Abstract
Cortical and subcortical circuitry are thought to play distinct roles in the generation of sleep oscillations and global state control, respectively. Here we silenced a subset of neocortical layer 5 pyramidal and archicortical dentate gyrus granule cells in male mice by ablating SNAP25. This markedly increased wakefulness and reduced rebound of electroencephalographic slow-wave activity after sleep deprivation, suggesting a role for the cortex in both vigilance state control and sleep homeostasis.
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Data availability
A sample dataset with spectral data and sleep scoring results used to generate key analyses presented in this paper is available on Figshare (https://doi.org/10.6084/m9.figshare.14737569). Source data are provided with this paper. Raw data from electrophysiological and passive infrared recordings are available from the corresponding authors upon reasonable request.
Code availability
Custom-made MATLAB code for key analyses is deposited on Figshare (https://doi.org/10.6084/m9.figshare.14737578). Code used for additional analyses is available from the corresponding authors upon reasonable request.
References
Saper, C. B. & Fuller, P. M. Wake–sleep circuitry: an overview. Curr. Opin. Neurobiol. 44, 186–192 (2017).
Liu, D. & Dan, Y. A motor theory of sleep–wake control: arousal–action circuit. Annu. Rev. Neurosci. 42, 27–46 (2019).
Vyazovskiy, V. V. et al. Cortical firing and sleep homeostasis. Neuron 63, 865–878 (2009).
Borbély, A. A., Daan, S., Wirz-Justice, A. & Deboer, T. The two-process model of sleep regulation: a reappraisal. J. Sleep. Res. 25, 131–143 (2016).
Huber, R., Felice Ghilardi, M., Massimini, M. & Tononi, G. Local sleep and learning. Nature 430, 78–81 (2004).
Vyazovskiy, V. V. et al. Local sleep in awake rats. Nature 472, 443–447 (2011).
Krueger, J., Nguyen, J. T., Dykstra-Aiello, C. J. & Taishi, P. Local sleep. Sleep. Med. Rev. 43, 14–21 (2019).
Krone, L. B. & Vyazovskiy, V. V. Unresponsive or just asleep? do local slow waves in the perilesional cortex have a function? Brain 143, 3513–3515 (2020).
Beltramo, R. et al. Layer-specific excitatory circuits differentially control recurrent network dynamics in the neocortex. Nat. Neurosci. 16, 227–234 (2013).
Chauvette, S., Volgushev, M. & Timofeev, I. Origin of active states in local neocortical networks during slow sleep oscillation. Cereb. Cortex 20, 2660–2674 (2010).
Sanchez-Vives, M. V. & McCormick, D. A. Cellular and network mechanisms of rhytmic recurrent activity in neocortex. Nat. Neurosci. 3, 1027–1034 (2000).
Buzsáki, G. Theta oscillations in the hippocampus. Neuron 33, 325–340 (2002).
Boyce, R., Glasgow, S. D., Williams, S. & Adamantidis, A. Causal evidence for the role of REM sleep theta rhythm in contextual memory consolidation. Science 352, 812–816 (2016).
Sanchez-Vives, M. V., McCormick, D. A., Sanchez-Vives, M. V. & McCormick, D. A. Cellular and network mechanisms of rhythmic recurrent activity in neocortex. Nat. Neurosci. 3, 1027–1034 (2000).
Hoerder-Suabedissen, A. et al. Cell-specific loss of SNAP25 from cortical projection neurons allows normal development but causes subsequent neurodegeneration. Cereb. Cortex 29, 2148–2159 (2019).
Gerfen, C. R., Paletzki, R. & Heintz, N. GENSAT BAC cre-recombinase driver lines to study the functional organization of cerebral cortical and basal ganglia circuits. Neuron 80, 1368–1383 (2013).
Lefort, S., Tomm, C., Floyd Sarria, J.-C. & Petersen, C. C. H. The excitatory neuronal network of the C2 barrel column in mouse primary somatosensory cortex. Neuron 61, 301–316 (2009).
