Molecular interrogation of hypothalamic organization reveals distinct dopamine neuronal subtypes

Single-cell and spatial omics: exploring hypothalamic heterogeneity

Elucidating the complex dynamic cellular organization in the hypothalamus is critical for understanding its role in coordinating fundamental body functions. Over the past decade, single-cell and spatial omics technologies have significantly evolved, overcoming initial technical challenges in capturing and analyzing individual cells. These high-throughput omics technologies now offer a remarkable opportunity to comprehend the complex spatiotemporal patterns of transcriptional diversity and cell-type characteristics across the entire hypothalamus. Current single-cell and single-nucleus RNA sequencing methods comprehensively quantify gene expression by exploring distinct phenotypes across various subregions of the hypothalamus. However, single-cell/single-nucleus RNA sequencing requires isolating the cell/nuclei from the tissue, potentially resulting in the loss of spatial information concerning neuronal networks. Spatial transcriptomics methods, by bypassing the cell dissociation, can elucidate the intricate spatial organization of neural networks through their imaging and sequencing technologies. In this review, we highlight the applicative value of single-cell and spatial transcriptomics in exploring the complex molecular-genetic diversity of hypothalamic cell types, driven by recent high-throughput achievements.

scQA: A dual-perspective cell type identification model for single cell transcriptome data

Single-cell RNA sequencing technologies have been pivotal in advancing the development of algorithms for clustering heterogeneous cell populations. Existing methods for utilizing scRNA-seq data to identify cell types tend to neglect the beneficial impact of dropout events and perform clustering focusing solely on quantitative perspective. Here, we introduce a novel method named scQA, notable for its ability to concurrently identify cell types and cell type-specific key genes from both qualitative and quantitative perspectives. In contrast to other methods, scQA not only identifies cell types but also extracts key genes associated with these cell types, enabling bidirectional clustering for scRNA-seq data. Through an iterative process, our approach aims to minimize the number of landmarks to approximately a dozen while maximizing the inclusion of quasi-trend-preserved genes with dropouts both qualitatively and quantitatively. It then clusters cells by employing an ingenious label propagation strategy, obviating the requirement for a predetermined number of cell types. Validated on 20 publicly available scRNA-seq datasets, scQA consistently outperforms other salient tools. Furthermore, we confirm the effectiveness and potential biological significance of the identified key genes through both external and internal validation. In conclusion, scQA emerges as a valuable tool for investigating cell heterogeneity due to its distinctive fusion of qualitative and quantitative facets, along with bidirectional clustering capabilities. Furthermore, it can be seamlessly integrated into border scRNA-seq analyses. The source codes are publicly available at https://github.com/LD-Lyndee/scQA.

Hypothalamic neurons fully or partially expressing the dopaminergic phenotype: development, distribution, functioning and functional significance. A review

The hypothalamus is a key link in neuroendocrine regulations, which are provided by neuropeptides and dopamine. Until the late 1980 s, it was believed that, along with peptidergic neurons, hypothalamus contained dopaminergic neurons. Over time, it has been shown that besides dopaminergic neurons expressing the dopamine transporter and dopamine-synthesizing enzymes - tyrosine hydroxylase (TH) and aromatic L-amino acid decarboxylase (AADC) - the hypothalamus contains neurons expressing only TH, only AADC, both enzymes or only dopamine transporter. The end secretory product of TH neurons is L-3,4-dihydroxyphenylalanine, while that of AADC neurons and bienzymatic neurons lacking the dopamine transporter is dopamine. During ontogenesis, especially in the perinatal period, monoenzymatic neurons predominate in the hypothalamic neuroendocrine centers. It is assumed that L-3,4-dihydroxyphenylalanine and dopamine are released into the neuropil, cerebral ventricles, and blood vessels, participating in the regulation of target cell differentiation in the perinatal period and the functioning of target cells in adulthood.

Mapping GABAergic projections that mediate feeding

Neuroscience offers important insights into the pathogenesis and treatment of obesity by investigating neural circuits underpinning appetite and feeding. Gamma-aminobutyric acid (GABA), one of the most abundant neurotransmitters in the brain, and its associated receptors represent an array of pharmacologically targetable mediators of appetite signalling. Targeting the GABAergic system is therefore an increasingly investigated approach to obesity treatment. However, the many GABAergic projections that control feeding have yet to be collectively analysed. This review provides a comprehensive analysis of the relationship between GABAergic signalling and appetite by examining both foundational studies and the results of newly emerging chemogenetic/optogenetic experiments. A current snapshot of these efforts to map GABAergic projections influencing appetite is provided, and potential avenues for further investigation are provided.

Does the brain make prolactin?

The prolactin receptor (Prlr) is widely expressed in the brain, particularly in the hypothalamus. Prolactin also has an increasing range of well-characterised effects on central nervous system function. Because of this, over many years, there has been interest in whether the hormone itself is also expressed within the brain, perhaps acting as a neuropeptide to regulate brain function via its receptor in neurons. The aim of this invited review is to critically evaluate the evidence for brain production of prolactin. Unlike the evidence for the Prlr, evidence for brain prolactin is inconsistent and variable. A range of different antibodies have been used, each characterising a different distribution of prolactin-like immunoreactivity. Prolactin mRNA has been detected in the brain, but only at levels markedly lower than seen in the pituitary gland. Importantly, it has largely only been detected by highly sensitive amplification-based techniques, and the extreme sensitivity means there is a risk of false-positive data. Modern in situ hybridisation methods and single-cell RNA sequencing have not provided supporting evidence, but it is hard to prove a negative! Finally, I acknowledge and discuss the possibility that prolactin might be produced in the brain under specific circumstances, such as to promote a neuroprotective response to cell damage. Collectively, however, based on this analysis, I have formed the opinion that brain production of prolactin is unlikely, and even if occurs, it is of little physiological consequence. Most, if not all of the brain actions of prolactin can be explained by pituitary prolactin gaining access to the brain.

A bipolar disorder-associated missense variant alters adenylyl cyclase 2 activity and promotes mania-like behavior

The single nucleotide polymorphism rs13166360, causing a substitution of valine (Val) 147 to leucine (Leu) in the adenylyl cyclase 2 (ADCY2), has previously been associated with bipolar disorder (BD). Here we show that the disease-associated ADCY2 missense mutation diminishes the enzyme´s capacity to generate the second messenger 3',5'-cylic adenosine monophosphate (cAMP) by altering its subcellular localization. We established mice specifically carrying the Val to Leu substitution using CRISPR/Cas9-based gene editing. Mice homozygous for the Leu variant display symptoms of a mania-like state accompanied by cognitive impairments. Mutant animals show additional characteristic signs of rodent mania models, i.e., they are hypersensitive to amphetamine, the observed mania-like behaviors are responsive to lithium treatment and the Val to Leu substitution results in a shifted excitatory/inhibitory synaptic balance towards more excitation. Exposure to chronic social defeat stress switches homozygous Leu variant carriers from a mania- to a depressive-like state, a transition which is reminiscent of the alternations characterizing the symptomatology in BD patients. Single-cell RNA-seq (scRNA-seq) revealed widespread Adcy2 mRNA expression in numerous hippocampal cell types. Differentially expressed genes particularly identified from glutamatergic CA1 neurons point towards ADCY2 variant-dependent alterations in multiple biological processes including cAMP-related signaling pathways. These results validate ADCY2 as a BD risk gene, provide insights into underlying disease mechanisms, and potentially open novel avenues for therapeutic intervention strategies.

