Key result
Python tools spikeNburst and nicespike enable robust, scalable analysis of high-density multi-electrode array recordings.
Why the study?
Analysing extensive HD-MEA datasets is challenging due to artefact removal, spike sorting, and accurate neuronal synchronization assessment limitations in existing methods.
The introduction of 'spikeNburst' and 'nicespike' provides a robust, scalable, and user-friendly framework for analyzing high-density multi-electrode array data in neural networks.
May aid preclinical neuronal research; leaves open translation to cardiovascular applications.
High-density multi-electrode arrays (HD-MEAs) enable the recording of in vitro neuronal activity with exceptional spatial and temporal resolution. However, analysing these extensive datasets presents challenges, such as artefact removal, spike sorting, and accurate assessments of neuronal synchronization. Here, we introduce a structured protocol for conducting comprehensive HD-MEA analyses using two Python-based tools: ‘ spikeNburst ’ and ‘ nicespike ’. This protocol provides a scalable and systematic approach to processing HD-MEA recordings, ensuring efficient data handling and robust analytical outcomes. The spikeNburst tool incorporates advanced methodologies for spike train filtering, burst and network burst detection, and synchronization analysis. Complementing this, we have implemented a full analysis pipeline in the nicespike tool, featuring GPU-accelerated spike sorting via template matching with Kilosort , enabling accurate identification of neuronal units across multiple electrodes. This protocol ensures more precise analyses by reducing redundancy and overestimation inherent in single-channel approaches. Moreover, both tools offer graphical user and command-line interfaces, ensuring accessibility for diverse user needs. We validated our protocol on in vitro neuronal culture recordings, demonstrating its suitability to identify somatic and dendritic features of neuronal units, characterize bursting behaviour, and quantify synchronization at both unit and network levels. By addressing critical limitations of existing methods, spikeNburst and nicespike provide a robust, scalable, and user-friendly framework for HD-MEA data analysis, enhancing the study of neural network dynamics and single-cell activity in detail.
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Wolff et al. (2025) studied In vitro neuronal activity. 'spikeNburst' and 'nicespike' software tools was evaluated on Identification of somatic and dendritic features, bursting behaviour, and synchronization. The Python-based tools 'spikeNburst' and 'nicespike' provide a robust and scalable framework for analyzing high-density multi-electrode array recordings of in vitro neuronal activity.
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