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Ford K, Zuin E, Righelli D, Medina E, Schoch H, Singletary K, Muheim C, Frank MG, Hicks SC, Risso D, Peixoto L. A Global Transcriptional Atlas of the Effect of Sleep Deprivation in the Mouse Frontal Cortex. bioRxiv 2023:2023.11.28.569011. [PMID: 38076891 PMCID: PMC10705260 DOI: 10.1101/2023.11.28.569011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Sleep deprivation (SD) has negative effects on brain function. Sleep problems are prevalent in neurodevelopmental, neurodegenerative and psychiatric disorders. Thus, understanding the molecular consequences of SD is of fundamental importance in neuroscience. In this study, we present the first simultaneous bulk and single-nuclear (sn)RNA sequencing characterization of the effects of SD in the mouse frontal cortex. We show that SD predominantly affects glutamatergic neurons, specifically in layers 4 and 5, and produces isoform switching of thousands of transcripts. At both the global and cell-type specific level, SD has a large repressive effect on transcription, down-regulating thousands of genes and transcripts; underscoring the importance of accounting for the effects of sleep loss in transcriptome studies of brain function. As a resource we provide extensive characterizations of cell types, genes, transcripts and pathways affected by SD; as well as tutorials for data analysis.
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Affiliation(s)
- Kaitlyn Ford
- Department of Translational Medicine and Physiology, Sleep and Performance Research Center. Elson S. Floyd College of Medicine. Washington State University, Spokane, WA
| | - Elena Zuin
- Department of Biology, University of Padova, Italy
- Department of Statistical Sciences, University of Padova, Italy
| | - Dario Righelli
- Department of Statistical Sciences, University of Padova, Italy
| | - Elizabeth Medina
- Department of Translational Medicine and Physiology, Sleep and Performance Research Center. Elson S. Floyd College of Medicine. Washington State University, Spokane, WA
| | - Hannah Schoch
- Department of Translational Medicine and Physiology, Sleep and Performance Research Center. Elson S. Floyd College of Medicine. Washington State University, Spokane, WA
| | - Kristan Singletary
- Department of Translational Medicine and Physiology, Sleep and Performance Research Center. Elson S. Floyd College of Medicine. Washington State University, Spokane, WA
| | - Christine Muheim
- Department of Translational Medicine and Physiology, Sleep and Performance Research Center. Elson S. Floyd College of Medicine. Washington State University, Spokane, WA
| | - Marcos G Frank
- Department of Translational Medicine and Physiology, Sleep and Performance Research Center. Elson S. Floyd College of Medicine. Washington State University, Spokane, WA
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, MD, USA
| | - Davide Risso
- Department of Statistical Sciences, University of Padova, Italy
| | - Lucia Peixoto
- Department of Translational Medicine and Physiology, Sleep and Performance Research Center. Elson S. Floyd College of Medicine. Washington State University, Spokane, WA
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Miladinović Đ, Muheim C, Bauer S, Spinnler A, Noain D, Bandarabadi M, Gallusser B, Krummenacher G, Baumann C, Adamantidis A, Brown SA, Buhmann JM. SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species. PLoS Comput Biol 2019; 15:e1006968. [PMID: 30998681 PMCID: PMC6490936 DOI: 10.1371/journal.pcbi.1006968] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 04/30/2019] [Accepted: 03/20/2019] [Indexed: 11/18/2022] Open
Abstract
Understanding sleep and its perturbation by environment, mutation, or medication remains a central problem in biomedical research. Its examination in animal models rests on brain state analysis via classification of electroencephalographic (EEG) signatures. Traditionally, these states are classified by trained human experts by visual inspection of raw EEG recordings, which is a laborious task prone to inter-individual variability. Recently, machine learning approaches have been developed to automate this process, but their generalization capabilities are often insufficient, especially across animals from different experimental studies. To address this challenge, we crafted a convolutional neural network-based architecture to produce domain invariant predictions, and furthermore integrated a hidden Markov model to constrain state dynamics based upon known sleep physiology. Our method, which we named SPINDLE (Sleep Phase Identification with Neural networks for Domain-invariant LEearning) was validated using data of four animal cohorts from three independent sleep labs, and achieved average agreement rates of 99%, 98%, 93%, and 97% with scorings from five human experts from different labs, essentially duplicating human capability. It generalized across different genetic mutants, surgery procedures, recording setups and even different species, far exceeding state-of-the-art solutions that we tested in parallel on this task. Moreover, we show that these scored data can be processed for downstream analyzes identical to those from human-scored data, in particular by demonstrating the ability to detect mutation-induced sleep alteration. We provide to the scientific community free usage of SPINDLE and benchmarking datasets as an online server at https://sleeplearning.ethz.ch. Our aim is to catalyze high-throughput and well-standardized experimental studies in order to improve our understanding of sleep.
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Affiliation(s)
- Đorđe Miladinović
- Department of Computer Science, ETH Zurich, Zürich, Switzerland
- Max Planck Institute for Intelligent Systems, Tübingen, Germany
| | - Christine Muheim
- Chronobiology and Sleep Research Group, University of Zurich, Zürich, Switzerland
- Department of Biomedical Sciences, Washington State University, Spokane, Washington, United States of America
| | - Stefan Bauer
- Department of Computer Science, ETH Zurich, Zürich, Switzerland
- Max Planck Institute for Intelligent Systems, Tübingen, Germany
| | - Andrea Spinnler
- Chronobiology and Sleep Research Group, University of Zurich, Zürich, Switzerland
| | - Daniela Noain
- Department of Neurology, University Hospital Zurich, Zürich, Switzerland
| | | | | | | | - Christian Baumann
- Department of Neurology, University Hospital Zurich, Zürich, Switzerland
| | | | - Steven A. Brown
- Chronobiology and Sleep Research Group, University of Zurich, Zürich, Switzerland
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