Vecchia, D. et al. Temporal sharpening of sensory responses by layer V in the mouse primary somatosensory cortex. Curr. Biol. 30, 1589–1599 (2020).
Washbourne, P. et al. Genetic ablation of the t-SNARE SNAP-25 distinguishes mechanisms of neuroexocytosis. Nat. Neurosci. 5, 19–26 (2002).
Funk, C. M., Honjoh, S., Rodriguez, A. V., Cirelli, C. & Tononi, G. Local slow waves in superficial layers of primary cortical areas during REM sleep. Curr. Biol. 26, 396–403 (2016).
Huber, R., Deboer, T. & Tobler, I. Effects of sleep deprivation on sleep and sleep EEG in three mouse strains: empirical data and simulations. Brain Res. 857, 8–19 (2000).
Franken, P., Dijk, D. J., Tobler, I. & Borbely, A. A. Sleep deprivation in rats: effects on EEG power spectra, vigilance states, and cortical temperature. Am. J. Physiol. 261, R198–R208 (1991).
Huber, R., Deboer, T. & Tobler, I. Topography of EEG dynamics after sleep deprivation in mice. J. Neurophysiol. 84, 1888–1893 (2000).
Albrecht, U. & Foster, R. G. Placing ocular mutants into a functional context: a chronobiological approach. Methods 28, 465–477 (2002).
Frank, M. G. & Heller, H. C. The function(s) of sleep. Handb. Exp. Pharmacol. 253, 3–34 (2019).
Morairty, S. R. et al. A role for cortical nNOS/NK1 neurons in coupling homeostatic sleep drive to EEG slow wave activity. Proc. Natl Acad. Sci. USA 110, 20272–20277 (2013).
Tossell, K. et al. Sleep deprivation triggers somatostatin neurons in prefrontal cortex to initiate nesting and sleep via the preoptic and lateral hypothalamus. Preprint at bioRxiv https://doi.org/10.1101/2020.07.01.179671 (2020).
Prasad, J. A., Carroll, B. J. & Sherman, S. M. Layer 5 corticofugal projections from diverse cortical areas: variations on a pattern of thalamic and extrathalamic targets. J. Neurosci. 40, 5785–5796 (2020).
Massimini, M., Huber, R., Ferrarelli, F., Hill, S. & Tononi, G. The sleep slow oscillation as a traveling wave. J. Neurosci. 24, 6862–6870 (2004).
Krone, L. et al. Top-down control of arousal and sleep: fundamentals and clinical implications. Sleep. Med. Rev. 31, 17–24 (2017).
Gent, T. C., Bandarabadi, M., Herrera, C. G. & Adamantidis, A. Thalamic dual control of sleep and wakefulness. Nat. Neurosci. 21, 974–984 (2018).
Hayashi, Y. et al. Cells of a common developmental origin regulate REM/non-REM sleep and wakefulness in mice. Science 350, 957–961 (2015).
Thomas, C. W., Guillaumin, M. C. C., McKillop, L. E., Achermann, P. & Vyazovskiy, V. V. Global sleep homeostasis reflects temporally and spatially integrated local cortical neuronal activity. Elife 9, e54148 (2020).
Sheroziya, M. & Timofeev, I. Global intracellular slow-wave dynamics of the thalamocortical system. J. Neurosci. 34, 8875–8893 (2014).
Verhage, M. et al. Synaptic assembly of the brain in the absence of neurotransmitter secretion. Science 287, 864–869 (2000).
Molnár, Z. et al. Normal development of embryonic thalamocortical connectivity in the absence of evoked synaptic activity. J. Neurosci. 22, 10313–10323 (2002).
Krueger, J. & Obál, F. A neuronal group theory of sleep function. J. Sleep. Res. 2, 63–69 (1993).
McKillop, L. E. et al. Effects of aging on cortical neural dynamics and local sleep homeostasis in mice. J. Neurosci. 38, 3911–3928 (2018).
Sakata, S. & Harris, K. D. Laminar structure of spontaneous and sensory-evoked population activity in auditory cortex. Neuron 64, 404–418 (2009).
