Single-Cell RNA-Seq Reveals Hypothalamic Cell Diversity

Potential role of tanycyte-derived neurogenesis in Alzheimer's disease

Tanycytes, specialized ependymal cells located in the hypothalamus, play a crucial role in the generation of new neurons that contribute to the neural circuits responsible for regulating the systemic energy balance. The precise coordination of the gene networks controlling neurogenesis in naive and mature tanycytes is essential for maintaining homeostasis in adulthood. However, our understanding of the molecular mechanisms and signaling pathways that govern the proliferation and differentiation of tanycytes into neurons remains limited. This article aims to review the recent advancements in research into the mechanisms and functions of tanycyte-derived neurogenesis. Studies employing lineage-tracing techniques have revealed that the neurogenesis specifically originating from tanycytes in the hypothalamus has a compensatory role in neuronal loss and helps maintain energy homeostasis during metabolic diseases. Intriguingly, metabolic disorders are considered early biomarkers of Alzheimer's disease. Furthermore, the neurogenic potential of tanycytes and the state of newborn neurons derived from tanycytes heavily depend on the maintenance of mild microenvironments, which may be disrupted in Alzheimer's disease due to the impaired blood-brain barrier function. However, the specific alterations and regulatory mechanisms governing tanycyte-derived neurogenesis in Alzheimer's disease remain unclear. Accumulating evidence suggests that tanycyte-derived neurogenesis might be impaired in Alzheimer's disease, exacerbating neurodegeneration. Confirming this hypothesis, however, poses a challenge because of the lack of long-term tracing and nucleus-specific analyses of newborn neurons in the hypothalamus of patients with Alzheimer's disease. Further research into the molecular mechanisms underlying tanycyte-derived neurogenesis holds promise for identifying small molecules capable of restoring tanycyte proliferation in neurodegenerative diseases. This line of investigation could provide valuable insights into potential therapeutic strategies for Alzheimer's disease and related conditions.

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.

Multitissue single-cell analysis reveals differential cellular and molecular sensitivity between fructose and high-fat high-sucrose diets

Metabolic syndrome (MetS), a conglomerate of dysregulated metabolic traits that vary between individuals, is partially driven by modern diets high in fat, sucrose, or fructose and their interactions with host genes in metabolic tissues. To elucidate the roles of individual tissues and cell types in diet-induced MetS, we performed single-cell RNA sequencing on the hypothalamus, liver, adipose tissue, and small intestine of mice fed high-fat high-sucrose (HFHS) or fructose diets. We found that hypothalamic neurons were sensitive to fructose, while adipose progenitor cells and macrophages were responsive to HFHS. Ligand-receptor analysis revealed lipid metabolism and inflammation networks among peripheral tissues driven by HFHS, while both diets stimulated synaptic remodeling within the hypothalamus. mt-Rnr2, a top responder to both diets, mitigated diet-induced MetS by stimulating thermogenesis. Our study demonstrates that HFHS and fructose diets have differential cell type and network targets but also share regulators such as mt-Rnr2 to affect MetS risk.

Single-cell genomics meets systems neuroscience: Insights from mapping the brain circuitry of stress

Responses to external and internal dangers is essential for survival and homeostatic regulation. Hypothalamic corticotropin-releasing hormone neurons (CRHNs) play a pivotal role in regulating neuroendocrine responses to fear and stress. In recent years, the application of neurogenetic tools, such as fiber photometry, chemogenetics and optogenetics, have provided new insights into the dynamic neuronal responses of CRHNs during stressful events, offering new perspectives into their functional significance in mediating neurobehavioural responses to stress. Transsynaptic viral tracers have facilitated the comprehensive mapping of neuronal inputs to CRHNs. Furthermore, the development and application of innovative single-cell genomic tools combined with viral tracing have begun to pave the way for a deeper understanding of the transcriptional profiles of neural circuit components, enabling molecular-anatomical circuit mapping. Here, I will discuss how these systems neuroscience approaches and novel single-cell genomic methods are advancing the molecular and functional mapping of stress neurocircuits, their associated challenges and future directions.

m6A deficiency impairs hypothalamic neurogenesis of feeding-related neurons in mice and human organoids and leads to adult obesity in mice

N6-methyladenosine (m6A), the most prevalent internal modification on mRNAs, plays important roles in the nervous system. Whether neurogenesis in the hypothalamus, a region critical for controlling appetite, is regulated by m6A signaling, especially in humans, remains unclear. Here, we showed that deletion of m6A writer Mettl14 in the mouse embryonic hypothalamus led to adult obesity, with impaired glucose-insulin homeostasis and increased energy intake. Mechanistically, deletion of Mettl14 leads to hypothalamic arcuate nucleus neurogenesis deficits with reduced generation of feeding-related neurons and dysregulation of neurogenesis-related m6A-tagged transcripts. Deletion of m6A writer Mettl3 or m6A reader Ythdc1 shared similar phenotypes. METTL14 or YTHDC1 knockdown also led to reduced generation of feeding-related neurons in human brain subregion-specific arcuate nucleus organoids. Our studies reveal a conserved role of m6A signaling in arcuate nucleus neurogenesis in mice and human organoids and shed light on the developmental basis of epitranscriptomic regulation of food intake and energy homeostasis.

Decoupled GNNs based on multi-view contrastive learning for scRNA-seq data clustering

Clustering is pivotal in deciphering cellular heterogeneity in single-cell RNA sequencing (scRNA-seq) data. However, it suffers from several challenges in handling the high dimensionality and complexity of scRNA-seq data. Especially when employing graph neural networks (GNNs) for cell clustering, the dependencies between cells expand exponentially with the number of layers. This results in high computational complexity, negatively impacting the model's training efficiency. To address these challenges, we propose a novel approach, called decoupled GNNs, based on multi-view contrastive learning (scDeGNN), for scRNA-seq data clustering. Firstly, this method constructs two adjacency matrices to generate distinct views, and trains them using decoupled GNNs to derive the initial cell feature representations. These representations are then refined through a multilayer perceptron and a contrastive learning layer, ensuring the consistency and discriminability of the learned features. Finally, the learned representations are fused and applied to the cell clustering task. Extensive experimental results on nine real scRNA-seq datasets from various organisms and tissues show that the proposed scDeGNN method significantly outperforms other state-of-the-art scRNA-seq data clustering algorithms across multiple evaluation metrics.

Enhanced single-cell RNA-seq embedding through gene expression and data-driven gene-gene interaction integration

Single-cell RNA sequencing (scRNA-seq) provides unprecedented insights into cellular heterogeneity, enabling detailed analysis of complex biological systems at single-cell resolution. However, the high dimensionality and technical noise inherent in scRNA-seq data pose significant analytical challenges. While current embedding methods focus primarily on gene expression levels, they often overlook crucial gene-gene interactions that govern cellular identity and function. To address this limitation, we present a novel embedding approach that integrates both gene expression profiles and data-driven gene-gene interactions. Our method first constructs a Cell-Leaf Graph (CLG) using random forest models to capture regulatory relationships between genes, while simultaneously building a K-Nearest Neighbor Graph (KNNG) to represent expression similarities between cells. These graphs are then combined into an Enriched Cell-Leaf Graph (ECLG), which serves as input for a graph neural network to compute cell embeddings. By incorporating both expression levels and gene-gene interactions, our approach provides a more comprehensive representation of cellular states. Extensive evaluation across multiple datasets demonstrates that our method enhances the detection of rare cell populations and improves downstream analyses such as visualization, clustering, and trajectory inference. This integrated approach represents a significant advance in single-cell data analysis, offering a more complete framework for understanding cellular diversity and dynamics.

