Abstract
We have developed a methodology to study particle adhesion in the microvascular environment using microfluidic, image-derived microvascular networks on a chip accompanied by Computational Fluid Dynamics (CFD) analysis of fluid flow and particle adhesion. Microfluidic networks, obtained from digitization of in vivo microvascular topology were prototyped using soft-lithography techniques to obtain semicircular cross sectional microvascular networks in polydimethylsiloxane (PDMS). Dye perfusion studies indicated the presence of well-perfused as well as stagnant regions in a given network. Furthermore, microparticle adhesion to antibody coated networks was found to be spatially non-uniform as well. These findings were broadly corroborated in the CFD analyses. Detailed information on shear rates and particle fluxes in the entire network, obtained from the CFD models, were used to show global adhesion trends to be qualitatively consistent with current knowledge obtained using flow chambers. However, in comparison with a flow chamber, this method represents and incorporates elements of size and complex morphology of the microvasculature. Particle adhesion was found to be significantly localized near the bifurcations in comparison with the straight sections over the entire network, an effect not observable with flow chambers. In addition, the microvascular network chips are resource effective by providing data on particle adhesion over physiologically relevant shear range from even a single experiment. The microfluidic microvascular networks developed in this study can be readily used to gain fundamental insights into the processes leading to particle adhesion in the microvasculature.