A Semi-Automated MEA Spike sorting (SAMS) method for high throughput assessment of cultured neurons

Abstract Neurons derived from human pluripotent stem cells (hPSCs) are valuable models for studying brain development and developing therapies for brain disorders. Evaluating human-derived neurons requires assessing their electrical activity, which can be achieved using multi-electrode arrays (MEAs) for extracellular recordings. Because each electrode channel generally detects activity from multiple neurons, resolving the activity of single neurons requires a process called spike sorting. However, currently available spike sorting methods are not optimized for the analysis of hPSC-derived neurons, and require complex workflows and time-consuming manual intervention. Here, we introduce a Semi-Automated MEA Spike sorting software (SAMS) designed specifically for low-density MEA recordings of cultured neurons. SAMS outperforms commercially available automated spike sorting algorithms in terms of accuracy and greatly reduces computational and human processing time. By providing an accessible, efficient, and integrated platform for spike sorting, SAMS enhances the resolution and utility of MEA in disease modeling and drug development using human-derived neurons. Highlights SAMS is designed and optimized for high throughput analysis of hPSC-derived neurons. SAMS is more efficient and accurate compared to recommended spike-sorting software. SAMS resolves phenotypic differences previously not observed without spike sorting. SAMS is an open-source software. Competing Interest Statement The authors have declared no competing interest.