Transcriptomic and spatial GABAergic neuron subtypes in zona incerta mediate distinct innate behaviors

Understanding the anatomical connection and behaviors of transcriptomic neuron subtypes is critical to delineating cell type-specific functions in the brain. Here we integrated single-nucleus transcriptomic sequencing, in vivo circuit mapping, optogenetic and chemogenetic approaches to dissect the molecular identity and function of heterogeneous GABAergic neuron populations in the zona incerta (ZI) in mice, a region involved in modulating various behaviors. By microdissecting ZI for transcriptomic and spatial gene expression analyses, our results revealed two non-overlapping Ecel1- and Pde11a-expressing GABAergic neurons with dominant expression in the rostral and medial zona incerta (ZIrEcel1 and ZImPde11a), respectively. The GABAergic projection from ZIrEcel1 to periaqueductal gray mediates self-grooming, while the GABAergic projection from ZImPde11a to the oral part of pontine reticular formation promotes transition from sleep to wakefulness. Together, our results revealed the molecular markers, spatial organization and specific neuronal circuits of two discrete GABAergic projection neuron populations in segregated subregions of the ZI that mediate distinct innate behaviors, advancing our understanding of the functional organization of the brain.

The paraventricular nucleus of the hypothalamus: a key node in the control of behavioural states

The paraventricular nucleus (PVN) of the hypothalamus contains diverse populations of neuropeptide-producing neurons. These include neurons that synthesise oxytocin, vasopressin, corticotropin-releasing hormone, thyrotropin-releasing hormone and somatostatin. While it is well established that these neurons control the secretion of neuroendocrine hormones, there is growing evidence that they also control the expression of important homeostatic behaviours. Here we review recent data showing a critical role of PVN neurons in controlling arousal, social behaviour, defensive behaviour and pain. Collectively, this suggests that the PVN is a key node in a wider neural network controlling behavioural states.

Central FGF19 signaling enhances energy homeostasis and adipose tissue thermogenesis through sympathetic activation in obese mice

Fibroblast growth factor 19 (FGF19) signaling in the brain is associated with body weight loss, reduced food intake, and improved glycemic control in obese mice through unclear mechanisms. Here, we investigated the effects of central FGF19 administration on peripheral tissues, focusing on adipose tissue and its contributions to body weight loss. Using single-cell RNA sequencing of the adult murine hypothalamus, we found that FGF19 has the potential to target multiple cell populations, including astrocytes-tanycytes, microglia, neurons, and oligodendrocytes. Central delivery of FGF19 decreased body weight gain and ameliorated glucose-insulin homeostasis in diet-induced obese (DIO) mice. These results were accompanied by increased energy expenditure and reduced peripheric inflammation. Notably, these effects were attributable to the increased activity of thermogenic adipocytes, as upregulated thermogenic markers in brown and inguinal adipose tissue and improved cold tolerance were induced by central FGF19. However, under blunted sympathetic activity, the described effects were abolished. Moreover, cold exposure induced upregulation of FGF19 receptors and coreceptors specifically in the hypothalamus, suggesting a critical metabolic adaptation for thermoregulation and energy homeostasis. Our findings indicate that central FGF19 signaling improves energy homeostasis in DIO mice, at least in part, by stimulating sympathetic activity and adipose tissue thermogenesis. These findings highlight FGF19's potential as a therapeutic target for obesity and metabolic disorders.NEW & NOTEWORTHY Although most studies associate central fibroblast growth factor 19 (FGF19) with reduced food intake, our findings highlight its role in enhancing thermogenesis in white and brown adipose tissues through sympathetic activation. Central FGF19 not only regulates feeding but also drives peripheral adaptations critical for energy homeostasis and body weight control under obesogenic conditions. These insights underscore the significance of top-down mechanisms in FGF19 action and its therapeutic potential for combating obesity.

The ARCCRABP1 neurons play a crucial role in the regulation of energy homeostasis

Recent single-cell RNA sequencing study suggested that CRABP1 expressing neurons in the arcuate nucleus (ARCCRABP1 neurons) were a distinct group of neurons. However, the physiological role of ARCCRABP1 neurons remains unexplored. Here, we demonstrated that ARCCRABP1 neurons played a crucial role in regulation of energy homeostasis in male mice. Ablation of ARCCRABP1 neurons resulted in obesity and a diabetic phenotype in mice. By employing chemogenetic or optogenetic manipulation techniques, the inhibition and activation of ARCCRABP1 neurons resulted in an increase and decrease in food intake, respectively. The axon terminals from these ARCCRABP1 neurons project to several brain regions implicated in feeding regulation such as PVH, BNST, PBN, and NTS. Optogenetic manipulation of these axons within these brain regions resulted in significant alterations of food intake behavior in mice. Furthermore, the electrophysiological studies demonstrated that the activation of ARCCRABP1 neurons induces depolarization in POMC neurons in the hypothalamus. The hormone stimulation studies showed that most of the ARCCRABP1 neurons respond to insulin. Collectively, our findings demonstrate that ARCCRABP1 neurons represent a distinct neuronal subtype involved in energy homeostasis regulation.

Raptin, a sleep-induced hypothalamic hormone, suppresses appetite and obesity

Sleep deficiency is associated with obesity, but the mechanisms underlying this connection remain unclear. Here, we identify a sleep-inducible hypothalamic protein hormone in humans and mice that suppresses obesity. This hormone is cleaved from reticulocalbin-2 (RCN2), and we name it Raptin. Raptin release is timed by the circuit from vasopressin-expressing neurons in the suprachiasmatic nucleus to RCN2-positive neurons in the paraventricular nucleus. Raptin levels peak during sleep, which is blunted by sleep deficiency. Raptin binds to glutamate metabotropic receptor 3 (GRM3) in neurons of the hypothalamus and stomach to inhibit appetite and gastric emptying, respectively. Raptin-GRM3 signaling mediates anorexigenic effects via PI3K-AKT signaling. Of note, we verify the connections between deficiencies in the sleeping state, impaired Raptin release, and obesity in patients with sleep deficiency. Moreover, humans carrying an RCN2 nonsense variant present with night eating syndrome and obesity. These data define a unique hormone that suppresses food intake and prevents obesity.

Polygenic enrichment analysis in multi-omics levels identifies cell/tissue specific associations with schizophrenia based on single-cell RNA sequencing data

OBJECTIVE

Understanding the specific cellular origin and tissue heterogeneity in schizophrenia is critically important for exploring the disease etiology. This study aims to investigate these aspects by performing multiple analyses based on omics data.

METHOD

We performed single-cell disease relevance score (scDRS) algorithm to link brain single-cell RNA sequencing (scRNA-seq) with schizophrenia risk across multi-omics scales at single-cell resolution. This approach identified cell types with overexpression of schizophrenia-related genes implicated by multi-omics panels (ATAC-seq, RNA-seq, TWAS, and GWAS). Schizophrenia-related genes from these multi-omics panels were extracted and combined with scRNA-seq data to calculate scDRS. Subsequently, the cell-type vs. disease association and tissue heterogeneity were assessed using scDRS for each omics panel.

RESULTS

We identified two novel cell subpopulations in the brain that differentially express SCUBE3 (59 cells, 7.0 %) and FN1 (21 cells, 2.5 %). At the individual cell level, schizophrenia-associated cell subpopulations included microglial cell associated with ATAC-seq panel (Passociation = 0.002, Pheterogeneity = 0.009) and deep layer neuron suggestively associated with GWAS panel (Passociation = 0.033, Pheterogeneity = 0.017). At the brain tissue level, microglial cell was significantly associated with cortical plate in ATAC-seq panel (Passociation = 0.002, Pheterogeneity = 0.011). Gene level analysis identified several genes associated with schizophrenia across multi-omics panels.

CONCLUSIONS

Our study outlines the signature of cell subpopulations, brain regions, and disease risk genes in schizophrenia at single-cell resolution across multi-omics scales. These findings provide a reference for future precision medicine approaches targeting specific cell types and brain regions in schizophrenia.

