Combinative in vitro studies and computational model to predict 3D cell migration response to drug insult

Metalloproteinases are involved in the regulation of prenatal tooth morphogenesis

During development, tooth germs undergo various morphological changes resulting from interactions between the oral epithelium and ectomesenchyme. These processes are influenced by the extracellular matrix, the composition of which, along with cell adhesion and signaling, is regulated by metalloproteinases. Notably, these include matrix metalloproteinases (MMPs), a disintegrin and metalloproteinases (ADAMs), and a disintegrin and metalloproteinases with thrombospondin motifs (ADAMTSs). Our analysis of previously published scRNAseq datasets highlight that these metalloproteinases show dynamic expression patterns during tooth development, with expression in a wide range of cell types, suggesting multiple roles in tooth morphogenesis. To investigate this, Marimastat, a broad-spectrum inhibitor of MMPs, ADAMs, and ADAMTSs, was applied to ex vivo cultures of mouse molar tooth germs. The treated samples exhibited significant changes in tooth germ size and morphology, including an overall reduction in size and an inversion of the typical bell shape. The cervical loop failed to extend, and the central area of the inner enamel epithelium protruded. Marimastat treatment also disrupted proliferation, cell polarization, and organization compared with control tooth germs. In addition, a decrease in laminin expression was observed, leading to a disruption in continuity of the basement membrane at the epithelial-mesenchymal junction. Elevated hypoxia-inducible factor 1-alpha gene (Hif-1α) expression correlated with a disruption to blood vessel development around the tooth germs. These results reveal the crucial role of metalloproteinases in tooth growth, shape, cervical loop elongation, and the regulation of blood vessel formation during prenatal tooth development.NEW & NOTEWORTHY Inhibition of metalloproteinases during tooth development had a wide-ranging impact on molar growth affecting proliferation, cell migration, and vascularization, highlighting the diverse role of these proteins in controlling development.

Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology

We provide an overview on the use of biological assays to calibrate and initialize mechanism-based models of cancer phenomena. Although artificial intelligence methods currently dominate the landscape in computational oncology, mathematical models that seek to explicitly incorporate biological mechanisms into their formalism are of increasing interest. These models can guide experimental design and provide insights into the underlying mechanisms of cancer progression. Historically, these models have included a myriad of parameters that have been difficult to quantify in biologically relevant systems, limiting their practical insights. Recently, however, there has been much interest calibrating biologically based models with the quantitative measurements available from (for example) RNA sequencing, time-resolved microscopy, and in vivo imaging. In this contribution, we summarize how a variety of experimental methods quantify tumor characteristics from the molecular to tissue scales and describe how such data can be directly integrated with mechanism-based models to improve predictions of tumor growth and treatment response.

Modeling the Leukemia Microenviroment In Vitro

Over the last decade, the active role of the microenvironment in the pathogenesis, development and drug resistance of B cell malignancies has been clearly established. It is known that the tissue microenvironment promotes proliferation and drug resistance of leukemic cells suggesting that successful treatments of B cell malignancies must target the leukemic cells within these compartments. However, the cross-talk occurring between cancer cells and the tissue microenvironment still needs to be fully elucidated. In solid tumors, this lack of knowledge has led to the development of new and more complex in vitro models able to successfully mimic the in vivo settings, while only a few simplified models are available for haematological cancers, commonly relying only on the co-culture with stabilized stromal cells and/or the addition of limited cocktails of cytokines. Here, we will review the known cellular and molecular interactions occurring between monoclonal B lymphocytes and their tissue microenvironment and the current literature describing innovative in vitro models developed in particular to study chronic lymphocytic leukemia (CLL). We will also elaborate on the possibility to further improve such systems based on the current knowledge of the key molecules/signals present in the microenvironment. In particular, we think that future models should be developed as 3D culture systems with a higher level of cellular and molecular complexity, to replicate microenvironmental-induced signaling. We believe that innovative 3D-models may therefore improve the knowledge on pathogenic mechanisms leading to the dissemination and homing of leukemia cells and consequently the identification of therapeutic targets.

Computational models of migration modes improve our understanding of metastasis

Tumor cells migrate through changing microenvironments of diseased and healthy tissue, making their migration particularly challenging to describe. To better understand this process, computational models have been developed for both the ameboid and mesenchymal modes of cell migration. Here, we review various approaches that have been used to account for the physical environment's effect on cell migration in computational models, with a focus on their application to understanding cancer metastasis and the related phenomenon of durotaxis. We then discuss how mesenchymal migration models typically simulate complex cell–extracellular matrix (ECM) interactions, while ameboid migration models use a cell-focused approach that largely ignores ECM when not acting as a physical barrier. This approach greatly simplifies or ignores the mechanosensing ability of ameboid migrating cells and should be reevaluated in future models. We conclude by describing future model elements that have not been included to date but would enhance model accuracy.

