Co-cultured sensory neuron classification using extracellular electrophysiology and machine learning approaches for enhancing analgesic screening

<i>Objective.</i>Chronic pain affects over 20% of the adult population in the United States, posing a substantial personal as well as economic burden and contributing to the ongoing opioid crisis. Effective, non-addictive chronic pain treatments are urgently needed. Traditional drug discovery methods have failed to identify novel, non-addictive compounds, highlighting the need for alternative approaches such as phenotypic screening. Our lab developed a phenotypic screening assay using extracellular electrophysiological recordings from co-cultures of human induced pluripotent stem cell sensory neurons and glia. This study aimed to identify responsive neuronal subtypes within these presumptively heterogeneous cultures.<i>Approach.</i>We induced an inflammation-like state using tumor necrosis factor alpha and evaluated acute responses to nociceptor agonist capsaicin, which targets transient receptor potential vanilloid-1. By employing unsupervised learning, we labeled responsive cells based on changes in mean firing rates (MFR). We then used the labeled cells' baseline activity to train and validate five classifiers<i>. Main results.</i>None of the classifiers outperformed the others in regards to accuracy. Nonetheless, an RUS-boosted ensemble of decision trees achieved an AUC-ROC of 0.877 classifying nociceptors in an unseen labeled culture.<i>Significance</i>. The notable accuracy suggests that machine learning techniques could be employed to enhance microelectrode array-based neuronal phenotypic assays as readouts (e.g. MFR) can be weighted based on target cell type (e.g. nociceptors).