Hypothalamic GHRH

Despite initial discovery in pancreatic tumors, GHRH is a 44-amino acid peptide primarily expressed in the hypothalamus. Recent RNA sequencing clarifies GHRH expression: predominantly hypothalamic in humans, with some basal ganglia presence, while extending to additional central nervous system (CNS) regions in other species. GHRH binds to its G-protein coupled receptor (GHRHR) in the arcuate (ARC), ventromedial (VMH), and periventricular (PeN) nuclei of the hypothalamus to exert its effects. Notably, the highest non-brain expression is found in somatotroph cells of the pituitary, directly targeting growth hormone (GH) production. GHRH is the primary regulator of pulsatile GH secretion, counteracted by somatostatin. While early models proposed alternating GHRH/somatostatin bursts, others implicate somatostatin as the primary regulator of GH pulse timing. These models fail to fully explain species and gender differences, particularly regarding nutritional status. The discovery of ghrelin, acting via GHS-R1a on GHRH neurons, significantly advanced understanding of GH regulation. Ghrelin interacts intricately with GHRH, modulating its expression and neuronal activity. Ghrelin also exerts GHRH-independent GH stimulation and synergizes with GHRH. The crucial role of GHRH in GH regulation is demonstrated by its key involvement in the action of other GH regulators, such as leptin, neuropeptide Y (NPY), and orexins. However, these interactions have also revealed that the physiological effects of GHRH extend far beyond its canonical role as a GH secretagogue. In this context, GHRH is thought to be a key regulator of the sleep-wake cycle and may be involved in whole-body energy homeostasis. The objective of this review is to summarize the current knowledge on GHRH and to discuss the potential pleiotropic effect of this hypothalamic neuropeptide, far beyond its classical action as regulator of the somatotroph axis.

Gene trajectory inference for single-cell data by optimal transport metrics

Single-cell RNA sequencing has been widely used to investigate cell state transitions and gene dynamics of biological processes. Current strategies to infer the sequential dynamics of genes in a process typically rely on constructing cell pseudotime through cell trajectory inference. However, the presence of concurrent gene processes in the same group of cells and technical noise can obscure the true progression of the processes studied. To address this challenge, we present GeneTrajectory, an approach that identifies trajectories of genes rather than trajectories of cells. Specifically, optimal transport distances are calculated between gene distributions across the cell-cell graph to extract gene programs and define their gene pseudotemporal order. Here we demonstrate that GeneTrajectory accurately extracts progressive gene dynamics in myeloid lineage maturation. Moreover, we show that GeneTrajectory deconvolves key gene programs underlying mouse skin hair follicle dermal condensate differentiation that could not be resolved by cell trajectory approaches. GeneTrajectory facilitates the discovery of gene programs that control the changes and activities of biological processes.

Control of tuberal hypothalamic development and its implications in metabolic disorders

The tuberal hypothalamus regulates a range of crucial physiological processes, including energy homeostasis and metabolism. In this Review, we explore the intricate molecular mechanisms and signalling pathways that control the development of the tuberal hypothalamus, focusing on aspects that shape metabolic outcomes. Major developmental events are discussed in the context of their effect on the establishment of both functional hypothalamic neuronal circuits and brain-body interfaces that are pivotal to the control of metabolism. Emerging evidence indicates that aberrations in molecular pathways during tuberal hypothalamic development contribute to metabolic dysregulation. Understanding the molecular underpinnings of tuberal hypothalamic development provides a comprehensive view of neurodevelopmental processes and offers a promising avenue for future targeted interventions to prevent and treat metabolic disorders.

Single-Cell Landscape of the Cochlea Revealed Cell-Type-Specific Diversification in Hipposideros armiger Based on PacBio Long-Read Sequencing

Echolocation represents one of the most rapid adaptive sensorimotor modulation behaviors observed in mammals, establishing bats as one of the most evolutionarily successful mammals. Bats rely on high-frequency hearing for survival, but our understanding of its cellular molecular basis is scattered and segmented. Herein, we constructed the first single-cell transcriptomic landscape of the cochlea in Hipposideros armiger, a CF-FM bat, using a PacBio-optimized genome and compared it with the results obtained from unoptimized original genomes. Sixteen distinct cell types were distributed across five spatial regions of the cochlea. Notably, through hematoxylin and eosin staining and fluorescence in situ hybridization, we identified new types of spiral ganglion neuron (SGN) cells in the cochlea of H. armiger. These SGN cells are likely critical for auditory perception and may have driven the adaptive evolution of high-frequency hearing in this species. Furthermore, we uncovered the differentiation relationships of among specific cell types, such as the transition from supporting cells to hair cells. Using the cochlear cell atlas as a reference, cell types susceptible to deafness-associated genes (in the human) were also identified. In summary, this study provides novel insights into the cellular and molecular mechanisms underlying the adaptive high-frequency hearing in bats and highlights potential candidate cell types and genes for therapeutic interventions in hearing loss.

Brain-wide cell-type-specific transcriptomic signatures of healthy ageing in mice

Biological ageing can be defined as a gradual loss of homeostasis across various aspects of molecular and cellular function1,2. Mammalian brains consist of thousands of cell types3, which may be differentially susceptible or resilient to ageing. Here we present a comprehensive single-cell RNA sequencing dataset containing roughly 1.2 million high-quality single-cell transcriptomes of brain cells from young adult and aged mice of both sexes, from regions spanning the forebrain, midbrain and hindbrain. High-resolution clustering of all cells results in 847 cell clusters and reveals at least 14 age-biased clusters that are mostly glial types. At the broader cell subclass and supertype levels, we find age-associated gene expression signatures and provide a list of 2,449 unique differentially expressed genes (age-DE genes) for many neuronal and non-neuronal cell types. Whereas most age-DE genes are unique to specific cell types, we observe common signatures with ageing across cell types, including a decrease in expression of genes related to neuronal structure and function in many neuron types, major astrocyte types and mature oligodendrocytes, and an increase in expression of genes related to immune function, antigen presentation, inflammation, and cell motility in immune cell types and some vascular cell types. Finally, we observe that some of the cell types that demonstrate the greatest sensitivity to ageing are concentrated around the third ventricle in the hypothalamus, including tanycytes, ependymal cells, and certain neuron types in the arcuate nucleus, dorsomedial nucleus and paraventricular nucleus that express genes canonically related to energy homeostasis. Many of these types demonstrate both a decrease in neuronal function and an increase in immune response. These findings suggest that the third ventricle in the hypothalamus may be a hub for ageing in the mouse brain. Overall, this study systematically delineates a dynamic landscape of cell-type-specific transcriptomic changes in the brain associated with normal ageing that will serve as a foundation for the investigation of functional changes in ageing and the interaction of ageing and disease.

Age-related cerebral ventriculomegaly occurs in patients with primary ciliary dyskinesia

Primary ciliary dyskinesia (PCD) is a genetic disorder causing motile ciliary dysfunction primarily affecting the respiratory and reproductive systems. However, the impact of PCD on the central nervous system remains poorly understood. Rodent models of PCD exhibit marked hydrocephalus leading to early animal mortality, however, most humans with PCD do not develop hydrocephalus for unknown reasons. We hypothesized that patients with PCD exhibit sub-clinical ventriculomegaly related to ependymal motile ciliary dysfunction. We demonstrated highly specific expression levels of known PCD-related genes in human brain multiciliated ependymal cells (p < 0.0001). To assess ventricular size, computed tomography sinus images from patients with PCD (n = 33) and age/sex-matched controls (n = 64) were analysed. Patients with PCD displayed significantly larger ventricular areas (p < 0.0001) and Evans index (p < 0.01), indicating ventriculomegaly that was consistent across all genetic subgroups. Ventricular enlargement correlated positively with increasing age in patients with PCD compared to controls (p < 0.001). Additionally, chart review demonstrated a high prevalence (39%) of neuropsychiatric/neurological disorders in adult PCD patients that did not correlate with degree of ventriculomegaly. Our findings suggest that patients with PCD may have unrecognized, mild ventriculomegaly which correlates with ageing, potentially attributable to ependymal ciliary dysfunction. Further study is required to determine causality, and whether ventricular enlargement contributes to neuropsychiatric/neurological or other morbidity in PCD.

