A novel unsupervised analysis of electrophysiological signals reveals new sleep substages in mice

摘要

Sleep science is entering a new era, thanks to new data-driven analysis approaches that, combined with mouse gene–editing technologies, show a promise in functional genomics and translational research. However, the investigation of sleep is time consuming and not suitable for large-scale phenotypic datasets, mainly due to the need for subjective manual annotations of electrophysiological states. Moreover, the heterogeneous nature of sleep, with all its physiological aspects, is not fully accounted for by the current system of sleep stage classification. In this study, we present a new data-driven analysis approach offering a plethora of novel features for the characterization of sleep. This novel approach allowed for identifying several substages of sleep that were hidden to standard analysis. For each of these substages, we report an independent set of homeostatic responses following sleep deprivation. By using our new substages classification, we have identified novel differences among various genetic backgrounds. Moreover, in a specific experiment with the Zfhx3 mouse line, a recent circadian mutant expressing both shortening of the circadian period and abnormal sleep architecture, we identified specific sleep states that account for genotypic differences at specific times of the day. These results add a further level of interaction between circadian clock and sleep homeostasis and indicate that dissecting sleep in multiple states is physiologically relevant and can lead to the discovery of new links between sleep phenotypes and genetic determinants. Therefore, our approach has the potential to significantly enhance the understanding of sleep physiology through the study of single mutations. Moreover, this study paves the way to systematic high-throughput analyses of sleep. Author summary Sleep is a heterogeneous process determined by a number of genetic and epigenetic factors. To investigate the biology of sleep, animal models, such as mice, are extensively used in sleep studies, and large-scale phenotypic datasets are required to reach meaningful conclusions. Currently, manual annotations of electrophysiological states by a researcher is the gold-standard approach to classifying sleep stages. Only a few sleep states are identified through such manual annotations, namely non-rapid-eye-movement (NREM) and rapid-eye-movement (REM) sleep. In this work, we present a new computational approach that identified multiple new substages of sleep based on standard electroencephalography (EEG)/electromyography (EMG) features. Using this new approach, we studied each individual identified state and discovered that many of these states respond to the basic principles of sleep physiology: for example, some states homeostatically respond to sleep deprivation. We also applied our method to different mouse strains and to a circadian mutant line of mice. In all experimental groups, we were able to refine our understanding by associating specific substages with the genetic variations. We conclude that our new unbiased computational approach can help refine the study of sleep by further dissecting sleep biology.