Modeling keratinocyte wound healing dynamics: Cell-cell adhesion promotes sustained collective migration

Mesenchymal Stem Cell Extracellular Vesicles from Tissue-Mimetic System Enhance Epidermal Regeneration via Formation of Migratory Cell Sheets

BACKGROUND

The secretome of adipose-derived mesenchymal stem cells (ASCs) offers a unique approach to understanding and treating wounds, including the critical process of epidermal regeneration orchestrated by keratinocytes. However, 2D culture techniques drastically alter the secretory dynamics of ASCs, which has led to ambiguity in understanding which secreted compounds (e.g., growth factors, exosomes, reactive oxygen species) may be driving epithelialization.

METHODS

A novel tissue-mimetic 3D hydrogel system was utilized to enhance the retainment of a more regenerative ASC phenotype and highlight the functional secretome differences between 2D and 3D. Subsequently, the ASC-secretome was stratified by molecular weight and the presence/absence of extracellular vesicles (EVs). The ASC-secretome fractions were then evaluated to assess for the capacity to augment specific keratinocyte activities.

RESULTS

Culture of ASCs within the tissue-mimetic system enhanced protein secretion ~ 50%, exclusively coming from the > 100 kDa fraction. The ASC-secretome ability to modulate epithelialization functions, including migration, proliferation, differentiation, and morphology, resided within the "> 100 kDa" fraction, with the 3D ASC-secretome providing the greatest improvement. 3D ASC EV secretion was enhanced two-fold and exhibited dose-dependent effects on epidermal regeneration. Notably, ASC-EVs induced morphological changes in keratinocytes reminiscent of native regeneration, including formation of stratified cell sheets. However, only 3D-EVs promoted collective cell sheet migration and an epithelial-to-mesenchymal-like transition in keratinocytes, whereas 2D-EVs contained an anti-migratory stimulus.

CONCLUSION

This study demonstrates how critical the culture environment is on influencing ASC-secretome regenerative capabilities. Additionally, the critical role of EVs in modulating epidermal regeneration is revealed and their translatability for future clinical therapies is discussed.

Estimation of Parameter Distributions for Reaction-Diffusion Equations with Competition using Aggregate Spatiotemporal Data

Reaction-diffusion equations have been used to model a wide range of biological phenomenon related to population spread and proliferation from ecology to cancer. It is commonly assumed that individuals in a population have homogeneous diffusion and growth rates; however, this assumption can be inaccurate when the population is intrinsically divided into many distinct subpopulations that compete with each other. In previous work, the task of inferring the degree of phenotypic heterogeneity between subpopulations from total population density has been performed within a framework that combines parameter distribution estimation with reaction-diffusion models. Here, we extend this approach so that it is compatible with reaction-diffusion models that include competition between subpopulations. We use a reaction-diffusion model of glioblastoma multiforme, an aggressive type of brain cancer, to test our approach on simulated data that are similar to measurements that could be collected in practice. We use Prokhorov metric framework and convert the reaction-diffusion model to a random differential equation model to estimate joint distributions of diffusion and growth rates among heterogeneous subpopulations. We then compare the new random differential equation model performance against other partial differential equation models' performance. We find that the random differential equation is more capable at predicting the cell density compared to other models while being more time efficient. Finally, we use k-means clustering to predict the number of subpopulations based on the recovered distributions.

Bifurcation analysis of critical values for wound closure outcomes in wound healing experiments

A nonlinear partial differential equation containing a nonlocal advection term and a diffusion term is analyzed to study wound closure outcomes in wound healing experiments. There is an extensive literature of similar models for wound healing experiments. In this paper we study the character of wound closure in these experiments in terms of the sensing radius of cells and the force of cell-cell adhesion. We prove a bifurcation result which differentiates uniform closure of the wound from nonuniform closure of the wound, based on a critical value [Formula: see text] of the force of cell-cell adhesion parameter [Formula: see text]. For [Formula: see text] the steady state solution [Formula: see text] of the model is stable and the wound closes uniformly. For [Formula: see text] the steady state solution [Formula: see text] of the model is unstable and the wound closes nonuniformly. We provide numerical simulations of the model to illustrate our results.

