Open AccessMultiple Particle Tracking Detects Changes in Brain Extracellular Matrix and Predicts Neurodevelopmental Age
Author(s) -
Michael J. McKenna,
David B. Shackelford,
Hugo Pontes,
Brendan Ball,
Elizabeth Nance
Publication year - 2021
Publication title -
acs nano
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.554
H-Index - 382
eISSN - 1936-086X
pISSN - 1936-0851
DOI - 10.1021/acsnano.1c00394
Subject(s) - extracellular matrix , neuroscience , nanoparticle tracking analysis , extracellular , materials science , computer science , biology , microbiology and biotechnology , microvesicles , biochemistry , microrna , gene
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.