Single-Cell RNA-Seq Reveals Hypothalamic Cell Diversity

Agrp-negative arcuate NPY neurons drive feeding under positive energy balance via altering leptin responsiveness in POMC neurons

Neuropeptide Y (NPY) in the arcuate nucleus (ARC) is known as one of the most critical regulators of feeding. However, how NPY promotes feeding under obese conditions is unclear. Here, we show that positive energy balance, induced by high-fat diet (HFD) or in genetically obese leptin-receptor-deficient mice, leads to elevated Npy2r expression especially on proopiomelanocortin (POMC) neurons, which also alters leptin responsiveness. Circuit mapping identified a subset of ARC agouti-related peptide (Agrp)-negative NPY neurons that control these Npy2r expressing POMC neurons. Chemogenetic activation of this newly discovered circuitry strongly drives feeding, while optogenetic inhibition reduces feeding. Consistent with that, lack of Npy2r on POMC neurons leads to reduced food intake and fat mass. This suggests that under energy surplus conditions, when ARC NPY levels generally drop, high-affinity NPY2R on POMC neurons is still able to drive food intake and enhance obesity development via NPY released predominantly from Agrp-negative NPY neurons.

SSNMDI: a novel joint learning model of semi-supervised non-negative matrix factorization and data imputation for clustering of single-cell RNA-seq data

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) technology attracts extensive attention in the biomedical field. It can be used to measure gene expression and analyze the transcriptome at the single-cell level, enabling the identification of cell types based on unsupervised clustering. Data imputation and dimension reduction are conducted before clustering because scRNA-seq has a high 'dropout' rate, noise and linear inseparability. However, independence of dimension reduction, imputation and clustering cannot fully characterize the pattern of the scRNA-seq data, resulting in poor clustering performance. Herein, we propose a novel and accurate algorithm, SSNMDI, that utilizes a joint learning approach to simultaneously perform imputation, dimensionality reduction and cell clustering in a non-negative matrix factorization (NMF) framework. In addition, we integrate the cell annotation as prior information, then transform the joint learning into a semi-supervised NMF model. Through experiments on 14 datasets, we demonstrate that SSNMDI has a faster convergence speed, better dimensionality reduction performance and a more accurate cell clustering performance than previous methods, providing an accurate and robust strategy for analyzing scRNA-seq data. Biological analysis are also conducted to validate the biological significance of our method, including pseudotime analysis, gene ontology and survival analysis. We believe that we are among the first to introduce imputation, partial label information, dimension reduction and clustering to the single-cell field.

AVAILABILITY AND IMPLEMENTATION

The source code for SSNMDI is available at https://github.com/yushanqiu/SSNMDI.

Large-Scale Integration of Single-Cell RNA-Seq Data Reveals Astrocyte Diversity and Transcriptomic Modules across Six Central Nervous System Disorders

The dysfunction of astrocytes in response to environmental factors contributes to many neurological diseases by impacting neuroinflammation responses, glutamate and ion homeostasis, and cholesterol and sphingolipid metabolism, which calls for comprehensive and high-resolution analysis. However, single-cell transcriptome analyses of astrocytes have been hampered by the sparseness of human brain specimens. Here, we demonstrate how large-scale integration of multi-omics data, including single-cell and spatial transcriptomic and proteomic data, overcomes these limitations. We created a single-cell transcriptomic dataset of human brains by integration, consensus annotation, and analyzing 302 publicly available single-cell RNA-sequencing (scRNA-seq) datasets, highlighting the power to resolve previously unidentifiable astrocyte subpopulations. The resulting dataset includes nearly one million cells that span a wide variety of diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), multiple sclerosis (MS), epilepsy (Epi), and chronic traumatic encephalopathy (CTE). We profiled the astrocytes at three levels, subtype compositions, regulatory modules, and cell-cell communications, and comprehensively depicted the heterogeneity of pathological astrocytes. We constructed seven transcriptomic modules that are involved in the onset and progress of disease development, such as the M2 ECM and M4 stress modules. We validated that the M2 ECM module could furnish potential markers for AD early diagnosis at both the transcriptome and protein levels. In order to accomplish a high-resolution, local identification of astrocyte subtypes, we also carried out a spatial transcriptome analysis of mouse brains using the integrated dataset as a reference. We found that astrocyte subtypes are regionally heterogeneous. We identified dynamic cell-cell interactions in different disorders and found that astrocytes participate in key signaling pathways, such as NRG3-ERBB4, in epilepsy. Our work supports the utility of large-scale integration of single-cell transcriptomic data, which offers new insights into underlying multiple CNS disease mechanisms where astrocytes are involved.