Brown, L., Hasan, S., Foster, R. G. & Peirson, S. N. COMPASS: Continuous Open Mouse Phenotyping of Activity and Sleep Status. Wellcome Open Res. 1, 2 (2016).
Schmid, B., Helfrich-Förster, C. & Yoshii, T. A new ImageJ plug-in ‘ActogramJ’ for chronobiological analyses. J. Biol. Rhythms 26, 464–467 (2011).
Howell, G. T. & Lacroix, G. L. Decomposing interactions using GLM in combination with the COMPARE, LMATRIX and MMATRIX subcommands in SPSS. Tutorials Quant. Methods Psychol. 8, 1–22 (2012).
Howell, D. C. Statistical Methods for Psychology (Cengage Learning, 2012).
Achermann, P. & Borbély, A. A. Coherence analysis of the human sleep electroencephalogram. Neuroscience 85, 1195–1208 (1998).
Tabachnick, B. G. & Fidell, L. S. in International Encyclopedia of Statistical Science. (ed. Lovric, M.) 902–904 (Springer Berlin Heidelberg, 2011).
Magill, P. J. et al. Changes in functional connectivity within the rat striatopallidal axis during global brain activation in vivo. J. Neurosci. 26, 6318–6329 (2006).
Paxinos, G. & Franklin, K. B. J. The Mouse Brain in Stereotaxic Coordinates (Elsevier Academic Press, 2019).
Schindelin, J. et al. Fiji: an open-source platform for biological image analysis. Nat. Methods 9, 676–682 (2012).
Grant, E., Hoerder-Suabedissen, A. & Molnar, Z. The regulation of corticofugal fiber targeting by retinal inputs. Cereb. Cortex 26, 1336–1348 (2016).
Acknowledgements
We thank T. Jahans-Price and X. Wang for help with establishing the microlesion protocol; M. Sabanovic for advice on statistics and figure production; K. Parley for support in the histological procedures; all members of the laboratory of V.V.V. for kind help with surgery assistance, animal care and sleep deprivation. We are very grateful to E. Mann, University of Oxford, for advice on the laminar analysis and to A. van den Pol, Yale University, for the generous gift of rabbit anti-Hcrt antibody. This work was supported by the Wellcome Trust PhD studentships 203971/Z/16/Z (to L.B.K.) and 109059/Z/15/Z (to C.B.-D.). T.Y. was supported by The Uehara Memorial Foundation Overseas Postdoctoral Fellowships, and The Naito Grant for Studying Overseas and a Wellcome Trust grant (106174/Z/14/Z). M.C.K. was supported by a Berrow Foundation Lord Florey Scholarship. M.C.C.G. was supported by a Biotechnology and Biological Sciences Research Council (BBSRC) DTP grant (BB/J014427/1) and by a Clarendon Scholarship (provided by the University of Oxford). V.v.d.V. and L.E.M. were supported by Novo Nordisk Postdoctoral Fellowships run in partnership with the University of Oxford. L.E.M. was also supported by a Sir Paul Nurse Junior Research Fellowship at Linacre College, Oxford. S.N.P. and S.K.E.T. are funded by the BBSRC (grant no. BB/S015817/1). The laboratory of Z.M. received funding from the UK Medical Research Council (G00900901), the Royal Society, St John’s College Research Centre, the Anatomical Society and Einstein Stiftung. Z.M. is an Einstein Visiting Fellow at Charité-Universitätsmedizin Berlin (host B. Eickholt for 2020–2024), and lead researcher at Oxford Martin School, University of Oxford. This work was further supported by a Wellcome Trust Strategic Award (098461/Z/12/Z), a John Fell OUP Research Fund grant (131/032) and Medical Research Council (UK) grants MR/N026039/1 and MR/S01134X/1.
