Han, Z. Z.; Deane, J. E.

Multi-electrode arrays (MEAs) generate mid- to high-throughput electrophysiological data widely used to characterise neural network activity in vitro. Downstream analysis of recordings from Axion Biosystems instruments, however, has long depended on the manufacturers closed-source pipeline (AxIS Navigator, NeuralMetric Tool, AxIS Metric Plotting Tool), limiting customisation and integration into flexible Python workflows. We present py-mea-flow, an open-source Python package providing end-to-end analysis of Axion Biosystems spk binary files. Our package enables spike extraction, inter-spike interval burst detection, network burst detection and spike synchrony. We also provide tools for preparation of publication-ready visualisations including raster plots with spike-detection-rate histograms, plate-layout heatmaps, time-course trajectories, single timepoint violin plots and statistical comparisons. Outputs reproduce the manufacturers NeuralMetric Tool reference with near perfect accuracy in all benchmarked metrics. A mea-flow command-line interface enables batch processing without writing Python code. As a proof of concept, we applied py-mea-flow to human iPSC-derived cortical neurons with CRISPRi-mediated LGI2 knockdown. This revealed a reproducible network hyperexcitability phenotype in which burst and network burst frequencies were elevated while spike synchrony remained unchanged. Availability and implementation: The package is distributed on PyPI (pip install py-mea-flow), and version-controlled at https://github.com/Molecularbiologyworld/py-mea-flow. User feedback can be provided via the GitHub issues tab.