diff_classifier: Parallelization of multi-particle tracking video analyses

Organotypic whole hemisphere brain slice models to study the effects of donor age and oxygen-glucose-deprivation on the extracellular properties of cortical and striatal tissue

BACKGROUND

The brain extracellular environment is involved in many critical processes associated with neurodevelopment, neural function, and repair following injury. Organization of the extracellular matrix and properties of the extracellular space vary throughout development and across different brain regions, motivating the need for platforms that provide access to multiple brain regions at different stages of development. We demonstrate the utility of organotypic whole hemisphere brain slices as a platform to probe regional and developmental changes in the brain extracellular environment. We also leverage whole hemisphere brain slices to characterize the impact of cerebral ischemia on different regions of brain tissue.

RESULTS

Whole hemisphere brain slices taken from postnatal (P) day 10 and P17 rats retained viable, metabolically active cells through 14 days in vitro (DIV). Oxygen-glucose-deprivation (OGD), used to model a cerebral ischemic event in vivo, resulted in reduced slice metabolic activity and elevated cell death, regardless of slice age. Slices from P10 and P17 brains showed an oligodendrocyte and microglia-driven proliferative response after OGD exposure, higher than the proliferative response seen in DIV-matched normal control slices. Multiple particle tracking in oxygen-glucose-deprived brain slices revealed that oxygen-glucose-deprivation impacts the extracellular environment of brain tissue differently depending on brain age and brain region. In most instances, the extracellular space was most difficult to navigate immediately following insult, then gradually provided less hindrance to extracellular nanoparticle diffusion as time progressed. However, changes in diffusion were not universal across all brain regions and ages.

CONCLUSIONS

We demonstrate whole hemisphere brain slices from P10 and P17 rats can be cultured up to two weeks in vitro. These brain slices provide a viable platform for studying both normal physiological processes and injury associated mechanisms with control over brain age and region. Ex vivo OGD impacted cortical and striatal brain tissue differently, aligning with preexisting data generated in in vivo models. These data motivate the need to account for both brain region and age when investigating mechanisms of injury and designing potential therapies for cerebral ischemia.

Surfactants influence polymer nanoparticle fate within the brain

Drug delivery to the brain is limited by poor penetration of pharmaceutical agents across the blood-brain barrier (BBB), within the brain parenchyma, and into specific cells of interest. Nanotechnology can overcome these barriers, but its ability to do so is dependent on nanoparticle physicochemical properties including surface chemistry. Surface chemistry can be determined by a number of factors, including by the presence of stabilizing surfactant molecules introduced during the formulation process. Nanoparticles coated with poloxamer 188 (F68), poloxamer 407 (F127), and polysorbate 80 (P80) have demonstrated uptake in BBB endothelial cells and enhanced accumulation within the brain. However, the impact of surfactants on nanoparticle fate, and specifically on brain extracellular diffusion or intracellular targeting, must be better understood to design nanotherapeutics to efficiently overcome drug delivery barriers in the brain. Here, we evaluated the effect of the biocompatible and commonly used surfactants cholic acid (CHA), F68, F127, P80, and poly (vinyl alcohol) (PVA) on poly (lactic-co-glycolic acid)-poly (ethylene glycol) (PLGA-PEG) nanoparticle transport to and within the brain. The inclusion of these surfactant molecules decreases diffusive ability through brain tissue, reflecting the surfactant's role in encouraging cellular interaction at short length and time scales. After in vivo administration, PLGA-PEG/P80 nanoparticles demonstrated enhanced penetration across the BBB and subsequent internalization within neurons and microglia. Surfactants incorporated into the formulation of PLGA-PEG nanoparticles therefore represent an important design parameter for controlling nanoparticle fate within the brain.

