Reproducible and fully automated testing of nocifensive behavior in mice.

Publisher: Elsevier Inc Country of Publication: United States NLM ID: 9918227360606676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2667-2375 (Electronic) Linking ISSN: 26672375 NLM ISO Abbreviation: Cell Rep Methods Subsets: MEDLINE

Original Publication: [New York] : Elsevier Inc., [2021]-

Pain*/diagnosis ; Pain Threshold*/physiology; Mice ; Animals ; Pain Measurement ; Reproducibility of Results ; Behavior, Animal/physiology

Pain in rodents is often inferred from their withdrawal from noxious stimulation. Threshold stimulus intensity or response latency is used to quantify pain sensitivity. This usually involves applying stimuli by hand and measuring responses by eye, which limits reproducibility and throughput. We describe a device that standardizes and automates pain testing by providing computer-controlled aiming, stimulation, and response measurement. Optogenetic and thermal stimuli are applied using blue and infrared light, respectively. Precise mechanical stimulation is also demonstrated. Reflectance of red light is used to measure paw withdrawal with millisecond precision. We show that consistent stimulus delivery is crucial for resolving stimulus-dependent variations in withdrawal and for testing with sustained stimuli. Moreover, substage video reveals "spontaneous" behaviors for consideration alongside withdrawal metrics to better assess the pain experience. The entire process was automated using machine learning. RAMalgo (reproducible automated multimodal algometry) improves the standardization, comprehensiveness, and throughput of preclinical pain testing.
Competing Interests: Declaration of interests The authors have filed a US patent (18/371.847) for the device described in this work.
(Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Comment in: Cell Rep Methods. 2023 Dec 18;3(12):100668. doi: 10.1016/j.crmeth.2023.100668. (PMID: 38086383)

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Keywords: CP: neuroscience; artificial intelligence; automation; behavior; machine learning; nociception; optogenetics; pain; reproducibility; sensitivity; somatosensory

Date Created: 20231122 Date Completed: 20231221 Latest Revision: 20241020

20241020

PMC10783627

10.1016/j.crmeth.2023.100650

37992707