615-23. [DOI: 10.1093/intimm/dxn020] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
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