Clustering single-cell RNA sequencing data via iterative smoothing and self-supervised discriminative embedding

Single-cell transcriptome sequencing (scRNA-seq) is a high-throughput technique used to study gene expression at the single-cell level. Clustering analysis is a commonly used method in scRNA-seq data analysis, helping researchers identify cell types and uncover interactions between cells. However, the choice of a robust similarity metric in the clustering procedure is still an open challenge due to the complex underlying structures of the data and the inherent noise in data acquisition. Here, we propose a deep clustering method for scRNA-seq data called scRISE (scRNA-seq Iterative Smoothing and self-supervised discriminative Embedding model) to resolve this challenge. The model consists of two main modules: an iterative smoothing module based on graph autoencoders designed to denoise the data and refine the pairwise similarity in turn to gradually incorporate cell structural features and enrich the data information; and a self-supervised discriminative embedding module with adaptive similarity threshold for partitioning samples into correct clusters. Our approach has shown improved quality of data representation and clustering on seventeen scRNA-seq datasets against a number of state-of-the-art deep learning clustering methods. Furthermore, utilizing the scRISE method in biological analysis against the HNSCC dataset has unveiled 62 informative genes, highlighting their potential roles as therapeutic targets and biomarkers.

Neural circuits for taste sensation

The sense of taste arises from the detection of chemicals in food by taste buds, the peripheral cellular detectors for taste. Although numerous studies have extensively investigated taste buds, research on neural circuits from primary taste neurons innervating taste buds to the central nervous system has only recently begun owing to recent advancements in neuroscience research tools. This minireview focuses primarily on recent reports utilizing advanced neurogenetic tools across relevant brain regions.

Brain RFamide Neuropeptides in Stress-Related Psychopathologies

The RFamide peptide family is a group of proteins that share a common C-terminal arginine-phenylalanine-amide motif. To date, the family comprises five groups in mammals: neuropeptide FF, LPXRFamides/RFamide-related peptides, prolactin releasing peptide, QRFP, and kisspeptins. Different RFamide peptides have their own cognate receptors and are produced by different cell populations, although they all can also bind to neuropeptide FF receptors with different affinities. RFamide peptides function in the brain as neuropeptides regulating key aspects of homeostasis such as energy balance, reproduction, and cardiovascular function. Furthermore, they are involved in the organization of the stress response including modulation of pain. Considering the interaction between stress and various parameters of homeostasis, the role of RFamide peptides may be critical in the development of stress-related neuropathologies. This review will therefore focus on the role of RFamide peptides as possible key hubs in stress and stress-related psychopathologies. The neurotransmitter coexpression profile of RFamide-producing cells is also discussed, highlighting its potential functional significance. The development of novel pharmaceutical agents for the treatment of stress-related disorders is an ongoing need. Thus, the importance of RFamide research is underlined by the emergence of peptidergic and G-protein coupled receptor-based therapeutic targets in the pharmaceutical industry.

Exploratory Rearing Is Governed by Hypothalamic Melanin-Concentrating Hormone Neurons According to Locus Ceruleus

Information seeking, such as standing on tiptoes to look around in humans, is observed across animals and helps survival. Its rodent analog-unsupported rearing on hind legs-was a classic model in deciphering neural signals of cognition and is of intense renewed interest in preclinical modeling of neuropsychiatric states. Neural signals and circuits controlling this dedicated decision to seek information remain largely unknown. While studying subsecond timing of spontaneous behavioral acts and activity of melanin-concentrating hormone (MCH) neurons (MNs) in behaving male and female mice, we observed large MN activity spikes that aligned to unsupported rears. Complementary causal, loss and gain of function, analyses revealed specific control of rear frequency and duration by MNs and MCHR1 receptors. Activity in a key stress center of the brain-the locus ceruleus noradrenaline cells-rapidly inhibited MNs and required functional MCH receptors for its endogenous modulation of rearing. By defining a neural module that both tracks and controls rearing, these findings may facilitate further insights into biology of information seeking.

Different electrophysiological profiles of genetically labelled dopaminergic neurons in the mouse midbrain and olfactory bulb

Dopaminergic (DA) neurons play pivotal roles in diverse brain functions, spanning movement, reward processing and sensory perception. DA neurons are most abundant in the midbrain (Substantia Nigra pars compacta [SNC] and Ventral Tegmental Area [VTA]) and the olfactory bulb (OB) in the forebrain. Interestingly, a subtype of OB DA neurons is capable of regenerating throughout life, while a second class is exclusively born during embryonic development. Compelling evidence in SNC and VTA also indicates substantial heterogeneity in terms of morphology, connectivity and function. To further investigate this heterogeneity and directly compare form and function of midbrain and forebrain bulbar DA neurons, we performed immunohistochemistry and whole-cell patch-clamp recordings in ex vivo brain slices from juvenile DAT-tdTomato mice. After confirming the penetrance and specificity of the dopamine transporter (DAT) Cre line, we compared soma shape, passive membrane properties, voltage sags and action potential (AP) firing across midbrain and forebrain bulbar DA subtypes. We found that each DA subgroup within midbrain and OB was highly heterogeneous, and that DA neurons across the two brain areas are also substantially different. These findings complement previous work in rats as well as gene expression and in vivo datasets, further questioning the existence of a single "dopaminergic" neuronal phenotype.

Transient expression of the neuropeptide galanin modulates peripheral‑to‑central connectivity in the somatosensory thalamus during whisker development in mice

The significance of transient neuropeptide expression during postnatal brain development is unknown. Here, we show that galanin expression in the ventrobasal thalamus of infant mice coincides with whisker map development and modulates subcortical circuit wiring. Time-resolved neuroanatomy and single-nucleus RNA-seq identified complementary galanin (Gal) and galanin receptor 1 (Galr1) expression in the ventrobasal thalamus and the principal sensory nucleus of the trigeminal nerve (Pr5), respectively. Somatodendritic galanin release from the ventrobasal thalamus was time-locked to the first postnatal week, when Gal1R+ Pr5 afferents form glutamatergic (Slc17a6+) synapses for the topographical whisker map to emerge. RNAi-mediated silencing of galanin expression disrupted glutamatergic synaptogenesis, which manifested as impaired whisker-dependent exploratory behaviors in infant mice, with behavioral abnormalities enduring into adulthood. Pharmacological probing of receptor selectivity in vivo corroborated that target recognition and synaptogenesis in the thalamus, at least in part, are reliant on agonist-induced Gal1R activation in inbound excitatory axons. Overall, we suggest a neuropeptide-dependent developmental mechanism to contribute to the topographical specification of a fundamental sensory neurocircuit in mice.

Maternal dietary fat during lactation shapes single nucleus transcriptomic profile of postnatal offspring hypothalamus in a sexually dimorphic manner in mice

Maternal overnutrition during lactation predisposes offspring to develop metabolic diseases and exacerbates the relevant syndromes in males more than females in later life. The hypothalamus is a heterogenous brain region that regulates energy balance. Here we combined metabolic trait quantification of mother and offspring mice under low and high fat diet (HFD) feeding during lactation, with single nucleus transcriptomic profiling of their offspring hypothalamus at peak lacation to understand the cellular and molecular alterations in response to maternal dietary pertubation. We found significant expansion in neuronal subpopulations including histaminergic (Hdc), arginine vasopressin/retinoic acid receptor-related orphan receptor β (Avp/Rorb) and agouti-related peptide/neuropeptide Y (AgRP/Npy) in male offspring when their mothers were fed HFD, and increased Npy-astrocyte interactions in offspring responding to maternal overnutrition. Our study provides a comprehensive offspring hypothalamus map at the peak lactation and reveals how the cellular subpopulations respond to maternal dietary fat in a sex-specific manner during development.

microRNA-33 controls hunger signaling in hypothalamic AgRP neurons

AgRP neurons drive hunger, and excessive nutrient intake is the primary driver of obesity and associated metabolic disorders. While many factors impacting central regulation of feeding behavior have been established, the role of microRNAs in this process is poorly understood. Utilizing unique mouse models, we demonstrate that miR-33 plays a critical role in the regulation of AgRP neurons, and that loss of miR-33 leads to increased feeding, obesity, and metabolic dysfunction in mice. These effects include the regulation of multiple miR-33 target genes involved in mitochondrial biogenesis and fatty acid metabolism. Our findings elucidate a key regulatory pathway regulated by a non-coding RNA that impacts hunger by controlling multiple bioenergetic processes associated with the activation of AgRP neurons, providing alternative therapeutic approaches to modulate feeding behavior and associated metabolic diseases.

