Automated phenotyping and advanced data mining exemplified in rats transgenic for Huntington's disease.

Abstract

The need for improving throughput, validity, and reliability in the behavioral characterization of rodents may benefit from integrating automated intra-home-cage-screening systems allowing the simultaneous detection of multiple behavioral and physiological parameters in parallel. To test this hypothesis, transgenic Huntington's disease (tgHD) rats were repeatedly screened within phenotyping home-cages (PhenoMaster and IntelliCage for rats), where spontaneous activity, feeding, drinking, temperature, and metabolic performance were continuously measured. Cognition and emotionality were evaluated within the same environment by means of operant learning procedures and refined analysis of the behavioral display under conditions of novelty. This investigator-independent approach was further correlated with behavioral display of the animals in classical behavioral assays. Multivariate analysis (MVA) including Principle Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) was used to explore correlation patterns of variables within and across the two genotypes. The automated systems traced previously undetected aspects in the phenotype of tgHD rats (circadian activity, energy metabolism, rearing), and out of those spontaneous free rearing correlated with individual performance in the accelerod test. PCA revealed a segregation by genotype in juvenile tgHD rats that differed from adult animals, being further resolved by PLS-DA detecting "temperature" (juvenile) and "rearing" (adult) as phenotypic key variables in the tgHD model. Intra-home-cage phenotyping in combination with MVA, is capable of characterizing a complex phenotype by detecting novel physiological and behavioral markers with high sensitivity and standardization using fewer human resources. A broader application of automated systems for large-scale screening is encouraged.