A novel unsupervised analysis of electrophysiological signals reveals new sleep substages in mice (original) (raw)
Related papers
2019
Sleep is a critical process that is well-conserved across mammalian species, and perhaps most animals, yet its functions and underlying mechanisms remain poorly understood. Identification of genes and pathways that can influence sleep may shed new light on these functions. Genomic screens enable the detection of previously unsuspected molecular processes that influence sleep. In this study, we report results from a large-scale phenotyping study of sleep-wake parameters for a population of single-gene knockout mice. Sleep-wake parameters were measured using a high throughput, non-invasive piezoelectric system called PiezoSleep. Knockout mice generated on a C57BL6/N (B6N) background were monitored for sleep and wake parameters for five days. By analyzing data from over 6000 mice representing 343 single gene knockout lines, we identified 122 genes influencing traits like sleep duration and bout length that have not been previously implicated in sleep, including those that affect sleep ...
Identification of Novel Sleep Related Genes from Large Scale Phenotyping Experiments in Mice
2017
OF DISSERTATION IDENTIFICATION OF NOVEL SLEEP RELATED GENES FROM LARGE SCALE PHENOTYPING EXPERIMENTS IN MICE Humans spend a third of their lives sleeping but very little is known about the physiological and genetic mechanisms controlling sleep. Increased data from sleep phenotyping studies in mouse and other species, genetic crosses, and gene expression databases can all help improve our understanding of the process. Here, we present analysis of our own sleep data from the large-scale phenotyping program at The Jackson Laboratory (JAX), to identify the best gene candidates and phenotype predictors for influencing sleep traits. The original knockout mouse project (KOMP) was a worldwide collaborative effort to produce embryonic stem (ES) cell lines with one of mouse’s 21,000 protein coding genes knocked out. The objective of KOMP2 is to phenotype as many as of these lines as feasible, with each mouse studied over a ten-week period (www.mousephenotype.org). The phenotyping for sleep be...
Sleep
Significant differences in many aspects of sleep/wake activity among inbred strains of mice suggest genetic influences on the control of sleep. A number of genetic techniques, including transgenesis, random and targeted mutagenesis, and analysis of quantitative trait loci may be used to identify genetic loci. To take full advantage of these genetic approaches in mice, a comprehensive and robust description of behavioral states has been developed. An existing automated sleep scoring algorithm, designed for sleep analysis in rats, has been examined for acceptability in the analysis of baseline sleep structure and the response to sleep deprivation in mice. This algorithm was validated in three inbred strains (C57BL/6J, C3HeB/FeJ, 129X1/SvJ) and one hybrid line (C57BL/6J X C3HeB/FeJ). Overall accuracy rates for behavioral state detection (mean±SE) using this system in mice were: waking, 98. 8%±0.4; NREM sleep, 97.1%±0.5; and REM sleep, 89.7%±1.4. Characterization of sleep has been extended to include measurements of sleep consolidation and fragmentation, REM sleep latency, and delta density decline with sleep. An experimental protocol is suggested for acquiring baseline sleep data for genetic studies. This sleep recording protocol, scoring, and analysis system is designed to facilitate the understanding of genetic basis of sleep structure.
Sleep, 2020
Identifying the precise neuronal networks activated during paradoxical sleep (PS, also called REM sleep) has been a challenge since its discovery. Similarly, our understanding of the homeostatic mechanisms regulating PS, whether through external modulation by circadian and ultradian drives or via intrinsic homeostatic regulation, is still limited, largely due to interfering factors rendering the investigation difficult. Indeed, none of the studies published so far were able to manipulate PS without significantly altering slow-wave sleep and/or stress level, thus introducing a potential bias in the analyses. With the aim of achieving a better understanding of PS homeostasis, we developed a new method based on automated scoring of vigilance states—using electroencephalogram and electromyogram features—and which involves closed-loop PS deprivation through the induction of cage floor movements when PS is detected. Vigilance states were analyzed during 6 and 48 h of PS deprivation as wel...