A single dorsal vagal complex circuit mediates the aversive and anorectic responses to GLP1R agonists

GLP-1 receptor agonists (GLP1RAs) effectively reduce feeding to treat obesity, although nausea and other aversive side effects of these drugs can limit their use. Brainstem circuits that promote satiation and that mediate the physiologic control of body weight can be distinguished from those that cause aversion. It remains unclear whether brainstem Glp1r neurons contribute to the normal regulation of energy balance and whether GLP1RAs control appetite via circuits distinct from those that mediate aversive responses, however. Hence, we defined roles for AP and NTS Glp1r-expressing neurons (APGlp1r and NTSGlp1r neurons, respectively) in the physiologic control of body weight, the GLP1RA-dependent suppression of food intake, and the GLP1RA-mediated stimulation of aversive responses. While silencing non-aversive NTSGlp1r neurons interfered with the physiologic restraint of feeding and body weight, restoring NTSGlp1r neuron Glp1r expression on an otherwise Glp1r-null background failed to enable long-term body weight suppression by GLP1RAs. In contrast, selective Glp1r expression in APGlp1r neurons restored both aversive responses and long-term body weight suppression by GLP1RAs. Thus, while non-aversive NTSGlp1r neurons control physiologic feeding, aversive APGlp1r neurons mediate both the anorectic and weight loss effects of GLP1RAs, dictating the functional inseparability of these pharmacologic GLP1RA responses at a circuit level.

Brain aging and rejuvenation at single-cell resolution

Brain aging leads to a decline in cognitive function and a concomitant increase in the susceptibility to neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. A key question is how changes within individual cells of the brain give rise to age-related dysfunction. Developments in single-cell "omics" technologies, such as single-cell transcriptomics, have facilitated high-dimensional profiling of individual cells. These technologies have led to new and comprehensive characterizations of brain aging at single-cell resolution. Here, we review insights gleaned from single-cell omics studies of brain aging, starting with a cell-type-centric overview of age-associated changes and followed by a discussion of cell-cell interactions during aging. We highlight how single-cell omics studies provide an unbiased view of different rejuvenation interventions and comment on the promise of combinatorial rejuvenation approaches for the brain. Finally, we propose new directions, including models of brain aging and neural stem cells as a focal point for rejuvenation.

Comparative analysis of adenosine 1 receptor expression and function in hippocampal and hypothalamic neurons

BACKGROUND

Adenosine, an ATP degradation product, is a sleep pressure factor. The adenosine 1 receptor (A1R) reports sleep need. Histaminergic neurons (HN) of the tuberomamillary nucleus (TMN) fire exclusively during wakefulness and promote arousal. All of them express GABAA receptors and are inhibited by GABA. Does adenosine contribute to their silencing?

SUBJECTS AND TREATMENT

Responses to adenosine were studied in mouse brain slices and primary dissociated cultures. For HN identification single-cell (sc)RT-PCR, reporter protein and pharmacology were used. Hippocampal Dentate Gyrus granular layer cells (DGgc) were studied in parallel.

METHODS

Firing frequency was recorded in patch-clamp configuration or by microelectrode arrays. A1R-expression was studied by scRT-PCR and semiquantitative PCR.

RESULTS

Most DGgc were inhibited through A1R, detected with scRT-PCR in 7 out of 10 PDZd2-positive DGgc; all HN were A1R negative. One HN out of 25 was inhibited by adenosine. The A1R mRNA level in the hippocampus was 6 times higher than in the caudal (posterior) hypothalamus. Response to adenosine was weaker in hypothalamic compared to hippocampal cultures.

CONCLUSIONS

Most HN are not inhibited by adenosine.

Single-cell Technology in Stem Cell Research

Single-cell technology (SCT), which enables the examination of the fundamental units comprising biological organs, tissues, and cells, has emerged as a powerful tool, particularly in the field of biology, with a profound impact on stem cell research. This innovative technology opens new pathways for acquiring cell-specific data and gaining insights into the molecular pathways governing organ function and biology. SCT is not only frequently used to explore rare and diverse cell types, including stem cells, but it also unveils the intricacies of cellular diversity and dynamics. This perspective, crucial for advancing stem cell research, facilitates non-invasive analyses of molecular dynamics and cellular functions over time. Despite numerous investigations into potential stem cell therapies for genetic disorders, degenerative conditions, and severe injuries, the number of approved stem cell-based treatments remains limited. This limitation is attributed to the various heterogeneities present among stem cell sources, hindering their widespread clinical utilization. Furthermore, stem cell research is intimately connected with cutting-edge technologies, such as microfluidic organoids, CRISPR technology, and cell/tissue engineering. Each strategy developed to overcome the constraints of stem cell research has the potential to significantly impact advanced stem cell therapies. Drawing on the advantages and progress achieved through SCT-based approaches, this study aims to provide an overview of the advancements and concepts associated with the utilization of SCT in stem cell research and its related fields.

Deconvolving Bulk Transcriptomics Samples to Obtain Cell Type Proportion Estimates

The brain, like many organs in the body, comprises an array of different cell types that act in concert to achieve its many functions. These cell types vary dramatically in molecular composition, morphology, spatial distribution, electrical activity, signaling, and association with sensory processing and behavior. As a result, a comprehensive investigation of molecular alterations in disease or perturbation studies needs to account for cell type heterogeneity in the brain. Bulk profiling of tissue is widely used in neuroscience, and is now a relatively mature field, with a range of experimental, technical, and computational approaches that are broadly and consistently used. One major question in bulk profiling, however, is the contribution of individual cell types to the overall bulk signature. Recently, advances in scale and sensitivity of single-cell methods have provided cell type-specific signatures in health and disease, offering a solution to the problem of inferring cell type contributions to bulk signatures. This is the main goal of bulk deconvolution, which aims to deconvolve cell type-specific signatures and proportions from bulk data, either de novo or using reference profiles obtained from single-cell data. Here, we present an overview of some basic principles of deconvolution, followed by a general workflow on applying deconvolution methods to bulk RNA-seq data in order to assess compositional differences in individual cell types that may be associated with experimental variable of interests.

Prokineticin 2 protein is diurnally expressed in PER2-containing clock neurons in the mouse suprachiasmatic nucleus

Expression of prokineticin 2 (PK2) mRNA in the suprachiasmatic nucleus (SCN), also known as the brain's clock, exhibits circadian oscillations with peak levels midday, zeitgeber time (ZT) 4, and almost undetectable levels during night. This circadian expression profile has substantially contributed to the suggested role of PK2 as an SCN output molecule involved in transmitting circadian rhythm of behavior and physiology. Due to unreliable specificity of PK2 antibodies, the 81 amino acid protein has primarily been studied at the mRNA level and correlation between circadian oscillating mRNAs and protein products are infrequent. Hence, data on PK2 protein expression in the SCN is lacking. In this study a thorough validation of a commercial PK2 antibody for immunohistochemistry (IHC) was performed followed by fluorescence IHC on SCN mouse brain sections at six consecutive ZTs over a 24-h cycle (12:12 light-dark, ZT0 =light ON whereas ZT12 =light OFF). Data were visualized and processed using confocal microscopy. Results showed that PK2 protein expression diurnally oscillates with calculated peak expression ZT5:40 ± 1:40 h. Opposite than described for PK2 mRNA, PK2 immunoreactivity was detectable at all times during the 24-h cycle. PK2 was primarily located in neurons of the shell compartment and > 80 % of these neurons co-expressed the core clock protein PER2. In conclusion, PK2 protein expression oscillates as the mRNA, supporting the suggested role of PK2 as a SCN molecule involved in circadian rhythm regulation.

scIALM: A method for sparse scRNA-seq expression matrix imputation using the Inexact Augmented Lagrange Multiplier with low error

Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology that quantifies gene expression profiles of specific cell populations at the single-cell level, providing a foundation for studying cellular heterogeneity and patient pathological characteristics. It is effective for developmental, fertility, and disease studies. However, the cell-gene expression matrix of single-cell sequencing data is often sparse and contains numerous zero values. Some of the zero values derive from noise, where dropout noise has a large impact on downstream analysis. In this paper, we propose a method named scIALM for imputation recovery of sparse single-cell RNA data expression matrices, which employs the Inexact Augmented Lagrange Multiplier method to use sparse but clean (accurate) data to recover unknown entries in the matrix. We perform experimental analysis on four datasets, calling the expression matrix after Quality Control (QC) as the original matrix, and comparing the performance of scIALM with six other methods using mean squared error (MSE), mean absolute error (MAE), Pearson correlation coefficient (PCC), and cosine similarity (CS). Our results demonstrate that scIALM accurately recovers the original data of the matrix with an error of 10e-4, and the mean value of the four metrics reaches 4.5072 (MSE), 0.765 (MAE), 0.8701 (PCC), 0.8896 (CS). In addition, at 10%-50% random masking noise, scIALM is the least sensitive to the masking ratio. For downstream analysis, this study uses adjusted rand index (ARI) and normalized mutual information (NMI) to evaluate the clustering effect, and the results are improved on three datasets containing real cluster labels.

