Single-Cell RNA-Seq Reveals Hypothalamic Cell Diversity

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.

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.

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 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.

Enhanced in situ spatial proteomics by effective combination of MALDI imaging and LC-MS/MS

Highly specialized cells are fundamental for 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 cost 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 on 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 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.

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.

Photoperiod, but not progesterone, has a strong impact upon the transcriptome of the medio-basal hypothalamus in female goats and ewes

Photoperiod is the main environmental driver of seasonal responses in organisms living at temperate and polar latitudes. Other external cues such as food and temperature, and internal cues including hormones, intervene to fine-tune phasing of physiological functions to the solar year. In mammals, the medio-basal hypothalamus (MBH) is the key integrator of these cues, which orchestrates a wide array of seasonal functions, including breeding. Here, using RNAseq and RT-qPCR, we demonstrate that molecular components of the photoperiodic response previously identified in ewes are broadly conserved in does (female goats, Capra hircus), with a common core of ∼50 genes. This core group can be defined as the "MBH seasonal trancriptome", which includes key players of the pars tuberalis-tanycytes neuroendocrine retrograde pathway that governs intra-MBH photoperiodic switches of triiodothyronine (T3) production (Tshb, Eya3, Dio2 and SlcO1c1), the two histone methyltransferases Suv39H2 and Ezh2 and the secreted protein Vmo1. Prior data in ewes revealed that T3 and estradiol (E2), both key hormones for the proper timing of seasonal breeding, differentially impact the MBH seasonal transcriptome, and identified cellular and molecular targets through which these hormones might act. In contrast, information regarding the potential impact of progesterone (P4) upon the MBH transcriptome was nonexistent. Here, we demonstrate that P4 has no discernible transcriptional impact in either does or ewes. Taken together, our data show that does and ewes possess a common core set of photoperiod-responsive genes in the MBH and conclusively demonstrate that P4 is not a key regulator of the MBH transcriptome.

Single-cell transcriptomic reveals a cell atlas and diversity of chicken amygdala responded to social hierarchy

Amygdala serves as a highly cellular, heterogeneous brain region containing excitatory and inhibitory neurons and is involved in the dopamine and serotoninergic neuron systems. An increasing number of studies have revealed the underpinned mechanism mediating social hierarchy in mammal and vertebrate, however, there are rare studies conducted on how amygdala on social hierarchy in poultry. In this study, we conducted food competition tests and determined the social hierarchy of the rooster. We performed cross-species analysis with mammalian amygdala, and found that cell types of human and rhesus monkeys were more closely related and that of chickens were more distant. We identified 26 clusters and divided them into 10 main clusters, of which GABAergic and glutamatergic neurons were associated with social behaviors. In conclusion, our results provide to serve the developmental studies of the amygdala neuron system and new insights into the underpinned mechanism of social hierarchy in roosters.

Modeling hypothalamic pathophysiology in vitro for metabolic, circadian, and sleep disorders

The hypothalamus, a small and intricate brain structure, orchestrates numerous neuroendocrine functions through specialized neurons and nuclei. Disruption of this complex circuitry can result in various diseases, including metabolic, circadian, and sleep disorders. Advances in in vitro models and their integration with new technologies have significantly benefited research on hypothalamic function and pathophysiology. We explore existing in vitro hypothalamic models and address their challenges and limitations as well as translational findings. We also highlight how collaborative efforts among multidisciplinary teams are essential to develop relevant and translational experimental models capable of replicating intricate neural circuits and neuroendocrine pathways, thereby advancing our understanding of therapeutic targets and drug discovery in hypothalamus-related disorders.

Variant-to-function analysis of the childhood obesity chr12q13 locus implicates rs7132908 as a causal variant within the 3' UTR of FAIM2

The ch12q13 locus is among the most significant childhood obesity loci identified in genome-wide association studies. This locus resides in a non-coding region within FAIM2; thus, the underlying causal variant(s) presumably influence disease susceptibility via cis-regulation. We implicated rs7132908 as a putative causal variant by leveraging our in-house 3D genomic data and public domain datasets. Using a luciferase reporter assay, we observed allele-specific cis-regulatory activity of the immediate region harboring rs7132908. We generated isogenic human embryonic stem cell lines homozygous for either rs7132908 allele to assess changes in gene expression and chromatin accessibility throughout a differentiation to hypothalamic neurons, a key cell type known to regulate feeding behavior. The rs7132908 obesity risk allele influenced expression of FAIM2 and other genes and decreased the proportion of neurons produced by differentiation. We have functionally validated rs7132908 as a causal obesity variant that temporally regulates nearby effector genes and influences neurodevelopment and survival.

scTPC: a novel semisupervised deep clustering model for scRNA-seq data

MOTIVATION

Continuous advancements in single-cell RNA sequencing (scRNA-seq) technology have enabled researchers to further explore the study of cell heterogeneity, trajectory inference, identification of rare cell types, and neurology. Accurate scRNA-seq data clustering is crucial in single-cell sequencing data analysis. However, the high dimensionality, sparsity, and presence of "false" zero values in the data can pose challenges to clustering. Furthermore, current unsupervised clustering algorithms have not effectively leveraged prior biological knowledge, making cell clustering even more challenging.

