Biology subjects

McKillop, L. E.

Publications and source records attributed to McKillop, L. E..

5 recordsLinked to original sources

bioRxiv2022

Effects of clozapine-N-oxide and compound 21 on sleep in laboratory mice

Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) are chemogenetic tools for remote control of targeted cell populations using chemical actuators that bind to modified receptors. Despite the popularity of DREADDs in neuroscience and sleep research, potential effects of the DREADD actuator clozapine-N-oxide (CNO) on sleep have never been systematically tested. Here we show that intraperitoneal injections of commonly used CNO doses (1, 5, and 10 mg/kg) alter sleep in wild-type male laboratory mice. Using electroencephalography (EEG) and electromyography (EMG) to analyse sleep, we found a dose-dependent suppression of rapid eye-movement (REM) sleep, changes in EEG spectral power during non-REM (NREM) sleep, and altered sleep architecture in a pattern previously reported for clozapine. Effects of CNO on sleep could arise from back-metabolism to clozapine or binding to endogenous neurotransmitter receptors. Interestingly, we found that the novel DREADD actuator, compound 21 (C21, 3 mg/kg), similarly modulates sleep despite a lack of back-metabolism to clozapine. Our results demonstrate that both CNO and C21 can modulate sleep of mice not expressing DREADD receptors. This implies that back-metabolism to clozapine is not the sole mechanism underlying side effects of chemogenetic actuators. Therefore, any chemogenetic experiment should include a DREADD-free control group injected with the same CNO, C21 or newly developed actuator. We suggest that electrophysiological sleep assessment could serve as a sensitive tool to test the biological inertness of novel chemogenetic actuators.

neuroscience↗

bioRxiv2021

Somnotate: A robust automated sleep stage classifier that exceeds human performance and identifies ambiguous states in mice

Electrophysiological recordings from freely behaving animals are a widespread and powerful mode of investigation in sleep research. These recordings generate large amounts of data that require sleep stage annotation (polysomnography), in which the data is parcellated according to three vigilance states: awake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Manual and computational annotation methods currently ignore intermediate states because the classification features become ambiguous. However, these intermediate states contain important information regarding vigilance state dynamics. Here, we present a new classifier, "Somnotate", which produces automated annotation accuracies that exceed human expert performance on mouse electrophysiological data, is robust to errors in the training data, compatible with different recording configurations, and maintains high performance during experimental interventions. Somnotate is a probabilistic classifier based on a combination of linear discriminant analysis (LDA) with a hidden Markov model (HMM). A unique feature of Somnotate is that it quantifies and reports the certainty of its annotations, enabling the experimenter to identify ambiguous recording periods in a principled manner. We leverage this feature to identify epochs that exhibit intermediate vigilance states, revealing that many of these cluster around state transitions, whereas others correspond to failed attempts to transition. We show that the success rates of different transitions can be experimentally manipulated and explain previously observed sleep patterns. Somnotate can thus facilitate the study of sleep stage transitions and offers new insight into the mechanisms underlying sleep-wake dynamics. Author summaryTypically, the three different vigilance states - awake, REM sleep, and non-REM sleep - exhibit distinct features that are readily recognised in electrophysiological recordings. However, particularly around vigilance state transitions, epochs often exhibit features from more than one state. These intermediate vigilance states pose challenges for existing manual and automated classification methods, and are hence often ignored. Here, we present Somnotate - an open-source, highly accurate and robust sleep stage classifier, which supports research into intermediate states and sleep stage dynamics. Somnotate quantifies and reports the certainty of its annotations, enabling the experimenter to identify abnormal epochs in a principled manner. We use this feature to identify intermediate states and to detect unsuccessful attempts to switch between vigilance states. This provides new insights into the mechanisms of vigilance state transitions in mice, and creates new opportunities for future experiments.

neuroscience↗

bioRxiv2020

Waking experience modulates sleep need in mice

Homeostatic regulation of sleep is reflected in the maintenance of a daily balance between sleep and wake. Although numerous internal and external factors can influence sleep, it is unclear whether and to what extent the process that keeps track of time spent awake is determined by the content of the waking experience. We hypothesised that alterations in environmental conditions may elicit different types of wakefulness, which will in turn influence both the capacity to sustain continuous wakefulness as well the rates of accumulating sleep pressure. To address this, we performed two experiments, where we compared wakefulness dominated by novel object exploration with either (i) the effects of voluntary wheel running (Experiment 1) or (ii) performance in a simple touchscreen task (Experiment 2). We find that voluntary wheel running results in longer wake episodes, as compared with exploratory behaviour; yet it does not lead to higher levels of EEG slow wave activity (SWA) during subsequent sleep. On the other hand, engagement in a touchscreen task, motivated by a food reward, results in lower SWA during subsequent sleep, as compared to exploratory wakefulness, even though the total duration of wakefulness was similar. Overall, our study suggests that sleep-wake behaviour is highly flexible within an individual, and that the homeostatic process that keeps track of time spent awake is sensitive to the nature of the waking experience. We therefore conclude that sleep dynamics are determined, to a large degree, by the interaction between the organism and the environment.

neuroscience↗

bioRxiv2020

The role of the hypothalamus in cortical arousaland sleep homeostasis

Sleep and wakefulness are not simple homogenous all-or-none states, but instead are characterized by rich dynamics of brain activity across many temporal and spatial scales. Rapid global state transitions between waking and sleeping are believed to be controlled by hypothalamic circuits, but the contribution of the hypothalamus to within-state changes of sleep and wake "intensity" remains largely unexplored. Here we show that stimulation of inhibitory neurons in the preoptic hypothalamus does not merely trigger awakening from sleep, but the resulting awake state is also characterized by increased cortical activity. This activation is associated with a faster build-up of sleep pressure, proportional to the arousal level. These findings show that hypothalamic systems thought to exclusively control global state switching, also regulate within-state "intensity", which we propose as a key intrinsic variable in shaping the architecture of sleep/wake states across the 24h day.

neuroscience↗

bioRxiv2019

Cortical neuronal activity determines the dynamics of local sleep homeostasis

The homeostatic regulation of sleep manifests as a relative constancy of its daily amount and intensity. Theoretical descriptions of this phenomenon define "Process S", a variable with dynamics dependent only on sleep-wake history, whose levels are reflected in electroencephalogram (EEG) slow wave activity (0.5 - 4 Hz) during sleep. Here we developed novel mathematical models of Process S in mice, assuming that its dynamics are a function of the deviation of cortical neuronal firing rates from a locally defined set-point, crucially without explicit knowledge of sleep-wake state. Our results suggest that Process S tracks global sleep-wake history through an integration of local cortical neuronal activity levels over time. We posit that, instead of reflecting sleep-wake-dependent changes in specific variables and serving their homeostatic regulation, Process S may be a time-keeping mechanism which enables individuals to obtain a species-specific and ecologically-relevant quantity of sleep, even in the absence of external temporal information.

neuroscience↗