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Multiple-particle tracking (MPT) is a microscopy technique capable of simultaneously tracking hundreds to thousands of nanoparticles in a biological sample and has been used extensively to characterize biological microenvironments, including the brain extracellular space (ECS). Machine learning techniques have been applied to MPT data sets to predict the diffusion mode of nanoparticle trajectories as well as more complex biological variables, such as biological age. In this study, we develop a machine learning pipeline to predict and investigate changes to the brain ECS due to injury using supervised classification and feature importance calculations. We first validate the pipeline on three related but distinct MPT data sets from the living brain ECS—age differences, region differences, and enzymatic degradation of ECS structure. We predict three ages with 86% accuracy, three regions with 90% accuracy, and healthy versus enzyme-treated tissue with 69% accuracy. Since injury across groups is normally compared with traditional statistical approaches, we first used linear mixed effects models to compare features between healthy control conditions and injury induced by two different oxygen glucose deprivation exposure times. We then used machine learning to predict injury state using MPT features. We show that the pipeline predicts between the healthy control, 0.5 h OGD treatment, and 1.5 h OGD treatment with 59% accuracy in the cortex and 66% in the striatum, and identifies nonlinear relationships between trajectory features that were not evident from traditional linear models. Our work demonstrates that machine learning applied to MPT data is effective across multiple experimental conditions and can find unique biologically relevant features of nanoparticle diffusion. 

Protein vesicles made from bioactive proteins have potential value in drug delivery, biocatalysis, and as artificial cells. As the proteins are produced recombinantly, the ability to precisely tune the protein sequence provides control not possible with polymeric vesicles. The tunability and biocompatibility motivated this work to develop protein vesicles using rationally designed protein building blocks to investigate how protein sequence influences vesicle self-assembly and properties. We have reported an elastin-like polypeptide (ELP) fused to an arginine-rich leucine zipper (ZR) and functional, globular proteins fused to a glutamate-rich leucine zipper (ZE) that self-assemble into protein vesicles when warmed from 4 to 25 °C due to the hydrophobic transition of ELP. Previously, we demonstrated the ability to tune vesicle properties by changing protein and salt concentration, ZE:ZR ratio, and warming rate. However, there is a limit to the properties that can be achieved via assembly conditions. In order to access a wider range of vesicle diameter and stability profiles, this work investigated how modifiying the hydrophobicity and length of the ELP sequence influenced self-assembly and the final properties of protein vesicles using mCherry as a model globular protein. The results showed that both transition temperature and diameter of protein vesicles were inversely correlated to the ELP guest residue hydrophobicity and the number of ELP pentapeptide repeats. Additionally, sequence manipulation enabled assembly of vesicles with properties not accessible by changes to assembly conditions. For example, introduction of tyrosine at 5 guest residue positions in ELP enabled formation of nanoscale vesicles stable at physiological salt concentration. This work yields design guidelines for modifying the ELP sequence to manipulate protein vesicle transition temperature, size and stability to achieve desired properties for particular biofunctional applications. 

Abstract 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.