Interpenetrating Networks of Collagen and Hyaluronic Acid That Serve as In Vitro Tissue Models for Assessing Macromolecular Transport.

High-fidelity preclinical in vitro tissue models can reduce the failure rate of drugs entering clinical trials. Collagen and hyaluronic acid (HA) are major components of the extracellular matrix of many native tissues and affect therapeutic macromolecule diffusion and recovery through tissues. Although collagen and HA are commonly used in tissue engineering, the physical and mechanical properties of these materials are variable and depend highly on processing conditions. In this study, HA was chemically modified and crosslinked via hydrazone bonds to form interpenetrating networks of crosslinked HA (HAX) with collagen (Col). These networks enabled a wide range of mechanical properties, including stiffness and swellability, and microstructures, such as pore morphology and size, that can better recapitulate diverse tissues. We utilized these interpenetrating ColHAX hydrogels as in vitro tissue models to examine macromolecular transport and recovery for early-stage drug screening. Hydrogel formulations with varying collagen and HAX concentrations imparted different gel properties based on the ratio of collagen to HAX. These gels were stable and swelled up to 170% of their original mass, and the storage moduli of the ColHAX gels increased over an order of magnitude by increasing collagen and HA concentration. Interestingly, when HAX concentration was constant and collagen concentration increased, both the pore size and spatial colocalization of collagen and HA increased. HA in the system dominated the ζ-potentials of the gels. The hydrogel and macromolecule properties impacted the mass transport and recovery of lysozyme, ß-lactoglobulin, and bovine serum albumin (BSA) from the ColHAX gels─large molecules were largely impacted by mesh size, whereas small molecules were influenced primarily by electrostatic forces. Overall, the tunable properties demonstrated by the ColHAX hydrogels can be used to mimic different tissues for early-stage assays to understand drug transport and its relationship to matrix properties.

Concentration-dependent diffusion of unlabeled protein within an in vitro hyaluronic acid matrix.

Diffusion and movement of subcutaneously injected biologics and high-concentration immunoglobulin G (IgG) therapeutics away from the injection site and through the subcutaneous (SC) tissue may be concentration dependent. This possibility was confirmed by in situ measurement of diffusion coefficients of unlabeled bovine IgG in phosphate-buffered saline within an in vitro hyaluronic acid matrix that represents the SC electrostatic environment. Diffusion decreased from 2.67 to 0.05 × 10-7 cm2 /s when IgG concentration increased from 25 to 73 mg/mL. The results demonstrated that in situ detection of unlabeled proteins within an in vitro SC environment provides another useful tool for the preclinical characterization of injectable biologics.

Mechanistic Computational Modeling of Implantable, Bioresorbable Drug Release Systems.

Implantable, bioresorbable drug delivery systems offer an alternative to current drug administration techniques; allowing for patient-tailored drug dosage, while also increasing patient compliance. Mechanistic mathematical modeling allows for the acceleration of the design of the release systems, and for prediction of physical anomalies that are not intuitive and may otherwise elude discovery. This study investigates short-term drug release as a function of water-mediated polymer phase inversion into a solid depot within hours to days, as well as long-term hydrolysis-mediated degradation and erosion of the implant over the next few weeks. Finite difference methods are used to model spatial and temporal changes in polymer phase inversion, solidification, and hydrolysis. Modeling reveals the impact of non-uniform drug distribution, production and transport of H+ ions, and localized polymer degradation on the diffusion of water, drug, and hydrolyzed polymer byproducts. Compared to experimental data, the computational model accurately predicts the drug release during the solidification of implants over days and drug release profiles over weeks from microspheres and implants. This work offers new insight into the impact of various parameters on drug release profiles, and is a new tool to accelerate the design process for release systems to meet a patient specific clinical need.

Investigation of macromolecular transport through tunable collagen hyaluronic acid matrices.

Therapeutic macromolecules possess properties such as size and electrostatic charge that will dictate their transport through subcutaneous (SC) tissue and ultimate bioavailability and efficacy. To improve therapeutic design, platforms that systematically measure the transport of macromolecules as a function of both drug and tissue properties are needed. We utilize a Transwell chamber with tunable collagen-hyaluronic acid (ColHA) hydrogels as an in vitro model to determine mass transport of macromolecules using non-invasive UV spectroscopy. Increasing hyaluronic acid (HA) concentration from 0 to 2 mg/mL within collagen gels decreases the mass transport of five macromolecules independent of size and charge and results in a maximum decrease in recovery of 23.3% in the case of bovine immunoglobulin G (IgG). However, in a pure 10 mg/mL HA solution, negatively-charged macromolecules bovine serum albumin (BSA), ß-lactoglobulin (BLg), dextran (Dex), and IgG had drastically increased recovery by 20-40% compared to their performance in ColHA matrices. This result was different from the positively-charged macromolecule Lysozyme (Lys), which, despite its small size, showed reduced recovery by 3% in pure HA. These results demonstrate two distinct regimes of mass transport within our tissue model. In the presence of both collagen and HA, increasing HA concentrations decrease mass transport; however, in the absence of collagen, the high negative charge of HA sequesters and increases residence time of positively-charged macromolecules and decreases residence time of negatively-charged macromolecules. Through our approach, ColHA hydrogels serve as a platform for the systematic evaluation of therapeutic macromolecule transport as a function of molecular characteristics.