Lateral mammillary body neurons in mouse brain are disproportionately vulnerable in Alzheimer's disease

The neural circuits governing the induction and progression of neurodegeneration and memory impairment in Alzheimer's disease (AD) are incompletely understood. The mammillary body (MB), a subcortical node of the medial limbic circuit, is one of the first brain regions to exhibit amyloid deposition in the 5xFAD mouse model of AD. Amyloid burden in the MB correlates with pathological diagnosis of AD in human postmortem brain tissue. Whether and how MB neuronal circuitry contributes to neurodegeneration and memory deficits in AD are unknown. Using 5xFAD mice and postmortem MB samples from individuals with varying degrees of AD pathology, we identified two neuronal cell types in the MB harboring distinct electrophysiological properties and long-range projections: lateral neurons and medial neurons. lateral MB neurons harbored aberrant hyperactivity and exhibited early neurodegeneration in 5xFAD mice compared with lateral MB neurons in wild-type littermates. Inducing hyperactivity in lateral MB neurons in wild-type mice impaired performance on memory tasks, whereas attenuating aberrant hyperactivity in lateral MB neurons ameliorated memory deficits in 5xFAD mice. Our findings suggest that neurodegeneration may be a result of genetically distinct, projection-specific cellular dysfunction and that dysregulated lateral MB neurons may be causally linked to memory deficits in AD.

Post-ischemic inflammatory response in the brain: Targeting immune cell in ischemic stroke therapy

An ischemic stroke occurs when the blood supply is obstructed to the vascular basin, causing the death of nerve cells and forming the ischemic core. Subsequently, the brain enters the stage of reconstruction and repair. The whole process includes cellular brain damage, inflammatory reaction, blood–brain barrier destruction, and nerve repair. During this process, the proportion and function of neurons, immune cells, glial cells, endothelial cells, and other cells change. Identifying potential differences in gene expression between cell types or heterogeneity between cells of the same type helps to understand the cellular changes that occur in the brain and the context of disease. The recent emergence of single-cell sequencing technology has promoted the exploration of single-cell diversity and the elucidation of the molecular mechanism of ischemic stroke, thus providing new ideas and directions for the diagnosis and clinical treatment of ischemic stroke.

CGRP physiology, pharmacology, and therapeutic targets: migraine and beyond

Calcitonin gene-related peptide (CGRP) is a neuropeptide with diverse physiological functions. Its two isoforms (α and β) are widely expressed throughout the body in sensory neurons as well as in other cell types, such as motor neurons and neuroendocrine cells. CGRP acts via at least two G protein-coupled receptors that form unusual complexes with receptor activity-modifying proteins. These are the CGRP receptor and the AMY1 receptor; in rodents, additional receptors come into play. Although CGRP is known to produce many effects, the precise molecular identity of the receptor(s) that mediates CGRP effects is seldom clear. Despite the many enigmas still in CGRP biology, therapeutics that target the CGRP axis to treat or prevent migraine are a bench-to-bedside success story. This review provides a contextual background on the regulation and sites of CGRP expression and CGRP receptor pharmacology. The physiological actions of CGRP in the nervous system are discussed, along with updates on CGRP actions in the cardiovascular, pulmonary, gastrointestinal, immune, hematopoietic, and reproductive systems and metabolic effects of CGRP in muscle and adipose tissues. We cover how CGRP in these systems is associated with disease states, most notably migraine. In this context, we discuss how CGRP actions in both the peripheral and central nervous systems provide a basis for therapeutic targeting of CGRP in migraine. Finally, we highlight potentially fertile ground for the development of additional therapeutics and combinatorial strategies that could be designed to modulate CGRP signaling for migraine and other diseases.