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Contributions
Z.M. and V.V.V. initiated and proposed the study with pilot experiments done by T.Y. L.B.K., T.Y., C.J.A., A.H.-S., Z.M. and V.V.V. designed the experiments. L.B.K., T.Y., M.C.C.G. and C.B.-D. conducted the electrophysiological experiments on the transgenic mouse model. L.B.K., C.B.-D. and M.C.K. conducted the electrophysiological experiments on wild-type mice. L.B.K., V.v.d.V., L.M.c.K., S.K.E.T. and S.N.P. conducted and analyzed the passive infrared recordings on the transgenic mouse model. L.B.K., M.C.K. and A.H.-S. performed the histology. A.H.-S. and Z.M. developed, validated and provided the transgenic mice. L.B.K., V.v.d.V., S.K.E.T. and V.V.V. analyzed the data. L.B.K. and V.V.V. wrote the manuscript. All authors discussed the results and commented on the manuscript.
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Extended data
Extended Data Fig. 1 Neuronal dynamics around OFF-ON transitions imply a leading role of layer 5 in population activity.
a) Average neuronal firing activity at the transitions from population OFF to ON periods during baseline NREM sleep (layer × time interaction: F(29,174) = 9.412, p < 0.001, two-factor repeated measures ANOVA). Note that firing rates are higher in layer 5 during the first 10 ms. b) Latency to the first spike for matched spike numbers during the first 200 ms of an ON period (main effect of layer: F(1,6) 86.301, p < 0.001, two-factor repeated-measures ANOVA). Note that the latency to the first spike is shorter in layer 5 irrespective of the total number of spikes in a given ON period. n = 7 wild type (C57L/6). Black asterisks indicate post-hoc contrasts with significant differences (*p < 0.05, **p < 0.01, ***p < 0.001). Data in panels a, b are presented as mean ± SEM (shaded areas). See Supplementary Table 1 for detailed results. L2/3, L5: Neocortical layers 2/3, 5. NREM: non-rapid eye movement sleep.
Extended Data Fig. 2 Comparison of Cre-expression in the Rbp4-Cre mouse line between neocortical layer 5, dentate gyrus and hypothalamus.
a) Coronal section of an Rbp4-Cre;Ai14;Snap25fl/+ mouse brain indicating areas that were further examined for Cre-expression, using confocal imaging of DAPI stained slices. b,c) Laser scanning confocal microscope images from neocortex (CTX, b) and ventrolateral preoptic hypothalamus (VLPO, c) of DAPI stained (blue) sections, showing the distribution of tdTomato+ cells in the two regions. The VPLO region is outlined with a white, dotted line. Cell counts on corresponding coronal sections in three brains revealed that 20.53 ± 0.98% (480/2342) of cortical L5 cells were tdTomato+, while only 1.15 ± 0.40% (35/3006) of hypothalamic cells expressed the red fluorescent indicator. d) Coronal section of an Rbp4-Cre;Ai14;Snap25fl/+ mouse brain indicating the area of the dentate gyrus which was further examined for Cre-expression. e) Laser scanning confocal microscope image of dentate gyrus (DG) in a DAPI stained (blue) section. TdTomato+ cells were quantified in both the top and bottom blades of DG in three images, each from three different brains, and comprise 39.39 ± 3.72% of cells in the granule layer. As evident from the boxed regions in (a,d), the tdTomato+ cells in different brain regions vary in their fluorescence intensity, therefore the images in panels (b,c,e) were acquired with settings optimised to show the tdTomato+ cells in each brain region. CTX: neocortex. DAPI: 4′,6-diamidino-2-phenylindole. DG: dentate gyrus. VLPO: ventrolateral preoptic hypothalamus. Scale bars: 1 mm (a,d), 100 μm (b,c,e).
Extended Data Fig. 3 The suprachiasmatic nucleus of the hypothalamus is void of Cre + cells and spared of fibre tracts in the Rbp4-Cre driver line.
a) Epifluorescence image of an Rbp4-Cre;Ai14;Snap25fl/+ brain section at the level of the suprachiasmatic nucleus (SCN). The section was counterstained with DAPI (blue). Box indicates approximate region from which image in (b) was taken. b) High-magnification epifluorescence image of the SCN region. Rbp4-Cre;Ai14 axons are shown in red, cell nuclei stained with DAPI in blue. Note that there are no Cre+ cells located within the SCN (outlined with white dotted lines), and very few of the dense axon bundles pass through the SCN. DAPI: 4′,6-diamidino-2-phenylindole. SCN: suprachiasmatic nucleus. Scale bars: 1 mm (a), 100 µm (b).
