Annemarie Zimprich1, Manuela A Östereicher2, Lore Becker2, Petra Dirscherl3, Luise Ernst4, Helmut Fuchs2, Valerie Gailus-Durner2, Lillian Garrett5, Florian Giesert4, Lisa Glasl3, Angelika Hummel3, Jan Rozman6, Martin Hrabě de Angelis7, Daniela Vogt-Weisenhorn3, Wolfgang Wurst8, Sabine M Hölter5. 1. Developmental Genetics, Technische Universität München-Weihenstephan, c/o Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; Institute of Developmental Genetics, Helmholtz Zentrum München, Ingolstaedter Landstr.1, 85764 Neuherberg, Germany. Electronic address: annemarie.zimprich@helmholtz-muenchen.de. 2. German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, Ingolstaedter Landstr. 1, 85764 Neuherberg, Germany. 3. Institute of Developmental Genetics, Helmholtz Zentrum München, Ingolstaedter Landstr.1, 85764 Neuherberg, Germany. 4. Developmental Genetics, Technische Universität München-Weihenstephan, c/o Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; Institute of Developmental Genetics, Helmholtz Zentrum München, Ingolstaedter Landstr.1, 85764 Neuherberg, Germany. 5. German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, Ingolstaedter Landstr. 1, 85764 Neuherberg, Germany; Institute of Developmental Genetics, Helmholtz Zentrum München, Ingolstaedter Landstr.1, 85764 Neuherberg, Germany. 6. German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, Ingolstaedter Landstr. 1, 85764 Neuherberg, Germany; German Center for Diabetes Research (DZD), Helmholtz Zentrum München, Ingolstaedter Landstr. 1, 85764 Neuherberg, Germany. 7. German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, Ingolstaedter Landstr. 1, 85764 Neuherberg, Germany; German Center for Diabetes Research (DZD), Helmholtz Zentrum München, Ingolstaedter Landstr. 1, 85764 Neuherberg, Germany; Chair of Experimental Genetics, Technische Universität München-Weihenstephan, c/o Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany. 8. Institute of Developmental Genetics, Helmholtz Zentrum München, Ingolstaedter Landstr.1, 85764 Neuherberg, Germany; Chair of Developmental Genetics, Technische Universität München-Weihenstephan, c/o Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; German Center for Neurodegenerative Diseases (DZNE), Site Munich, Feodor-Lynen-Str. 17, 81377 Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Adolf-Butenandt-Institut, Ludwig-Maximilians-Universität München, Schillerstr. 44, 80336 Munich, Germany.
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.
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.
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