RESULTS

This study investigates a semisupervised clustering model called scTPC, which integrates the triplet constraint, pairwise constraint, and cross-entropy constraint based on deep learning. Specifically, the model begins by pretraining a denoising autoencoder based on a zero-inflated negative binomial distribution. Deep clustering is then performed in the learned latent feature space using triplet constraints and pairwise constraints generated from partial labeled cells. Finally, to address imbalanced cell-type datasets, a weighted cross-entropy loss is introduced to optimize the model. A series of experimental results on 10 real scRNA-seq datasets and five simulated datasets demonstrate that scTPC achieves accurate clustering with a well-designed framework.

AVAILABILITY AND IMPLEMENTATION

scTPC is a Python-based algorithm, and the code is available from https://github.com/LF-Yang/Code or https://zenodo.org/records/10951780.

Single cell tracing of Pomc neurons reveals recruitment of 'Ghost' subtypes with atypical identity in a mouse model of obesity

The hypothalamus contains a remarkable diversity of neurons that orchestrate behavioural and metabolic outputs in a highly plastic manner. Neuronal diversity is key to enabling hypothalamic functions and, according to the neuroscience dogma, it is predetermined during embryonic life. Here, by combining lineage tracing of hypothalamic pro-opiomelanocortin (Pomc) neurons with single-cell profiling approaches in adult male mice, we uncovered subpopulations of 'Ghost' neurons endowed with atypical molecular and functional identity. Compared to 'classical' Pomc neurons, Ghost neurons exhibit negligible Pomc expression and are 'invisible' to available neuroanatomical approaches and promoter-based reporter mice for studying Pomc biology. Ghost neuron numbers augment in diet-induced obese mice, independent of neurogenesis or cell death, but weight loss can reverse this shift. Our work challenges the notion of fixed, developmentally programmed neuronal identities in the mature hypothalamus and highlight the ability of specialised neurons to reversibly adapt their functional identity to adult-onset obesogenic stimuli.

Single-Nucleus Transcriptome Profiling from the Hippocampus of a PTSD Mouse Model and CBD-Treated Cohorts

Post-traumatic stress disorder (PTSD) is the most common psychiatric disorder after a catastrophic event; however, the efficacious treatment options remain insufficient. Increasing evidence suggests that cannabidiol (CBD) exhibits optimal therapeutic effects for treating PTSD. To elucidate the cell-type-specific transcriptomic pathology of PTSD and the mechanisms of CBD against this disease, we conducted single-nucleus RNA sequencing (snRNA-seq) in the hippocampus of PTSD-modeled mice and CBD-treated cohorts. We constructed a mouse model by adding electric foot shocks following exposure to single prolonged stress (SPS+S) and tested the freezing time, anxiety-like behavior, and cognitive behavior. CBD was administrated before every behavioral test. The PTSD-modeled mice displayed behaviors resembling those of PTSD in all behavioral tests, and CBD treatment alleviated all of these PTSD-like behaviors (n = 8/group). Three mice with representative behavioral phenotypes were selected from each group for snRNA-seq 15 days after the SPS+S. We primarily focused on the excitatory neurons (ExNs) and inhibitory neurons (InNs), which accounted for 68.4% of the total cell annotations. A total of 88 differentially upregulated genes and 305 differentially downregulated genes were found in the PTSD mice, which were found to exhibit significant alterations in pathways and biological processes associated with fear response, synaptic communication, protein synthesis, oxidative phosphorylation, and oxidative stress response. A total of 63 overlapping genes in InNs were identified as key genes for CBD in the treatment of PTSD. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that the anti-PTSD effect of CBD was related to the regulation of protein synthesis, oxidative phosphorylation, oxidative stress response, and fear response. Furthermore, gene set enrichment analysis (GSEA) revealed that CBD also enhanced retrograde endocannabinoid signaling in ExNs, which was found to be suppressed in the PTSD group. Our research may provide a potential explanation for the pathogenesis of PTSD and facilitate the discovery of novel therapeutic targets for drug development. Moreover, it may shed light on the therapeutic mechanisms of CBD.

Hypothalamic GABAergic Neurons Expressing Cellular Retinoic Acid Binding Protein 1 (CRABP1) Are Sensitive to Metabolic Status and Liraglutide in Male Mice

INTRODUCTION

Owing to their privileged anatomical location, neurons of the arcuate nucleus of the hypothalamus (ARC) play critical roles in sensing and responding to metabolic signals such as leptin and glucagon-like peptide 1 (GLP-1). In addition to the well-known proopiomelanocortin (POMC)- and agouti-related peptide (AgRP)-expressing neurons, subpopulations of GABAergic neurons are emerging as key regulators of energy balance. However, the precise identity of these metabolic neurons is still elusive. Here, we identified and characterized the molecular signature of a novel population of GABAergic neurons of the ARC expressing Cellular retinoic acid binding protein 1 (Crabp1).

METHODS

Using a combination of immunohistochemistry and in situ hybridization techniques, we investigated the expression of Crabp1 across the mouse brain and characterized the molecular identity of Crabp1ARC neurons. We also determined whether Crabp1ARC neurons are sensitive to fasting, leptin, and GLP1R agonism by assessing cFOS immunoreactivity as a marker of neuronal activity.

RESULTS

Crabp1ARC neurons represent a novel GABAergic neuronal population robustly enriched in the ARC and are distinct from the prototypical melanocortin neurons. Crabp1ARC neurons overlap with three subpopulations of yet uncharacterized ARC neurons expressing Htr3b, Tbx19, and Tmem215. Notably, Crabp1ARC neurons express receptors for metabolic hormones and their activity is modulated by the nutritional state and GLP1R agonism.

