Ameboid cell motility: A model and inverse problem, with an application to live cell imaging data

RAB1A regulates glioma cellular proliferation and invasion via the mTOR signaling pathway and epithelial-mesenchymal transition

Aim: We aimed at investigating the mechanism of RAB1A proliferation and invasion in gliomas. Materials & methods: Genome-wide expression profile data and immunohistochemistry were analyzed to assess RAB1A expression in gliomas. The Transwell assay, wound healing assay, brain slice coculture model, cellular fluorescence and intracranial xenograft model of nude mice were used to determine the proliferation and invasion of glioma cells. Results & conclusion: RAB1A was highly expressed in gliomas compared with normal brain tissue. The overall survival time of glioma patients with high RAB1A expression was significantly shortened. RAB1A regulated the activity of RAC1 by inhibiting the mTOR signaling pathway, affecting actin polymerization, cell morphology and cell polarity. RAB1A downregulation inhibited the epithelial-mesenchymal transition, proliferation and invasion of glioma cells.

A mechanical model for morphological response of endothelial cells under combined wall shear stress and cyclic stretch loadings

The shape and morphology of endothelial cells (ECs) lining the blood vessels are a good indicator for atheroprone and atheroprotected sites. ECs of blood vessels experience both wall shear stress (WSS) and cyclic stretch (CS). These mechanical stimuli influence the shape and morphology of ECs. A few models have been proposed for predicting the morphology of ECs under WSS or CS. In the present study, a mathematical cell population model is developed to simulate the morphology of ECs under combined WSS and CS conditions. The model considers the cytoskeletal filaments, cell-cell interactions, and cell-extracellular matrix interactions. In addition, the reorientation and polymerization of microfilaments are implemented in the model. The simulations are performed for different conditions: without mechanical stimuli, under pure WSS, under pure CS, and under combined WSS and CS. The results are represented as shape and morphology of ECs, shape index, and angle of orientation. The model is validated qualitatively and quantitatively with several experimental studies, and good agreement with experimental studies is achieved. To the best of our knowledge, it is the first model for predicting the morphology of ECs under combined WSS and CS condition. The model can be used to indicate the atheroprone regions of a patient's artery.

Probing cellular mechanoadaptation using cell-substrate de-adhesion dynamics: experiments and model

Physical properties of the extracellular matrix (ECM) are known to regulate cellular processes ranging from spreading to differentiation, with alterations in cell phenotype closely associated with changes in physical properties of cells themselves. When plated on substrates of varying stiffness, fibroblasts have been shown to exhibit stiffness matching property, wherein cell cortical stiffness increases in proportion to substrate stiffness up to 5 kPa, and subsequently saturates. Similar mechanoadaptation responses have also been observed in other cell types. Trypsin de-adhesion represents a simple experimental framework for probing the contractile mechanics of adherent cells, with de-adhesion timescales shown to scale inversely with cortical stiffness values. In this study, we combine experiments and computation in deciphering the influence of substrate properties in regulating de-adhesion dynamics of adherent cells. We first show that NIH 3T3 fibroblasts cultured on collagen-coated polyacrylamide hydrogels de-adhere faster on stiffer substrates. Using a simple computational model, we qualitatively show how substrate stiffness and cell-substrate bond breakage rate collectively influence de-adhesion timescales, and also obtain analytical expressions of de-adhesion timescales in certain regimes of the parameter space. Finally, by comparing stiffness-dependent experimental and computational de-adhesion responses, we show that faster de-adhesion on stiffer substrates arises due to force-dependent breakage of cell-matrix adhesions. In addition to illustrating the utility of employing trypsin de-adhesion as a biophysical tool for probing mechanoadaptation, our computational results highlight the collective interplay of substrate properties and bond breakage rate in setting de-adhesion timescales.

One-dimensional viscoelastic cell motility models

In this paper we consider a class of one-dimensional cell motility models with increasing complexities beginning with a kinematic model and ending with a model based on viscoelastic theory. In many of these models, we establish the existence of traveling cell solutions and show numerically that the solutions of the time-dependent problem converge to the traveling cell solutions as t → ∞. As a result, we are able to predict the eventual length and speed of the cell.

Cell physician: reading cell motion: a mathematical diagnostic technique through analysis of single cell motion

Cell motility is an essential phenomenon in almost all living organisms. It is natural to think that behavioral or shape changes of a cell bear information about the underlying mechanisms that generate these changes. Reading cell motion, namely, understanding the underlying biophysical and mechanochemical processes, is of paramount importance. The mathematical model developed in this paper determines some physical features and material properties of the cells locally through analysis of live cell image sequences and uses this information to make further inferences about the molecular structures, dynamics, and processes within the cells, such as the actin network, microdomains, chemotaxis, adhesion, and retrograde flow. The generality of the principals used in formation of the model ensures its wide applicability to different phenomena at various levels. Based on the model outcomes, we hypothesize a novel biological model for collective biomechanical and molecular mechanism of cell motion.

