REM sleep stabilizes hypothalamic representation of feeding behavior

We first recorded the activity profiles of LH^vgat neurons using in vivo calcium imaging from food-deprived, freely moving mice during exploratory behaviors and feeding. To achieve this, we genetically targeted the expression of the calcium indicator GCaMP6s to LH^vgat neurons by stereotactically injecting an adeno-associated virus (AAV5) carrying a Cre-dependent expression cassette of GCaMP6s (Syn-Flex-GCaMP6s) into the LH of vgat-IRES-Cre mice (Fig. 1 A, Lower Left). We observed long-lasting expression of GCaMP6s in the LH area (up to 9 wk; Fig. 1B). Note that there was no colocalization of LH^vgat with melanin concentrating hormone (MCH)-positive neurons (SI Appendix, Fig. S1A), another REM sleep-active neuron population in the LH (35, 36). Transduced animals were chronically implanted with both EEG/electromyogram (EMG) electrodes and a gradient refractive index (GRIN) lens for simultaneous sleep–wake recording and optical imaging (up to 8 d) with a miniature fluorescence microscope (37), respectively (Fig. 1 A–C, Right and SI Appendix, Fig. S1B). We longitudinally recorded the calcium transients from GCaMP6s-expressing LH^vgat neurons, concomitant to EEG/EMG signals and behavioral video-tracking during a free-feeding task that did not involve instrumental learning or behavioral conditioning (Fig. 1 D and F and Movie S1).

We first investigated whether LH^vgat neuron populations reliably encode feeding (i.e., consummatory aspect), food approach (i.e., appetitive aspect), or nonfeeding behaviors using a cross-correlation analysis. Population vectors were computed from the fluorescence intensity values of single LH^vgat neurons for each recorded frame of an imaging session and correlated with each other (Fig. 1H). Each of these vectors represents the activity pattern of LH^vgat neuron population for a single frame, and the correlation indicates the similarity between two frames (Fig. 1G). Hereafter, we will use the term “map” to describe the average of LH^vgat neuron activity patterns for all frames recorded during feeding, food approach, or nonfeeding behaviors. This analysis revealed that LH^vgat neuron activity patterns during feeding were highly similar and that they were distinct from those observed during nonfeeding behaviors (Fig. 1I). The robust dichotomy between feeding and nonfeeding activity patterns was further captured by unsupervised hierarchical clustering with high sensitivity and specificity (Fig. 1 J–L and SI Appendix, Fig. S3 A–F).

To assess the stability of this representation, we computed an index of similarity by correlating the activity patterns of single frames occurring during individual feeding and food approach bouts to the corresponding map (i.e., averaged activity patterns observed during the respective behavior). We found that the feeding representation was stable over successive feeding bouts (Fig. 2A) and independent of a change of overall LH^vgat neuron population activity (SI Appendix, Fig. S3 G and H), whereas the one for food approach decreased linearly (Fig. 2B). Note that the stability of the representation depends on whether the cells changed their activity in a coordinated or individualized manner (SI Appendix, Fig. S4A).

We then classified single LH^vgat cells based on their activity profiles during food approach and feeding to reflect their contribution to the appetitive or consummatory aspects of feeding behavior, respectively (Fig. 2C). We found that subsets of LH^vgat neurons were maximally active during food intake (feeding-max, 28%), food approach (food approach-max, 30%), or when animals were outside the food quadrant and not eating (nonfeeding-max, 24%, Fig. 2 C and D), hereafter referred to as nonoverlapping “clusters.” Some neurons did not show any preferential activity during the scored behaviors (unspecific feeding, 17%, Fig. 2 C and D). The specificity of this functional classification was further confirmed by within-group post hoc statistical analysis of mean activity level (Fig. 2D) and affinity propagation clustering (38), an unsupervised clustering method (SI Appendix, Fig. S5 A–C and G–I). Interestingly, the activity of feeding-max and unspecific cells linearly decreased over successive feeding bouts, while the activity of nonfeeding-max neurons increased (Fig. 2E). Further, whereas the activity of nonfeeding-max neurons increased across food approaches, the activity of the other clusters decreased (Fig. 2F). These activity changes between the onset and the end of the recording session led to a decreased specificity of the functional clustering (SI Appendix, Fig. S4B) and a redistribution of LH^vgat neuron clusters (SI Appendix, Fig. S4 C and D).