CONCLUSION

Crabp1ARC neurons represent a novel heterogeneous population of GABAergic neurons sensitive to metabolic status.

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.

A brainstem-hypothalamus neuronal circuit reduces feeding upon heat exposure

Empirical evidence suggests that heat exposure reduces food intake. However, the neurocircuit architecture and the signalling mechanisms that form an associative interface between sensory and metabolic modalities remain unknown, despite primary thermoceptive neurons in the pontine parabrachial nucleus becoming well characterized1. Tanycytes are a specialized cell type along the wall of the third ventricle2 that bidirectionally transport hormones and signalling molecules between the brain's parenchyma and ventricular system3-8. Here we show that tanycytes are activated upon acute thermal challenge and are necessary to reduce food intake afterwards. Virus-mediated gene manipulation and circuit mapping showed that thermosensing glutamatergic neurons of the parabrachial nucleus innervate tanycytes either directly or through second-order hypothalamic neurons. Heat-dependent Fos expression in tanycytes suggested their ability to produce signalling molecules, including vascular endothelial growth factor A (VEGFA). Instead of discharging VEGFA into the cerebrospinal fluid for a systemic effect, VEGFA was released along the parenchymal processes of tanycytes in the arcuate nucleus. VEGFA then increased the spike threshold of Flt1-expressing dopamine and agouti-related peptide (Agrp)-containing neurons, thus priming net anorexigenic output. Indeed, both acute heat and the chemogenetic activation of glutamatergic parabrachial neurons at thermoneutrality reduced food intake for hours, in a manner that is sensitive to both Vegfa loss-of-function and blockage of vesicle-associated membrane protein 2 (VAMP2)-dependent exocytosis from tanycytes. Overall, we define a multimodal neurocircuit in which tanycytes link parabrachial sensory relay to the long-term enforcement of a metabolic code.

scDAC: deep adaptive clustering of single-cell transcriptomic data with coupled autoencoder and Dirichlet process mixture model

MOTIVATION

Clustering analysis for single-cell RNA sequencing (scRNA-seq) data is an important step in revealing cellular heterogeneity. Many clustering methods have been proposed to discover heterogenous cell types from scRNA-seq data. However, adaptive clustering with accurate cluster number reflecting intrinsic biology nature from large-scale scRNA-seq data remains quite challenging.

RESULTS

Here, we propose a single-cell Deep Adaptive Clustering (scDAC) model by coupling the Autoencoder (AE) and the Dirichlet Process Mixture Model (DPMM). By jointly optimizing the model parameters of AE and DPMM, scDAC achieves adaptive clustering with accurate cluster numbers on scRNA-seq data. We verify the performance of scDAC on five subsampled datasets with different numbers of cell types and compare it with 15 widely used clustering methods across nine scRNA-seq datasets. Our results demonstrate that scDAC can adaptively find accurate numbers of cell types or subtypes and outperforms other methods. Moreover, the performance of scDAC is robust to hyperparameter changes.

AVAILABILITY AND IMPLEMENTATION

The scDAC is implemented in Python. The source code is available at https://github.com/labomics/scDAC.

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.

Distinct and shared genetic architectures of gestational diabetes mellitus and type 2 diabetes

Gestational diabetes mellitus (GDM) is a common metabolic disorder affecting more than 16 million pregnancies annually worldwide1,2. GDM is related to an increased lifetime risk of type 2 diabetes (T2D)1-3, with over a third of women developing T2D within 15 years of their GDM diagnosis. The diseases are hypothesized to share a genetic predisposition1-7, but few studies have sought to uncover the genetic underpinnings of GDM. Most studies have evaluated the impact of T2D loci only8-10, and the three prior genome-wide association studies of GDM11-13 have identified only five loci, limiting the power to assess to what extent variants or biological pathways are specific to GDM. We conducted the largest genome-wide association study of GDM to date in 12,332 cases and 131,109 parous female controls in the FinnGen study and identified 13 GDM-associated loci, including nine new loci. Genetic features distinct from T2D were identified both at the locus and genomic scale. Our results suggest that the genetics of GDM risk falls into the following two distinct categories: one part conventional T2D polygenic risk and one part predominantly influencing mechanisms disrupted in pregnancy. Loci with GDM-predominant effects map to genes related to islet cells, central glucose homeostasis, steroidogenesis and placental expression.

A molecularly defined NAcSh D1 subtype controls feeding and energy homeostasis

Orchestrating complex behavioral states, such as approach and consumption of food, is critical for survival. In addition to hypothalamus neuronal circuits, the nucleus accumbens (NAc) also plays an important role in controlling appetite and satiety in responses to changing external stimuli. However, the specific neuronal subtypes of NAc involved as well as how the humoral and neuronal signals coordinate to regulate feeding remain incompletely understood. Here, we deciphered the spatial diversity of neuron subtypes of the NAc shell (NAcSh) and defined a dopamine receptor D1(Drd1)- and Serpinb2-expressing subtype located in NAcSh encoding food consumption. Chemogenetics- and optogenetics-mediated regulation of Serpinb2 + neurons bidirectionally regulates food seeking and consumption specifically. Circuitry stimulation revealed the NAcSh Serpinb2 →LH LepR projection controls refeeding and can overcome leptin-mediated feeding suppression. Furthermore, NAcSh Serpinb2 + neuron ablation reduces food intake and upregulates energy expenditure resulting in body weight loss. Together, our study reveals a neural circuit consisted of molecularly distinct neuronal subtype that bidirectionally regulates energy homeostasis, which can serve as a potential therapeutic target for eating disorders.

