Federico Bolaños1, Jeff M LeDue1, Timothy H Murphy2. 1. Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Vancouver, British Columbia, V6T 1Z3, Canada; Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, V6T 1Z3, Canada. 2. Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Vancouver, British Columbia, V6T 1Z3, Canada; Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, V6T 1Z3, Canada. Electronic address: thmurphy@mail.ubc.ca.
Abstract
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.
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.
Authors: Timothy H Murphy; Nicholas J Michelson; Jamie D Boyd; Tony Fong; Luis A Bolanos; David Bierbrauer; Teri Siu; Matilde Balbi; Federico Bolanos; Matthieu Vanni; Jeff M LeDue Journal: Elife Date: 2020-05-15 Impact factor: 8.140