Multiple Particle Tracking Detects Changes in Brain Extracellular Matrix and Predicts Neurodevelopmental Age.
Multiple Particle Tracking Detects Changes in Brain Extracellular Matrix and Predicts Neurodevelopmental Age.
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DOI：
10.1021/acsnano.1c00394
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发表时间：
2021-05-25
期刊：
影响因子：
17.1
通讯作者：
Nance E
中科院分区：
文献类型：
--
作者：
McKenna M;Shackelford D;Ferreira Pontes H;Ball B;Nance E
脑细胞外基质（ECM）结构介导神经发育和功能的许多方面。因此，探索脑ECM的结构变化可以深入了解神经发育机制，损伤引起的神经功能丧失以及病理性衰老和神经系统疾病的有害影响。我们证明了使用多粒子跟踪（MPT）探测脑ECM微观结构变化的能力。我们在从14-70天大的大鼠中收集的器官型大鼠脑切片中对胶体稳定的聚苯乙烯纳米颗粒进行了MPT。我们的分析揭示了纳米颗粒在脑细胞外空间的扩散能力和年龄之间的反比关系。此外，在整个发育过程中，皮质中有效ECM孔径的分布向更小的孔转移。我们使用从纳米颗粒轨迹中提取的原始数据和特征来训练一个能够高精度预测实际年龄的增强决策树。总的来说，这项工作展示了MPT与机器学习相结合的实用性，用于测量大脑ECM结构的变化并预测相关的复杂特征，如实际年龄。这将有助于进一步了解脑ECM在发育和衰老中的作用，以及损伤导致异常神经元功能的具体机制。此外，这种方法有可能开发机器学习模型，能够根据大脑微环境微观结构的变化检测损伤的存在或指示损伤的程度。
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.
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影响因子：
5.8
作者：
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影响因子：
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DOI：
10.1073/pnas.1509323112
发表时间：
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影响因子：
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DOI：
10.1523/jneurosci.2980-06.2006
发表时间：
2006-10-18
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
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作者：
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通讯作者：
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