High-throughput automated olfactory phenotyping of group-housed mice
Behavioural phenotyping of mice is often compromised by manual interventions of the experimenter and limited throughput. Here, we describe a fully automated behaviour setup that allows for quantitative analysis of mouse olfaction with minimized experimenter involvement. Mice are group-housed and tag...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
17 December 2019
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| In: |
Frontiers in behavioral neuroscience
Year: 2019, Volume: 13 |
| ISSN: | 1662-5153 |
| DOI: | 10.3389/fnbeh.2019.00267 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.3389/fnbeh.2019.00267 Verlag, kostenfrei, Volltext: https://www.frontiersin.org/articles/10.3389/fnbeh.2019.00267/full 
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| Author Notes: | Janine K. Reinert, Andreas T. Schaefer and Thomas Kuner |
| Summary: | Behavioural phenotyping of mice is often compromised by manual interventions of the experimenter and limited throughput. Here, we describe a fully automated behaviour setup that allows for quantitative analysis of mouse olfaction with minimized experimenter involvement. Mice are group-housed and tagged with unique RFID chips. They can freely initiate trials and are automatically trained on a go/no-go task, learning to distinguish a rewarded from an unrewarded odour. Further, odour discrimination tasks and detailed training aspects can be set for each animal individually for automated execution without direct experimenter intervention. The procedure described here, from initial RFID implantation to discrimination of complex odour mixtures at high accuracy, can be completed within less than 2 months with cohorts of up to 25 male mice. Apart from the presentation of monomolecular odours, the setup can generate arbitrary mixtures and dilutions from any set of odours to create complex stimuli, enabling demanding behavioural analyses at high-throughput. |
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| Item Description: | Gesehen am 05.03.2020 |
| Physical Description: | Online Resource |
| ISSN: | 1662-5153 |
| DOI: | 10.3389/fnbeh.2019.00267 |