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.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Crabbe, J.C., Wahlsten, D. & Dudek, B.C. Genetics of mouse behavior: interactions with laboratory environment. Science 284, 1670–1672 (1999).
Almasy, L. & Blangero, J. Endophenotypes as quantitative risk factors for psychiatric disease: rationale and study design. Am. J. Med. Genet. 105, 42–44 (2001).
Gottesman, I.I. & Gould, T.D. The endophenotype concept in psychiatry: etymology and strategic intentions. Am. J. Psychiatry 160, 636–645 (2003).
Glahn, D.C. et al. Arguments for the sake of endophenotypes: examining common misconceptions about the use of endophenotypes in psychiatric genetics. Am. J. Med. Genet. B Neuropsychiatr. Genet. 165B, 122–130 (2014).
Kas, M.J. & Van Ree, J.M. Dissecting complex behaviours in the post-genomic era. Trends Neurosci. 27, 366–369 (2004).
Papaleo, F., Lipska, B.K. & Weinberger, D.R. Mouse models of genetic effects on cognition: relevance to schizophrenia. Neuropharmacology 62, 1204–1220 (2012).
Tucci, V., Lassi, G. & Kas, M.J. Current understanding of the interplay between catechol-O-methyltransferase genetic variants, sleep, brain development and cognitive performance in schizophrenia. CNS Neurol. Disord. Drug Targets 11, 292–298 (2012).
Papaleo, F., Burdick, M.C., Callicott, J.H. & Weinberger, D.R. Epistatic interaction between COMT and DTNBP1 modulates prefrontal function in mice and in humans. Mol. Psychiatry 19, 311–316 (2014).
Flint, J. & Munafo, M.R. The endophenotype concept in psychiatric genetics. Psychol. Med. 37, 163–180 (2007).
Munafo, M.R., Bowes, L., Clark, T.G. & Flint, J. Lack of association of the COMT (Val158/108 Met) gene and schizophrenia: a meta-analysis of case-control studies. Mol. Psychiatry 10, 765–770 (2005).
Bains, R.S. et al. Assessing mouse behaviour throughout the light/dark cycle using automated in-cage analysis tools. J. Neurosci. Methods 300, 37–47 (2017).
Lassi, G. et al. Loss of Gnas imprinting differentially affects REM/NREM sleep and cognition in mice. PLoS Genet. 8, e1002706 (2012).
Balzani, E. et al. The Zfhx3-mediated axis regulates sleep and interval timing in mice. Cell Rep. 16, 615–621 (2016).
Tucci, V. et al. Dominant beta-catenin mutations cause intellectual disability with recognizable syndromic features. J. Clin. Invest. 124, 1468–1482 (2014).
Lassi, G. et al. Working-for-food behaviors: a preclinical study in Prader-Willi mutant mice. Genetics 204, 1129–1138 (2016).
Maggi, S. et al. A cross-laboratory investigation of timing endophenotypes in mouse behavior. Timing Time Percept. 2, 35–50 (2014).
Ward, R.D., Kellendonk, C., Kandel, E.R. & Balsam, P.D. Timing as a window on cognition in schizophrenia. Neuropharmacology 62, 1175–1181 (2012).
Teixeira, S. et al. Time perception distortion in neuropsychiatric and neurological disorders. CNS Neurol. Disord. Drug Targets 12, 567–582 (2013).
Hinton, S.C. et al. Motor timing variability increases in preclinical Huntington's disease patients as estimated onset of motor symptoms approaches. J. Int. Neuropsychol. Soc. 13, 539–543 (2007).
Tinazzi, M. et al. Impaired temporal processing of tactile and proprioceptive stimuli in cerebellar degeneration. PLoS One 8, e78628 (2013).
Henley, S.M. et al. Degradation of cognitive timing mechanisms in behavioural variant frontotemporal dementia. Neuropsychologia 65, 88–101 (2014).
Crawley, J.N. Behavioral phenotyping strategies for mutant mice. Neuron 57, 809–818 (2008).
Mandillo, S. et al. Reliability, robustness, and reproducibility in mouse behavioral phenotyping: a cross-laboratory study. Physiol. Genomics 34, 243–255 (2008).
Hart, P.C. et al. Experimental models of anxiety for drug discovery and brain research. Methods Mol. Biol. 602, 299–321 (2010).