Topical Delivery of Cell-Penetrating Peptide-Modified Human Growth Hormone for Enhanced Wound Healing

Protein drugs have been emerging as a class of promising therapeutics. However, their topical application has been limited by their high molecular weight and poor permeability to the cell membrane. In this study, we aimed to enhance human growth hormone (hGH) permeability for topical application by conjugation of TAT peptide, a cell-penetrating peptide, to hGH via crosslinker. After TAT was conjugated to hGH, TAT-hGH was purified by affinity chromatography. TAT-hGH significantly increased cell proliferation compared with the control. Interestingly, the effect of TAT-hGH was higher than hGH at the same concentration. Furthermore, the conjugation of TAT to hGH enhanced the permeability of TAT-hGH across the cell membrane without affecting its biological activity in vitro. In vivo, the topical application of TAT-hGH into scar tissue markedly accelerated wound healing. Histological results showed that TAT-hGH dramatically promoted the re-epithelialization of wounds in the initial stage. These results demonstrate TAT-hGH as a new therapeutic potential drug for wound healing treatment. This study also provides a new method for topical protein application via enhancement of their permeability.

A mathematical model of wound healing in bovine corneal endothelium

We developed a mathematical model to describe healing processes in bovine corneal endothelial (BCE) cells in culture, triggered by mechanical wounds with parallel edges. Previous findings from our laboratory show that, in these cases, BCE monolayers exhibit an approximately constant healing velocity. Also, that caspase-dependent apoptosis occurs, with the fraction of apoptotic cells increasing with the distance traveled by the healing edge. In addition, in this study we report the novel findings that, for wound scratch assays performed preserving the basal extracellular matrix: i) the healing cells increase their en face surface area in a characteristic fashion, and ii) the average length of the segments of the cell columns actively participating in the healing process increases linearly with time. These latter observations preclude the utilization of standard traveling wave formalisms to model wound healing in BCE cells. Instead, we developed and studied a simple phenomenological model based on a plausible formula for the spreading dynamics of the individual healing cells, that incorporates original evidence about the process in BCE cells. The model can be simulated to: i) obtain an approximately constant healing velocity; ii) reproduce the profile of the healing cell areas, and iii) obtain approximately linear time dependences of the mean cell area and average length of the front active segments per column. In view of its accuracy to account for the experimental observations, the model can also be acceptably employed to quantify the appearance of apoptotic cells during BCE wound healing. The strategy utilized here could offer a novel formal framework to represent modifications undergone by some epithelial cell lines during wound healing.

Genome-wide analysis of dysregulated RNA-binding proteins and alternative splicing genes in keloid

Introduction: The pathogenesis of keloids remains unclear. Methods: In this study, we analyzed RNA-Seq data (GSE113619) of the local skin tissue of 8 keloid-prone individuals (KPI) and 6 healthy controls (HC) before and 42 days after trauma from the gene expression omnibus (GEO) database. The differential alternative splicing (AS) events associated with trauma healing between KPIs and HCs were identifified, and their functional differences were analyzed by gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) pathways. The co-expression relationship of differentially alternative splicing genes and differentially expressed RNA binding proteins (RBPs) was established subsequently. Results: A total of 674 differential AS events between the KD42 and the KD0 and 378 differential AS events between the HD42 and the HD0 were discovered. Notably, most of the differential genes related to keloids are enriched in actin, microtubule cells, and cortical actin cytoskeletal tissue pathway. We observed a signifificant association between AS genes (EPB41, TPM1, NF2, PARD3) and trauma healing in KPIs and HCs. We also found that the differential expression of healthy controls-specifific trauma healing-related RBPs (TKT, FDPS, SAMHD1) may affect the response of HCs to trauma healing by regulating the AS of downstream trauma healing-related genes such as DCN and DST. In contrast, KPIs also has specifific differential expression of trauma healing related RBPs (S100A9, HspB1, LIMA1, FBL), which may affect the healing response of KPIs to trauma by regulating the AS of downstream trauma healing-related genes such as FN1 and TPM1. Discussion: Our results were innovative in revealing early wound healing-related genes (EPB41, TPM1, NF2, PARD3) in KPI from the perspective of AS regulated by RBPs.

Learning anisotropic interaction rules from individual trajectories in a heterogeneous cellular population

Interacting particle system (IPS) models have proven to be highly successful for describing the spatial movement of organisms. However, it is challenging to infer the interaction rules directly from data. In the field of equation discovery, the weak-form sparse identification of nonlinear dynamics (WSINDy) methodology has been shown to be computationally efficient for identifying the governing equations of complex systems from noisy data. Motivated by the success of IPS models to describe the spatial movement of organisms, we develop WSINDy for the second-order IPS to learn equations for communities of cells. Our approach learns the directional interaction rules for each individual cell that in aggregate govern the dynamics of a heterogeneous population of migrating cells. To sort a cell according to the active classes present in its model, we also develop a novel ad hoc classification scheme (which accounts for the fact that some cells do not have enough evidence to accurately infer a model). Aggregated models are then constructed hierarchically to simultaneously identify different species of cells present in the population and determine best-fit models for each species. We demonstrate the efficiency and proficiency of the method on several test scenarios, motivated by common cell migration experiments.