Coexpression network analysis of the adult brain sheds light on the pathogenic mechanism of DDR1 in schizophrenia and bipolar disorder

DDR1 has been linked to schizophrenia (SCZ) and bipolar disorder (BD) in association studies. DDR1 encodes 58 distinct transcripts, which can be translated into five isoforms (DDR1a-e) and are expressed in the brain. However, the transcripts expressed in each brain cell type, their functions and their involvement in SCZ and BD remain unknown. Here, to infer the processes in which DDR1 transcripts are involved, we used transcriptomic data from the human brain dorsolateral prefrontal cortex of healthy controls (N = 936) and performed weighted gene coexpression network analysis followed by enrichment analyses. Then, to explore the involvement of DDR1 transcripts in SCZ (N = 563) and BD (N = 222), we studied the association of coexpression modules with disease and performed differential expression and transcript significance analyses. Some DDR1 transcripts were distributed across five coexpression modules identified in healthy controls (MHC). MHC1 and MHC2 were enriched in the cell cycle and proliferation of astrocytes and OPCs; MHC3 and MHC4 were enriched in oligodendrocyte differentiation and myelination; and MHC5 was enriched in neurons and synaptic transmission. Most of the DDR1 transcripts associated with SCZ and BD pertained to MHC1 and MHC2. Altogether, our results suggest that DDR1 expression might be altered in SCZ and BD via the proliferation of astrocytes and OPCs, suggesting that these processes are relevant in psychiatric disorders.

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.

scAMAC: self-supervised clustering of scRNA-seq data based on adaptive multi-scale autoencoder

Cluster assignment is vital to analyzing single-cell RNA sequencing (scRNA-seq) data to understand high-level biological processes. Deep learning-based clustering methods have recently been widely used in scRNA-seq data analysis. However, existing deep models often overlook the interconnections and interactions among network layers, leading to the loss of structural information within the network layers. Herein, we develop a new self-supervised clustering method based on an adaptive multi-scale autoencoder, called scAMAC. The self-supervised clustering network utilizes the Multi-Scale Attention mechanism to fuse the feature information from the encoder, hidden and decoder layers of the multi-scale autoencoder, which enables the exploration of cellular correlations within the same scale and captures deep features across different scales. The self-supervised clustering network calculates the membership matrix using the fused latent features and optimizes the clustering network based on the membership matrix. scAMAC employs an adaptive feedback mechanism to supervise the parameter updates of the multi-scale autoencoder, obtaining a more effective representation of cell features. scAMAC not only enables cell clustering but also performs data reconstruction through the decoding layer. Through extensive experiments, we demonstrate that scAMAC is superior to several advanced clustering and imputation methods in both data clustering and reconstruction. In addition, scAMAC is beneficial for downstream analysis, such as cell trajectory inference. Our scAMAC model codes are freely available at https://github.com/yancy2024/scAMAC.

An analogue of the Prolactin Releasing Peptide reduces obesity and promotes adult neurogenesis

Hypothalamic Adult Neurogenesis (hAN) has been implicated in regulating energy homeostasis. Adult-generated neurons and adult Neural Stem Cells (aNSCs) in the hypothalamus control food intake and body weight. Conversely, diet-induced obesity (DIO) by high fat diets (HFD) exerts adverse influence on hAN. However, the effects of anti-obesity compounds on hAN are not known. To address this, we administered a lipidized analogue of an anti-obesity neuropeptide, Prolactin Releasing Peptide (PrRP), so-called LiPR, to mice. In the HFD context, LiPR rescued the survival of adult-born hypothalamic neurons and increased the number of aNSCs by reducing their activation. LiPR also rescued the reduction of immature hippocampal neurons and modulated calcium dynamics in iPSC-derived human neurons. In addition, some of these neurogenic effects were exerted by another anti-obesity compound, Liraglutide. These results show for the first time that anti-obesity neuropeptides influence adult neurogenesis and suggest that the neurogenic process can serve as a target of anti-obesity pharmacotherapy.

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.

A hyperparameter-randomized ensemble approach for robust clustering across diverse datasets

Clustering analysis is widely used to group objects by similarity, but for complex datasets such as those produced by single-cell analysis, the currently available clustering methods are limited by accuracy, robustness, ease of use, and interpretability. To address these limitations, we developed an ensemble clustering method with hyperparameter randomization that outperforms other methods across a broad range of single-cell and synthetic datasets, without the need for manual hyperparameter selection. In addition to hard cluster labels, it also outputs soft cluster memberships to characterize continuum-like regions and per cell overlap scores to quantify the uncertainty in cluster assignment. We demonstrate the improved clustering interpretability from these features by tracing the intermediate stages between handwritten digits in the MNIST dataset, and between tanycyte subpopulations in the hypothalamus. This approach improves the quality of clustering and subsequent downstream analyses for single-cell datasets, and may also prove useful in other fields of data analysis.

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.

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.

The neuropeptidergic connectome of C. elegans

Efforts are ongoing to map synaptic wiring diagrams, or connectomes, to understand the neural basis of brain function. However, chemical synapses represent only one type of functionally important neuronal connection; in particular, extrasynaptic, "wireless" signaling by neuropeptides is widespread and plays essential roles in all nervous systems. By integrating single-cell anatomical and gene-expression datasets with biochemical analysis of receptor-ligand interactions, we have generated a draft connectome of neuropeptide signaling in the C. elegans nervous system. This network is characterized by high connection density, extended signaling cascades, autocrine foci, and a decentralized topology, with a large, highly interconnected core containing three constituent communities sharing similar patterns of input connectivity. Intriguingly, several key network hubs are little-studied neurons that appear specialized for peptidergic neuromodulation. We anticipate that the C. elegans neuropeptidergic connectome will serve as a prototype to understand how networks of neuromodulatory signaling are organized.