Multiple Particle Tracking Detects Changes in Brain Extracellular Matrix and Predicts Neurodevelopmental Age

Brain extracellular matrix (ECM) structure mediates many aspects of neural development and function. Probing structural changes in brain ECM could thus provide insights into mechanisms of neurodevelopment, the loss of neural function in response to injury, and the detrimental effects of pathological aging and neurological disease. We demonstrate the ability to probe changes in brain ECM microstructure using multiple particle tracking (MPT). We performed MPT of colloidally stable polystyrene nanoparticles in organotypic rat brain slices collected from rats aged 14-70 days old. Our analysis revealed an inverse relationship between nanoparticle diffusive ability in the brain extracellular space and age. Additionally, the distribution of effective ECM pore sizes in the cortex shifted to smaller pores throughout development. We used the raw data and features extracted from nanoparticle trajectories to train a boosted decision tree capable of predicting chronological age with high accuracy. Collectively, this work demonstrates the utility of combining MPT with machine learning for measuring changes in brain ECM structure and predicting associated complex features such as chronological age. This will enable further understanding of the roles brain ECM play in development and aging and the specific mechanisms through which injuries cause aberrant neuronal function. Additionally, this approach has the potential to develop machine learning models capable of detecting the presence of injury or indicating the extent of injury based on changes in the brain microenvironment microstructure.

Microrheology for biomaterial design

Microrheology analyzes the microscopic behavior of complex materials by measuring the diffusion and transport of embedded particle probes. This experimental method can provide valuable insight into the design of biomaterials with the ability to connect material properties and biological responses to polymer-scale dynamics and interactions. In this review, we discuss how microrheology can be harnessed as a characterization method complementary to standard techniques in biomaterial design. We begin by introducing the core principles and instruments used to perform microrheology. We then review previous studies that incorporate microrheology in their design process and highlight biomedical applications that have been supported by this approach. Overall, this review provides rationale and practical guidance for the utilization of microrheological analysis to engineer novel biomaterials.

Nanoparticle-microglial interaction in the ischemic brain is modulated by injury duration and treatment

Cerebral ischemia is a major cause of death in both neonates and adults, and currently has no cure. Nanotechnology represents one promising area of therapeutic development for cerebral ischemia due to the ability of nanoparticles to overcome biological barriers in the brain. ex vivo injury models have emerged as a high-throughput alternative that can recapitulate disease processes and enable nanoscale probing of the brain microenvironment. In this study, we used oxygen-glucose deprivation (OGD) to model ischemic injury and studied nanoparticle interaction with microglia, resident immune cells in the brain that are of increasing interest for therapeutic delivery. By measuring cell death and glutathione production, we evaluated the effect of OGD exposure time and treatment with azithromycin (AZ) on slice health. We found a robust injury response with 0.5 hr of OGD exposure and effective treatment after immediate application of AZ. We observed an OGD-induced shift in microglial morphology toward increased heterogeneity and circularity, and a decrease in microglial number, which was reversed after treatment. OGD enhanced diffusion of polystyrene-poly(ethylene glycol) (PS-PEG) nanoparticles, improving transport and ability to reach target cells. While microglial uptake of dendrimers or quantum dots (QDs) was not enhanced after injury, internalization of PS-PEG was significantly increased. For PS-PEG, AZ treatment restored microglial uptake to normal control levels. Our results suggest that different nanoparticle platforms should be carefully screened before application and upon doing so; disease-mediated changes in the brain microenvironment can be leveraged by nanoscale drug delivery devices for enhanced cell interaction.

Predicting in situ nanoparticle behavior using multiple particle tracking and artificial neural networks

Predictive models of nanoparticle transport can drive design of nanotherapeutic platforms to overcome biological barriers and achieve localized delivery. In this paper, we demonstrate the ability of artificial neural networks to predict both nanoparticle properties, such as size and protein adsorption, and aspects of the brain microenvironment, such as cell internalization, viscosity, and brain region by using large (>100 000) trajectory datasets collected via multiple particle tracking in in vitro gel models of the brain and cultured organotypic brain slices. Our neural network achieved a 0.75 recall score when predicting gel viscosity based on trajectory datasets, compared to 0.49 using an obstruction scaling model. When predicting in situ nanoparticle size based on trajectory datasets, neural networks achieved a 0.90 recall score compared to 0.83 using an optimized Stokes-Einstein predictor. To distinguish between nanoparticles of different sizes in more complex nanoparticle mixtures, our neural network achieved up to a recall score of 0.85. Even in cases of more nuanced output variables where mathematical models are not available, such as protein adhesion, neural networks retained the ability to distinguish between particle populations (recall score of 0.89). These findings demonstrate how trajectory datasets in combination with machine learning techniques can be used to characterize the particle-microenvironment interaction space.