The role of cell-matrix adhesion and cell migration in breast tumor growth and progression.

During breast cancer progression, there is typically increased collagen deposition resulting in elevated extracellular matrix rigidity. This results in changes to cell-matrix adhesion and cell migration, impacting processes such as the epithelial-mesenchymal transition (EMT) and metastasis. We aim to investigate the roles of cell-matrix adhesion and cell migration on breast tumor growth and progression by studying the impacts of different types of extracellular matrices and their rigidities. We embedded MCF7 spheroids within three-dimensional (3D) collagen matrices and agarose matrices. MCF7 cells adhere to collagen but not agarose. Contrasting the results between these two matrices allows us to infer the role of cell-matrix adhesion. We found that MCF7 spheroids exhibited the fastest growth rate when embedded in a collagen matrix with a rigidity of 5.1 kPa (0.5 mg/mL collagen), whereas, for the agarose matrix, the rigidity for the fastest growth rate is 15 kPa (1.0% agarose) instead. This discrepancy is attributable to the presence of cell adhesion molecules in the collagen matrix, which initiates collagen matrix remodeling and facilitates cell migration from the tumor through the EMT. As breast tumors do not adhere to agarose matrices, it is suitable to simulate the cell-cell interactions during the early stage of breast tumor growth. We conducted further analysis to characterize the stresses exerted by the expanding spheroid on the agarose matrix. We identified two distinct MCF7 cell populations, namely, those that are non-dividing and those that are dividing, which exerted low and high expansion stresses on the agarose matrix, respectively. We confirmed this using Western blot which showed the upregulation of proliferating cell nuclear antigen, a proliferation marker, in spheroids grown in the 1.0% agarose (≈13 kPa). By treating the embedded MCF7 spheroids with an inhibitor or activator of myosin contractility, we showed that the optimum spheroids' growth can be increased or decreased, respectively. This finding suggests that tumor growth in the early stage, where cell-cell interaction is more prominent, is determined by actomyosin tension, which alters cell rounding pressure during cell division. However, when breast tumors begin generating collagen into the surrounding matrix, collagen remodeling triggers EMT to promote cell migration and invasion, ultimately leading to metastasis.

ContrastivePose: A contrastive learning approach for self-supervised feature engineering for pose estimation and behavorial classification of interacting animals.

In recent years, supervised machine learning models trained on videos of animals with pose estimation data and behavior labels have been used for automated behavior classification. Applications include, for example, automated detection of neurological diseases in animal models. However, we identify two potential problems of such supervised learning approach. First, such models require a large amount of labeled data but the labeling of behaviors frame by frame is a laborious manual process that is not easily scalable. Second, such methods rely on handcrafted features obtained from pose estimation data that are usually designed empirically. In this paper, we propose to overcome these two problems using contrastive learning for self-supervised feature engineering on pose estimation data. Our approach allows the use of unlabeled videos to learn feature representations and reduce the need for handcrafting of higher-level features from pose positions. We show that this approach to feature representation can achieve better classification performance compared to handcrafted features alone, and that the performance improvement is due to contrastive learning on unlabeled data rather than the neural network architecture. The method has the potential to reduce the bottleneck of scarce labeled videos for training and improve performance of supervised behavioral classification models for the study of interaction behaviors in animals.

Importance of modelling hERG binding in predicting drug-induced action potential prolongations for drug safety assessment.

Reduction of the rapid delayed rectifier potassium current (I Kr) via drug binding to the human Ether-à-go-go-Related Gene (hERG) channel is a well recognised mechanism that can contribute to an increased risk of Torsades de Pointes. Mathematical models have been created to replicate the effects of channel blockers, such as reducing the ionic conductance of the channel. Here, we study the impact of including state-dependent drug binding in a mathematical model of hERG when translating hERG inhibition to action potential changes. We show that the difference in action potential predictions when modelling drug binding of hERG using a state-dependent model versus a conductance scaling model depends not only on the properties of the drug and whether the experiment achieves steady state, but also on the experimental protocols. Furthermore, through exploring the model parameter space, we demonstrate that the state-dependent model and the conductance scaling model generally predict different action potential prolongations and are not interchangeable, while at high binding and unbinding rates, the conductance scaling model tends to predict shorter action potential prolongations. Finally, we observe that the difference in simulated action potentials between the models is determined by the binding and unbinding rate, rather than the trapping mechanism. This study demonstrates the importance of modelling drug binding and highlights the need for improved understanding of drug trapping which can have implications for the uses in drug safety assessment.

Learning transmission dynamics modelling of COVID-19 using comomodels.

The COVID-19 epidemic continues to rage in many parts of the world. In the UK alone, an array of mathematical models have played a prominent role in guiding policymaking. Whilst considerable pedagogical material exists for understanding the basics of transmission dynamics modelling, there is a substantial gap between the relatively simple models used for exposition of the theory and those used in practice to model the transmission dynamics of COVID-19. Understanding these models requires considerable prerequisite knowledge and presents challenges to those new to the field of epidemiological modelling. In this paper, we introduce an open-source R package, comomodels, which can be used to understand the complexities of modelling the transmission dynamics of COVID-19 through a series of differential equation models. Alongside the base package, we describe a host of learning resources, including detailed tutorials and an interactive web-based interface allowing dynamic investigation of the model properties. We then use comomodels to illustrate three key lessons in the transmission of COVID-19 within R Markdown vignettes.

Integration of a microfluidic multicellular coculture array with machine learning analysis to predict adverse cutaneous drug reactions.

Adverse cutaneous reactions are potentially life-threatening skin side effects caused by drugs administered into the human body. The availability of a human-specific in vitro platform that can prospectively screen drugs and predict this risk is therefore of great importance to drug safety. However, since adverse cutaneous drug reactions are mediated by at least 2 distinct mechanisms, both involving systemic interactions between liver, immune and dermal tissues, existing in vitro skin models have not been able to comprehensively recapitulate these complex, multi-cellular interactions to predict the skin-sensitization potential of drugs. Here, we report a novel in vitro drug screening platform, which comprises a microfluidic multicellular coculture array (MCA) to model different mechanisms-of-action using a collection of simplistic cellular assays. The resultant readouts are then integrated with a machine-learning algorithm to predict the skin sensitizing potential of systemic drugs. The MCA consists of 4 cell culture compartments connected by diffusion microchannels to enable crosstalk between hepatocytes that generate drug metabolites, antigen-presenting cells (APCs) that detect the immunogenicity of the drug metabolites, and keratinocytes and dermal fibroblasts, which collectively determine drug metabolite-induced FasL-mediated apoptosis. A single drug screen using the MCA can simultaneously generate 5 readouts, which are integrated using support vector machine (SVM) and principal component analysis (PCA) to classify and visualize the drugs as skin sensitizers or non-skin sensitizers. The predictive performance of the MCA and SVM classification algorithm is then validated through a pilot screen of 11 drugs labelled by the US Food and Drug Administration (FDA), including 7 skin-sensitizing and 4 non-skin sensitizing drugs, using stratified 4-fold cross-validation (CV) on SVM. The predictive performance of our in vitro model achieves an average of 87.5% accuracy (correct prediction rate), 75% specificity (prediction rate of true negative drugs), and 100% sensitivity (prediction rate of true positive drugs). We then employ the MCA and the SVM training algorithm to prospectively identify the skin-sensitizing likelihood and mechanism-of-action for obeticholic acid (OCA), a farnesoid X receptor (FXR) agonist which has undergone clinical trials for non-alcoholic steatohepatitis (NASH) with well-documented cutaneous side effects.