Single-cell genomics reveals region-specific developmental trajectories underlying neuronal diversity in the human hypothalamus

The development and diversity of neuronal subtypes in the human hypothalamus has been insufficiently characterized. To address this, we integrated transcriptomic data from 241,096 cells (126,840 newly generated) in the prenatal and adult human hypothalamus to reveal a temporal trajectory from proliferative stem cell populations to mature hypothalamic cell types. Iterative clustering of the adult neurons identified 108 robust transcriptionally distinct neuronal subtypes representing 10 hypothalamic nuclei. Pseudotime trajectories provided insights into the genes driving formation of these nuclei. Comparisons to single-cell transcriptomic data from the mouse hypothalamus suggested extensive conservation of neuronal subtypes despite certain differences in species-enriched gene expression. The uniqueness of hypothalamic neuronal lineages was examined developmentally by comparing excitatory lineages present in cortex and inhibitory lineages in ganglionic eminence, revealing both distinct and shared drivers of neuronal maturation across the human forebrain. These results provide a comprehensive transcriptomic view of human hypothalamus development through gestation and adulthood at cellular resolution.

Investigation of subfertility in the female Nsmf knockout mouse

OBJECTIVE

To study if a pituitary or ovarian defect contributes to subfertility of the female Nsmf knockout (KO) mouse, an animal model of the hypogonadotropic hypogonadism gene NSMF.

DESIGN

Analysis of hypothalamic, pituitary and ovarian gene expression at baseline, serum gonadotropin levels before and after gonadotropin-releasing hormone (GnRH) stimulation, ovarian response and implantation after superovulation, gonadotropin effects after ovariectomy, and ovarian NSMF protein expression.

SETTING

University research laboratory.

PATIENTS

None; mice were used.

INTERVENTIONS

Gonadotropin-releasing hormone stimulation, superovulation, and ovariectomy in separate experiments.

MAIN OUTCOME MEASURES

Gene expression in the hypothalamus, pituitary, and ovary; ovarian response and implantation after superovulation; serum gonadotropins after GnRH stimulation and ovariectomy; Western blot to measure ovarian NSMF expression.

RESULTS

We found increased hypothalamic Kiss1, Gnrh1, and Jak2 mRNA expression in female Nsmf KO vs. wild type (WT) mice. However, pituitary gonadotropin, and GnRH receptor gene expression was not affected, and serum gonadotropin levels were normal. Gonadotropins increased after ovariectomy for both groups. Baseline Kiss1, Fshr, Prkaca, Prkar1a, and Gdf9 ovarian mRNA expression was increased and Cyp19a1 expression was decreased in Nsmf KO mice, while superovulated Nsmf KO mice had reduced ovarian Kiss1r, Prkar1a, and Fshr mRNA expression, 50% less oocytes, and normal implantation. Western blot demonstrated NSMF protein expression in the ovary of WT mice.

CONCLUSIONS

Altered hypothalamic and ovarian gene expression was demonstrated in female Nsmf KO mice. It is possible that increased hypothalamic Gnrh1 and Kiss1 mRNA expression could compensate for reduced NSMF enabling a normal pituitary gonadotropin response. Impaired superovulation response, altered ovarian gene expression, and decreased number of oocytes indicate ovarian dysfunction, but a uterine factor cannot be excluded. These findings provide an anatomic basis for future mechanistic studies of subfertility in female Nsmf KO mice.

The structural and functional complexity of the integrative hypothalamus

The hypothalamus ("hypo" meaning below, and "thalamus" meaning bed) consists of regulatory circuits that support basic life functions that ensure survival. Sitting at the interface between peripheral, environmental, and neural inputs, the hypothalamus integrates these sensory inputs to influence a range of physiologies and behaviors. Unlike the neocortex, in which a stereotyped cytoarchitecture mediates complex functions across a comparatively small number of neuronal fates, the hypothalamus comprises upwards of thousands of distinct cell types that form redundant yet functionally discrete circuits. With single-cell RNA sequencing studies revealing further cellular heterogeneity and modern photonic tools enabling high-resolution dissection of complex circuitry, a new era of hypothalamic mapping has begun. Here, we provide a general overview of mammalian hypothalamic organization, development, and connectivity to help welcome newcomers into this exciting field.

Dissecting calvarial bones and sutures at single-cell resolution

Cranial bones constitute a protective shield for the vulnerable brain tissue, bound together as a rigid entity by unique immovable joints known as sutures. Cranial sutures serve as major growth centres for calvarial morphogenesis and have been identified as a niche for mesenchymal stem cells (MSCs) and/or skeletal stem cells (SSCs) in the craniofacial skeleton. Despite the established dogma of cranial bone and suture biology, technological advancements now allow us to investigate these tissues and structures at unprecedented resolution and embrace multiple novel biological insights. For instance, a decrease or imbalance of representation of SSCs within sutures might underlie craniosynostosis; dural sinuses enable neuroimmune crosstalk and are newly defined as immune hubs; skull bone marrow acts as a myeloid cell reservoir for the meninges and central nervous system (CNS) parenchyma in mediating immune surveillance, etc. In this review, we revisit a growing body of recent studies that explored cranial bone and suture biology using cutting-edge techniques and have expanded our current understanding of this research field, especially from the perspective of development, homeostasis, injury repair, resident MSCs/SSCs, immunosurveillance at the brain's border, and beyond.

