My lab studies sleep and circadian rhythms in mice, using a forward genetics approach called quantitative trait loci analysis. Historically, QTL mapping was limited by a lack of genetic diversity in the experimental populations, but recent advances have allowed more robust mapping, down to individual genes.

Traditional sleep monitoring protocols in rodents entail a trade off between accuracy and easy of use. Some monitoring devices, like the EEG, require invasive procedures. Other methods, like wheel running activity, only roughly correlate with sleep and wake. Our lab developed a novel sleep phenotyping technology, which uses a piezoelectric motion sensor placed at the bottom of the mouse cage. A computer classifier identifies sleep from the characteristic 3 Hz mouse breathing rhythm, which is the dominant motion during sleep. This system is noninvasive, high throughput, and 90-95% accurate compared to human observation and EEG.

Currently, I am analyzing data from several projects that employ the piezoelectric system for sleep phenotyping. The main project is a screen of over 1600 Collaborative Cross mice, which are a large genetic reference population derived from eight founder inbred strains. Analysis of this data set identified two significant QTL, including one for activity onset following sleep deprivation, which mapped to a single gene on chromosome 9. This work was published in Genome Research. In the future, we will expand the genotyping, since only a subset of mice were genotyped and used in the original analysis, and characterize the gene that we identified.

Another project involves the effects of sleep on recovery from traumatic brain injury. Mice are subjected to midline fluid percussion injury, and a control group is allowed to sleep ad libitum while an experimental group is sleep deprived. The recovery of the mice is characterized through cognitive and psychomotor tests, circulating hormone and cytokine levels, gene expression and brain histology. This work is in progress.

A third project looks at sleep and wake parameters in a mouse model of Alzheimer’s disease. We measured sleep, bout length, activity onset and peak activity in CD1 APP-PS1 mice versus wildtype controls of different ages. Our preliminary analysis identified significant age and genotype effects on sleep, activity onset and peak activity. In the future, we will record B6 Alzheimer’s mice and characterize an interesting pre-dawn activity phenotype that we have identified.