Computational Modelling of Nephron Progenitor Cell Movement and Aggregation During Kidney Organogenesis

AbstractDuring early kidney organogenesis, nephron progenitor (NP) cells move from the tip to the corner region of the ureteric bud (UB) branches in order to form the pretubular aggregate, the early structure giving rise to nephron formation. Chemotaxis and cell-cell adhesion differences are believed to drive cell patterning during this critical period of organogenesis, but the spatiotemporal organization of this process is incompletely understood.We applied a Cellular Potts model to explore to how these processes contribute to directed cell movement and aggregation. Model parameters were estimated based on fitting to experimental data obtained in ex vivo kidney explant and dissociation-reaggregation organoid culture studies.Our simulations indicated that optimal enrichment and aggregation of NP cells in the UB corner niche requires chemoattractant secretion from both the UB epithelial cells and the NP cells themselves, as well as differences in cell-cell adhesion energies. Furthermore, NP cells were observed, both experimentally and by modelling, to move at higher speed in the UB corner as compared to the tip region where they originated. The existence of different cell speed domains along the UB was confirmed using self-organizing map analysis.In summary, we demonstrated the suitability of a Cellular Potts Model approach to simulate cell movement and patterning during early nephrogenesis. Further refinement of the model should allow us to recapitulate the effects of developmental changes of cell phenotypes and molecular crosstalk during organ development.Author SummaryThe emergence of tissue patterns during vertebrate development is a major interest of both experimental research and biocomputational modelling. In this study, we established a Cellular Potts Model to explore cellular processes during early kidney development. The goal was to elucidate movements and aggregations of nephron progenitor cells. These precursor cells derive from mesenchymal cells around the ureteric buds and eventually form the epithelial structure of the nephron. Moreover, we wanted to explore computationally the mechanisms how these cells segregate from metanephric mesenchyme and move towards the location where the nephron will be formed. Utilizing the Compucell3D simulation software, we developed a model which assumes that nephron progenitor movement and aggregation is governed by only two mechanisms, i.e. cell-cell adhesion differences between cell types and nephron progenitor cell chemotaxis in response to chemoattractant secretion from two sources. These sources were either the epithelial cells of a static ureteric bud and/or the nephron progenitor cells themselves. The simulations indicated faster average cell speeds near the ureteric bud corner, the target region of cell movement and aggregation, and slower speeds near the place of origin, the tip of ureteric bud. The results were validated by comparison of the model predictions with experimental data from two ex vivo embryonic kidney models and a computational optimization protocol.

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Mechanisms of endothelial cell coverage by pericytes: computational modelling of cell wrapping and in vitro experiments

Journal of The Royal Society Interface ◽

10.1098/rsif.2019.0739 ◽

2020 ◽

Vol 17 (162) ◽

pp. 20190739

Author(s):

Kei Sugihara ◽

Saori Sasaki ◽

Akiyoshi Uemura ◽

Satoru Kidoaki ◽

Takashi Miura

Keyword(s):

Cell Adhesion ◽

Three Dimensional ◽

Computational Modelling ◽

Cellular Level ◽

Cellular Potts Model ◽

Contributing Factors ◽

Modelling Framework ◽

Cell Cell

Pericytes (PCs) wrap around endothelial cells (ECs) and perform diverse functions in physiological and pathological processes. Although molecular interactions between ECs and PCs have been extensively studied, the morphological processes at the cellular level and their underlying mechanisms have remained elusive. In this study, using a simple cellular Potts model, we explored the mechanisms for EC wrapping by PCs. Based on the observed in vitro cell wrapping in three-dimensional PC–EC coculture, the model identified four putative contributing factors: preferential adhesion of PCs to the extracellular matrix (ECM), strong cell–cell adhesion, PC surface softness and larger PC size. While cell–cell adhesion can contribute to the prevention of cell segregation and the degree of cell wrapping, it cannot determine the orientation of cell wrapping alone. While atomic force microscopy revealed that PCs have a larger Young’s modulus than ECs, the experimental analyses supported preferential ECM adhesion and size asymmetry. We also formulated the corresponding energy minimization problem and numerically solved this problem for specific cases. These results give biological insights into the role of PC–ECM adhesion in PC coverage. The modelling framework presented here should also be applicable to other cell wrapping phenomena observed in vivo .