Investigations on the Wound Healing Potential of Tilapia Piscidin (TP)2-5 and TP2-6

Wound healing is a highly orchestrated process involving many cell types, such as keratinocytes, fibroblasts and endothelial cells. This study aimed to evaluate the potential application of synthetic peptides derived from tilapia piscidin (TP)2, TP2-5 and TP2-6 in skin wound healing. The treatment of HaCaT keratinocytes with TP2-5 and TP2-6 did not cause cytotoxicity, but did enhance cell proliferation and migration, which could be attributed to the activation of epidermal growth factor receptor signaling. In CCD-966SK fibroblasts, although TP2-5 (31.25 μg/mL) and TP2-6 (125 μg/mL) showed cytotoxic effects, we observed the significant promotion of cell proliferation and migration at low concentrations. In addition, collagen I, collagen III, and keratinocyte growth factor were upregulated by the peptides. We further found that TP2-5 and TP2-6 showed pro-angiogenic properties, including the enhancement of human umbilical vein endothelial cell (HUVEC) migration and the promotion of neovascularization. In a murine model, wounds treated topically with TP2-5 and TP2-6 were reduced by day 2 post-injury and healed significantly faster than untreated wounds. Taken together, these findings demonstrate that both TP2-5 and TP2-6 have multifaceted effects when used as topical agents for accelerating wound healing.

A review of diabetic wound models-Novel insights into diabetic foot ulcer

Diabetic foot ulcer (DFU) is a major debilitating complication of diabetes. Many research investigations have been conducted with the aims to uncover the diabetic wound healing mechanisms, develop novel therapeutics, and screen bioactive wound dressings in order to improve the current management of DFU. These would have not been possible without the utilization of an appropriate wound model, especially in a diabetic wound context. This review focuses on the different in vitro research models used in DFU investigations such as the 2D scratch wound assay, 3D skin model, and 3D angiogenesis model as well as their limitations. The current efforts and challenges to apply the 2D and 3D in vitro models in a hyperglycemic context to provide insights into DFU modeling will be reviewed. Perspectives of utilizing 3D bioprinting and skin-on-the-chip model as a diabetic wound model in the future will also be highlighted. By leveraging knowledge from past experiences and current research, an improved experimental model for DFU is anticipated to be established in near future.

Recent advances in understanding the role of metabolic heterogeneities in cell migration

Migration is an energy-intensive, multi-step process involving cell adhesion, protrusion, and detachment. Each of these steps require cells to generate and consume energy, regulating their morphological changes and force generation. Given the need for energy to move, cellular metabolism has emerged as a critical regulator of both single cell and collective migration. Recently, metabolic heterogeneity has been highlighted as a potential determinant of collective cell behavior, as individual cells may play distinct roles in collective migration. Several tools and techniques have been developed and adapted to study cellular energetics during migration including live-cell probes to characterize energy utilization and metabolic state and methodologies to sort cells based on their metabolic profile. Here, we review the recent advances in techniques, parsing the metabolic heterogeneities inherent in cell populations and their contributions to cell migration.

Learning differential equation models from stochastic agent-based model simulations

Agent-based models provide a flexible framework that is frequently used for modelling many biological systems, including cell migration, molecular dynamics, ecology and epidemiology. Analysis of the model dynamics can be challenging due to their inherent stochasticity and heavy computational requirements. Common approaches to the analysis of agent-based models include extensive Monte Carlo simulation of the model or the derivation of coarse-grained differential equation models to predict the expected or averaged output from the agent-based model. Both of these approaches have limitations, however, as extensive computation of complex agent-based models may be infeasible, and coarse-grained differential equation models can fail to accurately describe model dynamics in certain parameter regimes. We propose that methods from the equation learning field provide a promising, novel and unifying approach for agent-based model analysis. Equation learning is a recent field of research from data science that aims to infer differential equation models directly from data. We use this tutorial to review how methods from equation learning can be used to learn differential equation models from agent-based model simulations. We demonstrate that this framework is easy to use, requires few model simulations, and accurately predicts model dynamics in parameter regions where coarse-grained differential equation models fail to do so. We highlight these advantages through several case studies involving two agent-based models that are broadly applicable to biological phenomena: a birth-death-migration model commonly used to explore cell biology experiments and a susceptible-infected-recovered model of infectious disease spread.