The parenting hub of the hypothalamus is a focus of imprinted gene action

Imprinted genes are subject to germline epigenetic modification resulting in parental-specific allelic silencing. Although genomic imprinting is thought to be important for maternal behaviour, this idea is based on serendipitous findings from a small number of imprinted genes. Here, we undertook an unbiased systems biology approach, taking advantage of the recent delineation of specific neuronal populations responsible for controlling parental care, to test whether imprinted genes significantly converge to regulate parenting behaviour. Using single-cell RNA sequencing datasets, we identified a specific enrichment of imprinted gene expression in a recognised "parenting hub", the galanin-expressing neurons of the preoptic area. We tested the validity of linking enriched expression in these neurons to function by focusing on MAGE family member L2 (Magel2), an imprinted gene not previously linked to parenting behaviour. We confirmed expression of Magel2 in the preoptic area galanin expressing neurons. We then examined the parenting behaviour of Magel2-null(+/p) mice. Magel2-null mothers, fathers and virgin females demonstrated deficits in pup retrieval, nest building and pup-directed motivation, identifying a central role for this gene in parenting. Finally, we show that Magel2-null mothers and fathers have a significant reduction in POA galanin expressing cells, which in turn contributes to a reduced c-Fos response in the POA upon exposure to pups. Our findings identify a novel imprinted gene that impacts parenting behaviour and, moreover, demonstrates the utility of using single-cell RNA sequencing data to predict gene function from expression and in doing so here, have identified a purposeful role for genomic imprinting in mediating parental behaviour.

Early Alzheimer's disease pathology in human cortex involves transient cell states

Cellular perturbations underlying Alzheimer's disease (AD) are primarily studied in human postmortem samples and model organisms. Here, we generated a single-nucleus atlas from a rare cohort of cortical biopsies from living individuals with varying degrees of AD pathology. We next performed a systematic cross-disease and cross-species integrative analysis to identify a set of cell states that are specific to early AD pathology. These changes-which we refer to as the early cortical amyloid response-were prominent in neurons, wherein we identified a transitional hyperactive state preceding the loss of excitatory neurons, which we confirmed by acute slice physiology on independent biopsy specimens. Microglia overexpressing neuroinflammatory-related processes also expanded as AD pathology increased. Finally, both oligodendrocytes and pyramidal neurons upregulated genes associated with β-amyloid production and processing during this early hyperactive phase. Our integrative analysis provides an organizing framework for targeting circuit dysfunction, neuroinflammation, and amyloid production early in AD pathogenesis.

Tracking cell-type-specific temporal dynamics in human and mouse brains

Progenitor cells are critical in preserving organismal homeostasis, yet their diversity and dynamics in the aged brain remain underexplored. We introduced TrackerSci, a single-cell genomic method that combines newborn cell labeling and combinatorial indexing to characterize the transcriptome and chromatin landscape of proliferating progenitor cells in vivo. Using TrackerSci, we investigated the dynamics of newborn cells in mouse brains across various ages and in a mouse model of Alzheimer's disease. Our dataset revealed diverse progenitor cell types in the brain and their epigenetic signatures. We further quantified aging-associated shifts in cell-type-specific proliferation and differentiation and deciphered the associated molecular programs. Extending our study to the progenitor cells in the aged human brain, we identified conserved genetic signatures across species and pinpointed region-specific cellular dynamics, such as the reduced oligodendrogenesis in the cerebellum. We anticipate that TrackerSci will be broadly applicable to unveil cell-type-specific temporal dynamics in diverse systems.

Novel human pluripotent stem cell-derived hypothalamus organoids demonstrate cellular diversity

The hypothalamus is a region of the brain that plays an important role in regulating body functions and behaviors. There is a growing interest in human pluripotent stem cells (hPSCs) for modeling diseases that affect the hypothalamus. Here, we established an hPSC-derived hypothalamus organoid differentiation protocol to model the cellular diversity of this brain region. Using an hPSC line with a tyrosine hydroxylase (TH)-TdTomato reporter for dopaminergic neurons (DNs) and other TH-expressing cells, we interrogated DN-specific pathways and functions in electrophysiologically active hypothalamus organoids. Single-cell RNA sequencing (scRNA-seq) revealed diverse neuronal and non-neuronal cell types in mature hypothalamus organoids. We identified several molecularly distinct hypothalamic DN subtypes that demonstrated different developmental maturities. Our in vitro 3D hypothalamus differentiation protocol can be used to study the development of this critical brain structure and can be applied to disease modeling to generate novel therapeutic approaches for disorders centered around the hypothalamus.

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.

scAce: an adaptive embedding and clustering method for single-cell gene expression data

MOTIVATION

Since the development of single-cell RNA sequencing (scRNA-seq) technologies, clustering analysis of single-cell gene expression data has been an essential tool for distinguishing cell types and identifying novel cell types. Even though many methods have been available for scRNA-seq clustering analysis, the majority of them are constrained by the requirement on predetermined cluster numbers or the dependence on selected initial cluster assignment.