A subcortical feeding circuit linking an interoceptive node to jaw movement

The brain processes an array of stimuli, enabling the selection of appropriate behavioural responses, but the neural pathways linking interoceptive inputs to outputs for feeding are poorly understood1-3. Here we delineate a subcortical circuit in which brain-derived neurotrophic factor (BDNF)-expressing neurons in the ventromedial hypothalamus (VMH) directly connect interoceptive inputs to motor centres, controlling food consumption and jaw movements. VMHBDNF neuron inhibition increases food intake by gating motor sequences of feeding through projections to premotor areas of the jaw. When food is unavailable, VMHBDNF inhibition elicits consummatory behaviours directed at inanimate objects such as wooden blocks, and inhibition of perimesencephalic trigeminal area (pMe5) projections evokes rhythmic jaw movements. The activity of these neurons is decreased during food consumption and increases when food is in proximity but not consumed. Activity is also increased in obese animals and after leptin treatment. VMHBDNF neurons receive monosynaptic inputs from both agouti-related peptide (AgRP) and proopiomelanocortin neurons in the arcuate nucleus (Arc), and constitutive VMHBDNF activation blocks the orexigenic effect of AgRP activation. These data indicate an Arc → VMHBDNF → pMe5 circuit that senses the energy state of an animal and regulates consummatory behaviours in a state-dependent manner.

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.

Multiomics analyses reveal high yield-related genes in the hypothalamic-pituitary-ovarian/liver axis of chicken

Egg production, regulated by multiple tissues, is among the most important economic traits in poultry. However, current research only focuses on the hypothalamic-pituitary-ovarian axis, ignoring the most important organ for substance metabolism in the body, the liver. Eggs are rich in lipids, proteins, and other nutrients, which are biosynthesized in the liver. Therefore, here the liver was included in the study of the hypothalamic-pituitary axis. This study used hypothalamus (HH_vs_LH), pituitary (HP_vs_LP), liver (HL_vs_LL), and ovary (HO_vs_LO) tissue samples from high- and low-laying Chengkou mountain chickens (CMC) for epihistological, transcriptome and metabolomic analyses aimed at improving the reproductive performance of CMC. The results showed that the liver of the high-laying group was yellowish, the cell boundary was clear, and the lipid droplets were evenly distributed. The ovaries of the high-laying group had a complete sequence of hierarchical follicles, which were rich in yolk. In contrast, the ovaries of the low-laying group were atrophic, except for a few small yellow follicles, and numerous primordial follicles that remained. The transcriptome sequences yielded 167.11 Gb of clean data, containing 28,715 genes. Furthermore, 285, 822, 787, and 1,183 differentially expressed genes (DEG) were identified in HH_vs_LH, HP_vs_LP, HL_vs_LL and HO_vs_LO and the DEGs significantly enriched 77, 163, 170, 171 pathways, respectively. Metabolome sequencing yielded 21,808 peaks containing 4,006 metabolites. The differential metabolite analysis yielded 343 and 682 significantly different metabolites (SDM) that significantly enriched 136 and 87 pathways in the liver and ovaries, respectively. A combined analysis of the transcriptome and metabolome of the liver and ovaries identified "CYP51A1-4α-carboxy-stigmasta7, 24(24(1))-dien-3β-ol" and "ACSS1B-estrone 3-sulfate" and other multiple gene-metabolite pairs. The DEGs in the hypothalamus and pituitary mainly enriched signaling transduction. In contrast, the DEGs and SDMs in the liver and ovaries mainly enriched the substance metabolism pathways: "gap junction", "extracellular matrix (ECM)-receptor interaction", "Steroid biosynthesis", and "Steroid hormone biosynthesis". These results suggest that the hypothalamic-pituitary axis may affect egg production mainly by regulating lipid metabolism in the liver and ovaries.

Neuropeptide signaling network of Caenorhabditis elegans: from structure to behavior

Neuropeptides are abundant signaling molecules that control neuronal activity and behavior in all animals. Owing in part to its well-defined and compact nervous system, Caenorhabditis elegans has been one of the primary model organisms used to investigate how neuropeptide signaling networks are organized and how these neurochemicals regulate behavior. We here review recent work that has expanded our understanding of the neuropeptidergic signaling network in C. elegans by mapping the evolutionary conservation, the molecular expression, the receptor-ligand interactions, and the system-wide organization of neuropeptide pathways in the C. elegans nervous system. We also describe general insights into neuropeptidergic circuit motifs and the spatiotemporal range of peptidergic transmission that have emerged from in vivo studies on neuropeptide signaling. With efforts ongoing to chart peptide signaling networks in other organisms, the C. elegans neuropeptidergic connectome can serve as a prototype to further understand the organization and the signaling dynamics of these networks at organismal level.

Molecular and functional mapping of the neuroendocrine hypothalamus: a new era begins

BACKGROUND

Recent advances in neuroscience tools for single-cell molecular profiling of brain neurons have revealed an enormous spectrum of neuronal subpopulations within the neuroendocrine hypothalamus, highlighting the remarkable molecular and cellular heterogeneity of this brain area.

RATIONALE

Neuronal diversity in the hypothalamus reflects the high functional plasticity of this brain area, where multiple neuronal populations flexibly integrate a variety of physiological outputs, including energy balance, stress and fertility, through crosstalk mechanisms with peripheral hormones. Intrinsic functional heterogeneity is also observed within classically 'defined' subpopulations of neuroendocrine neurons, including subtypes with distinct neurochemical signatures, spatial organisation and responsiveness to hormonal cues.

AIM

The aim of this review is to critically evaluate past and current research on the functional diversity of hypothalamic neuroendocrine neurons and their plasticity. It focuses on how this neuronal plasticity in this brain area relates to metabolic control, feeding regulation and interactions with stress and fertility-related neural circuits.

CONCLUSION

Our analysis provides an original framework for improving our understanding of the hypothalamic regulation of hormone function and the development of neuroendocrine diseases.

Age, sex, and cell type-resolved hypothalamic gene expression across the pubertal transition in mice

BACKGROUND

The hypothalamus plays a central role in regulating puberty. However, our knowledge of the postnatal gene regulatory networks that control the pubertal transition in males and females is incomplete. Here, we investigate the age-, sex- and cell-type-specific gene regulation in the hypothalamus across the pubertal transition.

METHODS

We used RNA-seq to profile hypothalamic gene expression in male and female mice at five time points spanning the onset of puberty (postnatal days (PD) 12, 22, 27, 32, and 37). By combining this data with hypothalamic single nuclei RNA-seq data from pre- and postpubertal mice, we assigned gene expression changes to their most likely cell types of origin. In our colony, pubertal onset occurs earlier in male mice, allowing us to focus on genes whose expression is dynamic across ages and offset between sexes, and to explore the bases of sex effects.

RESULTS

Our age-by-sex pattern of expression enriched for biological pathways involved hormone production, neuronal activation, and glial maturation. Additionally, we inferred a robust expansion of oligodendrocytes precursor cells into mature oligodendrocytes spanning the prepubertal (PD12) to peri-pubertal (PD27) timepoints. Using spatial transcriptomic data from postpubertal mice, we observed the lateral hypothalamic area and zona incerta were the most oligodendrocyte-rich regions and that these cells expressed genes known to be involved in pubertal regulation.

CONCLUSION

Together, by incorporating multiple biological timepoints and using sex as a variable, we identified gene and cell-type changes that may participate in orchestrating the pubertal transition and provided a resource for future studies of postnatal hypothalamic gene regulation.

