Coculture strategies in bone tissue engineering: the impact of culture conditions on pluripotent stem cell populations

The use of pluripotent stem cell populations for bone tissue regeneration provides many opportunities and challenges within the bone tissue engineering field. For example, coculture strategies have been utilized to mimic embryological development of bone tissue, and particularly the critical intercellular signaling pathways. While research in bone biology over the last 20 years has expanded our understanding of these intercellular signaling pathways, we still do not fully understand the impact of the system's physical characteristics (orientation, geometry, and morphology). This review of coculture literature delineates the various forms of coculture systems and their respective outcomes when applied to bone tissue engineering. To understand fully the key differences between the different coculture methods, we must appreciate the underlying paradigms of physiological interactions. Recent advances have enabled us to extrapolate these techniques to larger dimensions and higher geometric resolutions. Finally, the contributions of bioreactors, micropatterned biomaterials, and biomaterial interaction platforms are evaluated to give a sense of the sophistication established by a combination of these concepts with coculture systems.

Nutrient diffusion and simple nth-order consumption in regenerative tissue and biocatalytic sensors

This contribution addresses intra-tissue molar density profiles for nutrients, oxygen, growth factors, and other essential ingredients that anchorage-dependent cells require for successful proliferation on biocompatible surfaces. One-dimensional transient and steady state models of the reaction-diffusion equation are solved to correct a few deficiencies in the first illustrative example of diffusion and zeroth-order rates of consumption in tissues with rectangular geometry, as discussed in Ref. [(Griffith and Swartz, 2006) 1]. The functional form of the molar density profile for each species depends on geometry and the magnitude of the species-specific intra-tissue Damköhler number. The tissue's central core is reactant starved at high consumption rates and low rates of intra-tissue diffusion when the Damköhler number exceeds its geometry-sensitive critical value. Ideal tissue engineering designs avoid the diffusion-limited regime such that attached cells are exposed to all of the ingredients required for proliferation everywhere within a regenerative matrix. Analytical and numerical molar density profiles that satisfy the unsteady state modified diffusion equation with pseudo-homogeneous n(th)-order rates of intra-tissue consumption (i.e., n=0,1,2) allow one to (i) predict von Kármán-Pohlhausen mass transfer boundary layer thicknesses, measured inward from the external biomaterial surface toward its central core, and, most importantly, (ii) estimate the time required to achieve steady state conditions for regenerative tissue growth and biocatalytic sensing.

Lab-on-a-chip devices as an emerging platform for stem cell biology

The advent of stem cell based therapies has brought regenerative medicine into an increased focus as a part of the modern medicine practice, with a potential to treat a myriad of intractable diseases in the future. Stem cells reside in a complex microenvironment presenting them with a multitude of potential cues that are chemical, physical, and mechanical in nature. Conventional techniques used for experiments involving stem cells can only poorly mimic the physiological context, and suffer from imprecise spatial and temporal control, low throughput, lack of scalability and reproducibility, and poor representation of the mechanical and physical cell microenvironment. Novel lab-on-a-chip platforms, on the other hand, can much better mimic the complexity of in vivo tissue milieu and provide a greater control of the parameter variation in a high throughput and scalable manner. This capability may be especially important for understanding the biology and cementing the clinical potential of stem cell based therapies. Here we review microfabrication- and microfluidics-based approaches to investigating the complex biology of stem cell responses to changes in the local microenvironment. In particular, we categorize each method based on the types of controlled inputs it can have on stem cells, including soluble biochemical factors, extracellular matrix interactions, homotypic and heterotypic cell-cell signaling, physical cues (e.g. oxygen tension, pH, temperature), and mechanical forces (e.g. shear, topography, rigidity). Finally, we outline the methods to perform large scale observations of stem cell phenotypes and high-throughput screening of cellular responses to a combination of stimuli, and many new emerging technologies that are becoming available specifically for stem cell applications.

Mass Transport in a Microchannel Bioreactor With a Porous Wall

A two-dimensional flow model has been developed to simulate mass transport in a microchannel bioreactor with a porous wall. A two-domain approach, based on the finite volume method, was implemented. For the fluid part, the governing equation used was the Navier–Stokes equation; for the porous medium region, the generalized Darcy–Brinkman–Forchheimer extended model was used. For the porous-fluid interface, a stress jump condition was enforced with a continuity of normal stress, and the mass interfacial conditions were continuities of mass and mass flux. Two parameters were defined to characterize the mass transports in the fluid and porous regions. The porous Damkohler number is the ratio of consumption to diffusion of the substrates in the porous medium. The fluid Damkohler number is the ratio of the substrate consumption in the porous medium to the substrate convection in the fluid region. The concentration results were found to be well correlated by the use of a reaction-convection distance parameter, which incorporated the effects of axial distance, substrate consumption, and convection. The reactor efficiency reduced with reaction-convection distance parameter because of reduced reaction (or flux), and smaller local effectiveness factor due to the lower concentration in Michaelis–Menten type reactions. The reactor was more effective, and hence, more efficient with the smaller porous Damkohler number. The generalized results could find applications for the design of bioreactors with a porous wall.