RESULTS

In this article, we propose an adaptive embedding and clustering method named scAce, which constructs a variational autoencoder to simultaneously learn cell embeddings and cluster assignments. In the scAce method, we develop an adaptive cluster merging approach which achieves improved clustering results without the need to estimate the number of clusters in advance. In addition, scAce provides an option to perform clustering enhancement, which can update and enhance cluster assignments based on previous clustering results from other methods. Based on computational analysis of both simulated and real datasets, we demonstrate that scAce outperforms state-of-the-art clustering methods for scRNA-seq data, and achieves better clustering accuracy and robustness.

AVAILABILITY AND IMPLEMENTATION

The scAce package is implemented in python 3.8 and is freely available from https://github.com/sldyns/scAce.

scASGC: An adaptive simplified graph convolution model for clustering single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) is now a successful technique for identifying cellular heterogeneity, revealing novel cell subpopulations, and forecasting developmental trajectories. A crucial component of the processing of scRNA-seq data is the precise identification of cell subpopulations. Although many unsupervised clustering methods have been developed to cluster cell subpopulations, the performance of these methods is vulnerable to dropouts and high dimensionality. In addition, most existing methods are time-consuming and fail to adequately account for potential associations between cells. In the manuscript, we present an unsupervised clustering method based on an adaptive simplified graph convolution model called scASGC. The proposed method builds plausible cell graphs, aggregates neighbor information using a simplified graph convolution model, and adaptively determines the most optimal number of convolution layers for various graphs. Experiments on 12 public datasets show that scASGC outperforms both classical and state-of-the-art clustering methods. In addition, in a study of mouse intestinal muscle containing 15,983 cells, we identified distinct marker genes based on the clustering results of scASGC. The source code of scASGC is available at https://github.com/ZzzOctopus/scASGC.

Development of a versatile LCM-Seq method for spatial transcriptomics of fluorescently tagged cholinergic neuron populations

Single-cell transcriptomics are powerful tools to define neuronal cell types based on co-expressed gene clusters. Limited RNA input in these technologies necessarily compromises transcriptome coverage and accuracy of differential expression analysis. We propose that bulk RNA-Seq of neuronal pools defined by spatial position offers an alternative strategy to overcome these technical limitations. We report a laser-capture microdissection (LCM)-Seq method which allows deep transcriptome profiling of fluorescently tagged neuron populations isolated with LCM from histological sections of transgenic mice. Mild formaldehyde fixation of ZsGreen marker protein, LCM sampling of ∼300 pooled neurons, followed by RNA isolation, library preparation and RNA-Seq with methods optimized for nanogram amounts of moderately degraded RNA enabled us to detect ∼15,000 different transcripts in fluorescently labeled cholinergic neuron populations. The LCM-Seq approach showed excellent accuracy in quantitative studies, allowing us to detect 2891 transcripts expressed differentially between the spatially defined and clinically relevant cholinergic neuron populations of the dorsal caudate-putamen and medial septum. In summary, the LCM-Seq method we report in this study is a versatile, sensitive, and accurate bulk sequencing approach to study the transcriptome profile and differential gene expression of fluorescently tagged neuronal populations isolated from transgenic mice with high spatial precision.

MTGDC: A Multi-Scale Tensor Graph Diffusion Clustering for Single-Cell RNA Sequencing Data

Single-cell RNA sequencing (scRNA-seq) is a new technology that focuses on the expression levels for each cell to study cell heterogeneity. Thus, new computational methods matching scRNA-seq are designed to detect cell types among various cell groups. Herein, we propose a Multi-scale Tensor Graph Diffusion Clustering (MTGDC) for single-cell RNA sequencing data. It has the following mechanisms: 1) To mine potential similarity distributions among cells, we design a multi-scale affinity learning method to construct a fully connected graph between cells; 2) For each affinity matrix, we propose an efficient tensor graph diffusion learning framework to learn high-order information among multi-scale affinity matrices. First, the tensor graph is explicitly introduced to measure cell-cell edges with local high-order relationship information. To further preserve more global topology structure information in the tensor graph, MTGDC implicitly considers the propagation of information via a data diffusion process by designing a simple and efficient tensor graph diffusion update algorithm. 3) Finally, we mix together the multi-scale tensor graphs to obtain the fusion high-order affinity matrix and apply it to spectral clustering. Experiments and case studies showed that MTGDC had obvious advantages over the state-of-art algorithms in robustness, accuracy, visualization, and speed.

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.

Orexin and MCH neurons: regulators of sleep and metabolism

Sleep-wake and fasting-feeding are tightly coupled behavioral states that require coordination between several brain regions. The mammalian lateral hypothalamus (LH) is a functionally and anatomically complex brain region harboring heterogeneous cell populations that regulate sleep, feeding, and energy metabolism. Significant attempts were made to understand the cellular and circuit bases of LH actions. Rapid advancements in genetic and electrophysiological manipulation help to understand the role of discrete LH cell populations. The opposing action of LH orexin/hypocretin and melanin-concentrating hormone (MCH) neurons on metabolic sensing and sleep-wake regulation make them the candidate to explore in detail. This review surveys the molecular, genetic, and neuronal components of orexin and MCH signaling in the regulation of sleep and metabolism.