Improved sleep scoring in mice reveals human-like stages
Rodents are the main animal model to study sleep. Yet, in spite of a large consensus on the distinction between rapid-eye-movements sleep (REM) and non-REM sleep (NREM) in both humans and rodent, there is still no equivalent in mice of the NREM subdivision classically described in humans.Here we propose a classification of sleep stages in mice, inspired by human sleep scoring. By using chronic recordings in medial prefrontal cortex (mPFC) and hippocampus we can classify three NREM stages with a stage N1 devoid of any low oscillatory activity and N3 with a high density of delta waves. These stages displayed the same evolution observed in human during the whole sleep or within sleep cycles. Importantly, as in human, N1 in mice is the first stage observed at sleep onset and is increased after sleep fragmentation in Orexin-/- mice, a mouse model of narcolepsy.We also show that these substages are associated to massive modification of neuronal activity. Moreover, considering these stages...
bioRxiv (Cold Spring Harbor Laboratory), 2020
The temporal distribution of sleep stages is critical for the study of sleep function, regulation, and disorders in higher vertebrates. This temporal distribution is typically determined polysomnographically. In laboratory rodents, scoring of electrocorticography (ECoG) and electromyography (EMG) recordings is usually performed manually, where 5-10 second epochs are categorized as one of three specific stages: wakefulness, rapid-eye-movement (REM) sleep and non-REM (NREM) sleep. This process is laborious, time-consuming, and particularly impractical for large experimental cohorts with recordings lasting longer than 24 hours. To circumvent this problem, we developed an open-source Python toolkit, Sleep Identification Enabled by Supervised Training Algorithms (SIESTA), that automates the detection of these three main behavioral stages in mice. Our supervised machine learning algorithm extracts features from the ECoG and EMG signals, then automatically scores recordings with a hierarchical classifier based on Bagging Random Forest approaches. We evaluated this approach on data collected from wild-type mice housed under both normal and different lighting conditions, as well as from a mutant mouse line with abnormal sleep phenotypes. To validate its performance on test data, we compared SIESTA with manually scored data and obtained F1 scores of 0.92 for wakefulness, 0.81 for REM, and 0.93 for NREM. SIESTA has a user-friendly interface that can be used without coding expertise. To our knowledge, this is the first time that such a strategy has been developed using all opensource and freely available resources, and our aim is that SIESTA becomes a useful tool that facilitates further research of sleep in rodent models. .
Novel method for high-throughput phenotyping of sleep in mice
Physiological Genomics, 2006
[PDF] [Full Text] [Abstract] , March 5, 2008; 28 (10): 2495-2505. [PDF] [Full Text] [Abstract] , March 10, 2008; 33 (1): 91-99. Physiol Genomics M. Mackiewicz, B. Paigen, N. Naidoo and A. I. Pack Analysis of the QTL for sleep homeostasis in mice: Homer1a is a likely candidate including high-resolution figures, can be found at: Updated information and services
Background: Why we sleep is still one of the most perplexing mysteries in biology. Strong evidence indicates that sleep is necessary for normal brain function and that sleep need is a tightly regulated process. Surprisingly, molecular mechanisms that determine sleep need are incompletely described. Moreover, very little is known about transcriptional changes that specifically accompany the accumulation and discharge of sleep need. Several studies have characterized differential gene expression changes following sleep deprivation. Much less is known, however, about changes in gene expression during the compensatory response to sleep deprivation (i.e. recovery sleep). Results: In this study we present a comprehensive analysis of the effects of sleep deprivation and subsequent recovery sleep on gene expression in the mouse cortex. We used a non-traditional analytical method for normalization of genome-wide gene expression data, Removal of Unwanted Variation (RUV). RUV improves detection of differential gene expression following sleep deprivation. We also show that RUV normalization is crucial to the discovery of differentially expressed genes associated with recovery sleep. Our analysis indicates that the majority of transcripts upregulated by sleep deprivation require 6 h of recovery sleep to return to baseline levels, while the majority of downregulated transcripts return to baseline levels within 1–3 h. We also find that transcripts that change rapidly during recovery (i.e. within 3 h) do so on average with a time constant that is similar to the time constant for the discharge of sleep need.
Sleep as a biological problem: an overview of frontiers in sleep research
The Journal of Physiological Sciences, 2015
Sleep is a physiological process not only for the rest of the body but also for several brain functions such as mood, memory, and consciousness. Nevertheless, the nature and functions of sleep remain largely unknown due to its extremely complicated nature and lack of optimized technology for the experiments. Here we review the recent progress in the biology of the mammalian sleep, which covers a wide range of research areas: the basic knowledge about sleep, the physiology of cerebral cortex in sleeping animals, the detailed morphological features of thalamocortical networks, the mechanisms underlying fluctuating activity of autonomic nervous systems during rapid eye movement sleep, the cutting-edge technology of tissue clearing for visualization of the whole brain, the ketogenesis-mediated homeostatic regulation of sleep, and the forward genetic approach for identification of novel genes involved in sleep. We hope this multifaceted review will be helpful for researchers who are interested in the biology of sleep.