Publications by Author: Roberto De Luca

Among the neuronal populations implicated in sleep-wake control, the ventrolateral preoptic (VLPO) nucleus has emerged as a key sleep-promoting center. However, the synaptic drives that regulate the VLPO to control arousal levels in vivo have not to date been identified. Here, we show that sleep-promoting galaninergic neurons within the VLPO nucleus, defined pharmacologically and by single-cell transcript analysis, are postsynaptic targets of lateral hypothalamic GABAergic (LHGABA) neurons and that activation of this pathway in vivo rapidly drives wakefulness. Ca2+ imaging from LHGABA neurons indicate that they are both wake and rapid eye movement (REM)-sleep active. Consistent with the potent arousal-promoting property of the LHGABA → VLPO pathway, presynaptic inputs to LHGABA neurons originate from several canonical stress- and arousal-related network nodes. This work represents the first demonstration that direct synaptic inhibition of the VLPO area can suppress sleep-promoting neurons to rapidly promote arousal.

The "dorsal pons", or "dorsal pontine tegmentum" (dPnTg), is part of the brainstem. It is a complex, densely packed region whose nuclei are involved in regulating many vital functions. Notable among them are the parabrachial nucleus, the Kölliker Fuse, the Barrington nucleus, the locus coeruleus, and the dorsal, laterodorsal, and ventral tegmental nuclei. In this study, we applied single-nucleus RNA-seq (snRNA-seq) to resolve neuronal subtypes based on their unique transcriptional profiles and then used multiplexed error robust fluorescence in situ hybridization (MERFISH) to map them spatially. We sampled  1 million cells across the dPnTg and defined the spatial distribution of over 120 neuronal subtypes. Our analysis identified an unpredicted high transcriptional diversity in this region and pinpointed the unique marker genes of many neuronal subtypes. We also demonstrated that many neuronal subtypes are transcriptionally similar between humans and mice, enhancing this study's translational value. Finally, we developed a freely accessible, GPU and CPU-powered dashboard ( http://harvard.heavy.ai:6273/ ) that combines interactive visual analytics and hardware-accelerated SQL into a data science framework to allow the scientific community to query and gain insights into the data.