Development of py-mea-flow for comprehensive analysis of multi-electrode array data and its application to LGI2-deficient neurons
- 1University of Cambridge
Abstract
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-span, 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-span command-line interface enables batch processing without writing Python code. As a proof of concept, we applied py-mea-span 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-span), and version-controlled at https://github.com/Molecularbiologyworld/py-mea-span. User feedback can be provided via the GitHub issues tab.
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BibTeX
@misc{han2026development,
title = {{Development of py-mea-flow for comprehensive analysis of multi-electrode array data and its application to LGI2-deficient neurons}},
author = {Z. Z. Han and J. E. Deane},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.14.759118},
url = {https://www.biorxiv.org/content/10.64898/2026.10.14.759118v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Development of py-mea-flow for comprehensive analysis of multi-electrode array data and its application to LGI2-deficient neurons
AU - Z. Z. Han
AU - J. E. Deane
PY - 2026
DA - 2026/10/14
PB - bioRxiv
DO - 10.64898/2026.10.14.759118
UR - https://www.biorxiv.org/content/10.64898/2026.10.14.759118v1
AB - 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-span, 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-span command-line interface enables batch processing without writing Python code. As a proof of concept, we applied py-mea-span 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-span), and version-controlled at https://github.com/Molecularbiologyworld/py-mea-span. User feedback can be provided via the GitHub issues tab.
ER -