Suppression of α-catenin and adherens junctions enhances epithelial cell proliferation and motility via TACE-mediated TGF-α autocrine/paracrine signaling

Sustained cell migration is essential for wound healing and cancer metastasis. The epidermal growth factor receptor (EGFR) signaling cascade is known to drive cell migration and proliferation. While the signal transduction downstream of EGFR has been extensively investigated, our knowledge of the initiation and maintenance of EGFR signaling during cell migration remains limited. The metalloprotease TACE (tumor necrosis factor alpha converting enzyme) is responsible for producing active EGFR family ligands in the via ligand shedding. Sustained TACE activity may perpetuate EGFR signaling and reduce a cell’s reliance on exogenous growth factors. Using a cultured keratinocyte model system, we show that depletion of α-catenin perturbs adherens junctions, enhances cell proliferation and motility, and decreases dependence on exogenous growth factors. We show that the underlying mechanism for these observed phenotypical changes depends on enhanced autocrine/paracrine release of the EGFR ligand transforming growth factor alpha in a TACE-dependent manner. We demonstrate that proliferating keratinocyte epithelial cell clusters display waves of oscillatory extracellular signal–regulated kinase (ERK) activity, which can be eliminated by TACE knockout, suggesting that these waves of oscillatory ERK activity depend on autocrine/paracrine signals produced by TACE. These results provide new insights into the regulatory role of adherens junctions in initiating and maintaining autocrine/paracrine signaling with relevance to wound healing and cellular transformation.

Biologically-informed neural networks guide mechanistic modeling from sparse experimental data

Biologically-informed neural networks (BINNs), an extension of physics-informed neural networks [1], are introduced and used to discover the underlying dynamics of biological systems from sparse experimental data. In the present work, BINNs are trained in a supervised learning framework to approximate in vitro cell biology assay experiments while respecting a generalized form of the governing reaction-diffusion partial differential equation (PDE). By allowing the diffusion and reaction terms to be multilayer perceptrons (MLPs), the nonlinear forms of these terms can be learned while simultaneously converging to the solution of the governing PDE. Further, the trained MLPs are used to guide the selection of biologically interpretable mechanistic forms of the PDE terms which provides new insights into the biological and physical mechanisms that govern the dynamics of the observed system. The method is evaluated on sparse real-world data from wound healing assays with varying initial cell densities [2].

In this work we extend equation learning methods to be feasible for biological applications with nonlinear dynamics and where data are often sparse and noisy. Physics-informed neural networks have recently been shown to approximate solutions of PDEs from simulated noisy data while simultaneously optimizing the PDE parameters. However, the success of this method requires the correct specification of the governing PDE, which may not be known in practice. Here, we present an extension of the algorithm that allows neural networks to learn the nonlinear terms of the governing system without the need to specify the mechanistic form of the PDE. Our method is demonstrated on real-world biological data from scratch assay experiments and used to discover a previously unconsidered biological mechanism that describes delayed population response to the scratch.

Cell proliferation and migration explain pore bridging dynamics in 3D printed scaffolds of different pore size

Tissue growth in bioscaffolds is influenced significantly by pore geometry, but how this geometric dependence emerges from dynamic cellular processes such as cell proliferation and cell migration remains poorly understood. Here we investigate the influence of pore size on the time required to bridge pores in thin 3D-printed scaffolds. Experimentally, new tissue infills the pores continually from their perimeter under strong curvature control, which leads the tissue front to round off with time. Despite the varied shapes assumed by the tissue during this evolution, we find that time to bridge a pore simply increases linearly with the overall pore size. To disentangle the biological influence of cell behaviour and the mechanistic influence of geometry in this experimental observation, we propose a simple reaction-diffusion model of tissue growth based on Porous-Fisher invasion of cells into the pores. First, this model provides a good qualitative representation of the evolution of the tissue; new tissue in the model grows at an effective rate that depends on the local curvature of the tissue substrate. Second, the model suggests that a linear dependence of bridging time with pore size arises due to geometric reasons alone, not to differences in cell behaviours across pores of different sizes. Our analysis suggests that tissue growth dynamics in these experimental constructs is dominated by mechanistic crowding effects that influence collective cell proliferation and migration processes, and that can be predicted by simple reaction-diffusion models of cells that have robust, consistent behaviours.

Learning Equations from Biological Data with Limited Time Samples

Equation learning methods present a promising tool to aid scientists in the modeling process for biological data. Previous equation learning studies have demonstrated that these methods can infer models from rich datasets; however, the performance of these methods in the presence of common challenges from biological data has not been thoroughly explored. We present an equation learning methodology comprised of data denoising, equation learning, model selection and post-processing steps that infers a dynamical systems model from noisy spatiotemporal data. The performance of this methodology is thoroughly investigated in the face of several common challenges presented by biological data, namely, sparse data sampling, large noise levels, and heterogeneity between datasets. We find that this methodology can accurately infer the correct underlying equation and predict unobserved system dynamics from a small number of time samples when the data are sampled over a time interval exhibiting both linear and nonlinear dynamics. Our findings suggest that equation learning methods can be used for model discovery and selection in many areas of biology when an informative dataset is used. We focus on glioblastoma multiforme modeling as a case study in this work to highlight how these results are informative for data-driven modeling-based tumor invasion predictions.

