Scratcher: An automated machine-vision tool for dissecting the neural basis of itch.

Itch or pruritus invokes a specific reflexive and repetitive directed nocifensive behavioural response, known as scratching. Recent decades have revealed neural circuits that are involved in the sensory and affective-motivational aspects of itch-induced scratching. However, most of these studies relied on manual subjective methods of quantifying scratching in laboratory mice and rats. Recent advances in deep learning have opened avenues for the development of computational tools to analyze animal behaviour in a reliable and automated manner. Further, combined with optogenetic and chemogenetic strategies, these tools can accelerate our understanding of neural circuits underlying itch and scratching. To that end, we have developed Scratcher, a GUI-based computational tool based on a real-time object detection algorithm that allows semi-supervised automated analysis of scratching behaviour in mice in a computationally inexpensive manner. We recorded chloroquine-induced acute itch as it developed, and determined the consequence of nail-trimming on acute-itch induced scratching with Scratcher. To probe the neural mechanisms underlying itch, we combined Scratcher with genetic circuit dissection using the Fos-TRAP mouse line. By targeting itch-activated neurons in the lateral parabrachial nucleus (LPBN) - a key brainstem hub for pruritic signal transmission - we demonstrated that LPBN activity modulates itch-evoked scratching. Together, we present a novel, easy-to-use computational tool to dissect molecular, cellular, and circuit mechanisms of itch and scratching.