In this work, we describe the fabrication and working of a modular microsystem that recapitulates the functions of the "Neurovascular Unit". The microdevice comprised a vertical stack of a ...poly(dimethylsiloxane) (PDMS) neural parenchymal chamber separated by a vascular channel via a microporous polycarbonate (PC) membrane. The neural chamber housed a mixture of neurons (~4%), astrocytes (~95%), and microglia (~1%). The vascular channel was lined with a layer of rat brain microvascular endothelial cell line (RBE4). Cellular components in the neural chamber and vascular channel showed viability (>90%). The neural cells fired inhibitory as well as excitatory potentials following 10 days of culture. The endothelial cells showed diluted-acetylated low density lipoprotein (dil-a-LDL) uptake, expressed von Willebrand factor (vWF) and zonula occludens (ZO-1) tight junctions, and showed decreased Alexafluor™-conjugated dextran leakage across their barriers significantly compared with controls (p < 0.05). When the vascular layer was stimulated with TNF-α for 6 h, about 75% of resident microglia and astrocytes on the neural side were activated significantly (p < 0.05 compared to controls) recapitulating tissue-mimetic responses resembling neuroinflammation. The impact of this microsystem lies in the fact that this biomimetic neurovascular platform might not only be harnessed for obtaining mechanistic insights for neurodegenerative disorders, but could also serve as a potential screening tool for central nervous system (CNS) therapeutics in toxicology and neuroinfectious diseases.
We present a “mixed-methodology” based system-level modeling and simulation for biochemical assays in lab-on-a-chip (LoC) devices. The methodology uses a combination of numerical schemes and ...analytical approaches to simulate biological and physicochemical processes, specifically, an integral approach for fluid flow and electric field, method of lines (MOL) and two-compartment models for biochemical reactions, and Fourier series-based model for analyte mixing. The solution procedure begins with decomposing the LoC device into a system of inter-connected components (e.g., channels and junctions) and the models are solved in a network fashion. Models are developed to accurately capture the multi-physics (e.g., flow, mixing, and reaction) behavior of individual components. The assembly of the components is facilitated via exchange of fluid flux and Fourier series coefficients (or average concentration) of analytes between various components, which enables network solution of the models. The system models are validated against both experimental and numerical models on various biochemical assays (e.g. immunoassays and enzymatic reactions), showing significant computational speedup (100–10,000-fold depending on the assay) without appreciably compromising accuracy (<10% error relative to numerical analysis).
Existing microfluidic devices,
e.g.
parallel plate flow chambers, do not accurately depict the geometry of microvascular networks
in vivo
. We have developed a synthetic microvascular network (SMN) ...on a polydimethalsiloxane (PDMS) chip that can serve as an
in vitro
model of the bifurcations, tortuosities, and cross-sectional changes found in microvascular networks
in vivo
. Microvascular networks from a cremaster muscle were mapped using a modified Geographical Information System, and then used to manufacture the SMNs on a PDMS chip. The networks were cultured with bovine aortic endothelial cells (BAEC), which reached confluency 3–4 days after seeding. Propidium iodide staining indicated viable and healthy cells showing normal behavior in these networks. Anti-ICAM-1 conjugated 2-μm microspheres adhered to BAEC cells activated with TNF-α in significantly larger numbers compared to control IgG conjugated microspheres. This preferential adhesion suggests that cultured cells retain an intact cytokine response in the SMN. This microfluidic system can provide novel insight into characterization of drug delivery particles and dynamic flow conditions in microvascular networks.
Please cite this paper as: Prabhakarpandian, Wang, Rea‐Ramsey, Sundaram, Kiani, and Pant (2011). Bifurcations: Focal Points of Particle Adhesion in Microvascular Networks. Microcirculation. 18(5), ...380–389.
Objective: Particle adhesion in vivo is dependent on the microcirculation environment, which features unique anatomical (bifurcations, tortuosity, cross‐sectional changes) and physiological (complex hemodynamics) characteristics. The mechanisms behind these complex phenomena are not well understood. In this study, we used a recently developed in vitro model of microvascular networks, called SMN, for characterizing particle adhesion patterns in the microcirculation.