A single-cell genomic strategy for alternative transcript start sites identification

Alternative transcription start sites (TSSs) usage plays a critical role in gene transcription regulation in mammals. However, precisely identifying alternative TSSs remains challenging at the genome-wide level. We report a single-cell genomic technology for alternative TSSs annotation and cell heterogeneity detection. In the method, we utilize Fluidigm C1 system to capture individual cells of interest, SMARTer cDNA synthesis kit to recover full-length cDNAs, then dual priming oligonucleotide system to specifically enrich TSSs for genomic analysis. We apply this method to a genome-wide study of alternative TSSs identification in two different IFN-β stimulated mouse embryonic fibroblasts (MEFs). The data clearly discriminate two IFN-β stimulated MEFs. Moreover, our results indicate 81% expressed genes in these two cell types containing multiple TSSs, which is much higher than previous predictions based on Cap-Analysis Gene Expression (CAGE) (58%) or empirical determination (54%) in various cell types. This indicates that alternative TSSs are more pervasive than expected and implies our strategy could position them at an unprecedented sensitivity. It would be helpful for elucidating their biological insights in future.

Molecularly stratified hypothalamic astrocytes are cellular foci for obesity

Astrocytes safeguard the homeostasis of the central nervous system1,2. Despite their prominent morphological plasticity under conditions that challenge the brain's adaptive capacity3-5, the classification of astrocytes, and relating their molecular make-up to spatially devolved neuronal operations that specify behavior or metabolism, remained mostly futile6,7. Although it seems unexpected in the era of single-cell biology, the lack of a major advance in stratifying astrocytes under physiological conditions rests on the incompatibility of 'neurocentric' algorithms that rely on stable developmental endpoints, lifelong transcriptional, neurotransmitter, and neuropeptide signatures for classification6-8 with the dynamic functional states, anatomic allocation, and allostatic plasticity of astrocytes1. Simplistically, therefore, astrocytes are still grouped as 'resting' vs. 'reactive', the latter referring to pathological states marked by various inducible genes3,9,10. Here, we introduced a machine learning-based feature recognition algorithm that benefits from the cumulative power of published single-cell RNA-seq data on astrocytes as a reference map to stepwise eliminate pleiotropic and inducible cellular features. For the healthy hypothalamus, this walk-back approach revealed gene regulatory networks (GRNs) that specified subsets of astrocytes, and could be used as landmarking tools for their anatomical assignment. The core molecular censuses retained by astrocyte subsets were sufficient to stratify them by allostatic competence, chiefly their signaling and metabolic interplay with neurons. Particularly, we found differentially expressed mitochondrial genes in insulin-sensing astrocytes and demonstrated their reciprocal signaling with neurons that work antagonistically within the food intake circuitry. As a proof-of-concept, we showed that disrupting Mfn2 expression in astrocytes reduced their ability to support dynamic circuit reorganization, a time-locked feature of satiety in the hypothalamus, thus leading to obesity in mice. Overall, our results suggest that astrocytes in the healthy brain are fundamentally more heterogeneous than previously thought and topologically mirror the specificity of local neurocircuits.

An excitatory projection from the basal forebrain to the ventral tegmental area that underlies anorexia-like phenotypes

Maladaptation in balancing internal energy needs and external threat cues may result in eating disorders. However, brain mechanisms underlying such maladaptations remain elusive. Here, we identified that the basal forebrain (BF) sends glutamatergic projections to glutamatergic neurons in the ventral tegmental area (VTA) in mice. Glutamatergic neurons in both regions displayed correlated responses to various stressors. Notably, in vivo manipulation of BF terminals in the VTA revealed that the glutamatergic BF → VTA circuit reduces appetite, increases locomotion, and elicits avoidance. Consistently, activation of VTA glutamatergic neurons reduced body weight, blunted food motivation, and caused hyperactivity with behavioral signs of anxiety, all hallmarks of typical anorexia symptoms. Importantly, activation of BF glutamatergic terminals in the VTA reduced dopamine release in the nucleus accumbens. Collectively, our results point to overactivation of the glutamatergic BF → VTA circuit as a potential cause of anorexia-like phenotypes involving reduced dopamine release.

Region-specific transcriptomic responses to obesity and diabetes in macaque hypothalamus

The hypothalamus plays a crucial role in the progression of obesity and diabetes; however, its structural complexity and cellular heterogeneity impede targeted treatments. Here, we profiled the single-cell and spatial transcriptome of the hypothalamus in obese and sporadic type 2 diabetic macaques, revealing primate-specific distributions of clusters and genes as well as spatial region, cell-type-, and gene-feature-specific changes. The infundibular (INF) and paraventricular nuclei (PVN) are most susceptible to metabolic disruption, with the PVN being more sensitive to diabetes. In the INF, obesity results in reduced synaptic plasticity and energy sensing capability, whereas diabetes involves molecular reprogramming associated with impaired tanycytic barriers, activated microglia, and neuronal inflammatory response. In the PVN, cellular metabolism and neural activity are suppressed in diabetic macaques. Spatial transcriptomic data reveal microglia's preference for the parenchyma over the third ventricle in diabetes. Our findings provide a comprehensive view of molecular changes associated with obesity and diabetes.

The Role of Central Oxytocin in Autonomic Regulation

Oxytocin (OXT), a neuropeptide originating from the hypothalamus and traditionally associated with peripheral functions in parturition and lactation, has emerged as a pivotal player in the central regulation of the autonomic nervous system (ANS). This comprehensive ANS, comprising sympathetic, parasympathetic, and enteric components, intricately combines sympathetic and parasympathetic influences to provide unified control. The central oversight of sympathetic and parasympathetic outputs involves a network of interconnected regions spanning the neuroaxis, playing a pivotal role in the real-time regulation of visceral function, homeostasis, and adaptation to challenges. This review unveils the significant involvement of the central OXT system in modulating autonomic functions, shedding light on diverse subpopulations of OXT neurons within the paraventricular nucleus of the hypothalamus and their intricate projections. The narrative progresses from the basics of central ANS regulation to a detailed discussion of the central controls of sympathetic and parasympathetic outflows. The subsequent segment focuses specifically on the central OXT system, providing a foundation for exploring the central role of OXT in ANS regulation. This review synthesizes current knowledge, paving the way for future research endeavors to unravel the full scope of autonomic control and understand multifaceted impact of OXT on physiological outcomes.