Identifying density-dependent interactions in collective cell behaviour

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.

The Impact of Spaceflight and Simulated Microgravity on Cell Adhesion

Microgravity induces a number of significant physiological changes in the cardiovascular, nervous, immune systems, as well as the bone tissue of astronauts. Changes in cell adhesion properties are one aspect affected during long-term spaceflights in mammalian cells. Cellular adhesion behaviors can be divided into cell-cell and cell-matrix adhesion. These behaviors trigger cell-cell recognition, conjugation, migration, cytoskeletal rearrangement, and signal transduction. Cellular adhesion molecule (CAM) is a general term for macromolecules that mediate the contact and binding between cells or between cells and the extracellular matrix (ECM). In this review, we summarize the four major classes of adhesion molecules that regulate cell adhesion, including integrins, immunoglobulin superfamily (Ig-SF), cadherins, and selectin. Moreover, we discuss the effects of spaceflight and simulated microgravity on the adhesion of endothelial cells, immune cells, tumor cells, stem cells, osteoblasts, muscle cells, and other types of cells. Further studies on the effects of microgravity on cell adhesion and the corresponding physiological behaviors may help increase the safety and improve the health of astronauts in space.

Learning partial differential equations for biological transport models from noisy spatio-temporal data

We investigate methods for learning partial differential equation (PDE) models from spatio-temporal data under biologically realistic levels and forms of noise. Recent progress in learning PDEs from data have used sparse regression to select candidate terms from a denoised set of data, including approximated partial derivatives. We analyse the performance in using previous methods to denoise data for the task of discovering the governing system of PDEs. We also develop a novel methodology that uses artificial neural networks (ANNs) to denoise data and approximate partial derivatives. We test the methodology on three PDE models for biological transport, i.e. the advection-diffusion, classical Fisher-Kolmogorov-Petrovsky-Piskunov (Fisher-KPP) and nonlinear Fisher-KPP equations. We show that the ANN methodology outperforms previous denoising methods, including finite differences and both local and global polynomial regression splines, in the ability to accurately approximate partial derivatives and learn the correct PDE model.

Practical parameter identifiability for spatio-temporal models of cell invasion

We examine the practical identifiability of parameters in a spatio-temporal reaction-diffusion model of a scratch assay. Experimental data involve fluorescent cell cycle labels, providing spatial information about cell position and temporal information about the cell cycle phase. Cell cycle labelling is incorporated into the reaction-diffusion model by treating the total population as two interacting subpopulations. Practical identifiability is examined using a Bayesian Markov chain Monte Carlo (MCMC) framework, confirming that the parameters are identifiable when we assume the diffusivities of the subpopulations are identical, but that the parameters are practically non-identifiable when we allow the diffusivities to be distinct. We also assess practical identifiability using a profile likelihood approach, providing similar results to MCMC with the advantage of being an order of magnitude faster to compute. Therefore, we suggest that the profile likelihood ought to be adopted as a screening tool to assess practical identifiability before MCMC computations are performed.

Ulmus parvifolia Accelerates Skin Wound Healing by Regulating the Expression of MMPs and TGF-β

Ulmus parvifolia is one of the medicinal plants used traditionally for treatment of wounds. We intended to investigate the wound healing effect of the powder of Ulmus parvifolia (UP) root bark in a mouse wound healing model. We also determined the mechanisms of effects of U. parvifolia in skin and skin wound healing effects using a keratinocyte model. Animal experiments showed that the wound lesions in the mice decreased with 200 mesh U. parvifolia root bark powder and were significantly reduced with treatment by UP, compared with those treated with Ulmus macrocarpa (UM). Results from in vitro experiments also revealed that UP extract promoted the migration of human skin keratinocytes. UP powder treatment upregulated the expression of the matrix metalloproteinase-2 and -9 protein and significantly increased transforming growth factor (TGF)-β levels. We confirmed that topical administration of the bark powder exerted a significant effect on skin wound healing by upregulating the expression of MMP and transforming growth factor-β. Our study suggests that U. parvifolia may be a potential candidate for skin wound healing including epidermal skin rejuvenation.