Matrix degradation regulates osteoblast protrusion dynamics and individual migration

Protrusions are one of the structures that cells use to sense their surrounding environment in a probing and exploratory manner as well as to communicate with other cells. In particular, osteoblasts embedded within a 3D matrix tend to originate a large number of protrusions compared to other type of cells. In this work, we study the role that mechanochemical properties of the extracellular matrix (ECM) play on the dynamics of these protrusions, namely, the regulation of the size and number of emanating structures. In addition, we also determine how the dynamics of the protrusions may lead the 3D movement of the osteoblasts. Significant differences were found in protrusion size and cell velocity, when degradation activity due to metalloproteases was blocked by means of an artificial broad-spectrum matrix metalloproteinase inhibitor, whereas stiffening of the matrix by introducing transglutaminase crosslinking, only induced slight changes in both protrusion size and cell velocity, suggesting that the ability of cells to create a path through the matrix is more critical than the matrix mechanical properties themselves. To confirm this, we developed a cell migration computational model in 3D including both the mechanical and chemical properties of the ECM as well as the protrusion mechanics, obtaining good agreement with experimental results.

A stochastic algorithm for accurately predicting path persistence of cells migrating in 3D matrix environments

Cell mobility plays a critical role in immune response, wound healing, and the rate of cancer metastasis and tumor progression. Mobility within a three-dimensional (3D) matrix environment can be characterized by the average velocity of cell migration and the persistence length of the path it follows. Computational models that aim to predict cell migration within such 3D environments need to be able predict both of these properties as a function of the various cellular and extra-cellular factors that influence the migration process. A large number of models have been developed to predict the velocity of cell migration driven by cellular protrusions in 3D environments. However, prediction of the persistence of a cell's path is a more tedious matter, as it requires simulating cells for a long time while they migrate through the model extra-cellular matrix (ECM). This can be a computationally expensive process, and only recently have there been attempts to quantify cell persistence as a function of key cellular or matrix properties. Here, we propose a new stochastic algorithm that can simulate and analyze 3D cell migration occurring over days with a computation time of minutes, opening new possibilities of testing and predicting long-term cell migration behavior as a function of a large variety of cell and matrix properties. In this model, the matrix elements are generated as needed and stochastically based on the biophysical and biochemical properties of the ECM the cell migrates through. This approach significantly reduces the computational resources required to track and calculate cell matrix interactions. Using this algorithm, we predict the effect of various cellular and matrix properties such as cell polarity, cell mechanoactivity, matrix fiber density, matrix stiffness, fiber alignment, and fiber binding site density on path persistence of cellular migration and the mean squared displacement of cells over long periods of time.

Contributions of 3D Cell Cultures for Cancer Research

Cancer cell lines have contributed immensely in understanding the complex physiology of cancers. They are excellent material for studies as they offer homogenous samples without individual variations and can be utilised with ease and flexibility. Also, the number of assays and end-points one can study is almost limitless; with the advantage of improvising, modifying or altering several variables and methods. Literally, a new dimension to cancer research has been achieved by the advent of 3Dimensional (3D) cell culture techniques. This approach increased many folds the ways in which cancer cell lines can be utilised for understanding complex cancer biology. 3D cell culture techniques are now the preferred way of using cancer cell lines to bridge the gap between the 'absolute in vitro' and 'true in vivo'. The aspects of cancer biology that 3D cell culture systems have contributed include morphology, microenvironment, gene and protein expression, invasion/migration/metastasis, angiogenesis, tumour metabolism and drug discovery, testing chemotherapeutic agents, adaptive responses and cancer stem cells. We present here, a comprehensive review on the applications of 3D cell culture systems for these aspects of cancers. J. Cell. Physiol. 232: 2679-2697, 2017. © 2016 Wiley Periodicals, Inc.

Modeling, signaling and cytoskeleton dynamics: integrated modeling-experimental frameworks in cell migration

Cell migration is a complex and multistep process involved in homeostasis maintenance, morphogenesis, and disease development, such as cancer metastasis. Modeling cell migration and the relevant cytoskeleton dynamics have profound implications for studying fundamental development and disease diagnosis. This review focuses on some recent models of both cell migration and migration-related cytoskeleton dynamics, addressing issues such as the difference between amoeboid and mesenchymal migration modes, and between single-cell migration and collective cell migration. The review also highlights the computational integration among variable external cues, especially the biochemical and mechanical signaling that affects cell migration. Finally, we aim to identify the gaps in our current knowledge and potential strategies to develop integrated modeling-experimental frameworks for multiscale behavior integrating gene expression, cell signaling, mechanics, and multicellular dynamics. WIREs Syst Biol Med 2017, 9:e1365. doi: 10.1002/wsbm.1365 For further resources related to this article, please visit the WIREs website.