Deep scRNA sequencing reveals a broadly applicable Regeneration Classifier and implicates antioxidant response in corticospinal axon regeneration

Despite substantial progress in understanding the biology of axon regeneration in the CNS, our ability to promote regeneration of the clinically important corticospinal tract (CST) after spinal cord injury remains limited. To understand regenerative heterogeneity, we conducted patch-based single-cell RNA sequencing on rare regenerating CST neurons at high depth following PTEN and SOCS3 deletion. Supervised classification with Garnett gave rise to a Regeneration Classifier, which can be broadly applied to predict the regenerative potential of diverse neuronal types across developmental stages or after injury. Network analyses highlighted the importance of antioxidant response and mitochondrial biogenesis. Conditional gene deletion validated a role for NFE2L2 (or NRF2), a master regulator of antioxidant response, in CST regeneration. Our data demonstrate a universal transcriptomic signature underlying the regenerative potential of vastly different neuronal populations and illustrate that deep sequencing of only hundreds of phenotypically identified neurons has the power to advance regenerative biology.

Unveiling Hypothalamic Molecular Signatures via Retrograde Viral Tracing and Single-Cell Transcriptomics

Despite the importance of hypothalamic neurocircuits in regulating homeostatic and survival-related behaviors, our understanding of the intrinsic molecular identities of neural components involved in these complex multi-synaptic interactions remains limited. In this study, we constructed a Cre recombinase-dependent pseudorabies virus (PRVs) capable of crossing synapses, coupled with transcriptome analysis of single upstream neurons post-infection. By utilizing this retrograde nuclear Connect-seq (nuConnect-seq) approach, we generated a single nuclei RNA-seq (snRNA-seq) dataset of 1,533 cells derived from the hypothalamus of CRH-IRES-Cre (CRH-Cre) mice. To ensure the technical validity of our nuConnect-seq dataset, we employed a label transfer technique against an integrated reference dataset of postnatal mouse hypothalamus comprising 152,524 QC-passed cells. The uniqueness of our approach lies in the integration of diverse datasets for validation, providing a more nuanced diversity of hypothalamic cell types. The presented validated dataset may deepen our understanding of hypothalamic neurocircuits and underscore the essential role of comprehensive integrated transcriptomic data for technical validity.

scFseCluster: a feature selection-enhanced clustering for single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) enables researchers to reveal previously unknown cell heterogeneity and functional diversity, which is impossible with bulk RNA sequencing. Clustering approaches are widely used for analyzing scRNA-seq data and identifying cell types and states. In the past few years, various advanced computational strategies emerged. However, the low generalization and high computational cost are the main bottlenecks of existing methods. In this study, we established a novel computational framework, scFseCluster, for scRNA-seq clustering analysis. scFseCluster incorporates a metaheuristic algorithm (Feature Selection based on Quantum Squirrel Search Algorithm) to extract the optimal gene set, which largely guarantees the performance of cell clustering. We conducted simulation experiments in several aspects to verify the performance of the proposed approach. scFseCluster performed very well on eight benchmark scRNA-seq datasets because of the optimal gene sets obtained using the Feature Selection based on Quantum Squirrel Search Algorithm. The comparative study demonstrated the significant advantages of scFseCluster over seven State-of-the-Art algorithms. In addition, our analysis shows that feature selection on high-variable genes can significantly improve clustering performance. In conclusion, our study demonstrates that scFseCluster is a highly versatile tool for enhancing scRNA-seq data clustering analysis.

Early life adversity shapes social subordination and cell type-specific transcriptomic patterning in the ventral hippocampus

Adverse events in early life can modulate the response to additional stressors later in life and increase the risk of developing psychiatric disorders. The underlying molecular mechanisms responsible for these effects remain unclear. Here, we uncover that early life adversity (ELA) in mice leads to social subordination. Using single-cell RNA sequencing (scRNA-seq), we identified cell type-specific changes in the transcriptional state of glutamatergic and GABAergic neurons in the ventral hippocampus of ELA mice after exposure to acute social stress in adulthood. These findings were reflected by an alteration in excitatory and inhibitory synaptic transmission induced by ELA in response to acute social stress. Finally, enhancing the inhibitory network function through transient diazepam treatment during an early developmental sensitive period reversed the ELA-induced social subordination. Collectively, this study significantly advances our understanding of the molecular, physiological, and behavioral alterations induced by ELA, uncovering a previously unknown cell type-specific vulnerability to ELA.

Microbial stimulation of oxytocin release from the intestinal epithelium via secretin signaling

Intestinal microbes impact the health of the intestine and organs distal to the gut. Limosilactobacillus reuteri is a human intestinal microbe that promotes normal gut transit, the anti-inflammatory immune system, wound healing, normal social behavior in mice, and prevents bone reabsorption. Oxytocin impacts these functions and oxytocin signaling is required for L. reuteri-mediated wound healing and social behavior; however, the events in the gut leading to oxytocin stimulation and beneficial effects are unknown. Here we report evolutionarily conserved oxytocin production in the intestinal epithelium through analysis of single-cell RNA-Seq datasets and imaging of human and mouse intestinal tissues. Moreover, human intestinal organoids produce oxytocin, demonstrating that the intestinal epithelium is sufficient to produce oxytocin. We find that L. reuteri facilitates oxytocin secretion from human intestinal tissue and human intestinal organoids. Finally, we demonstrate that stimulation of oxytocin secretion by L. reuteri is dependent on the gut hormone secretin, which is produced in enteroendocrine cells, while oxytocin itself is produced in enterocytes. Altogether, this work demonstrates that oxytocin is produced and secreted from enterocytes in the intestinal epithelium in response to secretin stimulated by L. reuteri. This work thereby identifies oxytocin as an intestinal hormone and provides mechanistic insight into avenues by which gut microbes promote host health.

Molecularly defined and spatially resolved cell atlas of the whole mouse brain

In mammalian brains, millions to billions of cells form complex interaction networks to enable a wide range of functions. The enormous diversity and intricate organization of cells have impeded our understanding of the molecular and cellular basis of brain function. Recent advances in spatially resolved single-cell transcriptomics have enabled systematic mapping of the spatial organization of molecularly defined cell types in complex tissues1-3, including several brain regions (for example, refs. 1-11). However, a comprehensive cell atlas of the whole brain is still missing. Here we imaged a panel of more than 1,100 genes in approximately 10 million cells across the entire adult mouse brains using multiplexed error-robust fluorescence in situ hybridization12 and performed spatially resolved, single-cell expression profiling at the whole-transcriptome scale by integrating multiplexed error-robust fluorescence in situ hybridization and single-cell RNA sequencing data. Using this approach, we generated a comprehensive cell atlas of more than 5,000 transcriptionally distinct cell clusters, belonging to more than 300 major cell types, in the whole mouse brain with high molecular and spatial resolution. Registration of this atlas to the mouse brain common coordinate framework allowed systematic quantifications of the cell-type composition and organization in individual brain regions. We further identified spatial modules characterized by distinct cell-type compositions and spatial gradients featuring gradual changes of cells. Finally, this high-resolution spatial map of cells, each with a transcriptome-wide expression profile, allowed us to infer cell-type-specific interactions between hundreds of cell-type pairs and predict molecular (ligand-receptor) basis and functional implications of these cell-cell interactions. These results provide rich insights into the molecular and cellular architecture of the brain and a foundation for functional investigations of neural circuits and their dysfunction in health and disease.