A custom-made AAV1 variant (AAV1-T593K) enables efficient transduction of Japanese quail neurons in vitro and in vivo

The widespread use of rodents in neuroscience has prompted the development of optimized viral variants for transduction of brain cells, in vivo. However, many of the viruses developed are less efficient in other model organisms, with birds being among the most resistant to transduction by current viral tools. Resultantly, the use of genetically-encoded tools and methods in avian species is markedly lower than in rodents; likely holding the field back. We sought to bridge this gap by developing custom viruses towards the transduction of brain cells of the Japanese quail. We first develop a protocol for culturing primary neurons and glia from quail embryos, followed by characterization of cultures via immunostaining, single cell mRNA sequencing, patch clamp electrophysiology and calcium imaging. We then leveraged the cultures for the rapid screening of various viruses, only to find that all yielded poor to no infection of cells in vitro. However, few infected neurons were obtained by AAV1 and AAV2. Scrutiny of the sequence of the AAV receptor found in quails led us to rationally design a custom-made AAV variant (AAV1-T593K; AAV1*) that exhibits improved transduction efficiencies in vitro and in vivo (14- and five-fold, respectively). Together, we present unique culturing method, transcriptomic profiles of quail's brain cells and a custom-tailored AAV1 for transduction of quail neurons in vitro and in vivo.

Bitter taste cells in the ventricular walls of the murine brain regulate glucose homeostasis

The median eminence (ME) is a circumventricular organ at the base of the brain that controls body homeostasis. Tanycytes are its specialized glial cells that constitute the ventricular walls and regulate different physiological states, however individual signaling pathways in these cells are incompletely understood. Here, we identify a functional tanycyte subpopulation that expresses key taste transduction genes including bitter taste receptors, the G protein gustducin and the gustatory ion channel TRPM5 (M5). M5 tanycytes have access to blood-borne cues via processes extended towards diaphragmed endothelial fenestrations in the ME and mediate bidirectional communication between the cerebrospinal fluid and blood. This subpopulation responds to metabolic signals including leptin and other hormonal cues and is transcriptionally reprogrammed upon fasting. Acute M5 tanycyte activation induces insulin secretion and acute diphtheria toxin-mediated M5 tanycyte depletion results in impaired glucose tolerance in diet-induced obese mice. We provide a cellular and molecular framework that defines how bitter taste cells in the ME integrate chemosensation with metabolism.

A cofunctional grouping-based approach for non-redundant feature gene selection in unannotated single-cell RNA-seq analysis

Feature gene selection has significant impact on the performance of cell clustering in single-cell RNA sequencing (scRNA-seq) analysis. A well-rounded feature selection (FS) method should consider relevance, redundancy and complementarity of the features. Yet most existing FS methods focus on gene relevance to the cell types but neglect redundancy and complementarity, which undermines the cell clustering performance. We develop a novel computational method GeneClust to select feature genes for scRNA-seq cell clustering. GeneClust groups genes based on their expression profiles, then selects genes with the aim of maximizing relevance, minimizing redundancy and preserving complementarity. It can work as a plug-in tool for FS with any existing cell clustering method. Extensive benchmark results demonstrate that GeneClust significantly improve the clustering performance. Moreover, GeneClust can group cofunctional genes in biological process and pathway into clusters, thus providing a means of investigating gene interactions and identifying potential genes relevant to biological characteristics of the dataset. GeneClust is freely available at https://github.com/ToryDeng/scGeneClust.

AllenDigger, a Tool for Spatial Expression Data Visualization, Spatial Heterogeneity Delineation, and Single-Cell Registration Based on the Allen Brain Atlas

Spatial transcriptomics can be used to capture cellular spatial organization and has facilitated new insights into different biological contexts, including developmental biology, cancer, and neuroscience. However, its wide application is still hindered by its technical challenges and immature data analysis methods. Allen Brain Atlas (ABA) provides a great source for spatial gene expression throughout the mouse brain at various developmental stages with in situ hybridization image data. To the best of our knowledge, the portal developed to access spatial expression data is not very useful to biologists. Here, we developed a toolkit to collect and preprocess expression data from the ABA and allow a friendlier query to visualize the spatial distribution of genes of interest, characterize the spatial heterogeneity of the brain, and register cells from single-cell transcriptomics data to fine anatomical brain regions via machine learning methods with high accuracy. AllenDigger will be very helpful to the community in precise spatial gene expression queries and add extra spatial information to further interpret the scRNA-seq data in a cost-effective manner.

