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An approach to monitoring home-cage behavior in mice that facilitates data sharing

Nature Protocols volume 13, pages 13311347 (2018) | Download Citation

Abstract

Genetically modified mice are used as models for a variety of human behavioral conditions. However, behavioral phenotyping can be a major bottleneck in mouse genetics because many of the classic protocols are too long and/or are vulnerable to unaccountable sources of variance, leading to inconsistent results between centers. We developed a home-cage approach using a Chora feeder that is controlled by—and sends data to—software. In this approach, mice are tested in the standard cages in which they are held for husbandry, which removes confounding variables such as the stress induced by out-of-cage testing. This system increases the throughput of data gathering from individual animals and facilitates data mining by offering new opportunities for multimodal data comparisons. In this protocol, we use a simple work-for-food testing strategy as an example application, but the approach can be adapted for other experiments looking at, e.g., attention, decision-making or memory. The spontaneous behavioral activity of mice in performing the behavioral task can be monitored 24 h a day for several days, providing an integrated assessment of the circadian profiles of different behaviors. We developed a Python-based open-source analytical platform (Phenopy) that is accessible to scientists with no programming background and can be used to design and control such experiments, as well as to collect and share data. This approach is suitable for large-scale studies involving multiple laboratories.

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Acknowledgements

We thank the IT group at the IIT for technical support. This study was supported by the European Commission FP7 Programme under project 223263 (PhenoScale).

Author information

Author notes

    • Edoardo Balzani
    •  & Matteo Falappa

    These authors contributed equally to this work.

Affiliations

  1. Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genova, Italy.

    • Edoardo Balzani
    • , Matteo Falappa
    •  & Valter Tucci
  2. Dipartimento di Neuroscienze, Riabilitazione, Oftalmologia, Genetica e Scienze Materno-Infantili (DINOGMI), Università degli Studi di Genova, Genova, Italy.

    • Matteo Falappa
  3. Department of Psychology, Koç University, Istanbul, Turkey.

    • Fuat Balci
  4. Research Center for Translational Medicine, Koç University, Istanbul, Turkey.

    • Fuat Balci

Authors

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Contributions

E.B. and M.F. wrote Phenopy and performed the experiments and the analyses. F.B. designed the original timing analyses. V.T. conceived, planned and supervised the project, and wrote the manuscript with the help of all co-authors.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Valter Tucci.

Integrated supplementary information

Supplementary figures

  1. 1.

    Top view of CHORA feeders

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figure 1, Supplementary Methods and Supplementary Tables 1 and 2.

Zip files

  1. 1.

    Supplementary Data

    Recorded data and Python example scripts.

Videos

  1. 1.

    Running Phenopy.

    This video shows how to set up a behavioral experimental protocol using Chora feeders and Phenopy.

  2. 2.

    Data analysis example.

    This video shows how to import, export, edit and analyze data using Phenopy.

  3. 3.

    Importing functions.

    This video shows how to upload a new importation function into Phenopy.

  4. 4.

    Importing a new analysis.

    This video shows how to upload a new analysis function into Phenopy.

About this article

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DOI

https://doi.org/10.1038/nprot.2018.031

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