Rosenthal, R. & Fode, K. The effect of experimenter bias on performance of the albino rat. Behav. Sci. 8, 183–189 (1963).
Holman, L., Head, M.L., Lanfear, R. & Jennions, M.D. Evidence of experimental bias in the life sciences: why we need blind data recording. PLoS Biol. 13, e1002190 (2015).
Longordo, F., Fan, J., Steimer, T., Kopp, C. & Luthi, A. Do mice habituate to 'gentle handling?' A comparison of resting behavior, corticosterone levels and synaptic function in handled and undisturbed C57BL/6J mice. Sleep 34, 679–681 (2011).
Costa, R., Tamascia, M.L., Nogueira, M.D., Casarini, D.E. & Marcondes, F.K. Handling of adolescent rats improves learning and memory and decreases anxiety. J. Am. Assoc. Lab. Anim. Sci. 51, 548–553 (2012).
Tucci, V., Hardy, A. & Nolan, P.M. A comparison of physiological and behavioural parameters in C57BL/6J mice undergoing food or water restriction regimes. Behav. Brain Res. 173, 22–29 (2006).
Tucci, V. et al. Gene-environment interactions differentially affect mouse strain behavioral parameters. Mamm. Genome 17, 1113–1120 (2006).
Frick, K.M., Stearns, N.A., Pan, J.Y. & Berger-Sweeney, J. Effects of environmental enrichment on spatial memory and neurochemistry in middle-aged mice. Learn. Mem. 10, 187–198 (2003).
Jankowsky, J.L. et al. Environmental enrichment mitigates cognitive deficits in a mouse model of Alzheimer's disease. J. Neurosci. 25, 5217–5224 (2005).
Rampon, C. et al. Enrichment induces structural changes and recovery from nonspatial memory deficits in CA1 NMDAR1-knockout mice. Nat. Neurosci. 3, 238–244 (2000).
van Praag, H., Kempermann, G. & Gage, F.H. Neural consequences of environmental enrichment. Nat. Rev. Neurosci. 1, 191–198 (2000).
Tucci, V. et al. Reaching and grasping phenotypes in the mouse (Mus musculus): a characterization of inbred strains and mutant lines. Neuroscience 147, 573–582 (2007).
Balci, F., Freestone, D. & Gallistel, C.R. Risk assessment in man and mouse. Proc. Natl. Acad. Sci. USA 106, 2459–2463 (2009).
Balci, F. et al. High-throughput automated phenotyping of two genetic mouse models of Huntington's disease. PLoS Curr. 5 http://dx.doi.org/10.1371/currents.hd.124aa0d16753f88215776fba102ceb29 (2013).
Gallistel, C.R., Balci, F., Freestone, D., Kheifets, A. & King, A. Automated, quantitative cognitive/behavioral screening of mice: for genetics, pharmacology, animal cognition and undergraduate instruction. J. Vis. Exp. (84)e51047 http://dx.doi.org/10.3791/51047 (2014).
Cordes, S. & Gallistel, C.R. Intact interval timing in circadian CLOCK mutants. Brain Res. 1227, 120–127 (2008).
Perkel, J.M. Programming: pick up Python. Nature 518, 125–126 (2015).
Dunlap, J.C. Molecular bases for circadian clocks. Cell 96, 271–290 (1999).
Mohawk, J.A. & Takahashi, J.S. Cell autonomy and synchrony of suprachiasmatic nucleus circadian oscillators. Trends Neurosci. 34, 349–358 (2011).
Balci, F. et al. Interval timing in genetically modified mice: a simple paradigm. Genes Brain Behav. 7, 373–384 (2008).
Cheng, K., Westwood, R. & Crystal, J.D. Memory variance in the peak procedure of timing in pigeons. J. Exp. Psychol. Anim. Behav. Process. 19, 68–76 (1993).
Church, R.M., Meck, W.H. & Gibbon, J. Application of scalar timing theory to individual trials. J. Exp. Psychol. Anim. Behav. Process. 20, 135–155 (1994).
Balci, F. et al. Acquisition of peak responding: what is learned? Behav. Process. 80, 67–75 (2009).