A molecularly defined and spatially resolved cell atlas of the whole mouse brain

In mammalian brains, tens of millions to billions of cells form complex interaction networks to enable a wide range of functions. The enormous diversity and intricate organization of cells in the brain have so far hindered our understanding of the molecular and cellular basis of its functions. Recent advances in spatially resolved single-cell transcriptomics have allowed systematic mapping of the spatial organization of molecularly defined cell types in complex tissues1-3. However, these approaches have only been applied to a few brain regions1-11 and a comprehensive cell atlas of the whole brain is still missing. Here, we imaged a panel of >1,100 genes in ~8 million cells across the entire adult mouse brain using multiplexed error-robust fluorescence in situ hybridization (MERFISH)12 and performed spatially resolved, single-cell expression profiling at the whole-transcriptome scale by integrating MERFISH and single-cell RNA-sequencing (scRNA-seq) data. Using this approach, we generated a comprehensive cell atlas of >5,000 transcriptionally distinct cell clusters, belonging to ~300 major cell types, in the whole mouse brain with high molecular and spatial resolution. Registration of the MERFISH images to the common coordinate framework (CCF) of the mouse brain further allowed systematic quantifications of the cell composition and organization in individual brain regions defined in the CCF. We further identified spatial modules characterized by distinct cell-type compositions and spatial gradients featuring gradual changes in the gene-expression profiles of cells. Finally, this high-resolution spatial map of cells, with a transcriptome-wide expression profile associated with each cell, allowed us to infer cell-type-specific interactions between several hundred pairs of molecularly defined cell types and predict potential 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 valuable resource for future functional investigations of neural circuits and their dysfunction in diseases.

Generation of hypothalamic neural stem cell-like cells in vitro from human pluripotent stem cells

When damaged, restoring the function of the hypothalamus is currently impossible. It is unclear whether neural stem cells exist in the hypothalamus. Studies have reported that adult rodent tanycytes around the third ventricle function as hypothalamic neural stem cell-like cells. However, it is currently impossible to collect periventricular cells from humans. We attempted to generate hypothalamic neural stem cell-like cells from human embryonic stem cells (ESCs). We focused on retina and anterior neural fold homeobox (RAX) because its expression is gradually restricted to tanycytes during the late embryonic stage. We differentiated RAX::VENUS knockin human ESCs (hESCs) into hypothalamic organoids and sorted RAX⁺ cells from mature organoids. The isolated RAX⁺ cells formed neurospheres and exhibited self-renewal and multipotency. Neurogenesis was observed when neurospheres were transplanted into the mouse hypothalamus. We isolated RAX⁺ hypothalamic neural stem cell-like cells from wild-type human ES organoids. This is the first study to differentiate human hypothalamic neural stem cell-like cells from pluripotent stem cells.

Single-cell discovery of the scene and potential immunotherapeutic target in hypopharyngeal tumor environment

Hypopharyngeal carcinoma is a cancer with the worst prognosis. We constructed the first single-cell transcriptome map for hypopharyngeal carcinoma and explored its underlying mechanisms. We systematically studied single-cell transcriptome data of 17,599 cells from hypopharyngeal carcinoma and paracancerous tissues. We identified categories of cells by dimensionality reduction and performed further subgroup analysis. Focusing on the potential mechanism in the cellular communication of hypopharyngeal carcinoma, we predicted ligand-receptor interactions and verified them via immunohistochemical and cellular experiments. In total, seven cell types were identified, including epithelial and myeloid cells. Subsequently, subgroup analysis showed significant tumor heterogeneity. Based on the pathological type of squamous cell carcinoma, we focused on intercellular communication between epithelial cells and various cells. We predicted the crosstalk and inferred the regulatory effect of cellular active ligands on the surface receptor of epithelial cells. From the top potential pairs, we focused on the BMPR2 receptor for further research, as it showed significantly higher expression in epithelial cancer tissue than in adjacent tissue. Further bioinformatics analysis, immunohistochemical staining, and cell experiments also confirmed its cancer-promoting effects. Overall, the single-cell perspective revealed complex crosstalk in hypopharyngeal cancer, in which BMPR2 promotes its proliferation and migration, providing a rationale for further study and treatment of this carcinoma.

scTSSR2: Imputing Dropout Events for Single-Cell RNA Sequencing Using Fast Two-Side Self-Representation