Concerted transcriptional regulation of the morphogenesis of hypothalamic neurons by ONECUT3

Acquisition of specialized cellular features is controlled by the ordered expression of transcription factors (TFs) along differentiation trajectories. Here, we find a member of the Onecut TF family, ONECUT3, expressed in postmitotic neurons that leave their Ascl1+/Onecut1/2+ proliferative domain in the vertebrate hypothalamus to instruct neuronal differentiation. We combined single-cell RNA-seq and gain-of-function experiments for gene network reconstruction to show that ONECUT3 affects the polarization and morphogenesis of both hypothalamic GABA-derived dopamine and thyrotropin-releasing hormone (TRH)+ glutamate neurons through neuron navigator-2 (NAV2). In vivo, siRNA-mediated knockdown of ONECUT3 in neonatal mice reduced NAV2 mRNA, as well as neurite complexity in Onecut3-containing neurons, while genetic deletion of Onecut3/ceh-48 in C. elegans impaired neurocircuit wiring, and sensory discrimination-based behaviors. Thus, ONECUT3, conserved across neuronal subtypes and many species, underpins the polarization and morphological plasticity of phenotypically distinct neurons that descend from a common pool of Ascl1+ progenitors in the hypothalamus.

SAE-Impute: imputation for single-cell data via subspace regression and auto-encoders

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) technology has emerged as a crucial tool for studying cellular heterogeneity. However, dropouts are inherent to the sequencing process, known as dropout events, posing challenges in downstream analysis and interpretation. Imputing dropout data becomes a critical concern in scRNA-seq data analysis. Present imputation methods predominantly rely on statistical or machine learning approaches, often overlooking inter-sample correlations.

RESULTS

To address this limitation, We introduced SAE-Impute, a new computational method for imputing single-cell data by combining subspace regression and auto-encoders for enhancing the accuracy and reliability of the imputation process. Specifically, SAE-Impute assesses sample correlations via subspace regression, predicts potential dropout values, and then leverages these predictions within an autoencoder framework for interpolation. To validate the performance of SAE-Impute, we systematically conducted experiments on both simulated and real scRNA-seq datasets. These results highlight that SAE-Impute effectively reduces false negative signals in single-cell data and enhances the retrieval of dropout values, gene-gene and cell-cell correlations. Finally, We also conducted several downstream analyses on the imputed single-cell RNA sequencing (scRNA-seq) data, including the identification of differential gene expression, cell clustering and visualization, and cell trajectory construction.

CONCLUSIONS

These results once again demonstrate that SAE-Impute is able to effectively reduce the droupouts in single-cell dataset, thereby improving the functional interpretability of the data.

Characteristics of blood-brain barrier heterogeneity between brain regions revealed by profiling vascular and perivascular cells

The blood-brain barrier (BBB) protects the brain and maintains neuronal homeostasis. BBB properties can vary between brain regions to support regional functions, yet how BBB heterogeneity occurs is poorly understood. Here, we used single-cell and spatial transcriptomics to compare the mouse median eminence, one of the circumventricular organs that has naturally leaky blood vessels, with the cortex. We identified hundreds of molecular differences in endothelial cells (ECs) and perivascular cells, including astrocytes, pericytes and fibroblasts. Using electron microscopy and an aqueous-based tissue-clearing method, we revealed distinct anatomical specializations and interaction patterns of ECs and perivascular cells in these regions. Finally, we identified candidate regionally enriched EC-perivascular cell ligand-receptor pairs. Our results indicate that both molecular specializations in ECs and unique EC-perivascular cell interactions contribute to BBB functional heterogeneity. This platform can be used to investigate BBB heterogeneity in other regions and may facilitate the development of central nervous system region-specific therapeutics.

Cocaine- and Amphetamine-Regulated Transcript Peptide in the Central Nervous System of the Gecko, Hemidactylus leschenaultii: Molecular Characterization, Neuroanatomical Organization, and Regulation by Neuropeptide Y

Neuropeptide cocaine- and amphetamine-regulated transcript (CART) is widely expressed in the brains of teleosts, amphibians, birds, and mammals and has emerged as a conserved regulator of energy balance across these vertebrate phyla. However, as yet, there is no information on CART in the reptilian brain. We characterized the cDNA encoding CART and mapped CART-containing elements in the brain of gecko, Hemidactylus leschenaultii (hl) using a specific anti-CART antiserum. We report a 683-bp hlcart transcript containing a 336-bp open reading frame, which encodes a putative 111-amino acid hl-preproCART. The 89-amino acid hl-proCART generated from hl-preproCART produced two putative bioactive hl-CART-peptides. These bioactive CART-peptides were > 93% similar with those in rats/humans. Although reverse transcription-polymerase chain reaction (RT-PCR) detected hlcart-transcript in the brain, CART-containing neurons/fibers were widely distributed in the telencephalon, diencephalon, mesencephalon, rhombencephalon, spinal cord, and retina. The mitral cells in olfactory bulb, neurons in the paraventricular, periventricular, arcuate (Arc), Edinger-Westphal, and brainstem nuclei were intensely CART-positive. In view of antagonistic roles of neuropeptide Y (NPY) and CART in energy balance in the framework of mammalian hypothalamus, we probed CART-NPY interaction in the hypothalamus of H. leschenaultii. Double immunofluorescence showed a dense NPY-innervation of Arc CART neurons. Ex vivo hypothalamic slices treated with NPY/NPY-Y1-receptor agonist significantly reduced hlcart-mRNA levels in the Arc-containing tissues and CART-ir in the dorsal-Arc. However, CART-ir in ventral-Arc was unaffected. NPY via Y1-receptors may regulate energy balance by inhibiting dArc CART neurons. This study on CART in a reptilian brain fills the current void in literature and underscores the conserved feature of the neuropeptide across the entire vertebrate phyla.

scDFN: enhancing single-cell RNA-seq clustering with deep fusion networks

Single-cell ribonucleic acid sequencing (scRNA-seq) technology can be used to perform high-resolution analysis of the transcriptomes of individual cells. Therefore, its application has gained popularity for accurately analyzing the ever-increasing content of heterogeneous single-cell datasets. Central to interpreting scRNA-seq data is the clustering of cells to decipher transcriptomic diversity and infer cell behavior patterns. However, its complexity necessitates the application of advanced methodologies capable of resolving the inherent heterogeneity and limited gene expression characteristics of single-cell data. Herein, we introduce a novel deep learning-based algorithm for single-cell clustering, designated scDFN, which can significantly enhance the clustering of scRNA-seq data through a fusion network strategy. The scDFN algorithm applies a dual mechanism involving an autoencoder to extract attribute information and an improved graph autoencoder to capture topological nuances, integrated via a cross-network information fusion mechanism complemented by a triple self-supervision strategy. This fusion is optimized through a holistic consideration of four distinct loss functions. A comparative analysis with five leading scRNA-seq clustering methodologies across multiple datasets revealed the superiority of scDFN, as determined by better the Normalized Mutual Information (NMI) and the Adjusted Rand Index (ARI) metrics. Additionally, scDFN demonstrated robust multi-cluster dataset performance and exceptional resilience to batch effects. Ablation studies highlighted the key roles of the autoencoder and the improved graph autoencoder components, along with the critical contribution of the four joint loss functions to the overall efficacy of the algorithm. Through these advancements, scDFN set a new benchmark in single-cell clustering and can be used as an effective tool for the nuanced analysis of single-cell transcriptomics.

Investigating the Impact of Fasting and Refeeding on Blood Biochemical Indicators and Transcriptional Profiles in the Hypothalamus and Subcutaneous Adipose Tissue in Geese

Fasting and refeeding systems can cause significant short-term fluctuations in nutrient and energy levels, triggering adaptive physiological responses in animals. This study examines the effects of fasting and refeeding on blood biochemical indicators and transcriptional profiles in the hypothalamus and subcutaneous adipose tissue of geese. Biochemical assays reveal that fasting significantly increases levels of free fatty acids and glucagon, while reducing concentrations of triglycerides, leptin, and insulin. Transcriptomic analyses identify a complex transcriptional response in both the hypothalamus and subcutaneous adipose tissue, affecting several metabolic pathways and key genes associated with feed intake and energy metabolism. In subcutaneous adipose tissue, fasting downregulates genes involved in fatty acid synthesis (LPL, SCD, and ACSL1) and upregulates PLIN2, a gene promoting lipid droplet degradation. Fasting affects a variety of metabolic pathways and critical genes in the hypothalamus, including Apelin, insulin, and mTOR signaling pathways. After fasting, the mRNA expression of NOG, GABRD, and IGFBP-1 genes in the hypothalamus are significantly upregulated, while proopiomelanocortin (POMC) gene expression is markedly downregulated. This study highlights the intricate biological responses to nutritional changes in geese, which adds to our understanding of energy balance and metabolic regulation in avian species.