Transfer learning for clustering single-cell RNA-seq data crossing-species and batch, case on uterine fibroids

Due to the high dimensionality and sparsity of the gene expression matrix in single-cell RNA-sequencing (scRNA-seq) data, coupled with significant noise generated by shallow sequencing, it poses a great challenge for cell clustering methods. While numerous computational methods have been proposed, the majority of existing approaches center on processing the target dataset itself. This approach disregards the wealth of knowledge present within other species and batches of scRNA-seq data. In light of this, our paper proposes a novel method named graph-based deep embedding clustering (GDEC) that leverages transfer learning across species and batches. GDEC integrates graph convolutional networks, effectively overcoming the challenges posed by sparse gene expression matrices. Additionally, the incorporation of DEC in GDEC enables the partitioning of cell clusters within a lower-dimensional space, thereby mitigating the adverse effects of noise on clustering outcomes. GDEC constructs a model based on existing scRNA-seq datasets and then applying transfer learning techniques to fine-tune the model using a limited amount of prior knowledge gleaned from the target dataset. This empowers GDEC to adeptly cluster scRNA-seq data cross different species and batches. Through cross-species and cross-batch clustering experiments, we conducted a comparative analysis between GDEC and conventional packages. Furthermore, we implemented GDEC on the scRNA-seq data of uterine fibroids. Compared results obtained from the Seurat package, GDEC unveiled a novel cell type (epithelial cells) and identified a notable number of new pathways among various cell types, thus underscoring the enhanced analytical capabilities of GDEC. Availability and implementation: https://github.com/YuzhiSun/GDEC/tree/main.

A telencephalon cell type atlas for goldfish reveals diversity in the evolution of spatial structure and cell types

Teleost fish form the largest group of vertebrates and show a tremendous variety of adaptive behaviors, making them critically important for the study of brain evolution and cognition. The neural basis mediating these behaviors remains elusive. We performed a systematic comparative survey of the goldfish telencephalon. We mapped cell types using single-cell RNA sequencing and spatial transcriptomics, resulting in de novo molecular neuroanatomy parcellation. Glial cells were highly conserved across 450 million years of evolution separating mouse and goldfish, while neurons showed diversity and modularity in gene expression. Specifically, somatostatin interneurons, famously interspersed in the mammalian isocortex for local inhibitory input, were curiously aggregated in a single goldfish telencephalon nucleus but molecularly conserved. Cerebral nuclei including the striatum, a hub for motivated behavior in amniotes, had molecularly conserved goldfish homologs. We suggest elements of a hippocampal formation across the goldfish pallium. Last, aiding study of the teleostan everted telencephalon, we describe substantial molecular similarities between goldfish and zebrafish neuronal taxonomies.

Attention-based deep clustering method for scRNA-seq cell type identification

Single-cell sequencing (scRNA-seq) technology provides higher resolution of cellular differences than bulk RNA sequencing and reveals the heterogeneity in biological research. The analysis of scRNA-seq datasets is premised on the subpopulation assignment. When an appropriate reference is not available, such as specific marker genes and single-cell reference atlas, unsupervised clustering approaches become the predominant option. However, the inherent sparsity and high-dimensionality of scRNA-seq datasets pose specific analytical challenges to traditional clustering methods. Therefore, a various deep learning-based methods have been proposed to address these challenges. As each method improves partially, a comprehensive method needs to be proposed. In this article, we propose a novel scRNA-seq data clustering method named AttentionAE-sc (Attention fusion AutoEncoder for single-cell). Two different scRNA-seq clustering strategies are combined through an attention mechanism, that include zero-inflated negative binomial (ZINB)-based methods dealing with the impact of dropout events and graph autoencoder (GAE)-based methods relying on information from neighbors to guide the dimension reduction. Based on an iterative fusion between denoising and topological embeddings, AttentionAE-sc can easily acquire clustering-friendly cell representations that similar cells are closer in the hidden embedding. Compared with several state-of-art baseline methods, AttentionAE-sc demonstrated excellent clustering performance on 16 real scRNA-seq datasets without the need to specify the number of groups. Additionally, AttentionAE-sc learned improved cell representations and exhibited enhanced stability and robustness. Furthermore, AttentionAE-sc achieved remarkable identification in a breast cancer single-cell atlas dataset and provided valuable insights into the heterogeneity among different cell subtypes.

Robust, scalable, and informative clustering for diverse biological networks

Clustering molecular data into informative groups is a primary step in extracting robust conclusions from big data. However, due to foundational issues in how they are defined and detected, such clusters are not always reliable, leading to unstable conclusions. We compare popular clustering algorithms across thousands of synthetic and real biological datasets, including a new consensus clustering algorithm-SpeakEasy2: Champagne. These tests identify trends in performance, show no single method is universally optimal, and allow us to examine factors behind variation in performance. Multiple metrics indicate SpeakEasy2 generally provides robust, scalable, and informative clusters for a range of applications.

Heralding a new era of oxytocinergic research: New tools, new problems?

According to classic neuroendocrinology, hypothalamic oxytocin cells can be categorized into parvo- and magnocellular neurons. However, research in the last decade provided ample evidence that this black-and-white model of oxytocin neurons is most likely oversimplified. Novel genetic, functional and morphological studies indicate that oxytocin neurons might be organized in functional modules and suggest the existence of five or more distinct oxytocinergic subpopulations. However, many of these novel, automated high-throughput techniques might be inherently biased and interpretation of acquired data needs to be approached with caution to enable drawing sound and reliable conclusions. In addition, the recent finding that astrocytes in various brain regions express functional oxytocin receptors represents a paradigm shift and challenges the view that oxytocin primarily acts as a direct peptidergic neurotransmitter. This review highlights the latest technical advances in oxytocinergic research, puts recent studies on the oxytocin system into context and formulates various provocative ideas based on novel findings that challenges various prevailing hypotheses and dogmas about oxytocinergic modulation.

Integrative neurocircuits that control metabolism and food intake

Systemic metabolism has to be constantly adjusted to the variance of food intake and even be prepared for anticipated changes in nutrient availability. Therefore, the brain integrates multiple homeostatic signals with numerous cues that predict future deviations in energy supply. Recently, our understanding of the neural pathways underlying these regulatory principles-as well as their convergence in the hypothalamus as the key coordinator of food intake, energy expenditure, and glucose metabolism-have been revealed. These advances have changed our view of brain-dependent control of metabolic physiology. In this Review, we discuss new concepts about how alterations in these pathways contribute to the development of prevalent metabolic diseases such as obesity and type 2 diabetes mellitus and how this emerging knowledge may provide new targets for their treatment.

Molecular consequences of peripheral Influenza A infection on cell populations in the murine hypothalamus

Infection with Influenza A virus (IAV) causes the well-known symptoms of the flu, including fever, loss of appetite, and excessive sleepiness. These responses, mediated by the brain, will normally disappear once the virus is cleared from the system, but a severe respiratory virus infection may cause long-lasting neurological disturbances. These include encephalitis lethargica and narcolepsy. The mechanisms behind such long lasting changes are unknown. The hypothalamus is a central regulator of the homeostatic response during a viral challenge. To gain insight into the neuronal and non-neuronal molecular changes during an IAV infection, we intranasally infected mice with an H1N1 virus and extracted the brain at different time points. Using single-nucleus RNA sequencing (snRNA-seq) of the hypothalamus, we identify transcriptional effects in all identified cell populations. The snRNA-seq data showed the most pronounced transcriptional response at 3 days past infection, with a strong downregulation of genes across all cell types. General immune processes were mainly impacted in microglia, the brain resident immune cells, where we found increased numbers of cells expressing pro-inflammatory gene networks. In addition, we found that most neuronal cell populations downregulated genes contributing to the energy homeostasis in mitochondria and protein translation in the cytosol, indicating potential reduced cellular and neuronal activity. This might be a preventive mechanism in neuronal cells to avoid intracellular viral replication and attack by phagocytosing cells. The change of microglia gene activity suggest that this is complemented by a shift in microglia activity to provide increased surveillance of their surroundings.