The single-cell RNA sequencing (scRNA-seq) technique begins a new era by revealing gene expression patterns at single-cell resolution, enabling studies of heterogeneity and transcriptome dynamics of complex tissues at single-cell resolution. However, existing large proportion of dropout events may hinder downstream analyses. Thus imputation of dropout events is an important step in analyzing scRNA-seq data. We develop scTSSR2, a new imputation method that combines matrix decomposition with the previously developed two-side sparse self-representation, leading to fast two-side sparse self-representation to impute dropout events in scRNA-seq data. The comparisons of computational speed and memory usage among different imputation methods show that scTSSR2 has distinct advantages in terms of computational speed and memory usage. Comprehensive downstream experiments show that scTSSR2 outperforms the state-of-the-art imputation methods. A user-friendly R package scTSSR2 is developed to denoise the scRNA-seq data to improve the data quality.

Distinct and shared genetic architectures of Gestational diabetes mellitus and Type 2 Diabetes Mellitus

Gestational diabetes mellitus (GDM) affects more than 16 million pregnancies annually worldwide and is related to an increased lifetime risk of Type 2 diabetes (T2D). The diseases are hypothesized to share a genetic predisposition, but there are few GWAS studies of GDM and none of them is sufficiently powered to assess whether any 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 8 novel 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 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. These results pave the way for an improved biological understanding of GDM pathophysiology and its role in the development and course of T2D.

Interactions between β-endorphin and kisspeptin neurons of the ewe arcuate nucleus are modulated by photoperiod

Opioid peptides are well-known modulators of the central control of reproduction. Among them, dynorphin coexpressed in kisspeptin (KP) neurons of the arcuate nucleus (ARC) has been thoroughly studied for its autocrine effect on KP release through κ opioid receptors. Other studies have suggested a role for β-endorphin (BEND), a peptide cleaved from the pro-opiomelanocortin precursor, on food intake and central control of reproduction. Similar to KP, BEND content in the ARC of sheep is modulated by day length and BEND modulates food intake in a dose-dependent manner. Because KP levels in the ARC vary with photoperiodic and metabolic status, a photoperiod-driven influence of BEND neurons on neighboring KP neurons is plausible. The present study aimed to investigate a possible modulatory action of BEND on KP neurons located in the ovine ARC. Using confocal microscopy, numerous KP appositions on BEND neurons were found but there was no photoperiodic variation of the number of these interactions in ovariectomized, estradiol-replaced ewes. By contrast, BEND terminals on KP neurons were twice as numerous under short days, in ewes having an activated gonadotropic axis, compared to anestrus ewes under long days. Injection of 5 μg BEND into the third ventricle of short-day ewes induced a significant and specific increase of activated KP neurons (16% vs. 9% in controls), whereas the percentage of overall activated (c-Fos positive) neurons, was similar between both groups. These data suggest a photoperiod-dependent influence of BEND on KP neurons of the ARC, which may influence gonadotropin-releasing hormone pulsatile secretion and inform KP neurons about metabolic status.

A neuroepithelial wave of BMP signalling drives anteroposterior specification of the tuberal hypothalamus

The tuberal hypothalamus controls life-supporting homeostatic processes, but despite its fundamental role, the cells and signalling pathways that specify this unique region of the central nervous system in embryogenesis are poorly characterised. Here, we combine experimental and bioinformatic approaches in the embryonic chick to show that the tuberal hypothalamus is progressively generated from hypothalamic floor plate-like cells. Fate-mapping studies show that a stream of tuberal progenitors develops in the anterior-ventral neural tube as a wave of neuroepithelial-derived BMP signalling sweeps from anterior to posterior through the hypothalamic floor plate. As later-specified posterior tuberal progenitors are generated, early specified anterior tuberal progenitors become progressively more distant from these BMP signals and differentiate into tuberal neurogenic cells. Gain- and loss-of-function experiments in vivo and ex vivo show that BMP signalling initiates tuberal progenitor specification, but must be eliminated for these to progress to anterior neurogenic progenitors. scRNA-Seq profiling shows that tuberal progenitors that are specified after the major period of anterior tuberal specification begin to upregulate genes that characterise radial glial cells. This study provides an integrated account of the development of the tuberal hypothalamus.