A sub-cellular viscoelastic model for cell population mechanics

Understanding the biomechanical properties and the effect of biomechanical force on epithelial cells is key to understanding how epithelial cells form uniquely shaped structures in two or three-dimensional space. Nevertheless, with the limitations and challenges posed by biological experiments at this scale, it becomes advantageous to use mathematical and 'in silico' (computational) models as an alternate solution. This paper introduces a single-cell-based model representing the cross section of a typical tissue. Each cell in this model is an individual unit containing several sub-cellular elements, such as the elastic plasma membrane, enclosed viscoelastic elements that play the role of cytoskeleton, and the viscoelastic elements of the cell nucleus. The cell membrane is divided into segments where each segment (or point) incorporates the cell's interaction and communication with other cells and its environment. The model is capable of simulating how cells cooperate and contribute to the overall structure and function of a particular tissue; it mimics many aspects of cellular behavior such as cell growth, division, apoptosis and polarization. The model allows for investigation of the biomechanical properties of cells, cell-cell interactions, effect of environment on cellular clusters, and how individual cells work together and contribute to the structure and function of a particular tissue. To evaluate the current approach in modeling different topologies of growing tissues in distinct biochemical conditions of the surrounding media, we model several key cellular phenomena, namely monolayer cell culture, effects of adhesion intensity, growth of epithelial cell through interaction with extra-cellular matrix (ECM), effects of a gap in the ECM, tensegrity and tissue morphogenesis and formation of hollow epithelial acini. The proposed computational model enables one to isolate the effects of biomechanical properties of individual cells and the communication between cells and their microenvironment while simultaneously allowing for the formation of clusters or sheets of cells that act together as one complex tissue.

A model of fibroblast motility on substrates with different rigidities

To function efficiently in the body, the biological cells must have the ability to sense the external environment. Mechanosensitivity toward the extracellular matrix was identified as one of the sensing mechanisms affecting cell behavior. It was shown experimentally that a fibroblast cell prefers locomoting over the stiffer substrate when given a choice between a softer and a stiffer substrate. In this article, we develop a discrete model of fibroblast motility with substrate-rigidity sensing. Our model allows us to understand the interplay between the cell-substrate sensing and the cell biomechanics. The model cell exhibits experimentally observed substrate rigidity sensing, which allows us to gain additional insights into the cell mechanosensitivity.

CellTrack: an open-source software for cell tracking and motility analysis

MOTIVATION

Cell motility is a critical part of many important biological processes. Automated and sensitive cell tracking is essential to cell motility studies where the tracking results can be used for diagnostic or curative decisions and where mathematical models can be developed to deepen our understanding of the mechanisms underlying cell motility.

RESULTS

We have developed CellTrack: a self-contained, extensible, and cross-platform software package for cell tracking and motility analysis. Besides the general purpose image enhancement, object segmentation and tracking algorithms, we have implemented a novel edge-based method for sensitive tracking of the cell boundaries, and constructed an ensemble of methods that achieves refined tracking results even under large displacements or deformations of the cells.

AVAILABILITY

CellTrack is an Open Source project and is freely available at http://db.cse.ohio-state.edu/CellTrack.

The large-scale digital cell analysis system: an open system for nonperturbing live cell imaging

The Large-Scale Digital Cell Analysis System (LSDCAS) was designed to provide a highly extensible open source live cell imaging system. Analysis of cell growth data has demonstrated a lack of perturbation in cells imaged using LSDCAS, through reference to cell growth data from cells growing in CO(2) incubators. LSDCAS consists of data acquisition, data management and data analysis software, and is currently a Core research facility at the Holden Comprehensive Cancer Center at the University of Iowa. Using LSDCAS analysis software, this report and others show that although phase-contrast imaging has no apparent effect on cell growth kinetics and viability, fluorescent image acquisition in the cell lines tested caused a measurable level of growth perturbation using LSDCAS. This report describes the current design of the system, reasons for the implemented design, and details its basic functionality. The LSDCAS software runs on the GNU/Linux operating system, and provides easy to use, graphical programs for data acquisition and quantitative analysis of cells imaged with phase-contrast or fluorescence microscopy (alone or in combination), and complete source code is freely available under the terms of the GNU Public Software License at the project website (http://lsdcas.engineering.uiowa.edu).

