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Infraslow noradrenergic locus coeruleus activity fluctuations are gatekeepers of the NREM–REM sleep cycle

Nature Neuroscience volume 28, pages 84–96 (2025)Cite this article

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Abstract

The noradrenergic locus coeruleus (LC) regulates arousal levels during wakefulness, but its role in sleep remains unclear. Here, we show in mice that fluctuating LC neuronal activity partitions non-rapid-eye-movement sleep (NREMS) into two brain–autonomic states that govern the NREMS–REMS cycle over ~50-s periods; high LC activity induces a subcortical–autonomic arousal state that facilitates cortical microarousals, whereas low LC activity is required for NREMS-to-REMS transitions. This functional alternation regulates the duration of the NREMS–REMS cycle by setting permissive windows for REMS entries during undisturbed sleep while limiting these entries to maximally one per ~50-s period during REMS restriction. A stimulus-enriched, stress-promoting wakefulness was associated with longer and shorter levels of high and low LC activity, respectively, during subsequent NREMS, resulting in more microarousal-induced NREMS fragmentation and delayed REMS onset. We conclude that LC activity fluctuations are gatekeepers of the NREMS–REMS cycle and that this role is influenced by adverse wake experiences.

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Fig. 1: LC activity surges during NREMS and associated brain and autonomic arousal correlates.
Fig. 2: LC activity troughs during NREMS allow REMS entries.
Fig. 3: REMS entries during REMS-R are restricted to LC activity troughs.
Fig. 4: Role of LC activity fluctuations in the natural NREMS–REMS cycle.
Fig. 5: SSD but not gentle SD disrupted subsequent sleep and enhanced LC activity.
Fig. 6: Acute LC inhibition suppressed sleep-disruptive effects of a stimulus-enriched wakefulness.
Fig. 7: Scheme summarizing the functional partitioning of NREMS and its relevance for the NREMS–REMS cycle.

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Data availability

Raw data from which all figure panels were obtained are publicly available from Zenodo (https://zenodo.org/records/8263449)57. Source data are provided with this paper.

Code availability

Major codes used in this study for data acquisition and analysis are available from our GitHub repository (https://github.com/Romain2-5/IntanLuthiLab) or upon direct request to the corresponding author.

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Acknowledgements

We thank P. Franken, S. Fulda, C. Lustenberger, M. Schmidt, F. Siclari, A. Stephan, M. Tafti and E. van Someren for discussion and reading of earlier versions of this manuscript. We acknowledge S. Astori and O. Zanoletti from the Carmen Sandi lab (École Polytechnique Fédérale de Lausanne) for carrying out the corticosterone measurements and A. Guillaume-Gentil for assistance with viral injections. We recognize S. Lecci for providing the first datasets relevant for this study. We also thank G. J. Feng and Y. Li for generously providing us with the first aliquots of the GRABNE sensor. The constructive comments of J. Buron, L. Banterle and P. Milanese on the manuscript are very appreciated. We are grateful to the animal caretakers, especially M. Blom and T. Tromme, and to L. Sériot for expert veterinary advice. This work was supported by the Swiss National Science Foundation (grants 184759 and 214851 to A.L.), a Faculty of Biology and Medicine Université de Lausanne doctoral fellowship to A.O.-F. and Etat de Vaud institutional funding to A.L.

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Author notes

  1. These authors contributed equally: Alejandro Osorio-Forero, Georgios Foustoukos, Romain Cardis.

Authors and Affiliations

  1. Department of Fundamental Neurosciences, University of Lausanne, Lausanne, Switzerland

    Alejandro Osorio-Forero, Georgios Foustoukos, Romain Cardis, Najma Cherrad, Christiane Devenoges, Laura M. J. Fernandez & Anita Lüthi

  2. Department of Sleep and Cognition, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands

    Alejandro Osorio-Forero

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Contributions

A.O.-F., G.F., R.C. and A.L. conceptualized the study. A.O.-F., G.F. and R.C. devised the methodology. A.O.-F., G.F., R.C. and N.C. carried out the investigation and developed analysis scripts. C.D. provided technical support. L.M.J.F. created the schemes and final graphs using Adobe Illustrator. A.L. acquired funding, supervised the study and wrote the original draft. All authors reviewed and edited the manuscript.

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Correspondence to Anita Lüthi.

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Nature Neuroscience thanks Danqian Liu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Histological verification of viral expression specificity and quantification of LC activity output during NREMS.