Extended Data Fig. 4 No overlap between orexin or melanin concentrating hormone-expressing cells with Rbp4-Cre + cells but dense fibre tracts in lateral hypothalamus.
a) Epifluorescence image of an Rbp4-Cre;Ai14;Snap25fl/+ brain hemisection at the level of the lateral hypothalamic area (LH), stained for melanin concentrating hormone (MCH) in green. Boxes indicate approximate regions from which images in (b,c) were taken. b,c) Laser scanning confocal microscope images of two representative sections of LH of the same brain as shown in (a) stained for MCH (b) or orexin/hypocretin (Hcrt; c). Rbp4-Cre;Ai14 cells and processes are shown in red, and nuclei are counterstained with DAPI (blue). Note that no MCH + cell was tdTom + (n = 3 brains, 692 MCH + cells), and no Hcrt+ cell was tdTom + (n = 3 brains, 469 Hcrt+ cells). Note the dense fine fibres surrounding cell bodies in LH, consistent with an axonal terminal field in that region. DAPI: 4′,6-diamidino-2-phenylindole. Hcrt: orexin/hypocretin. MCH: melanin concentrating hormone. tdTom: tdTomato. Scale bars: 1 mm (a), 100 μm (b,c).
Extended Data Fig. 5 The theta peak during REM sleep is shifted towards lower frequencies in cortical SNAP25-ablated mice.
EEG spectral power in the frequency range 4–10 Hz normalised to the mean spectral power over the entire EEG spectrum (0.5–30 Hz) during REM sleep on the baseline day. Note that peak theta activity is shifted towards lower frequencies in cKOs compared to CTRs in both the frontal and occipital EEG derivations. n = 5 CTR and n = 8 cKO for EEG spectral analysis. Data are presented as mean ± SEM (shaded areas). See Supplementary Table 1 for detailed results. cKO: conditional knockout animals. CTR: control animals. EEG: Electroencephalogram. REM: Rapid eye movement sleep.
Extended Data Fig. 6 Genotype differences in the amount of time spent in wake, NREM, and REM sleep during undisturbed baseline recordings are more pronounced in the dark period.
During the light period, the distribution of vigilance states is similar between genotypes with only a trend towards increased wakefulness and reduced NREM and REM sleep, while strong differences occur during the dark period (genotype × phase × vigilance state interaction: F(1,14) = 36.083, p < 0.001, three-way ANOVA). n = 6 CTR and n = 9 cKO for vigilance state analysis. Black asterisks indicate post-hoc contrasts with significant differences (*p < 0.05, **p < 0.01, ***p < 0.001), grey asterisks indicate post-hoc comparisons with P < 0.05, which do not reach significance after Bonferroni correction for multiple comparisons. Data is presented as group mean (red line), 95% confidence interval (pink box), and one standard deviation (blue box) with individual data points overlaid. See Supplementary Table 1 for detailed results. cKO: conditional knockout animals. CTR: control animals. NREM: Non-rapid eye movement sleep. REM: Rapid eye movement sleep.
Extended Data Fig. 7 Absolute time in NREM sleep following sleep deprivation is reduced in cortical SNAP25-ablated animals but relative NREM rebound does not differ between genotypes.
