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.

中文翻译：

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大脑微环境中扩散的高保真预测


多粒子追踪 （MPT） 是一种显微镜技术，能够同时追踪生物样品中成百上千的纳米颗粒，并已广泛用于表征生物微环境，包括大脑细胞外空间 （ECS）。机器学习技术已应用于 MPT 数据集，以预测纳米粒子轨迹的扩散模式以及更复杂的生物变量，例如生物年龄。在这项研究中，我们开发了一个机器学习管道，使用监督分类和特征重要性计算来预测和研究由于受伤而对大脑 ECS 的影响。我们首先在来自活体大脑 ECS 的三个相关但不同的 MPT 数据集上验证了管道——年龄差异、区域差异和 ECS 结构的酶降解。我们以 86% 的准确率预测三个年龄，以 90% 的准确率预测三个区域，以 69% 的准确率预测健康与酶处理组织的对比。由于通常将各组伤害与传统统计方法进行比较，因此我们首先使用线性混合效应模型来比较健康控制条件与两种不同氧葡萄糖剥夺暴露时间诱导的损伤之间的特征。然后，我们使用机器学习通过 MPT 功能预测受伤状态。我们表明，管道在健康对照、0.5 小时 OGD 治疗和 1.5 小时 OGD 治疗之间以 59% 的准确率和纹状体的准确率 66% 进行预测，并识别了传统线性模型中不明显的轨迹特征之间的非线性关系。我们的工作表明，应用于 MPT 数据的机器学习在多种实验条件下都是有效的，并且可以发现纳米粒子扩散的独特生物学相关特征。