a) Confocal micrographs taken from a representative DBH-Cre mouse expressing jGCaMP8s (green, same animal as Fig. 1c) in the LC. Dotted square, area expanded on the right, corresponding immunostaining for tyrosine hydroxylase (TH, magenta), and overlay. b) Scatter plot for the relationship between the dynamic range of the LC fluorescence signal (green dots) and the overlap of LC activity levels between Wake and NREMS (blue dots). This overlap was calculated up to a threshold of mean - 2 standard deviations of wake levels for the 10 mice included in Fig. 1. High dynamic ranges show the lowest overlap, whereas low dynamic ranges provide more variable overlap values. The mean±s.e.m. overlap indicated in the main text was calculated from the blue-colored datapoints. c) Example two-site fiber photometric recording from an animal that expresses jGCaMP8s in LC and GRABNE1h in somatosensory thalamus. Shown from the top are: Hypnogram, LC-jGCaMP8s and free NA-GRABNE1h fluorescence, S1 LFP sigma (σ, 10 – 15 Hz) power, CA1 LFP-derived θ/δ-ratio and absolute EMG levels. Portion of trace underlain in grey expanded on the right. Vertical lines, coordination between fluorescent signals. d) Mean±s.e.m. cross-correlation of LC activity with free NA levels for two-site fiber photometric recordings (n = 2), of which one is illustrated in panel c. Cross-correlation coefficients: 0.56 and 0.59 at a positive lag of 2.9 and 3.1 s for the 2 mice. Positive lag means increases in LC activity preceded NA augmentations. e) Anticorrelation of LC activity and σ power. e1) Time-frequency plot of S1 LFP in NREMS, aligned with LC activity (green). Same recording as in Fig. 1c. S1 LFP σ (10 – 15 Hz) power (blue) and individual sleep spindles (black dots) shown below. e2) Cross-correlations for LC fluorescence and S1 σ power from ZT0-ZT12 (same animal as e1) and mean±s.e.m. cross-correlation across n = 10 animals. Signals were anticorrelated with a time lag of 2.1 ± 0.5 s (n = 10) and showed side peaks at 48.3 ± 2.2 s, range 38.5–58.1 s, measured in n = 6/10 animals in which these side peaks were clearly detectable). Positive lag means LC activity increases preceded σ power declines. Vertical dotted lines, side peaks. Insets, zoom-ins with crosscorrelation magnitude and lag values.

Source data

Extended Data Fig. 2 Illustration of analysis routine to detect LC activity peaks and surges.

Step-by-step visual illustration of the algorithm explained in the Methods (‘Analysis procedures, Detection of LC peaks and activity surges’), starting from the original LC fluorescence signals. From top to bottom, traces shown are: Hypnogram (black), corresponding LC fluorescent signal (green), low-pass-(0.1 Hz) filtered signal (dark brown) with lower envelope (blue), subtracted trace (light brown). On this last signal, peak analysis was carried out using a threshold and peak prominence >60%. Detected peaks are indicated by red crosses. From these individual peaks, activity surges were identified, of which an example is given in red in the expanded trace portion surrounded by a blue rectangle.

Extended Data Fig. 3 Extended spectral analysis corresponding to LC activity peaks.

a) Spectral dynamics of S1 LFP signals corresponding to LC peaks that were classified based on whether they did not (black traces) or did co-occur with a MA (orange traces). Corresponding power dynamics for δ, σ and γ frequency bands are aligned vertically, together with heart rate and absolute EMG. Traces are means across n = 9 animals. Horizontal blue bars denote significance as calculated by false discovery rates (see Methods, Statistics). b) Quantification of peak values as done in Fig. 1i for EEG signals. Box-and-whisker plots quantifying peak values (0-1 s) for LC peaks without and with a MA, and corresponding heart rate and spectral bands for n = 9 mice (Bonferroni-corrected two-sided paired t-tests; for LC peak: t = −16.251, p = 2.07*10−7; for δ: t = 6.3263, p = 0.00023; for σ: t = 4.972, p = 0.0011; for γ: t = −4.8073, p = 0.0013). Box-and-whisker plots: center line, median; box edges, first and third quartiles; whiskers, minimum and maximum. c) Overlay of LC signal (dotted line) and δ power (continuous line) on an expanded time scale, with traces taken from panel a, with the same color code. Note the increase in δ power accompanying the onset of the LC activity peak that becomes inverted to reach negative values in the case of a MA-associated LC activity peak (orange trace). d) As c, for LC signal and γ power. Note that there is a decrease in power for non-MA associated peaks that becomes inverted to an increase for a MA-associated LC activity peak.

Source data

Extended Data Fig. 4 Additional examples of LC activity and spectral dynamics at NREMS-to-REMS transitions.

a) Recordings from three different mice during baseline recordings, presented as in Fig. 2a (means±s.e.m. of all transitions per mouse from ZT0-ZT2, number of transitions indicated for each animal). b) Recordings from three different mice during REMS-R, presented as in Fig. 3c (means±s.e.m. of all transitions per mouse from ZT0-ZT6, number of transitions indicated for each animal). The tendency for the EMG to increase during REMS in these mean traces is due to the interruption of REMS that leads to wake-up.