To assess the contribution of LH^vgat neurons to either wakefulness or REM sleep, we next performed simultaneous recordings of cellular activity and EEG/EMG signals from spontaneously sleeping animals. We observed that the activity of LH^vgat neurons was strongly modulated across sleep–wake states (Fig. 3A), with subsets of LH^vgat neurons maximally active during REM sleep (REM-max, 34%), wakefulness (wake-max, 17%), both wakefulness and REM sleep (wake/REM-max, 13%) or non-REM (NREM) sleep (NREM-max, 5%), and no selectivity for either of the sleep states or wakefulness (unspecific sleep, 23%, Fig. 3 B and C). The mean activity of neurons within wake-max, REM-max, and wake/REM-max, but not NREM-max or unspecific, clusters was significantly different across sleep–wake states and post hoc comparisons corroborated the classification of LH^vgat neurons across sleep–wake states (Fig. 3C). Unsupervised clustering further confirmed this result and changes in the classification thresholds mainly led to a reassignment from the wake/REM-max cluster to the wake-max group without significant changes in the other clusters (SI Appendix, Fig. S5 D–I).

Are LH^vgat neuron activity patterns during REM sleep similar to those occurring during awake feeding? To first test whether LH circuits contributing to awake feeding behaviors and sleep stages share common cellular substrates, we computed a coclassification matrix of the different LH^vgat neuron clusters associated with the feeding task and sleep–wake states (Fig. 4A). This revealed a large overlap between the different feeding and sleep clusters (Fig. 4A and SI Appendix, Fig. S6A). We found a significant modulation of the average activity of the neurons in the different feeding clusters across sleep–wake states. Whereas activity of all clusters was lowest during NREM sleep, it was highest during REM sleep for all functional clusters except the nonfeeding-max group (Fig. 4B)

At the population level, we next looked at the activity patterns during the different sleep–wake states. We found that the activity patterns of LH^vgat neurons during REM sleep showed high similarity to the awake feeding and food approach maps (Fig. 4C). While these patterns remained stable over time during REM sleep, they did not during NREM sleep and wakefulness, possibly due to lower activity and higher susceptibility to background noise (Fig. 4C). The similarity of the activity pattern to the feeding map was positively correlated with overall cell activity levels during REM sleep, but not NREM sleep or wakefulness, for all of the recorded animals and it was significantly higher than chance (Fig. 4 D and E). Patterns similar to food approach or nonfeeding maps were not found to be correlated with high population activity for any of the sleep–wake states (SI Appendix, Fig. S6 B and C).

