Flow shear stress regulates endothelial barrier function and expression of angiogenic factors in a 3D microfluidic tumor vascular model

Buchanan, Cara F.; Verbridge, Scott S.; Vlachos, Pavlos P.; Rylander, Marissa Nichole

doi:10.4161/19336918.2014.970001

Cited by 179 publications

(203 citation statements)

References 61 publications

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“…Buchanan reported increased secretion of pro-angiogenic factors when endothelial cells were co-cultured with MDA-MB-231 breast cancer cells [159,160]. However, higher shear stress (10 dyne cm rsif.royalsocietypublishing.org J. R. Soc.…”

Section: Peri-vascular Niche: Modelling Angiogenesis and Quiescence Omentioning

confidence: 99%

Tumour-on-a-chip: microfluidic models of tumour morphology, growth and microenvironment

Tsai

Trubelja

Shen

et al. 2017

J. R. Soc. Interface.

172

133

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Cancer remains one of the leading causes of death, albeit enormous efforts to cure the disease. To overcome the major challenges in cancer therapy, we need to have a better understanding of the tumour microenvironment (TME), as well as a more effective means to screen anti-cancer drug leads; both can be achieved using advanced technologies, including the emerging tumour-on-a-chip technology. Here, we review the recent development of the tumour-on-a-chip technology, which integrates microfluidics, microfabrication, tissue engineering and biomaterials research, and offers new opportunities for building and applying functional three-dimensional in vitro human tumour models for oncology research, immunotherapy studies and drug screening. In particular, tumour-on-a-chip microdevices allow well-controlled microscopic studies of the interaction among tumour cells, immune cells and cells in the TME, of which simple tissue cultures and animal models are not amenable to do. The challenges in developing the next-generation tumour-on-a-chip technology are also discussed.

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Section: Peri-vascular Niche: Modelling Angiogenesis and Quiescence Omentioning

confidence: 99%

Tumour-on-a-chip: microfluidic models of tumour morphology, growth and microenvironment

Tsai

Trubelja

Shen

et al. 2017

J. R. Soc. Interface.

172

133

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“…Matrix degrading enzymes present a natural decay and are produced by tumor cells so that the growth levels off in regions where the / MDE approaches the maximal volume fraction. As in (21), the extracellular matrix is non-motile; it is degraded by the net action of the enzymes and is continuously remodeled by tumor cells if there is enough space. Finally, the model points out to a dependence of diffusion coefficients on the ECM structure, a characteristic of heterogeneous ECM [71].…”

Section: Signaling Modelsmentioning

confidence: 99%

“…The mathematical model characterized by (21) has 14 parameters associated with the local dynamics of the paracrine signaling pathway and more than 50 % have to be estimated. Aiming to include the net effect of the matrix degrading enzymes on the tumor growth and on the extracellular matrix remodeling in such way that the amount of parameters is reduced, we consider a sub-class of the phase field tumor growth model developed in [77].…”