Meta-analysis of pre-clinical studies on the effects of opioid receptor ligands on food intake, motivation, and choice

The opioid receptors (OR) regulate food intake. Still, despite extensive pre-clinical research, the overall effects and individual contribution of the mu (MOR), kappa (KOR), and delta (DOR) OR subtypes to feeding behaviors and food intake remain unclear. To address this, we conducted a pre-registered systematic search and meta-analysis of rodent dose-response studies to evaluate the impact of central and peripheral administration of non-selective and selective OR ligands on intake, motivation, and choice of food. All studies had a high bias risk. Still, the meta-analysis confirmed the overall orexigenic and anorexigenic effects of OR agonists and antagonists, respectively. Our results support a larger orexigenic role for central MOR agonists among OR subtypes and that peripheral OR antagonists reduce motivation for and intake of preferred foods. In binary food choice studies, peripheral OR agonists selectively increase the intake of fat-preferred foods; in contrast, they did not increase the intake of sweet carbohydrate-preferred foods. Overall, these data support that OR regulation of intake, motivation, and choice is influenced by food macronutrient composition.

CL-Impute: A contrastive learning-based imputation for dropout single-cell RNA-seq data

BACKGROUND

Single-cell RNA-sequencing (scRNA-seq) technology has revolutionized the study of cell heterogeneity and biological interpretation at the single-cell level. However, the dropout events commonly present in scRNA-seq data can markedly reduce the reliability of downstream analysis. Existing imputation methods often overlook the discrepancy between the established cell relationship from dropout noisy data and reality, which limits their performances due to the learned untrustworthy cell representations.

METHOD

Here, we propose a novel approach called the CL-Impute (Contrastive Learning-based Impute) model for estimating missing genes without relying on preconstructed cell relationships. CL-Impute utilizes contrastive learning and a self-attention network to address this challenge. Specifically, the proposed CL-Impute model leverages contrastive learning to learn cell representations from the self-perspective of dropout events, whereas the self-attention network captures cell relationships from the global-perspective.

RESULTS

Experimental results on four benchmark datasets, including quantitative assessment, cell clustering, gene identification, and trajectory inference, demonstrate the superior performance of CL-Impute compared with that of existing state-of-the-art imputation methods. Furthermore, our experiment reveals that combining contrastive learning and masking cell augmentation enables the model to learn actual latent features from noisy data with a high rate of dropout events, enhancing the reliability of imputed values.

CONCLUSIONS

CL-Impute is a novel contrastive learning-based method to impute scRNA-seq data in the context of high dropout rate. The source code of CL-Impute is available at https://github.com/yuchen21-web/Imputation-for-scRNA-seq.

Sex shapes cell-type-specific transcriptional signatures of stress exposure in the mouse hypothalamus

Stress-related psychiatric disorders and the stress system show prominent differences between males and females, as well as strongly divergent transcriptional changes. Despite several proposed mechanisms, we still lack the understanding of the molecular processes at play. Here, we explore the contribution of cell types to transcriptional sex dimorphism using single-cell RNA sequencing. We identify cell-type-specific signatures of acute restraint stress in the paraventricular nucleus of the hypothalamus, a central hub of the stress response, in male and female mice. Further, we show that a history of chronic mild stress alters these signatures in a sex-specific way, and we identify oligodendrocytes as a major target for these sex-specific effects. This dataset, which we provide as an online interactive app, offers the transcriptomes of thousands of individual cells as a molecular resource for an in-depth dissection of the interplay between cell types and sex on the mechanisms of the stress response.

Angiotensin AT1A receptor signal switching in Agouti-related peptide neurons mediates metabolic rate adaptation during obesity

Resting metabolic rate (RMR) adaptation occurs during obesity and is hypothesized to contribute to failed weight management. Angiotensin II (Ang-II) type 1 (AT1A) receptors in Agouti-related peptide (AgRP) neurons contribute to the integrative control of RMR, and deletion of AT1A from AgRP neurons causes RMR adaptation. Extracellular patch-clamp recordings identify distinct cellular responses of individual AgRP neurons from lean mice to Ang-II: no response, inhibition via AT1A and Gαi, or stimulation via Ang-II type 2 (AT2) receptors and Gαq. Following diet-induced obesity, a subset of Ang-II/AT1A-inhibited AgRP neurons undergo a spontaneous G-protein "signal switch," whereby AT1A stop inhibiting the cell via Gαi and instead begin stimulating the cell via Gαq. DREADD-mediated activation of Gαi, but not Gαq, in AT1A-expressing AgRP cells stimulates RMR in lean and obese mice. Thus, loss of AT1A-Gαi coupling within the AT1A-expressing AgRP neuron subtype represents a molecular mechanism contributing to RMR adaptation.

Peptidergic and functional delineation of the Edinger-Westphal nucleus

Many neuronal populations that release fast-acting excitatory and inhibitory neurotransmitters in the brain also contain slower-acting neuropeptides. These facultative peptidergic cell types are common, but it remains uncertain whether neurons that solely release peptides exist. Our fluorescence in situ hybridization, genetically targeted electron microscopy, and electrophysiological characterization suggest that most neurons of the non-cholinergic, centrally projecting Edinger-Westphal nucleus in mice are obligately peptidergic. We further show, using anterograde projection mapping, monosynaptic retrograde tracing, angled-tip fiber photometry, and chemogenetic modulation and genetically targeted ablation in conjunction with canonical assays for anxiety, that this peptidergic population activates in response to loss of motor control and promotes anxiety responses. Together, these findings elucidate an integrative, ethologically relevant role for the Edinger-Westphal nucleus and functionally align the nucleus with the periaqueductal gray, where it resides. This work advances our understanding of peptidergic modulation of anxiety and provides a framework for future investigations of peptidergic systems.

Stratification of enterochromaffin cells by single-cell expression analysis

Dynamic interactions between gut mucosal cells and the external environment are essential to maintain gut homeostasis. Enterochromaffin (EC) cells transduce both chemical and mechanical signals and produce 5-hydroxytryptamine (5-HT) to mediate disparate physiological responses. However, the molecular and cellular basis for functional diversity of ECs remains to be adequately defined. Here, we integrated single-cell transcriptomics with spatial image analysis to identify fourteen EC clusters that are topographically organized along the gut. Subtypes predicted to be sensitive to the chemical environment and mechanical forces were identified that express distinct transcription factors and hormones. A Piezo2+ population in the distal colon was endowed with a distinctive neuronal signature. Using a combination of genetic, chemogenetic and pharmacological approaches, we demonstrated Piezo2+ ECs are required for normal colon motility. Our study constructs a molecular map for ECs and offers a framework for deconvoluting EC cells with pleiotropic functions.

A ligand-receptor interactome atlas of the zebrafish

Studies in zebrafish can unravel the functions of cellular communication and thus identify novel bench-to-bedside drugs targeting cellular communication signaling molecules. Due to the incomplete annotation of zebrafish proteome, the knowledge of zebrafish receptors, ligands, and tools to explore their interactome is limited. To address this gap, we de novo predicted the cellular localization of zebrafish reference proteome using deep learning algorithm. We combined the predicted and existing annotations on cellular localization of zebrafish proteins and created repositories of zebrafish ligands, membrane receptome, and interactome as well as associated diseases and targeting drugs. Unlike other tools, our interactome atlas is based on both the physical interaction data of zebrafish proteome and existing human ligand-receptor pair databases. The resources are available as R and Python scripts. DanioTalk provides a novel resource for researchers interested in targeting cellular communication in zebrafish, as we demonstrate in applications studying synapse and axo-glial interactome. DanioTalk methodology can be applied to build and explore the ligand-receptor atlas of other non-mammalian model organisms.

