An open-source, low-cost voluntary running activity tracking tool for in vivo rodent studies.

Autor: Deitzler GE; Department of Microbiology, Oregon State University, Corvallis, OR, United States of America., Bira NP; Collaborative Robotics and Intelligent Systems (CoRIS) Institute, Oregon State University, Corvallis, OR, United States of America., Davidson JR; Collaborative Robotics and Intelligent Systems (CoRIS) Institute, Oregon State University, Corvallis, OR, United States of America., David MM; Department of Microbiology, Oregon State University, Corvallis, OR, United States of America.; Department of Pharmaceutical Sciences, Oregon State University, Corvallis, OR, United States of America.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2022 Sep 09; Vol. 17 (9), pp. e0273865. Date of Electronic Publication: 2022 Sep 09 (Print Publication: 2022).
DOI: 10.1371/journal.pone.0273865
Abstrakt: In vivo rodent behavioral and physiological studies often benefit from measurement of general activity. However, many existing instruments necessary to track such activity are high in cost and invasive within home cages, some even requiring extensive separate cage systems, limiting their widespread use to collect data. We present here a low-cost open-source alternative that measures voluntary wheel running activity and allows for modulation and customization, along with a reproducible and easy to set-up code pipeline for setup and analysis in Arduino IDE and R. Our robust, non-invasive scalable voluntary running activity tracker utilizes readily accessible magnets, Hall effect sensors, and an Arduino microcontroller. Importantly, it can interface with existing rodent home cages and wheel equipment, thus eliminating the need to transfer the mice to an unfamiliar environment. The system was validated both for accuracy by a rotating motor used to simulate mouse behavior, and in vivo. Our recorded data is consistent with results found in the literature showing that the mice run between 3 to 16 kilometers per night, and accurately captures speed and distance traveled continuously on the wheel. Such data are critical for analysis of highly variable behavior in mouse models and allow for characterization of behavioral metrics such as general activity. This system provides a flexible, low-cost methodology, and minimizes the cost, infrastructure, and personnel required for tracking voluntary wheel activity.
Competing Interests: MMD has financial interests relative to the activity of Second Genome, and Second Genome could benefit from the outcomes of this research. This does not alter our adherence to PLOS ONE policies on sharing data and materials. The other authors have no conflicts of interest to declare that are relevant to the content of this article.
Databáze: MEDLINE
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