Single-cell RNA-based phenotyping reveals a pivotal role of thyroid hormone receptor alpha for hypothalamic development

Thyroid hormone and its receptor TRα1 play an important role in brain development. Several animal models have been used to investigate this function, including mice heterozygous for the TRα1R384C mutation, which confers receptor-mediated hypothyroidism. These mice display abnormalities in several autonomic functions, which was partially attributed to a developmental defect in hypothalamic parvalbumin neurons. However, whether other cell types in the hypothalamus are similarly affected remains unknown. Here, we used single-nucleus RNA sequencing to obtain an unbiased view on the importance of TRα1 for hypothalamic development and cellular diversity. Our data show that defective TRα1 signaling has surprisingly little effect on the development of hypothalamic neuronal populations, but it heavily affects hypothalamic oligodendrocytes. Using selective reactivation of the mutant TRα1 during specific developmental periods, we find that early postnatal thyroid hormone action seems to be crucial for proper hypothalamic oligodendrocyte maturation. Taken together, our findings underline the well-known importance of postnatal thyroid health for brain development and provide an unbiased roadmap for the identification of cellular targets of TRα1 action in mouse hypothalamic development.

Defining the age-dependent and tissue-specific circadian transcriptome in male mice

Cellular circadian clocks direct a daily transcriptional program that supports homeostasis and resilience. Emerging evidence has demonstrated age-associated changes in circadian functions. To define age-dependent changes at the systems level, we profile the circadian transcriptome in the hypothalamus, lung, heart, kidney, skeletal muscle, and adrenal gland in three age groups. We find age-dependent and tissue-specific clock output changes. Aging reduces the number of rhythmically expressed genes (REGs), indicative of weakened circadian control. REGs are enriched for the hallmarks of aging, adding another dimension to our understanding of aging. Analyzing differential gene expression within a tissue at four different times of day identifies distinct clusters of differentially expressed genes (DEGs). Increased variability of gene expression across the day is a common feature of aged tissues. This analysis extends the landscape for understanding aging and highlights the impact of aging on circadian clock function and temporal changes in gene expression.

Correspondence analysis for dimension reduction, batch integration, and visualization of single-cell RNA-seq data

Effective dimension reduction is essential for single cell RNA-seq (scRNAseq) analysis. Principal component analysis (PCA) is widely used, but requires continuous, normally-distributed data; therefore, it is often coupled with log-transformation in scRNAseq applications, which can distort the data and obscure meaningful variation. We describe correspondence analysis (CA), a count-based alternative to PCA. CA is based on decomposition of a chi-squared residual matrix, avoiding distortive log-transformation. To address overdispersion and high sparsity in scRNAseq data, we propose five adaptations of CA, which are fast, scalable, and outperform standard CA and glmPCA, to compute cell embeddings with more performant or comparable clustering accuracy in 8 out of 9 datasets. In particular, we find that CA with Freeman-Tukey residuals performs especially well across diverse datasets. Other advantages of the CA framework include visualization of associations between genes and cell populations in a "CA biplot," and extension to multi-table analysis; we introduce corralm for integrative multi-table dimension reduction of scRNAseq data. We implement CA for scRNAseq data in corral, an R/Bioconductor package which interfaces directly with single cell classes in Bioconductor. Switching from PCA to CA is achieved through a simple pipeline substitution and improves dimension reduction of scRNAseq datasets.

Developmental and functional relationships between hypothalamic tanycytes and embryonic radial glia

The hypothalamus is a key regulator of several homeostatic processes, such as circadian rhythms, energy balance, thirst, and thermoregulation. Recently, the hypothalamic third ventricle has emerged as a site of postnatal neurogenesis and gliogenesis. This hypothalamic neural stem potential resides in a heterogeneous population of cells known as tanycytes, which, not unlike radial glia, line the floor and ventrolateral walls of the third ventricle and extend a long process into the hypothalamic parenchyma. Here, we will review historical and recent data regarding tanycyte biology across the lifespan, focusing on the developmental emergence of these diverse cells from embryonic radial glia and their eventual role contributing to a fascinating, but relatively poorly characterized, adult neural stem cell niche.

scDCCA: deep contrastive clustering for single-cell RNA-seq data based on auto-encoder network