Differential CRH expression level determines efficiency of Cre- and Flp-dependent recombination

Corticotropin-releasing hormone expressing (CRH+) neurons are distributed throughout the brain and play a crucial role in shaping the stress responses. Mouse models expressing site-specific recombinases (SSRs) or reporter genes are important tools providing genetic access to defined cell types and have been widely used to address CRH+ neurons and connected brain circuits. Here, we investigated a recently generated CRH-FlpO driver line expanding the CRH system-related tool box. We directly compared it to a previously established and widely used CRH-Cre line with respect to the FlpO expression pattern and recombination efficiency. In the brain, FlpO mRNA distribution fully recapitulates the expression pattern of endogenous Crh. Combining both Crh locus driven SSRs driver lines with appropriate reporters revealed an overall coherence of respective spatial patterns of reporter gene activation validating CRH-FlpO mice as a valuable tool complementing existing CRH-Cre and reporter lines. However, a substantially lower number of reporter-expressing neurons was discerned in CRH-FlpO mice. Using an additional CRH reporter mouse line (CRH-Venus) and a mouse line allowing for conversion of Cre into FlpO activity (CAG-LSL-FlpO) in combination with intersectional and subtractive mouse genetic approaches, we were able to demonstrate that the reduced number of tdTomato reporter expressing CRH+ neurons can be ascribed to the lower recombination efficiency of FlpO compared to Cre recombinase. This discrepancy particularly manifests under conditions of low CRH expression and can be overcome by utilizing homozygous CRH-FlpO mice. These findings have direct experimental implications which have to be carefully considered when targeting CRH+ neurons using CRH-FlpO mice. However, the lower FlpO-dependent recombination efficiency also entails advantages as it provides a broader dynamic range of expression allowing for the visualization of cells showing stress-induced CRH expression which is not detectable in highly sensitive CRH-Cre mice as Cre-mediated recombination has largely been completed in all cells generally possessing the capacity to express CRH. These findings underscore the importance of a comprehensive evaluation of novel SSR driver lines prior to their application.

Transcriptomic profiling of Parkinson's disease brains reveals disease stage specific gene expression changes

Parkinson´s disease (PD) is a progressive neurodegenerative disorder characterized by both motor and non-motor symptoms. Aggravation of symptoms is mirrored by accumulation of protein aggregates mainly composed by alpha-synuclein in different brain regions, called Lewy bodies (LB). Previous studies have identified several molecular mechanisms as autophagy and inflammation playing a role in PD pathogenesis. Increased insights into mechanisms involved in early disease stages and driving the progression of the LB pathology are required for the development of disease-modifying strategies. Here, we aimed to elucidate disease stage-specific transcriptomic changes in brain tissue of well-characterized PD and control donors. We collected frontal cortex samples from 84 donors and sequenced both the coding and non-coding RNAs. We categorized our samples into groups based on their degree of LB pathology aiming to recapitulate a central aspect of disease progression. Using an analytical pipeline that corrected for sex, age at death, RNA quality, cell composition and unknown sources of variation, we found major disease stage-specific transcriptomic changes. Gene expression changes were most pronounced in donors at the disease stage when microscopic LB changes first occur in the sampled brain region. Additionally, we identified disease stage-specific enrichment of brain specific pathways and immune mechanisms. On the contrary, we showed that mitochondrial mechanisms are enriched throughout the disease course. Our data-driven approach also suggests a role for several poorly characterized lncRNAs in disease development and progression of PD. Finally, by combining genetic and epigenetic information, we highlighted two genes (MAP4K4 and PHYHIP) as candidate genes for future functional studies. Together our results indicate that transcriptomic dysregulation and associated functional changes are highly disease stage-specific, which has major implications for the study of neurodegenerative disorders.

Olfactory modulation of stress-response neural circuits

Stress responses, which are crucial for survival, are evolutionally conserved throughout the animal kingdom. The most common endocrine axis among stress responses is that triggered by corticotropin-releasing hormone neurons (CRHNs) in the hypothalamus. Signals of various stressors are detected by different sensory systems and relayed through individual neural circuits that converge on hypothalamic CRHNs to initiate common stress hormone responses. To investigate the neurocircuitry mechanisms underlying stress hormone responses induced by a variety of stressors, researchers have recently developed new approaches employing retrograde transsynaptic viral tracers, providing a wealth of information about various types of neural circuits that control the activity of CRHNs in response to stress stimuli. Here, we review earlier and more recent findings on the stress neurocircuits that converge on CRHNs, focusing particularly on olfactory systems that excite or suppress the activities of CRHNs and lead to the initiation of stress responses. Because smells are arguably the most important signals that enable animals to properly cope with environmental changes and survive, unveiling the regulatory mechanisms by which smells control stress responses would provide broad insight into how stress-related environmental cues are perceived in the animal brain.

Fast neurotransmitter identity of MCH neurons: Do contents depend on context?

Hypothalamic melanin-concentrating hormone (MCH) neurons participate in many fundamental neuroendocrine processes. While some of their effects can be attributed to MCH itself, others appear to depend on co-released neurotransmitters. Historically, the subject of fast neurotransmitter co-release from MCH neurons has been contentious, with data to support MCH neurons releasing GABA, glutamate, both, and neither. Rather than assuming a position in that debate, this review considers the evidence for all sides and presents an alternative explanation: neurochemical identity, including classical neurotransmitter content, is subject to change. With an emphasis on the variability of experimental details, we posit that MCH neurons may release GABA and/or glutamate at different points according to environmental and contextual factors. Through the lens of the MCH system, we offer evidence that the field of neuroendocrinology would benefit from a more nuanced and dynamic interpretation of neurotransmitter identity.

Brain-wide mapping of efferent projections of glutamatergic (Onecut3+ ) neurons in the lateral mouse hypothalamus

AIM

This study mapped the spatiotemporal positions and connectivity of Onecut3+ neuronal populations in the developing and adult mouse brain.

METHODS

We generated fluorescent reporter mice to chart Onecut3+ neurons for brain-wide analysis. Moreover, we crossed Onecut3-iCre and Mapt-mGFP (Tau-mGFP) mice to visualize axonal projections. A dual Cre/Flp-dependent AAV construct in Onecut3-iCre cross-bred with Slc17a6-FLPo mice was used in an intersectional strategy to map the connectivity of glutamatergic lateral hypothalamic neurons in the adult mouse.

RESULTS

We first found that Onecut3 marks a hitherto undescribed Slc17a6+ /Vglut2+ neuronal cohort in the lateral hypothalamus, with the majority expressing thyrotropin-releasing hormone. In the adult, Onecut3+ /Vglut2+ neurons of the lateral hypothalamus had both intra- and extrahypothalamic efferents, particularly to the septal complex and habenula, where they targeted other cohorts of Onecut3+ neurons and additionally to the neocortex and hippocampus. This arrangement suggests that intrinsic reinforcement loops could exist for Onecut3+ neurons to coordinate their activity along the brain's midline axis.

CONCLUSION

We present both a toolbox to manipulate novel subtypes of hypothalamic neurons and an anatomical arrangement by which extrahypothalamic targets can be simultaneously entrained.

A genetically encoded sensor measures temporal oxytocin release from different neuronal compartments

Oxytocin (OT), a peptide hormone and neuromodulator, is involved in diverse physiological and pathophysiological processes in the central nervous system and the periphery. However, the regulation and functional sequences of spatial OT release in the brain remain poorly understood. We describe a genetically encoded G-protein-coupled receptor activation-based (GRAB) OT sensor called GRABOT1.0. In contrast to previous methods, GRABOT1.0 enables imaging of OT release ex vivo and in vivo with suitable sensitivity, specificity and spatiotemporal resolution. Using this sensor, we visualize stimulation-induced OT release from specific neuronal compartments in mouse brain slices and discover that N-type calcium channels predominantly mediate axonal OT release, whereas L-type calcium channels mediate somatodendritic OT release. We identify differences in the fusion machinery of OT release for axon terminals versus somata and dendrites. Finally, we measure OT dynamics in various brain regions in mice during male courtship behavior. Thus, GRABOT1.0 provides insights into the role of compartmental OT release in physiological and behavioral functions.