Multi-feature-Based Robust Cell Tracking.

Cell tracking algorithms have been used to extract cell counts and motility information from time-lapse images of migrating cells. However, these algorithms often fail when the collected images have cells with spatially and temporally varying features, such as morphology, position, and signal-to-noise ratio. Consequently, state-of-the-art algorithms are not robust or reliable because they require manual inputs to overcome the cell feature changes. To address these issues, we present a fully automated, adaptive, and robust feature-based cell tracking algorithm for the accurate detection and tracking of cells in time-lapse images. Our algorithm tackles measurement limitations twofold. First, we use Hessian filtering and adaptive thresholding to detect the cells in images, overcoming spatial feature variations among the existing cells without manually changing the input thresholds. Second, cell feature parameters are measured, including position, diameter, mean intensity, area, and orientation, and these parameters are simultaneously used to accurately track the cells between subsequent frames, even under poor temporal resolution. Our technique achieved a minimum of 92% detection and tracking accuracy, compared to 16% from Mosaic and Trackmate. Our improved method allows for extended tracking and characterization of heterogeneous cell behavior that are of particular interest for intravital imaging users.

A forward reconstruction, holographic method to overcome the lens effect during 3D detection of semi-transparent, non-spherical particles.

Suspensions of semi-transparent particles such as polystyrene microparticles are commonly used as model systems in the study of micro-rheology, biology, and microfluidics. Holography is a valuable tool that allows one to obtain 3-D information for particle position and orientation, but forward reconstruction techniques often struggle to infer this information accurately for semi-transparent spheroids with an O(1) aspect ratio, since the lens effect from the particle introduces complex patterns. We propose a reconstruction method that uses image moment information to generate a mask over the sharp patterns from the lens effect and gives reasonable estimation of the 3-D position and orientation of the particle. The method proposed in this work uses the average particle geometry information to determine the process parameters and identify the appropriate detection zone. The average detection error for zc is less than 25% of the average particle thickness, and the average errors in the in-plane and out-of-plane orientations ϕ and θ are 2° and 4°, respectively. Our method provides comparable accuracy in the detection of the particle center of mass (xc, yc, zc) and in-plane orientation ϕ as a recent forward reconstruction method for semi-transparent particles proposed by Byeon et al. (H. Byeon, T. Go and S. J. Lee, Appl. Opt., 2016, 54, 2106-2112; H. Byeon, T. Go and S. J. Lee, Opt. Express, 2016, 24, 598-610). This method provides a clearly defined framework for identifying the particle's out-of-plane tilt angle θ. We finally demonstrate the applicability of the method to opaque, slender (aspect ratio AR ≫ 1) particles by analyzing the 3-D motion of E. coli cells from holographic video footage.

In vitro measurement of concentration of unlabeled protein within a hyaluronic acid matrix.

There are currently more than 560 therapeutic monoclonal antibodies (mAbs) at various stages of research and clinical testing, including candidates for administration by subcutaneous (SC) injection. Preclinical studies based on in vitro measurements of high molecular weight proteins within simulated SC matrices are assisting laboratory studies of interactions of injectable biotherapeutic proteins within the SC environment in relation to bioavailability. We report a new method for directly measuring diffusion of unlabeled, high molecular weight proteins injected into an in vitro matrix that simulates the negatively charged environment of the SC. The matrix consists of 10 mg/ml HA in a repurposed cell culture chamber. The measurement consists of pipetting triplicate 20 µl protein samples into the matrix, placing the chamber in a laboratory scanner, activating tryptophan residues in the protein at 280 nm, and imaging the resulting protein fluorescence at 384 nm over a 0.5-4 h time period thus tracking protein movement. This facile approach enables mapping of protein concentration as a function of time and distance within the matrix, and determination of diffusion coefficients, D, within ±10%. Bovine IgG and BSA gave D = 2.3 ± 0.2*10-7 and 4.6 ± 0.2*10-7 cm2 /s at 24°C, respectively, for initial protein concentrations of 21 mg/mL.

