Elsevier

Journal of Neuroscience Methods

Volume 300, 15 April 2018, Pages 77-91
Journal of Neuroscience Methods

Analysis of locomotor behavior in the German Mouse Clinic

https://doi.org/10.1016/j.jneumeth.2017.05.005Get rights and content

Highlights

  • Assessment of comprehensive locomotor phenotyping strategy.

  • Refinement of highly dimensional data sets by Principal Component Analysis.

  • Influence of equipment version, sex, body weight, age and genetic background.

  • No redundancies detected between different locomotor tests.

Abstract

Background

Generation and phenotyping of mutant mouse models continues to increase along with the search for the most efficient phenotyping tests. Here we asked if a combination of different locomotor tests is necessary for comprehensive locomotor phenotyping, or if a large data set from an automated gait analysis with the CatWalk system would suffice.

New method

First we endeavored to meaningfully reduce the large CatWalk data set by Principal Component Analysis (PCA) to decide on the most relevant parameters. We analyzed the influence of sex, body weight, genetic background and age. Then a combination of different locomotor tests was analyzed to investigate the possibility of redundancy between tests.

Result

The extracted 10 components describe 80% of the total variance in the CatWalk, characterizing different aspects of gait. With these, effects of CatWalk version, sex, body weight, age and genetic background were detected. In addition, the PCA on a combination of locomotor tests suggests that these are independent without significant redundancy in their locomotor measures.

Comparison with existing methods

The PCA has permitted the refinement of the highly dimensional CatWalk (and other tests) data set for the extraction of individual component scores and subsequent analysis.

Conclusion

The outcome of the PCA suggests the possibility to focus on measures of the front and hind paws, and one measure of coordination in future experiments to detect phenotypic differences. Furthermore, although the CatWalk is sensitive for detecting locomotor phenotypes pertaining to gait, it is necessary to include other tests for comprehensive locomotor phenotyping.

Introduction

Recent advances in genome editing technology have revolutionized and accelerated the generation of mutant mouse lines for modeling human disease. This technological cataclysm has brought with it an upsurge in demand for comprehensive behavioral phenotyping that is reflected in the rising number of large-scale phenotyping centers and consortia (Infrafrontier Consortium, 2015, de Angelis et al., 2015). To meet the demand, new sophisticated phenotyping devices have been developed to provide an automatic and detailed characterization of mice. The goal of automation is not only to speed up data acquisition and to make it more objective and replicable, but also to reduce the burden on the animal through less handling. Ideally, a few automated systems would be sufficient to comprehensively phenotype a mouse model. The questions repeatedly arising in this context are how valid are individual tests; which test is the most informative and how many tests are really needed. It would also be beneficial to know if parameters of one test could predict the utility of another test for more detailed phenotypic characterization. Such knowledge would enhance the efficacy of phenotyping strategies, which have to be outlined in license applications for experiments on animals, thus saving time, animals and money.

Here we explore these questions with respect to locomotor phenotyping. Would it be sufficient, for example, to use the Catwalk (CW) from Noldus; an automated gait analysis system with video based paw tracking (Hamers et al., 2001, Batka et al., 2014)? Previously the assessment of gait parameters involved footprint analysis, where the paws were dipped into ink and the animal walked across a paper. Approximately 20 parameters were measured from these prints, however with the automated CW system, around 230 parameters are measured. With this dramatic increase in parameters/data points data analysis has become more complex. Traditionally only single parameters or a few selected ones were analyzed. But it is hard to formulate a clear picture of the phenotype when dealing with a large amount of parameters for one test. Thus it is necessary to decide which of the parameters give most information and is the best for detecting disease-relevant phenotypes. Alternatively, a compression of the data may be more appropriate to garner a comprehensive overview. As such reducing the number of parameters under consideration (aka dimensions) to a few components (i.e. artificial variables) by Principal Component Analysis (PCA) improves data handling without significant information loss (Budaev, 2010, Gonik et al., 2012, Vannoni et al., 2014, Ferguson et al., 2013, Ohl et al., 2003). The subsequent analysis then runs on the extracted components that substitute for the more numerous original parameters.

