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

Abstract

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.

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

“…Glial migration and proliferation in response to hypoxic-ischemic events [ 91 , 92 ], which we also show in our data in response to OGD, has been associated with both the down-regulation of ECM protein expression [ 93 , 94 ] and increased expression of ECM degrading matrix metalloproteinases [ 95 , 96 ]. Changes in brain ECM structure and composition are known to impact the extracellular diffusion of both small molecules [ 83 , 97 ] and nano-sized probes [ 19 , 98 ]. Future work focused on quantifying ECM protein expression through 14DIV and following OGD would provide clarity.…”

“…We have recently demonstrated that multiple particle tracking (MPT) is capable of detecting dynamic changes in brain ECM microstructure and quantifying both ECS transport properties and geometry with submicron resolution [ 19 ]. Therefore, MPT can be a viable option to study regional differences in the brain extracellular microenvironment.…”

Where Nanosensors Meet Machine Learning: Prospects and Challenges in Detecting Disease X

“…Another way is to sequentially combine each constructed model, such that a new model is trained with the knowledge of the entire error that the whole ensemble has learnt so farsuch as gradient boosting techniques (Figure E-ii) . For example, an extreme gradient boosted tree (XGBoost) algorithm trained on fluorescent microscopy tracking of hundreds to thousands of poly­(ethylene glycol)-coated polystyrene nanoparticles effectively predicted the neurodevelopmental age of rats with 86.64% accuracy by studying changes in the brain extracellular matrix . Overall, tree-based ensemble algorithms have immense potential in dealing with complicated feature relationships arising from concurrent monitoring of multiple biomarkers, due to their inherent ability to form robust and generalizable models.…”

Transport in the Brain Extracellular Space: Diffusion, but Which Kind?

“…Respectively, to the exploration of the brain’s ECS, SPT methods gave reconstruction of the ECS’s topology and characteristic distances between cell providing transport channels [ 43 , 44 , 45 , 46 , 47 ]. Another usage is the SPT-based data is related to the fact of mutual interchangeability of studying mean-squared displacement (MSD) of random walker exploring either one long track or the evolution of the concentration distribution of a large ensemble of markers in the case of classic diffusion process (ergodicity).…”

“…The MSD in this case directly gives the value of the diffusion coefficient. Thus, analysing tracks of single particles in different locations of the brain’s ECS, it is possible to get a map of the distribution of local diffusion coefficients, which as revealed widely varies across the ECS [ 45 , 47 , 48 , 49 ].…”