Type I collagen hydrogels have been used successfully as three-dimensional substrates for cell culture and have shown promise as scaffolds for engineered tissues and tumors. A critical step in the development of collagen hydrogels as viable tissue mimics is quantitative characterization of hydrogel properties and their correlation with fabrication parameters, which enables hydrogels to be tuned to match specific tissues or fulfill engineering requirements. A significant body of work has been devoted to characterization of collagen I hydrogels; however, due to the breadth of materials and techniques used for characterization, published data are often disjoint and hence their utility to the community is reduced. This review aims to determine the parameter space covered by existing data and identify key gaps in the literature so that future characterization and use of collagen I hydrogels for research can be most efficiently conducted. This review is divided into three sections: (1) relevant fabrication parameters are introduced and several of the most popular methods of controlling and regulating them are described, (2) hydrogel properties most relevant for tissue engineering are presented and discussed along with their characterization techniques, (3) the state of collagen I hydrogel characterization is recapitulated and future directions are proposed. Ultimately, this review can serve as a resource for selection of fabrication parameters and material characterization methodologies in order to increase the usefulness of future collagenhydrogel-based characterization studies and tissue engineering experiments.

Endothelial cells lining blood vessels are exposed to various hemodynamic forces associated with blood flow. These include fluid shear, the tangential force derived from the friction of blood flowing across the luminal cell surface, tensile stress due to deformation of the vessel wall by transvascular flow, and normal stress caused by the hydrodynamic pressure differential across the vessel wall. While it is well known that these fluid forces induce changes in endothelial morphology, cytoskeletal remodeling, and altered gene expression, the effect of flow on endothelial organization within the context of the tumor microenvironment is largely unknown. Using a previously established microfluidic tumor vascular model, the objective of this study was to investigate the effect of normal (4 dyn/cm 2 ), low (1 dyn/cm 2 ), and high (10 dyn/cm 2 ) microvascular wall shear stress (WSS) on tumor-endothelial paracrine signaling associated with angiogenesis. It is hypothesized that high WSS will alter the endothelial phenotype such that vascular permeability and tumor-expressed angiogenic factors are reduced. Results demonstrate that endothelial permeability decreases as a function of increasing WSS, while co-culture with tumor cells increases permeability relative to mono-cultures. This response is likely due to shear stress-mediated endothelial cell alignment and tumor-VEGF-induced permeability. In addition, gene expression analysis revealed that high WSS (10 dyn/cm 2 ) significantly down-regulates tumor-expressed MMP9, HIF1, VEGFA, ANG1, and ANG2, all of which are important factors implicated in tumor angiogenesis. This result was not observed in tumor mono-cultures or static conditioned media experiments, suggesting a flow-mediated paracrine signaling mechanism exists with surrounding tumor cells that elicits a change in expression of angiogenic factors. Findings from this work have significant implications regarding low blood velocities commonly seen in the tumor vasculature, suggesting high shear stress-regulation of angiogenic activity is lacking in many vessels, thereby driving tumor angiogenesis.

Hyperpermeable tumor vessels are responsible for elevated interstitial fluid pressure and altered flow patterns within the tumor microenvironment. These aberrant hydrodynamic stresses may enhance tumor development by stimulating the angiogenic activity of endothelial cells lining the tumor vasculature. However, it is currently not known to what extent shear forces affect endothelial organization or paracrine signaling during tumor angiogenesis. The objective of this study was to develop a three-dimensional (3D), in vitro microfluidic tumor vascular model for coculture of tumor and endothelial cells under varying flow shear stress conditions. A central microchannel embedded within a collagen hydrogel functions as a single neovessel through which tumor-relevant hydrodynamic stresses are introduced and quantified using microparticle image velocimetry (μ-PIV). This is the first use of μ-PIV in a tumor representative, 3D collagen matrix comprised of cylindrical microchannels, rather than planar geometries, to experimentally measure flow velocity and shear stress. Results demonstrate that endothelial cells develop a confluent endothelium on the microchannel lumen that maintains integrity under physiological flow shear stresses. Furthermore, this system provides downstream molecular analysis capability, as demonstrated by quantitative RT-PCR, in which, tumor cells significantly increase expression of proangiogenic genes in response to coculture with endothelial cells under low flow conditions. This work demonstrates that the microfluidic in vitro cell culture model can withstand a range of physiological flow rates and permit quantitative measurement of wall shear stress at the fluid-collagen interface using μ-PIV optical flow diagnostics, ultimately serving as a versatile platform for elucidating the role of fluid forces on tumor-endothelial cross talk.

The topic of a-posteriori uncertainty quantification of Particle Image Velocimetry (PIV) data is recognized as crucial step to obtain accurate estimates of the uncertainty for a specific experiment. This is particularly relevant in the fluid dynamics community, especially when PIV measurements are employed for CFD data validation. In spite of the relevance of the subject, the first uncertainty quantification methods have been developed only in the last two years. The present work describes a comparative assessment of four approaches proposed in literature: the uncertainty surface method (Timmins et al, 2012), the particle disparity approach (Sciacchitano et al, 2013; called image-matching approach in this reference), the peak ratio approach (Charonko and Vlachos, 2013) and the correlation statistics method (Wieneke and Prevost, 2014). The analysis makes use of a data base produced during a dedicated experiment where the instantaneous velocity field is known with high accuracy by means of an independent and more accurate measurement system. Strengths and limitations of the four uncertainty quantification approaches observed within the different flow regimes and imaging conditions are discussed.