Variant-to-function analysis of the childhood obesity chr12q13 locus implicates rs7132908 as a causal variant within the 3' UTR of FAIM2

The ch12q13 obesity locus is among the most significant childhood obesity loci identified in genome-wide association studies. This locus resides in a non-coding region within FAIM2; thus, the underlying causal variant(s) presumably influence disease susceptibility via an influence on cis-regulation within the genomic region. We implicated rs7132908 as a putative causal variant at this locus leveraging a combination of our inhouse 3D genomic data, public domain datasets, and several computational approaches. Using a luciferase reporter assay in human primary astrocytes, we observed allele-specific cis-regulatory activity of the immediate region harboring rs7132908. Motivated by this finding, we went on to generate isogenic human embryonic stem cell lines homozygous for either rs7132908 allele with CRISPR-Cas9 homology-directed repair to assess changes in gene expression due to genotype and chromatin accessibility throughout a differentiation to hypothalamic neurons, a key cell type known to regulate feeding behavior. We observed that the rs7132908 obesity risk allele influenced the expression of FAIM2 along with other genes, decreased the proportion of neurons produced during differentiation, up-regulated cell death gene sets, and conversely down-regulated neuron differentiation gene sets. We have therefore functionally validated rs7132908 as a causal obesity variant which temporally regulates nearby effector genes at the ch12q13 locus and influences neurodevelopment and survival.

Control by the brain of vitamin A homeostasis

Vitamin A is a micronutrient essential for vertebrate animals maintained in homeostatic balance in the body; however, little is known about the control of this balance. This study investigated whether the hypothalamus, a key integrative brain region, regulates vitamin A levels in the liver and circulation. Vitamin A in the form of retinol or retinoic acid was stereotactically injected into the 3rd ventricle of the rat brain. Alternatively, retinoids in the mouse hypothalamus were altered through retinol-binding protein 4 (Rbp4) gene knockdown. This led to rapid change in the liver proteins controlling vitamin A homeostasis as well as vitamin A itself in liver and the circulation. Prolonged disruption of Rbp4 in the region of the arcuate nucleus of the mouse hypothalamus altered retinol levels in the liver. This supports the concept that the brain may sense retinoids and influence whole-body vitamin A homeostasis with a possible "vitaminostatic" role.

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.

Cell-type specific molecular signatures of aging revealed in a brain-wide transcriptomic cell-type atlas

Biological aging can be defined as a gradual loss of homeostasis across various aspects of molecular and cellular function. Aging is a complex and dynamic process which influences distinct cell types in a myriad of ways. The cellular architecture of the mammalian brain is heterogeneous and diverse, making it challenging to identify precise areas and cell types of the brain that are more susceptible to aging than others. Here, we present a high-resolution single-cell RNA sequencing dataset containing ~1.2 million high-quality single-cell transcriptomic profiles of brain cells from young adult and aged mice across both sexes, including areas spanning the forebrain, midbrain, and hindbrain. We find age-associated gene expression signatures across nearly all 130+ neuronal and non-neuronal cell subclasses we identified. We detect the greatest gene expression changes in non-neuronal cell types, suggesting that different cell types in the brain vary in their susceptibility to aging. We identify specific, age-enriched clusters within specific glial, vascular, and immune cell types from both cortical and subcortical regions of the brain, and specific gene expression changes associated with cell senescence, inflammation, decrease in new myelination, and decreased vasculature integrity. We also identify genes with expression changes across multiple cell subclasses, pointing to certain mechanisms of aging that may occur across wide regions or broad cell types of the brain. Finally, we discover the greatest gene expression changes in cell types localized to the third ventricle of the hypothalamus, including tanycytes, ependymal cells, and Tbx3+ neurons found in the arcuate nucleus that are part of the neuronal circuits regulating food intake and energy homeostasis. These findings suggest that the area surrounding the third ventricle in the hypothalamus may be a hub for aging in the mouse brain. Overall, we reveal a dynamic landscape of cell-type-specific transcriptomic changes in the brain associated with normal aging that will serve as a foundation for the investigation of functional changes in the aging process and the interaction of aging and diseases.

AgRP neurons are not indispensable for body weight maintenance in adult mice

In addition to their role in promoting feeding and obesity development, hypothalamic arcuate agouti-related protein/neuropeptide Y (AgRP/NPY) neurons are widely perceived to be indispensable for maintaining normal feeding and body weight in adults, and consistently, acute inhibition of AgRP neurons is known to reduce short-term food intake. Here, we adopted complementary methods to achieve nearly complete ablation of arcuate AgRP/NPY neurons in adult mice and report that lesioning arcuate AgRP/NPY neurons in adult mice causes no apparent alterations in ad libitum feeding or body weight. Consistent with previous studies, loss of AgRP/NPY neurons blunts fasting refeeding. Thus, our studies show that AgRP/NPY neurons are not required for maintaining ad libitum feeding or body weight homeostasis in adult mice.

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.

Atoh1 drives the heterogeneity of the pontine nuclei neurons and promotes their differentiation

Pontine nuclei (PN) neurons mediate the communication between the cerebral cortex andthe cerebellum to refine skilled motor functions. Prior studies showed that PN neurons fall into two subtypes based on their anatomic location and region-specific connectivity, but the extent of their heterogeneity and its molecular drivers remain unknown. Atoh1 encodes a transcription factor that is expressed in the PN precursors. We previously showed that partial loss of Atoh1 function in mice results in delayed PN development and impaired motor learning. In this study, we performed single-cell RNA sequencing to elucidate the cell state-specific functions of Atoh1 during PN development and found that Atoh1 regulates cell cycle exit, differentiation, migration, and survival of PN neurons. Our data revealed six previously not known PN subtypes that are molecularly and spatially distinct. We found that the PN subtypes exhibit differential vulnerability to partial loss of Atoh1 function, providing insights into the prominence of PN phenotypes in patients with ATOH1 missense mutations.