Clustering scRNA-seq data with the cross-view collaborative information fusion strategy

Single-cell RNA sequencing (scRNA-seq) technology has revolutionized biological research by enabling high-throughput, cellular-resolution gene expression profiling. A critical step in scRNA-seq data analysis is cell clustering, which supports downstream analyses. However, the high-dimensional and sparse nature of scRNA-seq data poses significant challenges to existing clustering methods. Furthermore, integrating gene expression information with potential cell structure data remains largely unexplored. Here, we present scCFIB, a novel information bottleneck (IB)-based clustering algorithm that leverages the power of IB for efficient processing of high-dimensional sparse data and incorporates a cross-view fusion strategy to achieve robust cell clustering. scCFIB constructs a multi-feature space by establishing two distinct views from the original features. We then formulate the cell clustering problem as a target loss function within the IB framework, employing a collaborative information fusion strategy. To further optimize scCFIB's performance, we introduce a novel sequential optimization approach through an iterative process. Benchmarking against established methods on diverse scRNA-seq datasets demonstrates that scCFIB achieves superior performance in scRNA-seq data clustering tasks. Availability: the source code is publicly available on GitHub: https://github.com/weixiaojiao/scCFIB.

Neurodevelopmental disorder-associated CYFIP2 regulates membraneless organelles and eIF2α phosphorylation via protein interactors and actin cytoskeleton

De novo variants in the Cytoplasmic FMR1-interacting protein 2 (CYFIP2) have been repeatedly associated with neurodevelopmental disorders and epilepsy, underscoring its critical role in brain development and function. While CYFIP2's role in regulating actin polymerization as part of the WAVE regulatory complex (WRC) is well-established, its additional molecular functions remain relatively unexplored. In this study, we performed unbiased quantitative proteomic analysis, revealing 278 differentially expressed proteins (DEPs) in the forebrain of Cyfip2 knock-out embryonic mice compared to wild-type mice. Unexpectedly, these DEPs, in conjunction with previously identified CYFIP2 brain interactors, included not only other WRC components but also numerous proteins associated with membraneless organelles (MLOs) involved in mRNA processing and translation within cells, including the nucleolus, stress granules, and processing bodies. Additionally, single-cell transcriptomic analysis of the Cyfip2 knock-out forebrain revealed gene expression changes linked to cellular stress responses and MLOs. We also observed morphological changes in MLOs in Cyfip2 knock-out brains and CYFIP2 knock-down cells under basal and stress conditions. Lastly, we demonstrated that CYFIP2 knock-down in cells, potentially through WRC-dependent actin regulation, suppressed the phosphorylation levels of the alpha subunit of eukaryotic translation initiation factor 2 (eIF2α), thereby enhancing protein synthesis. These results suggest a physical and functional connection between CYFIP2 and various MLO proteins and also extend CYFIP2's role within the WRC from actin regulation to influencing eIF2α phosphorylation and protein synthesis. With these dual functions, CYFIP2 may fine-tune the balance between MLO formation/dynamics and protein synthesis, a crucial aspect of proper mRNA processing and translation.

Single nucleus/cell RNA-seq of the chicken hypothalamic-pituitary-ovarian axis offers new insights into the molecular regulatory mechanisms of ovarian development

The hypothalamic-pituitary-ovarian (HPO) axis represents a central neuroendocrine network essential for reproductive function. Despite its critical role, the intrinsic heterogeneity within the HPO axis across vertebrates and the complex intercellular interactions remain poorly defined. This study provides the first comprehensive, unbiased, cell type-specific molecular profiling of all three components of the HPO axis in adult Lohmann layers and Liangshan Yanying chickens. Within the hypothalamus, pituitary, and ovary, seven, 12, and 13 distinct cell types were identified, respectively. Results indicated that the pituitary adenylate cyclase activating polypeptide (PACAP), follicle-stimulating hormone (FSH), and prolactin (PRL) signaling pathways may modulate the synthesis and secretion of gonadotropin-releasing hormone (GnRH), FSH, and luteinizing hormone (LH) within the hypothalamus and pituitary. In the ovary, interactions between granulosa cells and oocytes involved the KIT, CD99, LIFR, FN1, and ANGPTL signaling pathways, which collectively regulate follicular maturation. The SEMA4 signaling pathway emerged as a critical mediator across all three tissues of the HPO axis. Additionally, gene expression analysis revealed that relaxin 3 (RLN3), gastrin-releasing peptide (GRP), and cocaine- and amphetamine regulated transcripts (CART, also known as CARTPT) may function as novel endocrine hormones, influencing the HPO axis through autocrine, paracrine, and endocrine pathways. Comparative analyses between Lohmann layers and Liangshan Yanying chickens demonstrated higher expression levels of GRP, RLN3, CARTPT, LHCGR, FSHR, and GRPR in the ovaries of Lohmann layers, potentially contributing to their superior reproductive performance. In conclusion, this study provides a detailed molecular characterization of the HPO axis, offering novel insights into the regulatory mechanisms underlying reproductive biology.

ESCHR: a hyperparameter-randomized ensemble approach for robust clustering across diverse datasets

Clustering is widely used for single-cell analysis, but current methods are limited in accuracy, robustness, ease of use, and interpretability. To address these limitations, we developed an ensemble clustering method that outperforms other methods at hard clustering without the need for hyperparameter tuning. It also performs soft clustering to characterize continuum-like regions and quantify clustering uncertainty, demonstrated here by mapping the connectivity and intermediate transitions between MNIST handwritten digits and between hypothalamic tanycyte subpopulations. This hyperparameter-randomized ensemble approach improves the accuracy, robustness, ease of use, and interpretability of single-cell clustering, and may prove useful in other fields as well.

Phototaxis is a satiety-dependent behavioral sequence in Hydra vulgaris

Understanding how internal states such as satiety are connected to animal behavior is a fundamental question in neuroscience. Hydra vulgaris, a freshwater cnidarian with only 12 neuronal cell types, serves as a tractable model system for studying state-dependent behaviors. We found that starved hydras consistently move towards light, while fed hydras do not. By modeling this behavior as a set of three sequences of head orientation, jump distance and jump rate, we demonstrate that the satiety state only affects the rate of the animal jumping to a new position, while the orientation and jump distance are unaffected. These findings yield insights into how internal states in a simple organism, Hydra, affect specific elements of a behavior, and offer general principles for studying the relationship between state-dependent behaviors and their underlying molecular mechanisms.

scCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data

Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for characterizing cellular landscapes within complex tissues. Large-scale single-cell transcriptomics holds great potential for identifying rare cell types critical to the pathogenesis of diseases and biological processes. Existing methods for identifying rare cell types often rely on one-time clustering using partial or global gene expression. However, these rare cell types may be overlooked during the clustering phase, posing challenges for their accurate identification. In this paper, we propose a Cluster decomposition-based Anomaly Detection method (scCAD), which iteratively decomposes clusters based on the most differential signals in each cluster to effectively separate rare cell types and achieve accurate identification. We benchmark scCAD on 25 real-world scRNA-seq datasets, demonstrating its superior performance compared to 10 state-of-the-art methods. In-depth case studies across diverse datasets, including mouse airway, brain, intestine, human pancreas, immunology data, and clear cell renal cell carcinoma, showcase scCAD's efficiency in identifying rare cell types in complex biological scenarios. Furthermore, scCAD can correct the annotation of rare cell types and identify immune cell subtypes associated with disease, thereby offering valuable insights into disease progression.