Revisiting the Fisher-Kolmogorov-Petrovsky-Piskunov equation to interpret the spreading-extinction dichotomy

The Fisher-Kolmogorov-Petrovsky-Piskunov model, also known as the Fisher-KPP model, supports travelling wave solutions that are successfully used to model numerous invasive phenomena with applications in biology, ecology and combustion theory. However, there are certain phenomena that the Fisher-KPP model cannot replicate, such as the extinction of invasive populations. The Fisher-Stefan model is an adaptation of the Fisher-KPP model to include a moving boundary whose evolution is governed by a Stefan condition. The Fisher-Stefan model also supports travelling wave solutions; however, a key additional feature of the Fisher-Stefan model is that it is able to simulate population extinction, giving rise to a spreading-extinction dichotomy. In this work, we revisit travelling wave solutions of the Fisher-KPP model and show that these results provide new insight into travelling wave solutions of the Fisher-Stefan model and the spreading-extinction dichotomy. Using a combination of phase plane analysis, perturbation analysis and linearization, we establish a concrete relationship between travelling wave solutions of the Fisher-Stefan model and often-neglected travelling wave solutions of the Fisher-KPP model. Furthermore, we give closed-form approximate expressions for the shape of the travelling wave solutions of the Fisher-Stefan model in the limit of slow travelling wave speeds, c≪1.

Continuum descriptions of spatial spreading for heterogeneous cell populations: Theory and experiment

Variability in cell populations is frequently observed in both in vitro and in vivo settings. Intrinsic differences within populations of cells, such as differences in cell sizes or differences in rates of cell motility, can be present even within a population of cells from the same cell line. We refer to this variability as cell heterogeneity. Mathematical models of cell migration, for example, in the context of tumour growth and metastatic invasion, often account for both undirected (random) migration and directed migration that is mediated by cell-to-cell contacts and cell-to-cell adhesion. A key feature of standard models is that they often assume that the population is composed of identical cells with constant properties. This leads to relatively simple single-species homogeneous models that neglect the role of heterogeneity. In this work, we use a continuum modelling approach to explore the role of heterogeneity in spatial spreading of cell populations. We employ a three-species heterogeneous model of cell motility that explicitly incorporates different types of experimentally-motivated heterogeneity in cell sizes: (i) monotonically decreasing; (ii) uniform; (iii) non-monotonic; and (iv) monotonically increasing distributions of cell size. Comparing the density profiles generated by the three-species heterogeneous model with density profiles predicted by a more standard single-species homogeneous model reveals that when we are dealing with monotonically decreasing and uniform distributions a simple and computationally efficient single-species homogeneous model can be remarkably accurate in describing the evolution of a heterogeneous cell population. In contrast, we find that the simpler single-species homogeneous model performs relatively poorly when applied to non-monotonic and monotonically increasing distributions of cell sizes. Additional results for heterogeneity in parameters describing both undirected and directed cell migration are also considered, and we find that similar results apply.

miRNA-126 enhances viability, colony formation, and migration of keratinocytes HaCaT cells by regulating PI3 K/AKT signaling pathway

Wound healing is a basic biological process including proliferation and migration of keratinocyte. The effects of microRNAs on skin wound healing remain largely unexplored. This study aimed to investigate the role of microRNA-126 (miR-126) in human skin wound healing. Relative expression of miR-126 after injury was evaluated by qRT-PCR. Cell viability, colony formation, cycle distribution, migration, and the alternation of PI3 K/AKT pathway after miR-126 knockdown or overexpression were detected, respectively. In addition, potential target gene of miR-126 was also explored by luciferase assay. Results showed that miR-126 was up-regulated during skin wound healing. Moreover, overexpression of miR-126 promoted cell proliferation and migration, whereas inhibition of miR-126 led to the opposite effects. Additionally, we discovered that PLK2, which inhibited cell viability, colony formation and migration of keratinocyte, was a target gene of miR-126. The expression of PLK2 was negatively correlated with the level of miR-126 during wound healing. Finally, we demonstrated that overexpression of miR-126 significantly increased the expression of p-AKT, p-ERK2, and PI3 K, indicating that overexpression of miR-126 activated PI3 K/AKT signaling pathway. In conclusion, our results demonstrated that miR-126 acted as a critical regulator for promoting proliferation and migration in keratinocyte during skin wound healing.

In Vitro Wound Healing Assays to Investigate Epidermal Migration

Re-epithelialization after cutaneous injury is a complex and multifaceted process that incorporates numerous cellular components interacting in a myriad of pathways. One of the most crucial aspects of this process is the initiation of keratinocyte migration to fill the wound bed. Re-epithelialization involves both the individual and collective movement of epidermal cells under the control of integrated signaling paradigms. It is therefore essential to develop a simple methodology to dissect the basic movement of epidermal cells in vitro. Scratch assays are relatively simple experiments in which a single layer of cells are plated onto a prepared dish with multiple furrows created in the cell bed. The resulting cellular migration to fill the wound bed can then be imaged and processed quantitatively to investigate migration rates and other factors of interest. Here, we provide important adaptations to the classic scratch assay to make it a robust, reproducible, and quantitative tool for the evaluation of epidermal cell migration.