Extended Data Fig. 5 Reliability of closed-loop interference with σ power dynamics.

a) On-line detection of the rising phase (left) and the declining phase (right) of σ power for the periods of optogenetic stimulation and inhibition, respectively. The blue line symbolizes one cycle of the infraslow σ power fluctuations (from trough to peak to trough). The histograms show the probability of detecting the rising phase (blue) and the declining phase (yellow) of the baseline σ power fluctuation across all animals used for the data in Fig. 2b (n = 9) and Fig. 2c (n = 10), in the absence of subsequent light application. Horizontal box-and-whisker plots denote the group data, with circles showing the average phase per animal. The y-axis no longer applies to these group data. This analysis supports the reliable detection of the rising and declining slopes of the natural σ power fluctuations in all animals included. b) Real-time phase during which optogenetic stimulation (blue) and inhibition (yellow) were done to target the rising and declining slopes of the σ power fluctuations, respectively, followed by light exposure. The horizontal box-and-whisker plots show the average phase within the 5 s prior to LED-on. As σ power is much suppressed by optogenetic stimulation (see ref. 10), the detection becomes more variable for blue (rising phase) than for yellow (declining phase) data points. c) Correlation between mean detected phase and effect on REMS for optogenetic stimulation and inhibition. No correlation was observed between the optogenetic manipulations and the REMS entries (Left, Stimulation, R = 0.46, p = 0.21; Inhibition, R = 0.35, p = 0.33) or the relative amount of REMS (Right, Stimulation, R = 0.63, p = 0.08; Inhibition, R = 0.39, p = 0.26) within the stimulation periods. In a, b: Box-and-whisker plots: center line, median; box edges, top and bottom quartiles; whiskers, minimum and maximum.

Source data

Extended Data Fig. 6 Quantification of viral transfection efficiency.

a) Representative micrographs showing LC immunofluorescence for Jaws_EGFP (green) and TH (magenta), with overlay on the right. b) Mean transfection rate of viral injection, quantified as the ratio of Jaws_EGFP- and TH-expressing neurons in relation to the total number of TH-expressing neurons (n = 4 animals). c) Corresponding cell counts. In b and c: Box-and-whisker plots: center line, median; box edges, top and bottom quartiles; whiskers, ±1.5x interquartile ranges.

Source data

Extended Data Fig. 7 Validation of the efficiency and specificity of the REMS-R technique.

a) Box-and-whisker plots of times spent in REMS, wake and NREMS for baseline, REMS-R and yoked conditions. Experiments were done such that each animal was used for all three conditions. Paired data are connected with grey lines. Bonferroni-corrected two-sided paired t-tests; for REMS time; Baseline vs REMS-R, t = 5.822, p = 0.0004, for REMS-R vs yoked, t = −3.171, p = 0.0132; for wake time: Baseline vs REMS-R, t = −4.933, p = 0.0011, for REMS-R vs yoked, t = 1.611, p = 0.1459; for NREMS time: Baseline vs REMS-R, t = 4.346, p = 0.0025, for REMS-R vs yoked, t = −1.024, p = 0.3357. For box-and whisker plots: center line, median; box edges, first and third quartiles; whiskers, minimum and maximum. b) Mean±s.e.m. for hourly values of low-frequency δ power (1.5 – 4 Hz) in the 12-h light phase during which REMS-R was carried out from ZT0-ZT6. c) Mean±s.e.m. for cumulated time spent in REMS during the REMS-R and the subsequent recovery period in undisturbed conditions. Note the greater loss of REMS time during REMS-R compared to yoked animals, followed by recovery after the end of the REMS-R.

Source data

Extended Data Fig. 8 Validating the efficiency of Jaws-mediated inhibition of LC using histological and functional methods.

a) Representative fluorescent micrographs of 4 mice included in the experiment in Fig. 4c, d illustrating EGFP fluorescence of Jaws-expressing LC neurons and position of optic fibers on top of LC. b) Linear correlation between light-induced sleep spindle density changes and REMS entries for the experiment involving LC inhibition late in the undisturbed NREMS-REMS cycle (Fig. 4d). c) Summary data of control experiments for the experiments described in Fig. 4c, d using animals expressing non-light-sensitive (mCherry-expressing) viral constructs (n = 7). Sham corresponds to LED-off conditions. Paired t-tests or Wilcoxon signed-rank tests (Early: for NREMS: t = −2.3997, p = 0.053; for REMS: t = 0.1955, p = 0.8514; for Wake: t = 2.5490, p = 0.0435; for REMS entries: t = −0.4087, p = 0.6968, Late: for NREMS (Wilcoxon signed-rank test):V = 13, p = 0.9375; for REMS: t = 1.1423, p = 0.2968; for Wake: t = 0.2941, p = 0.8226; for REMS entries: t = 1.9133, p = 0.1042). Box-and-whisker plots: center line, median; box edges, first and third quartiles; whiskers, ±1.5x interquartile ranges, paired data connected with grey lines.

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Extended Data Fig. 9 Plasma corticosterone levels after SSD in comparison to undisturbed conditions at the same time of day.

Box-and-whisker plots, with grey lines connecting paired datasets taken at ZT4, once in baseline undisturbed sleep, once after a 4-h SSD for n = 8 animals. Two-sided paired t-test (t = −12.8, p = 4*10−6). (Box-and-whisker plots: center line, median; box edges, top and bottom quartiles; whiskers, ±1.5x interquartile ranges, with grey lines connecting paired data).

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Osorio-Forero, A., Foustoukos, G., Cardis, R. et al. Infraslow noradrenergic locus coeruleus activity fluctuations are gatekeepers of the NREM–REM sleep cycle. Nat Neurosci 28, 84–96 (2025). https://doi.org/10.1038/s41593-024-01822-0

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