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Characterization of Early Cortical Neural Network Development in Multiwell Microelectrode Array Plates
Citation:
Cotterill, E., D. Hall, K. Wallace, W. Mundy, S. Eglen, AND Tim Shafer. Characterization of Early Cortical Neural Network Development in Multiwell Microelectrode Array Plates. Journal of Biomolecular Screening. SAGE Publications USA, Thousand Oaks, CA, 21(5):510-519, (2016).
Impact/Purpose:
This manuscript describes the ontogeny of neural network activity in multi-well microelectrode array plates containing 48 wells. In addition, it describes novel analysis methods for data obtained from these plates. It is important for 2 reasons. First, it demonstrates that neural network ontogeny can be described by multiple parameters when networks are grown in these plates and that the ontogeny is similar to that of other platforms. Second, it describes the ability of mathematical approaches to separate data obtained from different age cultures, and estimates the minimum number of wells needed to separate different treatments. Both of these reasons are important to the ultimate development of an assay to screen compounds for potential developmental neurotoxicity based on this technology.
Description:
We examined the development of neural network activity using microelectrode array (MEA) recordings made in multi-well MEA plates (mwMEAs) over the first 12 days in vitro (DIV). In primary cortical cultures made from postnatal rats, action potential spiking activity was essentially absent on DIV 2 and developed rapidly between DIV 5 and 12. Spiking activity was primarily sporadic and unorganized at early DIV, and became progressively more organized with time in culture, with bursting parameters, synchrony and network bursting increasing between DIV 5 and 12. We selected 12 features to describe network activity and principal components analysis using these features demonstrated a general segregation of data by age at both the well and plate levels. Using a combination of random forest classifiers and Support Vector Machines, we demonstrated that 4 features (CV of within burst ISI, CV of IBI, network spike rate and burst rate) were sufficient to predict the age (either DIV 5, 7, 9 or 12) of each well recording with >65% accuracy. When restricting the classification problem to a binary decision, we found that classification improved dramatically, e.g. 95% accuracy for discriminating DIV 5 vs DIV 12 wells. Further, we present a novel resampling approach to determine the number of wells that might be needed for conducting comparisons of different treatments using mwMEA plates. Overall, these results demonstrate that network development on mwMEA plates is similar to development in single well MEAs, and that the increased throughput of mwMEAs will facilitate screening drugs, chemicals or disease states for effects on neurodevelopment.