Myosin phosphatase accelerates cutaneous wound healing by regulating migration and differentiation of epidermal keratinocytes via Akt signaling pathway in human and murine skin

Wound healing is a complex sequence of cellular and molecular processes such as inflammation, cell migration, proliferation and differentiation. ROCK is a widely investigated Ser/Thr kinase with important roles in rearranging the actomyosin cytoskeleton. ROCK inhibitors have already been approved to improve corneal endothelial wound healing. The purpose of this study was to investigate the functions of myosin phosphatase (MP or PPP1CB), a type-1 phospho-Ser/Thr-specific protein phosphatase (PP1), one of the counter enzymes of ROCK, in skin homeostasis and wound healing. To confirm our hypotheses, we applied tautomycin (TM), a selective PP1 inhibitor, on murine skin that caused the arrest of wound closure. TM suppressed scratch closure of HaCaT human keratinocytes without having influence on the survival of the cells. Silencing of, the regulatory subunit of MP (MYPT1 or PPP1R12A), had a negative impact on the migration of keratinocytes and it influenced the cell-cell adhesion properties by decreasing the impedance of HaCaT cells. We assume that MP differentially activates migration and differentiation of keratinocytes and plays a key role in the downregulation of transglutaminase-1 in lower layers of skin where no differentiation is required. MAPK Proteome Profiler analysis on human ex vivo biopsies with MYPT1-silencing indicated that MP contributes to the mediation of wound healing by regulating the Akt signaling pathway. Our findings suggest that MP plays a role in the maintenance of normal homeostasis of skin and the process of wound healing.

INVESTIGATION OF A STRUCTURED FISHER'S EQUATION WITH APPLICATIONS IN BIOCHEMISTRY

Recent biological research has sought to understand how biochemical signaling pathways, such as the mitogen-activated protein kinase (MAPK) family, influence the migration of a population of cells during wound healing. Fisher's Equation has been used extensively to model experimental wound healing assays due to its simple nature and known traveling wave solutions. This partial differential equation with independent variables of time and space cannot account for the effects of biochemical activity on wound healing, however. To this end, we derive a structured Fisher's Equation with independent variables of time, space, and biochemical pathway activity level and prove the existence of a self-similar traveling wave solution to this equation. We exhibit that these methods also apply to a general structured reaction-diffusion equation and a chemotaxis equation. We also consider a more complicated model with different phenotypes based on MAPK activation and numerically investigate how various temporal patterns of biochemical activity can lead to increased and decreased rates of population migration.

Extended logistic growth model for heterogeneous populations

Cell proliferation is the most important cellular-level mechanism responsible for regulating cell population dynamics in living tissues. Modern experimental procedures show that the proliferation rates of individual cells can vary significantly within the same cell line. However, in the mathematical biology literature, cell proliferation is typically modelled using a classical logistic equation which neglects variations in the proliferation rate. In this work, we consider a discrete mathematical model of cell migration and cell proliferation, modulated by volume exclusion (crowding) effects, with variable rates of proliferation across the total population. We refer to this variability as heterogeneity. Constructing the continuum limit of the discrete model leads to a generalisation of the classical logistic growth model. Comparing numerical solutions of the model to averaged data from discrete simulations shows that the new model captures the key features of the discrete process. Applying the extended logistic model to simulate a proliferation assay using rates from recent experimental literature shows that neglecting the role of heterogeneity can, at times, lead to misleading results.

Engineering Approaches to Study Cellular Decision Making

In their native environment, cells are immersed in a complex milieu of biochemical and biophysical cues. These cues may include growth factors, the extracellular matrix, cell-cell contacts, stiffness, and topography, and they are responsible for regulating cellular behaviors such as adhesion, proliferation, migration, apoptosis, and differentiation. The decision-making process used to convert these extracellular inputs into actions is highly complex and sensitive to changes both in the type of individual cue (e.g., growth factor dose/level, timing) and in how these individual cues are combined (e.g., homotypic/heterotypic combinations). In this review, we highlight recent advances in the development of engineering-based approaches to study the cellular decision-making process. Specifically, we discuss the use of biomaterial platforms that enable controlled and tailored delivery of individual and combined cues, as well as the application of computational modeling to analyses of the complex cellular decision-making networks.