The advances in single-cell ribonucleic acid sequencing (scRNA-seq) allow researchers to explore cellular heterogeneity and human diseases at cell resolution. Cell clustering is a prerequisite in scRNA-seq analysis since it can recognize cell identities. However, the high dimensionality, noises and significant sparsity of scRNA-seq data have made it a big challenge. Although many methods have emerged, they still fail to fully explore the intrinsic properties of cells and the relationship among cells, which seriously affects the downstream clustering performance. Here, we propose a new deep contrastive clustering algorithm called scDCCA. It integrates a denoising auto-encoder and a dual contrastive learning module into a deep clustering framework to extract valuable features and realize cell clustering. Specifically, to better characterize and learn data representations robustly, scDCCA utilizes a denoising Zero-Inflated Negative Binomial model-based auto-encoder to extract low-dimensional features. Meanwhile, scDCCA incorporates a dual contrastive learning module to capture the pairwise proximity of cells. By increasing the similarities between positive pairs and the differences between negative ones, the contrasts at both the instance and the cluster level help the model learn more discriminative features and achieve better cell segregation. Furthermore, scDCCA joins feature learning with clustering, which realizes representation learning and cell clustering in an end-to-end manner. Experimental results of 14 real datasets validate that scDCCA outperforms eight state-of-the-art methods in terms of accuracy, generalizability, scalability and efficiency. Cell visualization and biological analysis demonstrate that scDCCA significantly improves clustering and facilitates downstream analysis for scRNA-seq data. The code is available at https://github.com/WJ319/scDCCA.

scGMAAE: Gaussian mixture adversarial autoencoders for diversification analysis of scRNA-seq data

The progress of single-cell RNA sequencing (scRNA-seq) has led to a large number of scRNA-seq data, which are widely used in biomedical research. The noise in the raw data and tens of thousands of genes pose a challenge to capture the real structure and effective information of scRNA-seq data. Most of the existing single-cell analysis methods assume that the low-dimensional embedding of the raw data belongs to a Gaussian distribution or a low-dimensional nonlinear space without any prior information, which limits the flexibility and controllability of the model to a great extent. In addition, many existing methods need high computational cost, which makes them difficult to be used to deal with large-scale datasets. Here, we design and develop a depth generation model named Gaussian mixture adversarial autoencoders (scGMAAE), assuming that the low-dimensional embedding of different types of cells follows different Gaussian distributions, integrating Bayesian variational inference and adversarial training, as to give the interpretable latent representation of complex data and discover the statistical distribution of different types of cells. The scGMAAE is provided with good controllability, interpretability and scalability. Therefore, it can process large-scale datasets in a short time and give competitive results. scGMAAE outperforms existing methods in several ways, including dimensionality reduction visualization, cell clustering, differential expression analysis and batch effect removal. Importantly, compared with most deep learning methods, scGMAAE requires less iterations to generate the best results.

A critical update on the leptin-melanocortin system

The discovery of leptin in 1994 was an "eureka moment" in the field of neurometabolism that provided new opportunities to better understand the central control of energy balance and glucose metabolism. Rapidly, a prevalent model in the field emerged that pro-opiomelanocortin (POMC) neurons were key in promoting leptin's anorexigenic effects and that the arcuate nucleus of the hypothalamus (ARC) was a key region for the regulation of energy homeostasis. While this model inspired many important discoveries, a growing body of literature indicates that this model is now outdated. In this review, we re-evaluate the hypothalamic leptin-melanocortin model in light of recent advances that directly tackle previous assumptions, with a particular focus on the ARC. We discuss how segregated and heterogeneous these neurons are, and examine how the development of modern approaches allowing spatiotemporal, intersectional, and chemogenetic manipulations of melanocortin neurons has allowed a better definition of the complexity of the leptin-melanocortin system. We review the importance of leptin in regulating glucose homeostasis, but not food intake, through direct actions on ARC POMC neurons. We further highlight how non-POMC, GABAergic neurons mediate leptin's direct effects on energy balance and influence POMC neurons.