a) Time spent in vigilance states (wake, NREM, and REM) during the 18 h recovery time following sleep deprivation (genotype × vigilance state interaction: F(1,14) = 27.754, p < 0.001, mixed ANOVA). Note that cortical SNAP25-ablated animals (cKO) overall spent more time awake and less time in NREM and REM sleep compared to controls (CTR). b) Time course of NREM sleep on a sleep deprivation day compared between genotypes (genotype × time interaction: F(4,54) = 4.222, p = 0.004, mixed ANOVA). cKOs sleep less during the entire 12 h dark period following sleep deprivation. c) Rebound of NREM sleep time following sleep deprivation relative to individual baseline values. No differences were observed between genotypes. d) Change in duration of NREM episodes during the first hour after sleep deprivation (ZT6-7 of SD day) relative to the same time window on BL day. n = 6 CTR and n = 9 cKO for vigilance state analysis. Black asterisks indicate post-hoc contrasts with significant differences (*p < 0.05, **p < 0.01, ***p < 0.001). Data in panels a, c, d is presented as group mean (red line), 95% confidence interval (pink box), and one standard deviation (blue box) with individual data points overlaid. Data in panel b are presented as mean values ± SEM(shaded areas). See Supplementary Table 1 for detailed results. BL: baseline. cKO: conditional knockout animals. CTR: control animals. NREM: Non-rapid eye movement sleep. REM: Rapid eye movement sleep. SD: sleep deprivation. ZT: zeitgeber time.
Extended Data Fig. 8 The rebound of slow wave activity after sleep deprivation is specific to cortical areas but not layers.
Time course of NREM slow wave activity (SWA) after sleep deprivation (a) in the LFPs from layers 2/3 and 5 in primary motor cortex and (b) in the frontal and occipital EEG derivation. Note that cortical SNAP25-ablated animals (cKO) had lower initial SWA levels in the frontal EEG and LFP recordings across all layers, compared to controls (CTR). n = 5 CTR and n = 5 cKO for laminar analysis, n = 5 CTR and n = 8 cKO for EEG spectral analysis. Black asterisks indicate post-hoc contrasts with significant differences (*p < 0.05, **p < 0.01, ***p < 0.001). Data in panels a and b are presented as mean values ± SEM (shaded areas). See Supplementary Table 1 for detailed results. cKO: conditional knockout animals. CTR: control animals.
Extended Data Fig. 9 Relative EEG power spectra during sleep deprivation show an attenuated increase in theta activity cortical SNAP25-ablated animals.
Wake EEG spectral power during the 6-hour sleep deprivation shown as a frequency bin-wise percentage of 24 h baseline values. Note that the expected increase in theta-power during sleep deprivation, which is visible in CTR mice, is severely diminished in cKOs. n = 5 CTR and n = 8 cKO for EEG spectral analysis. Individual asterisks indicate spectral bins with significant differences in post-hoc comparison before (grey) and after (black) Bonferroni adjustment of α. Data are presented as mean values ± SEM (shaded areas). See Supplementary Table 1 for detailed results. cKO: conditional knockout animals. CTR: control animals. EEG: electroencephalogram.
Extended Data Fig. 10 Passive infrared recordings (PIR) show robustness of sleep phenotype to altered light-conditions.
a) Wake time estimates averaged over the last 3 days of PIR recordings under 12:12 light-dark (LD) conditions and over the first 3 days of constant darkness (DD) (main effect of genotype: F(1,15) = 18.604, p = 0.001, mixed ANOVA). Note that genotype differences in the daily amount of wakefulness persist in the absence of light. b) Time course of wakefulness in LD and DD conditions (genotype × time interaction: F(3.801,57.016) = 3.319, p = 0.018, mixed ANOVA). n = 7 CTR and n = 10 for PIR recordings. cKO: conditional knockout animals. Black asterisks indicate post-hoc contrasts with significant differences (*p < 0.05, **p < 0.01, ***p < 0.001), grey asterisks indicate post-hoc comparisons with P < 0.05, which do not reach significance after Bonferroni correction for multiple comparisons. Data in panel a is presented as group mean (red line), 95% confidence interval (pink box), and one standard deviation (blue box) with individual data points overlaid. Data in panel b are presented as mean values ± SEM (shaded areas). See Supplementary Table 1 for detailed results. CTR: control animals. LD: light:dark. DD: constant darkness. PIR: passive infrared recordings.
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Krone, L.B., Yamagata, T., Blanco-Duque, C. et al. A role for the cortex in sleep–wake regulation. Nat Neurosci 24, 1210–1215 (2021). https://doi.org/10.1038/s41593-021-00894-6
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DOI: https://doi.org/10.1038/s41593-021-00894-6
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