Stable Thermally-Modulated Nanodroplet Ultrasound Contrast Agents.

Liquid perfluorocarbon-based nanodroplets are stable enough to be used in extravascular imaging, but provide limited contrast enhancement due to their small size, incompressible core, and small acoustic impedance mismatch with biological fluids. Here we show a novel approach to overcoming this limitation by using a heating-cooling cycle, which we will refer to as thermal modulation (TM), to induce echogenicity of otherwise stable but poorly echogenic nanodroplets without triggering a transient phase shift. We apply thermal modulation to high-boiling point tetradecafluorohexane (TDFH) nanodroplets stabilized with a bovine serum albumin (BSA) shell. BSA-TDFH nanodroplets with an average diameter under 300 nanometers showed an 11.9 ± 5.4 mean fold increase in echogenicity on the B-mode and a 13.9 ± 6.9 increase on the nonlinear contrast (NLC) mode after thermal modulation. Once activated, the particles maintained their enhanced echogenicity (p < 0.001) for at least 13 h while retaining their nanoscale size. Our data indicate that thermally modulated nanodroplets can potentially serve as theranostic agents or sensors for various applications of contrast-enhanced ultrasound.

Monitoring heterogeneity in therapeutic samples using Schlieren.

Antigen, antibodies, and other therapeutic biomolecule solutions are likely to undergo physical and chemical processes during their development, manufacturing, transport, and storage. This can induce internal stresses in the sample, resulting in aggregation, heterogeneities, and an overall reduction in the sample quality, e.g., freeze-thawing of samples for storage. Monitoring mixing is thus crucial to ensure homogeneity and consistency while further optimizing downstream processes. We present a simple and portable all-lens Schlieren setup to detect, visualize, and quantify heterogeneities in the protein/antigen or other pharmaceutical solutions during and after thawing in real-time. We illustrate the capabilities of the proposed method by visualizing and quantifying heterogeneities during the thawing of BSA and IgG in four different formulation buffers. The local concentration gradients in a thawing sample lead to light intensity variations which are captured using the Schlieren technique. The sample heterogeneity can then be quantified by relating these light intensity variations to concentration gradients. To this end, we first measure the refractive index of the sample solutions, which varies linearly with the sample concentration. This linear relation is then used to extract the concentration gradient field from the light intensity data. We establish the validity of the proposed approach by demonstrating its accuracy in measuring the diffusion coefficient of a diffusing interface. The portability of the setup and its applicability to a wide range of pharmaceutical solutions make this Schlieren-based technique suitable for monitoring the mixing, heterogeneity, and stability of pharmaceutical samples.

Estimation of the probability density function of random displacements from images.

We introduce an image-based algorithm to find the probability density function (PDF) of particle displacements from a sequence of images. Conventionally methods based on cross correlation (CC) of image ensembles estimate the standard deviation of an assumed Gaussian PDF from the width of the CC peak. These methods are subject to limiting assumptions that the particle intensity profile and distribution of particle displacements are both Gaussian. Here, we introduce an approach to image-based probability estimation of displacement (iPED) without making any assumptions about the shape of particles' intensity profile or the PDF of the displacements. In addition, we provide a statistical convergence criterion for iPED to achieve an accurate estimate of the underlying PDF. We compare iPED's performance with the previous CC method for both Gaussian and non-Gaussian particle intensity profiles undergoing Gaussian or non-Gaussian processes. We validate iPED using synthetic images and show that it accurately resolves the PDF of particle displacements with no underlying assumptions. Finally, we demonstrate the application of iPED to real experimental data sets and evaluate its performance. In conclusion, this work presents a method for the estimation of the probability density function of random displacements from images. This method is generalized and independent of any assumptions about the underlying process and is applicable to any moving objects of any arbitrary shape.

Hydrodynamic attraction of bacteria to gas and liquid interfaces.

Near an interface, the distribution of swimming microorganisms such as bacteria is distinguished from inert colloidal particles because of the interfacial hydrodynamics induced by swimming. In this work, we use nontumbling flagellated bacteria, Escherichia coli, to study cell distribution near gas and liquid interfaces and compare it to the case of a solid wall. For low-viscosity ratios such as gas interfaces, we observe a stronger cell accumulation compared to that near liquid and solid surfaces. This contradicts known theoretical predictions. Therefore, we develop a model based on Brownian dynamics, including hydrodynamic effects and short-range physiochemical interactions between bacteria and interfaces. This model explains our experimental findings and can predict cell distribution near clean and surfactant-contaminated interfaces. By considering higher order singularities, this study helps explain bacteria orientation, trajectories, and cell density.