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Computational modelling of cell chain migration reveals mechanisms that sustain follow-the-leader behaviour

Journal of The Royal Society Interface ◽

10.1098/rsif.2011.0726 ◽

2012 ◽

Vol 9 (72) ◽

pp. 1576-1588 ◽

Cited By ~ 24

Author(s):

Michelle L. Wynn ◽

Paul M. Kulesa ◽

Santiago Schnell

Keyword(s):

Cancer Metastasis ◽

Computational Modelling ◽

Cell Movement ◽

Cell Contact ◽

Model Parameters ◽

Directional Bias ◽

Agent Based ◽

Chain Migration ◽

A Chain ◽

Cell Cell

Follow-the-leader chain migration is a striking cell migratory behaviour observed during vertebrate development, adult neurogenesis and cancer metastasis. Although cell–cell contact and extracellular matrix (ECM) cues have been proposed to promote this phenomenon, mechanisms that underlie chain migration persistence remain unclear. Here, we developed a quantitative agent-based modelling framework to test mechanistic hypotheses of chain migration persistence. We defined chain migration and its persistence based on evidence from the highly migratory neural crest model system, where cells within a chain extend and retract filopodia in short-lived cell contacts and move together as a collective. In our agent-based simulations, we began with a set of agents arranged as a chain and systematically probed the influence of model parameters to identify factors critical to the maintenance of the chain migration pattern. We discovered that chain migration persistence requires a high degree of directional bias in both lead and follower cells towards the target. Chain migration persistence was also promoted when lead cells maintained cell contact with followers, but not vice-versa. Finally, providing a path of least resistance in the ECM was not sufficient alone to drive chain persistence. Our results indicate that chain migration persistence depends on the interplay of directional cell movement and biased cell–cell contact.

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Computational modelling of nephron progenitor cell movement and aggregation during kidney organogenesis

Mathematical Biosciences ◽

10.1016/j.mbs.2021.108759 ◽

2021 ◽

pp. 108759

Author(s):

Pauli Tikka ◽

Moritz Mercker ◽

Ilya Skovorodkin ◽

Ulla Saarela ◽

Seppo Vainio ◽

...

Keyword(s):

Progenitor Cell ◽

Computational Modelling ◽

Cell Movement ◽

Kidney Organogenesis ◽

Nephron Progenitor

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Using approximate Bayesian computation to quantify cell-cell adhesion parameters in a cell migratory process

10.1101/068791 ◽

2016 ◽

Author(s):

Robert J. H. Ross ◽

R. E. Baker ◽

Andrew Parker ◽

M. J. Ford ◽

R. L. Mort ◽

...

Keyword(s):

Cell Adhesion ◽

Approximate Bayesian Computation ◽

Cell Interactions ◽

Model Parameters ◽

Bayesian Computation ◽

Accurate Identification ◽

A Cell ◽

Approximate Bayesian ◽

Set Up ◽

Cell Cell

AbstractIn this work we implement approximate Bayesian computational methods to improve the design of a wound-healing assay used to quantify cell-cell interactions. This is important as cell-cell interactions, such as adhesion and repulsion, have been shown to play an important role in cell migration. Initially, we demonstrate with a model of an ideal experiment that we are able to identify model parameters for agent motility and adhesion, given we choose appropriate summary statistics. Following this, we replace our model of an ideal experiment with a model representative of a practically realisable experiment. We demonstrate that, given the current (and commonly used) experimental set-up, model parameters cannot be accurately identified using approximate Bayesian computation methods. We compare new experimental designs through simulation, and show more accurate identification of model parameters is possible by expanding the size of the domain upon which the experiment is performed, as opposed to increasing the number of experimental repeats. The results presented in this work therefore describe time and cost-saving alterations for a commonly performed experiment for identifying cell motility parameters. Moreover, the results presented in this work will be of interest to those concerned with performing experiments that allow for the accurate identification of parameters governing cell migratory processes, especially cell migratory processes in which cell-cell adhesion or repulsion are known to play a significant role.

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A Mechanogenetic Model of Exercise-Induced Pulmonary Haemorrhage in the Thoroughbred Horse

Genes ◽

10.3390/genes10110880 ◽

2019 ◽

Vol 10 (11) ◽

pp. 880

Author(s):

Blott ◽

Cunningham ◽

Malkowski ◽

Brown ◽

Rauch

Keyword(s):