Improved in vitro models for preclinical drug and formulation screening focusing on 2D and 3D skin and cornea constructs

The present overview deals with current approaches for the improvement of in vitro models for preclinical drug and formulation screening which were elaborated in a joint project at the Center of Pharmaceutical Engineering of the TU Braunschweig. Within this project a special focus was laid on the enhancement of skin and cornea models. For this reason, first, a computation-based approach for in silico modeling of dermal cell proliferation and differentiation was developed. The simulation should for example enhance the understanding of the performed 2D in vitro tests on the antiproliferative effect of hyperforin. A second approach aimed at establishing in vivo-like dynamic conditions in in vitro drug absorption studies in contrast to the commonly used static conditions. The reported Dynamic Micro Tissue Engineering System (DynaMiTES) combines the advantages of in vitro cell culture models and microfluidic systems for the emulation of dynamic drug absorption at different physiological barriers and, later, for the investigation of dynamic culture conditions. Finally, cryopreserved shipping was investigated for a human hemicornea construct. As the implementation of a tissue-engineering laboratory is time-consuming and cost-intensive, commercial availability of advanced 3D human tissue is preferred from a variety of companies. However, for shipping purposes cryopreservation is a challenge to maintain the same quality and performance of the tissue in the laboratory of both, the provider and the customer.

Fibroblasts promote the collective invasion of ameloblastoma tumor cells in a 3D coculture model

Ameloblastoma is a benign tumor of the odontogenic epithelium with several histological subtypes. All subtypes of ameloblastoma contain abundant stroma; the tumor cells invade collectively into the surrounding tissues without losing intratumor cell attachments. However, the molecular mechanisms mediating ameloblastoma invasion remain unclear. Here, we evaluated the functional significance of the interactions between ameloblastoma tumor cells and stromal fibroblasts on collective cellular invasion using a three-dimensional cultivation method, double-layered collagen gel hemisphere (DL-CGH) culture. The AM-1 plexiform and AM-3 follicular human ameloblastoma cell lines and HFF-2 human fibroblasts were labeled with GFP and DsRed, respectively. Collective cellular invasion of ameloblastoma cells was assessed in the presence or absence of fibroblasts. Notably, without fibroblasts, AM-1 cells formed sharp, plexiform-like invasive processes, whereas AM-3 cells formed a series of blunt processes often observed during collective migration. In comparison, under the cocultures with HFF-2 fibroblasts, AM-3 cells formed tuft-like invasive processes and collectively invaded into outer layer more than that observed with AM-1 cells. Moreover, HFF-2 fibroblasts localized to the tips of the invasive tumor processes. These findings suggest that tumor-associated cells assist tumor cell invasion. Microscopic analysis of sectioned three-dimensional cultures revealed that AM-3/HFF-2 hemispheres were histologically similar to follicular ameloblastoma tumor samples. Therefore, our findings suggest that ameloblastoma subtypes exhibit distinct invasion patterns and that fibroblasts promote collective tumor invasion in follicular ameloblastoma.

Studying Electrotaxis in Microfluidic Devices

Collective cell migration is important in various physiological processes such as morphogenesis, cancer metastasis and cell regeneration. Such migration can be induced and guided by different chemical and physical cues. Electrotaxis, referring to the directional migration of adherent cells under stimulus of electric fields, is believed to be highly involved in the wound-healing process. Electrotactic experiments are conventionally conducted in Petri dishes or cover glasses wherein cells are cultured and electric fields are applied. However, these devices suffer from evaporation of the culture medium, non-uniformity of electric fields and low throughput. To overcome these drawbacks, micro-fabricated devices composed of micro-channels and fluidic components have lately been applied to electrotactic studies. Microfluidic devices are capable of providing cells with a precise micro-environment including pH, nutrition, temperature and various stimuli. Therefore, with the advantages of reduced cell/reagent consumption, reduced Joule heating and uniform and precise electric fields, microfluidic chips are perfect platforms for observing cell migration under applied electric fields. In this paper, I review recent developments in designing and fabricating microfluidic devices for studying electrotaxis, aiming to provide critical updates in this rapidly-growing, interdisciplinary field.

Review on experiment-based two- and three-dimensional models for wound healing

Traumatic and chronic wounds are a considerable medical challenge that affects many populations and their treatment is a monetary and time-consuming burden in an ageing society to the medical systems. Because wounds are very common and their treatment is so costly, approaches to reveal the responses of a specific wound type to different medical procedures and treatments could accelerate healing and reduce patient suffering. The effects of treatments can be forecast using mathematical modelling that has the predictive power to quantify the effects of induced changes to the wound-healing process. Wound healing involves a diverse and complex combination of biophysical and biomechanical processes. We review a wide variety of contemporary approaches of mathematical modelling of gap closure and wound-healing-related processes, such as angiogenesis. We provide examples of the understanding and insights that may be garnered using those models, and how those relate to experimental evidence. Mathematical modelling-based simulations can provide an important visualization tool that can be used for illustrational purposes for physicians, patients and researchers.