Emotionally clocked out: cell-type specific regulation of mood and anxiety by the circadian clock system in the brain

Circadian rhythms are self-sustained oscillations of biological systems that allow an organism to anticipate periodic changes in the environment and optimally align feeding, sleep, wakefulness, and the physiological and biochemical processes that support them within the 24 h cycle. These rhythms are generated at a cellular level by a set of genes, known as clock genes, which code for proteins that inhibit their own transcription in a negative feedback loop and can be perturbed by stress, a risk factor for the development of mood and anxiety disorders. A role for circadian clocks in mood and anxiety has been suggested for decades on the basis of clinical observations, and the dysregulation of circadian rhythms is a prominent clinical feature of stress-related disorders. Despite our understanding of central clock structure and function, the effect of circadian dysregulation in different neuronal subtypes in the suprachiasmatic nucleus (SCN), the master pacemaker region, as well as other brain systems regulating mood, including mesolimbic and limbic circuits, is just beginning to be elucidated. In the brain, circadian clocks regulate neuronal physiological functions, including neuronal activity, synaptic plasticity, protein expression, and neurotransmitter release which in turn affect mood-related behaviors via cell-type specific mechanisms. Both animal and human studies have revealed an association between circadian misalignment and mood disorders and suggest that internal temporal desynchrony might be part of the etiology of psychiatric disorders. To date, little work has been conducted associating mood-related phenotypes to cell-specific effects of the circadian clock disruptions. In this review, we discuss existing literature on how clock-driven changes in specific neuronal cell types might disrupt phase relationships among cellular communication, leading to neuronal circuit dysfunction and changes in mood-related behavior. In addition, we examine cell-type specific circuitry underlying mood dysfunction and discuss how this circuitry could affect circadian clock. We provide a focus for future research in this area and a perspective on chronotherapies for mood and anxiety disorders.

ScInfoVAE: interpretable dimensional reduction of single cell transcription data with variational autoencoders and extended mutual information regularization

Single-cell RNA-sequencing (scRNA-seq) data can serve as a good indicator of cell-to-cell heterogeneity and can aid in the study of cell growth by identifying cell types. Recently, advances in Variational Autoencoder (VAE) have demonstrated their ability to learn robust feature representations for scRNA-seq. However, it has been observed that VAEs tend to ignore the latent variables when combined with a decoding distribution that is too flexible. In this paper, we introduce ScInfoVAE, a dimensional reduction method based on the mutual information variational autoencoder (InfoVAE), which can more effectively identify various cell types in scRNA-seq data of complex tissues. A joint InfoVAE deep model and zero-inflated negative binomial distributed model design based on ScInfoVAE reconstructs the objective function to noise scRNA-seq data and learn an efficient low-dimensional representation of it. We use ScInfoVAE to analyze the clustering performance of 15 real scRNA-seq datasets and demonstrate that our method provides high clustering performance. In addition, we use simulated data to investigate the interpretability of feature extraction, and visualization results show that the low-dimensional representation learned by ScInfoVAE retains local and global neighborhood structure data well. In addition, our model can significantly improve the quality of the variational posterior.

Reliable Identification and Interpretation of Single-Cell Molecular Heterogeneity and Transcriptional Regulation using Dynamic Ensemble Pruning

Unsupervised clustering is an essential step in identifying cell types from single-cell RNA sequencing (scRNA-seq) data. However, a common issue with unsupervised clustering models is that the optimization direction of the objective function and the final generated clustering labels in the absence of supervised information may be inconsistent or even arbitrary. To address this challenge, a dynamic ensemble pruning framework (DEPF) is proposed to identify and interpret single-cell molecular heterogeneity. In particular, a silhouette coefficient-based indicator is developed to determine the optimization direction of the bi-objective function. In addition, a hierarchical autoencoder is employed to project the high-dimensional data onto multiple low-dimensional latent space sets, and then a clustering ensemble is produced in the latent space by the basic clustering algorithm. Following that, a bi-objective fruit fly optimization algorithm is designed to prune dynamically the low-quality basic clustering in the ensemble. Multiple experiments are conducted on 28 real scRNA-seq datasets and one large real scRNA-seq dataset from diverse platforms and species to validate the effectiveness of the DEPF. In addition, biological interpretability and transcriptional and post-transcriptional regulatory are conducted to explore biological patterns from the cell types identified, which could provide novel insights into characterizing the mechanisms.

Cell-specific transcriptome changes in the hypothalamic arcuate nucleus in a mouse deoxycorticosterone acetate-salt model of hypertension

A common preclinical model of hypertension characterized by low circulating renin is the "deoxycorticosterone acetate (DOCA)-salt" model, which influences blood pressure and metabolism through mechanisms involving the angiotensin II type 1 receptor (AT1R) in the brain. More specifically, AT1R within Agouti-related peptide (AgRP) neurons of the arcuate nucleus of the hypothalamus (ARC) has been implicated in selected effects of DOCA-salt. In addition, microglia have been implicated in the cerebrovascular effects of DOCA-salt and angiotensin II. To characterize DOCA-salt effects upon the transcriptomes of individual cell types within the ARC, we used single-nucleus RNA sequencing (snRNAseq) to examine this region from male C57BL/6J mice that underwent sham or DOCA-salt treatment. Thirty-two unique primary cell type clusters were identified. Sub-clustering of neuropeptide-related clusters resulted in identification of three distinct AgRP subclusters. DOCA-salt treatment caused subtype-specific changes in gene expression patterns associated with AT1R and G protein signaling, neurotransmitter uptake, synapse functions, and hormone secretion. In addition, two primary cell type clusters were identified as resting versus activated microglia, and multiple distinct subtypes of activated microglia were suggested by sub-cluster analysis. While DOCA-salt had no overall effect on total microglial density within the ARC, DOCA-salt appeared to cause a redistribution of the relative abundance of activated microglia subtypes. These data provide novel insights into cell-specific molecular changes occurring within the ARC during DOCA-salt treatment, and prompt increased investigation of the physiological and pathophysiological significance of distinct subtypes of neuronal and glial cell types.

CosTaL: an accurate and scalable graph-based clustering algorithm for high-dimensional single-cell data analysis

With the aim of analyzing large-sized multidimensional single-cell datasets, we are describing a method for Cosine-based Tanimoto similarity-refined graph for community detection using Leiden's algorithm (CosTaL). As a graph-based clustering method, CosTaL transforms the cells with high-dimensional features into a weighted k-nearest-neighbor (kNN) graph. The cells are represented by the vertices of the graph, while an edge between two vertices in the graph represents the close relatedness between the two cells. Specifically, CosTaL builds an exact kNN graph using cosine similarity and uses the Tanimoto coefficient as the refining strategy to re-weight the edges in order to improve the effectiveness of clustering. We demonstrate that CosTaL generally achieves equivalent or higher effectiveness scores on seven benchmark cytometry datasets and six single-cell RNA-sequencing datasets using six different evaluation metrics, compared with other state-of-the-art graph-based clustering methods, including PhenoGraph, Scanpy and PARC. As indicated by the combined evaluation metrics, Costal has high efficiency with small datasets and acceptable scalability for large datasets, which is beneficial for large-scale analysis.