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

Chronic pain affects over 20% of the adult population in the United States, posing a substantial 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 (hiPSC) sensory neurons and glia. This study aimed to identify responsive neuronal subtypes within these presumptively heterogeneous cultures. We induced an inflammation-like state using TNF-α and evaluated acute responses to nociceptor agonists/antagonist capsaicin and PF-05089771, which target TRVP1, and Nav1.7, respectively. By employing unsupervised learning, we labeled responsive cells based on changes in spike count and synchrony. We then used the labeled cells' baseline activity data to train and validate five classifiers, finding that a feed forward neural network yielded error values that were the most significant following pair-wise comparisons. The classifier achieved an 84% accuracy for classifying nociceptors in an unseen labeled culture. The notable accuracy suggests that machine learning techniques could be employed to enhance MEA-based neuronal phenotypic assays as cellular readouts (i.e. mean-firing rates) can be weighted based on a desired target cell (i.e. nociceptor). .