A single-cell transcriptomic map of the developing Atoh1 lineage identifies neural fate decisions and neuronal diversity in the hindbrain

Proneural transcription factors establish molecular cascades to orchestrate neuronal diversity. One such transcription factor, Atonal homolog 1 (Atoh1), gives rise to cerebellar excitatory neurons and over 30 distinct nuclei in the brainstem critical for hearing, breathing, and balance. Although Atoh1 lineage neurons have been qualitatively described, the transcriptional programs that drive their fate decisions and the full extent of their diversity remain unknown. Here, we analyzed single-cell RNA sequencing and ATOH1 DNA binding in Atoh1 lineage neurons of the developing mouse hindbrain. This high-resolution dataset identified markers for specific brainstem nuclei and demonstrated that transcriptionally heterogeneous progenitors require ATOH1 for proper migration. Moreover, we identified a sizable population of proliferating unipolar brush cell progenitors in the mouse Atoh1 lineage, previously described in humans as the origin of one medulloblastoma subtype. Collectively, our data provide insights into the developing mouse hindbrain and markers for functional assessment of understudied neuronal populations.

Fasting induces metabolic switches and spatial redistributions of lipid processing and neuronal interactions in tanycytes

The ependyma lining the third ventricle (3V) in the mediobasal hypothalamus plays a crucial role in energy balance and glucose homeostasis. It is characterized by a high functional heterogeneity and plasticity, but the underlying molecular mechanisms governing its features are not fully understood. Here, 5481 hypothalamic ependymocytes were cataloged using FACS-assisted scRNAseq from fed, 12h-fasted, and 24h-fasted adult male mice. With standard clustering analysis, typical ependymal cells and β2-tanycytes appear sharply defined, but other subpopulations, β1- and α-tanycytes, display fuzzy boundaries with few or no specific markers. Pseudospatial approaches, based on the 3V neuroanatomical distribution, enable the identification of specific versus shared tanycyte markers and subgroup-specific versus general tanycyte functions. We show that fasting dynamically shifts gene expression patterns along the 3V, leading to a spatial redistribution of cell type-specific responses. Altogether, we show that changes in energy status induce metabolic and functional switches in tanycyte subpopulations, providing insights into molecular and functional diversity and plasticity within the tanycyte population.

A subset of dopamine receptor-expressing neurons in the nucleus accumbens controls feeding and energy homeostasis

Orchestrating complex behaviors, such as approaching and consuming food, is critical for survival. In addition to hypothalamus neuronal circuits, the nucleus accumbens (NAc) also controls appetite and satiety. However, specific neuronal subtypes of the NAc that are involved and how the humoral and neuronal signals coordinate to regulate feeding remain incompletely understood. Here we decipher the spatial diversity of neuron subtypes of the NAc shell (NAcSh) and define a dopamine receptor D1-expressing and Serpinb2-expressing subtype controlling food consumption in male mice. Chemogenetics and optogenetics-mediated regulation of Serpinb2+ neurons bidirectionally regulate food seeking and consumption specifically. Circuitry stimulation reveals that the NAcShSerpinb2→LHLepR projection controls refeeding and can overcome leptin-mediated feeding suppression. Furthermore, NAcSh Serpinb2+ neuron ablation reduces food intake and upregulates energy expenditure, resulting in reduced bodyweight gain. Our study reveals a neural circuit consisting of a molecularly distinct neuronal subtype that bidirectionally regulates energy homeostasis, providing a potential therapeutic target for eating disorders.

Enhanced In Situ Spatial Proteomics by Effective Combination of MALDI Imaging and LC-MS/MS

Highly specialized cells are fundamental for the proper functioning of complex organs. Variations in cell-type-specific gene expression and protein composition have been linked to a variety of diseases. Investigation of the distinctive molecular makeup of these cells within tissues is therefore critical in biomedical research. Although several technologies have emerged as valuable tools to address this cellular heterogeneity, most workflows lack sufficient in situ resolution and are associated with high costs and extremely long analysis times. Here, we present a combination of experimental and computational approaches that allows a more comprehensive investigation of molecular heterogeneity within tissues than by either shotgun LC-MS/MS or MALDI imaging alone. We applied our pipeline to the mouse brain, which contains a wide variety of cell types that not only perform unique functions but also exhibit varying sensitivities to insults. We explored the distinct neuronal populations within the hippocampus, a brain region crucial for learning and memory that is involved in various neurological disorders. As an example, we identified the groups of proteins distinguishing the neuronal populations of the dentate gyrus (DG) and the cornu ammonis (CA) in the same brain section. Most of the annotated proteins matched the regional enrichment of their transcripts, thereby validating the method. As the method is highly reproducible, the identification of individual masses through the combination of MALDI-IMS and LC-MS/MS methods can be used for the much faster and more precise interpretation of MALDI-IMS measurements only. This greatly speeds up spatial proteomic analyses and allows the detection of local protein variations within the same population of cells. The method's general applicability has the potential to be used to investigate different biological conditions and tissues and a much higher throughput than other techniques making it a promising approach for clinical routine applications.

Identifying cell states in single-cell RNA-seq data at statistically maximal resolution

Single-cell RNA sequencing (scRNA-seq) has become a popular experimental method to study variation of gene expression within a population of cells. However, obtaining an accurate picture of the diversity of distinct gene expression states that are present in a given dataset is highly challenging because of the sparsity of the scRNA-seq data and its inhomogeneous measurement noise properties. Although a vast number of different methods is applied in the literature for clustering cells into subsets with 'similar' expression profiles, these methods generally lack rigorously specified objectives, involve multiple complex layers of normalization, filtering, feature selection, dimensionality-reduction, employ ad hoc measures of distance or similarity between cells, often ignore the known measurement noise properties of scRNA-seq measurements, and include a large number of tunable parameters. Consequently, it is virtually impossible to assign concrete biophysical meaning to the clusterings that result from these methods. Here we address the following problem: Given raw unique molecule identifier (UMI) counts of an scRNA-seq dataset, partition the cells into subsets such that the gene expression states of the cells in each subset are statistically indistinguishable, and each subset corresponds to a distinct gene expression state. That is, we aim to partition cells so as to maximally reduce the complexity of the dataset without removing any of its meaningful structure. We show that, given the known measurement noise structure of scRNA-seq data, this problem is mathematically well-defined and derive its unique solution from first principles. We have implemented this solution in a tool called Cellstates which operates directly on the raw data and automatically determines the optimal partition and cluster number, with zero tunable parameters. We show that, on synthetic datasets, Cellstates almost perfectly recovers optimal partitions. On real data, Cellstates robustly identifies subtle substructure within groups of cells that are traditionally annotated as a common cell type. Moreover, we show that the diversity of gene expression states that Cellstates identifies systematically depends on the tissue of origin and not on technical features of the experiments such as the total number of cells and total UMI count per cell. In addition to the Cellstates tool we also provide a small toolbox of software to place the identified cellstates into a hierarchical tree of higher-order clusters, to identify the most important differentially expressed genes at each branch of this hierarchy, and to visualize these results.

Micromanaging the neuroendocrine system - A review on miR-7 and the other physiologically relevant miRNAs in the hypothalamic-pituitary axis

The hypothalamic-pituitary axis is central to the functioning of the neuroendocrine system and essential for regulating physiological and behavioral homeostasis and coordinating fundamental body functions. The expanding line of evidence shows the indispensable role of the microRNA pathway in regulating the gene expression profile in the developing and adult hypothalamus and pituitary gland. Experiments provoking a depletion of miRNA maturation in the context of the hypothalamic-pituitary axis brought into focus a prominent involvement of miRNAs in neuroendocrine functions. There are also a few individual miRNAs and miRNA families that have been studied in depth revealing their crucial role in mediating the regulation of fundamental processes such as temporal precision of puberty timing, hormone production, fertility and reproduction capacity, and energy balance. Among these miRNAs, miR-7 was shown to be hypothalamus-enriched and the top one highly expressed in the pituitary gland, where it has a profound impact on gene expression regulation. Here, we review miRNA profiles, knockout phenotypes, and miRNA interaction (targets) in the hypothalamic-pituitary axis that advance our understanding of the roles of miRNAs in mammalian neurosecretion and related physiology.