Neuroscience

Sleep is not homogenous but contains a highly diverse microstructural composition influenced by neuromodulators. Prior methods used to measure neuromodulator levels in vivo have been limited by low time resolution or technical difficulties in achieving recordings in a freely moving setting, which is essential for natural sleep. In this protocol, we demonstrate the combination of electroencephalographic (EEG)/electromyographic (EMG) recordings with fiber photometric measurements of fluorescent biosensors for neuromodulators in freely moving mice. This allows for real-time assessment of extracellular neuromodulator levels during distinct phases of sleep with a high temporal resolution.

Sleep is a conserved biological process in the animal kingdom. Understanding the neural mechanisms underlying sleep state transitions is a fundamental goal of neurobiology, important for the development of new treatments for insomnia and other sleep-related disorders. Yet, brain circuits controlling this process remain poorly understood. A key technique in sleep research is to monitor in vivo neuronal activity in sleep-related brain regions across different sleep states. These sleep-related regions are usually located deeply in the brain. Here, we describe technical details and protocols for in vivo calcium imaging in the brainstem of sleeping mice. In this system, sleep-related neuronal activity in the ventrolateral medulla (VLM) is measured using simultaneous microendoscopic calcium imaging and electroencephalogram (EEG) recording. By aligning calcium and EEG signals, we demonstrate that VLM glutamatergic neurons display increased activity during the transition from wakefulness to non-rapid eye movement (NREM) sleep. The protocol described here can be applied to study neuronal activity in other deep brain regions involved in REM or NREM sleep.

The functions of sleep remain largely unclear, and even less is known about its role in development. A general strategy to tackle these questions is to disrupt sleep and measure the outcomes. However, some existing sleep deprivation methods may not be suitable for studying the effects of chronic sleep disruption, due to their lack of effectiveness and/or robustness, substantial stress caused by the deprivation method, or consuming a large quantity of time and manpower. More problems may be encountered when applying these existing protocols to young, developing animals, because of their likely heightened vulnerability to stressors, and difficulties in precisely monitoring sleep at young ages. Here, we report a protocol of automated sleep disruption in mice using a commercially available, shaking platform–based deprivation system. We show that this protocol effectively and robustly deprives both non-rapid-eye-movement (NREM) sleep and rapid-eye-movement (REM) sleep without causing a significant stress response, and does not require human supervision. This protocol uses adolescent mice, but the method also works with adult mice.

Graphical abstract

Automated sleep deprivation system. The platform of the deprivation chamber was programmed to shake in a given frequency and intensity to keep the animal awake while its brain and muscle activities were continuously monitored by electroencephalography and electromyography.

Sleep is a conserved neurobehavioral state observed in animals with sufficiently complex nervous systems and is critical for survival. While the exact function of sleep remains unknown, the lack of sleep can have a range of physiological and behavioral effects. Studies in invertebrates and vertebrates have identified conserved neural mechanisms and cellular pathways in control of sleep, wakefulness and arousal. Methodologies to measure sleep have ranged from EEG recordings in humans and rodents to in-depth analysis of locomotor patterns in flies, fish and worms. Here we focus on sleep measurements using activity monitoring in the highly versatile experimental model system, Drosophila melanogaster, which is amenable to a number of genetic, physiological and behavioral manipulations. Further, we also describe methods used to manipulate sleep and wakefulness to understand the neural regulation of sleep and how organisms balance sleep, wakefulness and behavioral arousal. Sleep as a behavioral state is regulated by a number of factors including food, environmental conditions, and genetic background. The methodologies described here provide, a high-throughput approach to study neural regulation of sleep and factors that affect this complex behavior.