Methods: SMNs were fabricated using soft‐lithography processes followed by particle adhesion studies using avidin and biotin‐conjugated microspheres. Particle adhesion patterns were subsequently analyzed using CFD‐based modeling.
Results: Experimental and modeling studies highlighted the complex and heterogeneous fluid flow patterns encountered by particles in microvascular networks resulting in significantly higher propensity of adhesion (>1.5×) near bifurcations compared with the branches of the microvascular networks.
Conclusion: Bifurcations are the focal points of particle adhesion in microvascular networks. Changing flow patterns and morphology near bifurcations are the primary factors controlling the preferential adhesion of functionalized particles in microvascular networks. SMNs provide an in vitro framework for understanding particle adhesion.
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.
Development of novel carriers and optimization of their design parameters has led to significant advances in the field of targeted drug delivery. Since carrier shape has recently been recognized as ...an important design parameter for drug delivery, we sought to investigate how carrier shape influences their flow in the vasculature and their ability to target the diseased site. Idealized synthetic microvascular networks (SMNs) were used for this purpose since they closely mimic key physical aspects of real vasculature and at the same time offer practical advantages in terms of ease of use and direct observation of particle flow. The attachment propensities of surface functionalized spheres, elliptical/circular disks and rods with dimensions ranging from 1
μm to 20
μm were compared by flowing them through bifurcating SMNs. Particles of different geometries exhibited remarkably different adhesion propensities. Moreover, introduction of a bifurcation as opposed to the commonly used linear channel resulted in significantly different flow and adhesion behaviors, which may have important implications in correlating these results to
in vivo behavior. This study provides valuable information for design of carriers for targeted drug delivery.
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Direct numerical simulation of a dilute suspension offinite-volumespheres requires computation of the time-varying fluid field and updating the particle momenta and positions, taking into account the ...effects due to particle–particle collisions. Collision calculations are inherently an orderN2poperation, whereNpis the number of particles in the system. Typical simulations contain 105–106particles making the brute force computation of collisions prohibitively expensive. An alternative algorithm, based on molecular-dynamic-simulation strategies, is proposed in this paper. A second consideration in simulating a finite-volume particle suspension is how the particle forces should be coupled back into the fluid calculation (so-called reverse coupling). Careful consideration of the energy budget for the particle and fluid phases indicates that interpolation schemes for forward and reverse coupling must be symmetric in order to ensure proper behavior of the overall energy balance. Asymmetric interpolation schemes will lead to errors of one plus the error of the least accurate interpolation method per iteration. Of course, global errors may be much larger due to a cumulative effect of the systematic deviation.
Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new ...paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed toward solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology.
•Self-driving labs (SDLs) combine automated experiments with AI to direct them.•Synthetic biology provides a unique opportunity for the development of SDLs.•The investment advises careful consideration of the biological questions targeted.•Here, we discuss challenges and opportunities in creating SDLs for synthetic biology.
Prognoses of Traumatic Brain Injury (TBI) outcomes are neither easily nor accurately determined from clinical indicators. This is due in part to the heterogeneity of damage inflicted to the brain, ...ultimately resulting in diverse and complex outcomes. Using a data-driven approach on many distinct data elements may be necessary to describe this large set of outcomes and thereby robustly depict the nuanced differences among TBI patients’ recovery. In this work, we develop a method for modeling large heterogeneous data types relevant to TBI. Our approach is geared toward the probabilistic representation of mixed continuous and discrete variables with missing values. The model is trained on a dataset encompassing a variety of data types, including demographics, blood-based biomarkers, and imaging findings. In addition, it includes a set of clinical outcome assessments at 3, 6, and 12 months post-injury. The model is used to stratify patients into distinct groups in an unsupervised learning setting. We use the model to infer outcomes using input data, and show that the collection of input data reduces uncertainty of outcomes over a baseline approach. In addition, we quantify the performance of a likelihood scoring technique that can be used to self-evaluate the extrapolation risk of prognosis on unseen patients.
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