Section: Signaling Modelsmentioning

confidence: 99%

Toward Predictive Multiscale Modeling of Vascular Tumor Growth

Oden

Lima

Almeida

et al. 2015

Arch Computat Methods Eng

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New directions in medical and biomedical sciences have gradually emerged over recent years that will change the way diseases are diagnosed and treated and are leading to the redirection of medicine toward patientspecific treatments. We refer to these new approaches for studying biomedical systems as predictive medicine, a new version of medical science that involves the use of advanced computer models of biomedical phenomena, high-performance computing, new experimental methods for model data calibration, modern imaging technologies, cutting-edge numerical algorithms for treating large stochastic systems, modern methods for model selection, calibration, validation, verification, and uncertainty quantification, and new approaches for drug design and delivery, all based on predictive models. The methodologies are designed to study events at multiple scales, from genetic data, to sub-cellular signaling mechanisms, to cell interactions, to tissue physics and chemistry, to organs in living human subjects. The present document surveys work on the development and implementation of predictive models of vascular tumor growth, covering aspects of what might be called modeling-and-experimentally based computational oncology. The work described is that of a multi-institutional team, centered at ICES with strong participation by members at M. D. Anderson Cancer Center and University of Texas at San Antonio. This exposition covers topics on signaling models, cell and cell-interaction models, tissue models based on multi-species mixture theories, models of angiogenesis, and beginning work of drug effects. A number of new parallel computer codes for implementing finite-element methods, multi-level Markov Chain Monte Carlo sampling methods, data classification methods, stochastic PDE solvers, statistical inverse algorithms for model calibration and validation, models of events at different spatial and temporal scales is presented. Importantly, new methods for model selection in the presence of uncertainties fundamental to predictive medical science, are described which are based on the notion of Bayesian model plausibilities. Also, as part of this general approach, new codes for determining the sensitivity of model outputs to variations in model parameters are described that provide a basis for assessing the importance of model parameters and controlling and reducing the number of relevant model parameters. Model specific data is to be accessible through careful and model-specific platforms in the Tumor Engineering Laboratory. We describe parallel computer platforms on which large-scale calculations are run as well as specific time-marching algorithms needed to treat stiff systems encountered in some phase-field mixture models. We also cover new non-invasive imaging and data classification methods that provide in vivo data for model validation. The study concludes with a brief discussion of future work and open challenges.

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“…The τ w values of 0.0, 0.1, and 1.5 dyn/cm 2 corresponded with shear stress rates (γ) of 0, 1.2, and 180 s −1 , respectively. The upper τ w was chosen as it is physiologically similar to that found in postcapillary vessels, 27,29,30 the low τ w mimics physiological conditions such as fluid perfusion through tissues, 31,32 and the 0.0 dyn/cm 2 mimics current static in vitro experimental designs.…”

Section: ■ Experimental Sectionmentioning

confidence: 99%

Cellular Shuttles: Monocytes/Macrophages Exhibit Transendothelial Transport of Nanoparticles under Physiological Flow

Moore¹,

Hauser²,

Gruber

et al. 2017

ACS Appl. Mater. Interfaces

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A major hurdle in the development of biomedical nanoparticles (NP) is understanding how they interact with complex biological systems and navigate biological barriers to arrive at pathological targets. It is becoming increasingly evident that merely controlling particle physicochemical properties may not be sufficient to mediate particle biodistribution in dynamic environments. Thus, researchers are increasingly turning toward more complex but likewise more physiological in vitro systems to study particle−-cell/particle−system interactions. An emerging paradigm is to utilize naturally migratory cells to act as so-called "Trojan horses" or cellular shuttles. We report here the use of monocytes/macrophages to transport NP across a confluent endothelial cell layer using a microfluidic in vitro model. With a custom-built flow chamber, we showed that physiological shear stress, when compared to low flow or static conditions, increased NP uptake by macrophages. We further provided a mathematical explanation for the effect of flow on NP uptake, namely that the physical exposure times of NP to cells is dictated by shear stress (i.e., flow rate) and results in increased particle uptake under flow. This study was extended to a multicellular, hydrodynamic in vitro model. Because monocytes are cells that naturally translocate across biological barriers, we utilized a monocyte/macrophage cell line as cellular NP transporters across an endothelial layer. In this exploratory study, we showed that monocyte/macrophage cells adhere to an endothelial layer and dynamically interact with the endothelial cells. The monocytes/macrophages took up NP and diapedesed across the endothelial layer with NP accumulating within the cellular uropod. These data illustrate that monocytes/macrophages may therefore act as active shuttles to deliver particles across endothelial barriers.

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Flow shear stress regulates endothelial barrier function and expression of angiogenic factors in a 3D microfluidic tumor vascular model

Cited by 179 publications

References 61 publications

Tumour-on-a-chip: microfluidic models of tumour morphology, growth and microenvironment

Tumour-on-a-chip: microfluidic models of tumour morphology, growth and microenvironment

Toward Predictive Multiscale Modeling of Vascular Tumor Growth

Cellular Shuttles: Monocytes/Macrophages Exhibit Transendothelial Transport of Nanoparticles under Physiological Flow

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