Running a PCA on behavioral data is not new; several groups have used it for analyzing data (Budaev, 2010, Gonik et al., 2012, Vannoni et al., 2014, Ferguson et al., 2013, Ohl et al., 2003). Yet running a PCA on a multidimensional data set (a high amount of animals and parameters, i.e. over 1000 cases and over 100 parameters, respectively) is not frequently done. With a few exceptions animal numbers are moderate (below 100 animals) and parameters analyzed small (below 50). To answer the above mentioned questions we apply a PCA to a multidimensional data set as collected by the CW, which, to our knowledge, has not been done so far. By including a fairly large sample size (over 1000 cases) we expect to produce reliable results, as factor instability is less likely to occur in a larger sample.

In this paper we focus on locomotor phenotypes, whereby the definition of a ‘locomotor phenotype’ is used very broadly. We include gait phenotypes (as analyzed by the CW), as well as activity (as in the Open Field (OF), home cage (HC) and SHIRPA) and motor ability phenotypes (as in the Grip Strength (GS), Rotarod (RR) and Vertical Pole (VP) test) into this term. Gait phenotypes describe how the animal moves, activity phenotypes describe how frequently the animal moves, and motor ability phenotypes describe in this case muscle strength, balance and coordination. To test for a disease-relevant locomotor phenotype generally one to several different tests are applied. Here we assess if the CW alone, with its large amount of parameters, can be sufficient to detect locomotor deficits and if a reduction in dimensions is appropriate to detect meaningful components. With historical data from our lab we ran a PCA to reduce the dimensions of the CW data. Furthermore, we characterized the resulting components and analyzed them with different statistical methods to evaluate the impact of different versions of the CW system (we have used two different versions in our lab), sex, body weight, age and genetics.

Likewise we ask if a combination of tests measuring locomotor behavior is necessary for comprehensive locomotor phenotyping, or if there are redundancies between tests. To answer these questions we included two data sets. In the first set we took historical data from our lab including parameters from the CW, OF and VP test. The second data set comprises publicly available data from the International Mouse Phenotyping Consortium (IMPC; (Brown and Moore, 2012)) website (https://www.mousephenotype.org) selecting different measures of motor performance (horizontal as well as vertical activity, muscle strength and coordination), which included parameters measured in the OF, SHIRPA, RR, GS and in the HC during indirect calorimetry at the German Mouse Clinic. With both data sets a PCA was run to see how the different parameters are distributed among the extracted components, thereby assessing possible redundancies between different tests.

Section snippets

Behavior

For all tests mice are transferred to the testing room at a minimum of 30 min prior to testing. Mutants are always concurrently tested with respective wild types and males prior to females. After each mouse the test apparatus is cleaned and disinfected. Different mutant lines were used in the behavioral assessments. For sample sizes by sex and genotype please refer to Table S1 in the Supporting Information. All experiments were approved by the government of Upper Bavaria, Germany.

Characterization of the ten principal components revealed the possibility to focus on a reduced parameter-set in future analysis

We firstly ran a PCA only on the CW data set to reduce dimensions and to analyze the impact of sex, body weight, age and genotype. Historical data of 1499 cases was used for the PCA analyzing CW data. As there was a change of the CW version (from 7.1 to XT) in our institute we took data from both, analyzing only parameters that were measured by the two. After the omission of one parameter due to low correlations in the correlation matrix (“StepSequence_Rb”), we analyzed 112 parameters. The

Discussion

In our study we performed PCAs on different data sets related to locomotor phenotypes to refine future data analysis and to investigate possible redundancies between different tests. Knowledge of the latter better informs future decisions on phenotyping strategies: if tests were redundant, one could focus on the simplest one. Our results suggest that for future CW analysis a primary focus can be on FP and HP measures, and not all individual paws, as well as one of the interlimb coordination

Conclusion

In conclusion, we have shown that a large amount of data points generated by the CW system can be meaningfully compacted via PCA to a few components. Here we extracted 10 components explaining 80% of the total variance in the data. The loadings of parameters onto components suggest the possibility to focus on FP and HP parameters neglecting the parameters for every single paw, as well as analyzing only one measure of coordination, i.e. couplings or phase dispersion, as long as the experimental

Acknowledgments

This work was supported by the German Science Foundation Collaborative Research Centre (CRC) 870 to L.E., by funds from the Bayerisches Staatsministerium für Bildung und Kultus, Wissenschaft und Kunst within Bavarian Research Network “Human Induced Pluripotent Stem Cells” (ForIPS) to F.G., the German Federal Ministry of Education and Research (BMBF) through the Integrated Network MitoPD (Mitochondrial endophenotypes of Morbus Parkinson), under the auspices of the e:Med Programme (grant 031A430E

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