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TitleCost effective raspberry pi-based radio frequency identification tagging of mice suitable for automated in vivo imaging.
Publication TypeJournal Article
Year of Publication2017
AuthorsBolaños, Federico, Jeff M. LeDue, and Timothy H. Murphy
JournalJ Neurosci Methods
Date Published2017 Jan 30
KeywordsAnimals, Automation, Laboratory, Behavior, Animal, Body Weight, Computers, Injections, Subcutaneous, Mice, Inbred C57BL, Mice, Transgenic, Radio Frequency Identification Device, Software, Wireless Technology

BACKGROUND: Automation of animal experimentation improves consistency, reduces potential for error while decreasing animal stress and increasing well-being. Radio frequency identification (RFID) tagging can identify individual mice in group housing environments enabling animal-specific tracking of physiological parameters.

NEW METHOD: We describe a simple protocol to radio frequency identification (RFID) tag and detect mice. RFID tags were injected sub-cutaneously after brief isoflurane anesthesia and do not require surgical steps such as suturing or incisions. We employ glass-encapsulated 125kHz tags that can be read within 30.2±2.4mm of the antenna. A raspberry pi single board computer and tag reader enable automated logging and cross platform support is possible through Python.

RESULTS: We provide sample software written in Python to provide a flexible and cost effective system for logging the weights of multiple mice in relation to pre-defined targets.

COMPARISON WITH EXISTING METHODS: The sample software can serve as the basis of any behavioral or physiological task where users will need to identify and track specific animals. Recently, we have applied this system of tagging to automated mouse brain imaging within home-cages.

CONCLUSIONS: We provide a cost effective solution employing open source software to facilitate adoption in applications such as automated imaging or tracking individual animal weights during tasks where food or water restriction is employed as motivation for a specific behavior.

Alternate JournalJ. Neurosci. Methods
PubMed ID27899319