Cell Adhesion ◽

Physical Model ◽

Cell Biology ◽

Pulmonary Haemorrhage ◽

Cell Stiffness ◽

Critical Parameters ◽

Primary Study ◽

Model Parameters ◽

Exercise Induced ◽

Cell Cell

Exercise-induced pulmonary haemorrhage (EIPH) occurs in horses performing high-intensity athletic activity. The application of physics principles to derive a ‘physical model’, which is coherent with existing physiology and cell biology data, shows that critical parameters for capillary rupture are cell–cell adhesion and cell stiffness (cytoskeleton organisation). Specifically, length of fracture in the capillary is a ratio between the energy involved in cell–cell adhesion and the stiffness of cells suggesting that if the adhesion diminishes and/or that the stiffness of cells increases EIPH is more likely to occur. To identify genes associated with relevant cellular or physiological phenotypes, the physical model was used in a post-genome-wide association study (GWAS) to define gene sets associated with the model parameters. The primary study was a GWAS of EIPH where the phenotype was based on weekly tracheal wash samples collected over a two-year period from 72 horses in a flat race training yard. The EIPH phenotype was determined from cytological analysis of the tracheal wash samples, by scoring for the presence of red blood cells and haemosiderophages. Genotyping was performed using the Illumina Equine SNP50 BeadChip and analysed using linear regression in PLINK. Genes within significant genome regions were selected for sets based on their GeneOntology biological process, and analysed using fastBAT. The gene set analysis showed that genes associated with cell stiffness (cytoskeleton organisation) and blood flow have the most significant impact on EIPH risk.

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Computational modelling of epidermal stratification highlights the importance of asymmetric cell division for predictable and robust layer formation

Journal of The Royal Society Interface ◽

10.1098/rsif.2014.0631 ◽

2014 ◽

Vol 11 (99) ◽

pp. 20140631 ◽

Cited By ~ 21

Author(s):

Alexander Gord ◽

William R. Holmes ◽

Xing Dai ◽

Qing Nie

Keyword(s):

Cell Adhesion ◽

Cell Division ◽

Asymmetric Cell Division ◽

Three Dimensional ◽

Interaction Strength ◽

Computational Modelling ◽

Protective Layer ◽

The Body ◽

Cell Interactions ◽

Cell Cell

Skin is a complex organ tasked with, among other functions, protecting the body from the outside world. Its outermost protective layer, the epidermis, is comprised of multiple cell layers that are derived from a single-layered ectoderm during development. Using a new stochastic, multi-scale computational modelling framework, the anisotropic subcellular element method, we investigate the role of cell morphology and biophysical cell–cell interactions in the formation of this layered structure. This three-dimensional framework describes interactions between collections of hundreds to thousands of cells and (i) accounts for intracellular structure and morphology, (ii) easily incorporates complex cell–cell interactions and (iii) can be efficiently implemented on parallel architectures. We use this approach to construct a model of the developing epidermis that accounts for the internal polarity of ectodermal cells and their columnar morphology. Using this model, we show that cell detachment, which has been previously suggested to have a role in this process, leads to unpredictable, randomized stratification and that this cannot be abrogated by adjustment of cell–cell adhesion interaction strength. Polarized distribution of cell adhesion proteins, motivated by epithelial polarization, can however eliminate this detachment, and in conjunction with asymmetric cell division lead to robust and predictable development.

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A Cellular Potts Model for Analyzing Cell Migration across Constraining Pillar Arrays

Axioms ◽

10.3390/axioms10010032 ◽

2021 ◽

Vol 10 (1) ◽

pp. 32

Author(s):

Marco Scianna ◽

Luigi Preziosi

Keyword(s):

Cell Migration ◽

Potts Model ◽

Large Fraction ◽

Cell Movement ◽

Stochastic Approach ◽

Cellular Potts Model ◽

Cell Dynamics ◽

Experimental Systems ◽

Pillar Arrays ◽

Constrained Environments

Cell migration in highly constrained environments is fundamental in a wide variety of physiological and pathological phenomena. In particular, it has been experimentally shown that the migratory capacity of most cell lines depends on their ability to transmigrate through narrow constrictions, which in turn relies on their deformation capacity. In this respect, the nucleus, which occupies a large fraction of the cell volume and is substantially stiffer than the surrounding cytoplasm, imposes a major obstacle. This aspect has also been investigated with the use of microfluidic devices formed by dozens of arrays of aligned polymeric pillars that limit the available space for cell movement. Such experimental systems, in particular, in the designs developed by the groups of Denais and of Davidson, were here reproduced with a tailored version of the Cellular Potts model, a grid-based stochastic approach where cell dynamics are established by a Metropolis algorithm for energy minimization. The proposed model allowed quantitatively analyzing selected cell migratory determinants (e.g., the cell and nuclear speed and deformation, and forces acting at the nuclear membrane) in the case of different experimental setups. Most of the numerical results show a remarkable agreement with the corresponding empirical data.

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