We then tested whether activation of LH^vgat neurons during REM sleep contributes to the maintenance of appropriate feeding behavior during wakefulness. To achieve this, we targeted the expression of the light-sensitive proton pump ArchT3.0 to LH^vgat neurons by stereotactically injecting an adeno-associated virus (AAV2) carrying a Cre-dependent expression cassette of ArchT (DIO-EF1a-ArchT3.0-eYFP, silencing) or YFP (DIO-EF1a-eYFP, control) into the LH of vgat-IRES-Cre mice (Fig. 5 A, Lower Right). We tested for the biological function of the opsin by recording the electrophysiological response to optogenetic silencing in mice that were chronically implanted with tetrodes inside a microdrive and an optical fiber in the LH (Fig. 5 A, Left). Of 18 recorded neurons, 8 showed a significant decrease in spike rate upon continuous illumination delivered selectively during REM sleep episodes (light-responsive, 44%) and 10 did not change their spike rates in response to the light delivery (light-nonresponsive, 56%, Fig. 5 B and C and SI Appendix, Fig. S7 B and C). Consistent with our imaging results (Fig. 3), we observed that light-responsive putative LH^vgat neurons increased their spiking activity at the onset of REM sleep and remained active until the transitions to wakefulness (SI Appendix, Fig. S7 C–E). An additional cohort of mice were implanted with cortical EEG electrodes, EMG wires, and bilateral optical fibers above the LH. These animals were fed ad libitum and habituated to the free-feeding task for three consecutive days. On the fourth day, LH^vgat neurons were optogenetically silenced selectively during REM sleep episodes prior to the free-feeding paradigm in both ArchT3.0- and YFP-transduced animals (Fig. 5 D and E). Interestingly, we found that silencing of LH^vgat neurons selectively during REM sleep significantly reduced average food intake during subsequent wakefulness in ArchT-transduced, as compared to control animals (YFP control vs. ArchT: +1.49 ± 9.09 vs. −29.06 ± 6.87%, Fig. 5F). Importantly, silencing during wakefulness in a separate set of animals had no effect on food intake (YFP control vs. ArchT: −13.10 ± 12.44 vs. −14.19 ± 17.86%, Fig. 5G), suggesting that this behavioral response is specific to REM sleep manipulation of LH^vgat neuron activity. Analysis of the behavioral sequence showed that the reduction of food consumption was due to a significant decrease of the initiation of food intake events (YFP control vs. ArchT: 23.00 ± 1.03 vs. 15.80 ± 2.29, Fig. 5H) rather than time spent exploring the food quadrant (YFP control vs. ArchT: 946.04 ± 119.72 s vs. 898.76 ± 187.91 s, Fig. 5I). The baseline values of food intake were similar between YFP control and the ArchT-transduced mice in both the REM- and the wake-silencing cohorts. Furthermore, neither REM-, nor wake-selective silencing altered the sleep–wake cycle parameters during the optogenetic procedure or the following free-feeding experiments (Fig. 5E and SI Appendix, Figs. S8 and S9, respectively).

To investigate whether REM sleep provides a window for activity tuning of neuronal network dynamics, we targeted the expression of GCaMP6s and ArchT to the right LH by stereotactic coinfusion of AAV5-Syn-Flex-GCaMP6s and AAV2-CAG-Flex-ArchT-tdTomato in vgat-IRES-Cre mice (Fig. 6A and Methods Summary). ∼74% of GCaMP6s-expressing LH^vgat neurons coexpressed ArchT-tdTomato (Fig. 6B and SI Appendix, Fig. S10 A and B). Mice were then instrumented with a GRIN lens for consecutive REM sleep-specific optogenetic silencing followed by imaging concomitant to EEG/EMG recordings during the free-feeding task (Fig. 6C and SI Appendix, Fig. S11A). We found that optogenetic silencing induced a significant change of the feeding map in comparison to their baseline representation. The change persisted for at least 4 d (Fig. 6 D and E and SI Appendix, Fig. S13 A–C). This change was not due to daily variation of the feeding map (SI Appendix, Fig. S12A) or changes in overall neuronal activity (SI Appendix, Figs. S12E and S13E) since both remained stable over days. Note that also the food approach map was significantly changed (SI Appendix, Fig. S13D), consistent with its variation within recording session (Fig. 2B) and across days (SI Appendix, Fig. S12B).

At the single-cell level, the activity of feeding-max neurons was significantly decreased during postsilencing feeding behavior but not during food approach or nonfeeding behavior (Fig. 6 F and G and SI Appendix, Fig. S13 G–I). This decrease was not due to changes in the cellular recruitment because the number of LH^vgat neurons per cluster remained stable over days, and after optogenetic silencing (Fig. 6F and SI Appendix, Fig. S12C), despite daily variation of the cellular composition of the clusters (SI Appendix, Figs. S12D and S13F). These effects were not due to photobleaching of GCaMP6s fluorescence upon laser light stimulation (SI Appendix, Fig. S11 B–I).