Feldmann, H.M., Golozoubova, V., Cannon, B. & Nedergaard, J. UCP1 ablation induces obesity and abolishes diet-induced thermogenesis in mice exempt from thermal stress by living at thermoneutrality. Cell Metab. 9, 203–209 (2009).
Lassi, G. et al. Deletion of the Snord116/SNORD116 alters sleep in mice and patients with Prader-Willi syndrome. Sleep 39, 637–644 (2016).
Balzani, E. et al. The Zfhx3-mediated axis regulates sleep and interval timing in mice. Cell Rep. 16(3), 615–621 (2016).
Tosun, T., Gür, F. & Balci, F. Mice plan decision strategies based on previously learned time intervals, locations, and probabilities. Proc. Natl. Acad. Sci. USA 113, 787–792 (2016).
Rossi, M.A. & Yin, H.H. Methods for studying habitual behavior in mice. Curr. Protoc. Neurosci. Chap. 8, Unit 8.29 (2012).
Aquili, L. et al. Behavioral flexibility is increased by optogenetic inhibition of neurons in the nucleus accumbens shell during specific time segments. Learn. Mem. 21(4), 223–231 (2014).
Cottrell, J.R. et al. Working memory impairment in calcineurin knock-out mice is associated with alterations in synaptic vesicle cycling and disruption of high-frequency synaptic and network activity in prefrontal cortex. J. Neurosci. 33(27) 10938–10949 (2013).
Bach, M.E. et al. Transient and selective overexpression of D2 receptors in the striatum causes persistent deficits in conditional associative learning. Proc. Natl. Acad. Sci. 105 16027–16032 (2008).
Papachristos, E.B. & Gallistel, C.R. Autoshaped head poking in the mouse: a quantitative analysis of the learning curve. J. Exp. Anal. Behav. 85, 293–308 (2006).
Gallistel, C.R. et al. Is matching innate? J. Exp. Anal. Behav. 87, 161–199 (2007).
Pinkston, J.W. & Lamb, R.J. Delay discounting in C57BL/6J and DBA/2J mice: adolescent-limited and life-persistent patterns of impulsivity. Behav. Neurosci. 125, 194–201 (2011).
Young, J.W., Light, G.A., Marston, H.M., Sharp, R. & Geyer, M.A. The 5-choice continuous performance test: evidence for a translational test of vigilance for mice. PLoS One 4 e4227 (2009).
Gourley, S.L. et al. Dissociable regulation of instrumental action within mouse prefrontal cortex. Eur. J. Neurosci. 32, 1726–1734 (2010).
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
Authors and Affiliations
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.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Integrated supplementary information
Supplementary Figure 1 Top view of CHORA feeders
This picture shows the top panel of the CHORA feeders. The power lever and the operational mode lever are indicated by a red arrow. The lever of the operational state is set in via cable modality. Switch the lever for wireless mode. Once the feeder is turned on, the communication modality cannot be changed. To change the communication modality, first switch off the feeder and then change the communication modality.
Supplementary information
Supplementary Text and Figures
Supplementary Figure 1, Supplementary Methods and Supplementary Tables 1 and 2. (PDF 763 kb)
Supplementary Data
Recorded data and Python example scripts. (ZIP 117880 kb)
Running Phenopy.
This video shows how to set up a behavioral experimental protocol using Chora feeders and Phenopy. (MP4 4370 kb)
Data analysis example.
This video shows how to import, export, edit and analyze data using Phenopy. (MOV 4714 kb)
Importing functions.
This video shows how to upload a new importation function into Phenopy. (MOV 2841 kb)
Importing a new analysis.
This video shows how to upload a new analysis function into Phenopy. (MOV 4635 kb)
Rights and permissions
About this article
Cite this article
Balzani, E., Falappa, M., Balci, F. et al. An approach to monitoring home-cage behavior in mice that facilitates data sharing. Nat Protoc 13, 1331–1347 (2018). https://doi.org/10.1038/nprot.2018.031
Published:
Issue Date:
DOI: https://doi.org/10.1038/nprot.2018.031
This article is cited by
-
Data repurposing from digital home cage monitoring enlightens new perspectives on mouse motor behaviour and reduction principle
Scientific Reports (2023)
-
A hierarchical 3D-motion learning framework for animal spontaneous behavior mapping
Nature Communications (2021)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.