Clonal diversity in carcinomas: its implications for tumour progression and the contribution made to it by epithelial-mesenchymal transitions

The progression of tumours to malignancy is commonly considered to arise through lineal evolution, a process in which mutations conferring pro-oncogenic cellular phenotypes are acquired by a succession of ever-more dominant clones. However, this model is at odds with the persistent polyclonality observed in many cancers. We propose that an alternative mechanism for tumour progression, called interclonal cooperativity, is likely to play a role at stages of tumour progression when mutations cause microenvironmental changes, such as occur with epithelial-mesenchymal transitions (EMTs). Interclonal cooperativity occurs when cancer cell-cancer cell interactions produce an emergent malignant phenotype from individually non-malignant clones. In interclonal cooperativity, the oncogenic mutations occur in different clones within the tumour that complement each other and cooperate in order to drive progression. This reconciles the accepted genetic and evolutionary basis of cancers with the observed polyclonality in tumours. Here, we provide a conceptual basis for examining the importance of cancer cell-cancer cell interactions to the behaviour of tumours and propose specific mechanisms by which clonal diversity in tumours, including that provided by EMTs, can drive the progression of tumours to malignancy.

A statistical pattern recognition approach for determining cellular viability and lineage phenotype in cultured cells and murine bone marrow

BACKGROUND

Cellular binding of annexin V and membrane permeability to 7-aminoactinomycin D (7AAD) are important tools for studying apoptosis and cell death by flow cytometry. Combining viability markers with cell surface marker expression is routinely used to study various cell lineages. Current classification methods using strict thresholds, or "gates," on the fluorescent intensity of these markers are subjective in nature and may not fully describe the phenotypes of interest. We have developed objective criteria for phenotypic boundary recognition through the application of statistical pattern recognition. This task was achieved using artificial neural networks (ANNs) that were trained to recognize subsets of cells with known phenotypes, and then used to determine decision boundaries based on statistical measures of similarity. This approach was then used to test the hypothesis that erythropoietin (EPO) inhibits apoptosis and cell death in erythroid precursor cells in murine bone marrow.

METHODS

Our method was developed for classification of viability using an in vitro cell system and then applied to an ex vivo analysis of murine late-stage erythroid progenitors. To induce apoptosis and cell death in vitro, an EPO-dependent human leukemic cell line, UT-7(EPO) cells were incubated without recombinant human erythropoietin (rhEPO) for 72 h. Five different ANNs were trained to recognize live, apoptotic, and dead cells using a "known" subset of the data for training, and a K-fold cross validation procedure for error estimation. The ANNs developed with the in vitro system were then applied to classify cells from an ex vivo study of rhEPO treated mice. Tg197 (human tumor necrosis-alpha transgenic mice, a model of anemia of chronic disease) received a single s.c. dose of 10,000 U/kg rhEPO and femoral bone marrow was collected 1, 2, 4, and 8 days after dosing. Femoral bone marrow cells were stained with TER-119 PE, CD71 APC enable identification of erythroid precursors, and annexin V FITC and 7AAD to identify the apoptotic and dead cells. During classification forward and side angle light scatter were also input to all pattern recognition systems.

RESULTS

Similar decision boundaries between live, apoptotic, and dead cells were consistently identified by the neural networks. The best performing network was a radial basis function multi-perceptron that produced an estimated average error rate of 4.5% +/- 0.9%. Using these boundaries, the following results were reached: depriving UT-7(EPO) cells of rhEPO induced apoptosis and cell death while the addition of rhEPO rescued the cells in a dose-dependent manner. In vivo, treatment with rhEPO resulted in an increase of live erythroid cells in the bone marrow to 119.8% +/- 9.8% of control at the 8 day time point. However, a statistically significant transient increase in TER-119(+) CD71(+) 7AAD(+) dead erythroid precursors was observed at the 1 and 2 day time points with a corresponding decrease in TER-119(+) CD71(+) 7AAD(-) Annexin V(-) live erythroid precursors, and no change in the number of TER-119(+) CD71(+) annexin V(+) 7AAD(-) apoptotic erythroid precursors in the bone marrow.

CONCLUSIONS

A statistical pattern recognition approach to viability classification provides an objective rationale for setting decision boundaries between "positive" and "negative" intensity measures in cytometric data. Using this approach we have confirmed that rhEPO inhibits apoptosis and cell death in an EPO dependent cell line in vitro, but failed to do so in vivo, suggesting EPO may not act as a simple antiapoptotic agent in the bone marrow. Rather, homeostatic mechanisms may regulate the pharmacodynamic response to rhEPO.