Dorsoventral Arrangement of Lateral Hypothalamus Populations in the Mouse Hypothalamus: a Prosomeric Genoarchitectonic Analysis

The lateral hypothalamus (LH) has a heterogeneous cytoarchitectonic organization that has not been elucidated in detail. In this work, we analyzed within the framework of the prosomeric model the differential expression pattern of 59 molecular markers along the ventrodorsal dimension of the medial forebrain bundle in the mouse, considering basal and alar plate subregions of the LH. We found five basal (LH1-LH5) and four alar (LH6-LH9) molecularly distinct sectors of the LH with neuronal cell groups that correlate in topography with previously postulated alar and basal hypothalamic progenitor domains. Most peptidergic populations were restricted to one of these LH sectors though some may have dispersed into a neighboring sector. For instance, histaminergic Hdc-positive neurons were mostly contained within the basal LH3, Nts (neurotensin)- and Tac2 (tachykinin 2)-expressing cells lie strictly within LH4, Hcrt (hypocretin/orexin)-positive and Pmch (pro-melanin-concentrating hormone)-positive neurons appeared within separate LH5 subdivisions, Pnoc (prepronociceptin)-expressing cells were mainly restricted to LH6, and Sst (somatostatin)-positive cells were identified within the LH7 sector. The alar LH9 sector, a component of the Foxg1-positive telencephalo-opto-hypothalamic border region, selectively contained Satb2-expressing cells. Published studies of rodent LH subdivisions have not described the observed pattern. Our genoarchitectonic map should aid in systematic approaches to elucidate LH connectivity and function.

scWECTA: A weighted ensemble classification framework for cell type assignment based on single cell transcriptome

Rapid advances in single-cell transcriptome analysis provide deeper insights into the study of tissue heterogeneity at the cellular level. Unsupervised clustering can identify potential cell populations in single-cell RNA-sequencing (scRNA-seq) data, but fail to further determine the identity of each cell. Existing automatic annotation methods using scRNA-seq data based on machine learning mainly use single feature set and single classifier. In view of this, we propose a Weighted Ensemble classification framework for Cell Type Annotation, named scWECTA, which improves the accuracy of cell type identification. scWECTA uses five informative gene sets and integrates five classifiers based on soft weighted ensemble framework. And the ensemble weights are inferred through the constrained non-negative least squares. Validated on multiple pairs of scRNA-seq datasets, scWECTA is able to accurately annotate scRNA-seq data across platforms and across tissues, especially for imbalanced data containing rare cell types. Moreover, scWECTA outperforms other comparable methods in balancing the prediction accuracy of common cell types and the unassigned rate of non-common cell types at the same time. The source code of scWECTA is freely available at https://github.com/ttren-sc/scWECTA.

Recent advances in understanding neuronal diversity and neural circuit complexity across different brain regions using single-cell sequencing

Neural circuits are characterized as interconnecting neuron networks connected by synapses. Some kinds of gene expression and/or functional changes of neurons and synaptic connections may result in aberrant neural circuits, which has been recognized as one crucial pathological mechanism for the onset of many neurological diseases. Gradual advances in single-cell sequencing approaches with strong technological advantages, as exemplified by high throughput and increased resolution for live cells, have enabled it to assist us in understanding neuronal diversity across diverse brain regions and further transformed our knowledge of cellular building blocks of neural circuits through revealing numerous molecular signatures. Currently published transcriptomic studies have elucidated various neuronal subpopulations as well as their distribution across prefrontal cortex, hippocampus, hypothalamus, and dorsal root ganglion, etc. Better characterization of brain region-specific circuits may shed light on new pathological mechanisms involved and assist in selecting potential targets for the prevention and treatment of specific neurological disorders based on their established roles. Given diverse neuronal populations across different brain regions, we aim to give a brief sketch of current progress in understanding neuronal diversity and neural circuit complexity according to their locations. With the special focus on the application of single-cell sequencing, we thereby summarize relevant region-specific findings. Considering the importance of spatial context and connectivity in neural circuits, we also discuss a few published results obtained by spatial transcriptomics. Taken together, these single-cell sequencing data may lay a mechanistic basis for functional identification of brain circuit components, which links their molecular signatures to anatomical regions, connectivity, morphology, and physiology. Furthermore, the comprehensive characterization of neuron subtypes, their distributions, and connectivity patterns via single-cell sequencing is critical for understanding neural circuit properties and how they generate region-dependent interactions in different context.

Imaging the Hypothalamo-Neurohypophysial System

The hypothalamo-neurohypophysial system (HNS) is a brain peptidergic neurosecretory apparatus which is composed of arginine vasopressin (AVP) and oxytocin (OXT) magnocellular neurones and their neuronal processes in the posterior pituitary (PP). In response to specific stimuli, AVP and OXT are secreted into the systemic circulation at the neurovascular interface of the PP, where they act as hormones, but they can also behave as neurotransmitters when released at the somatodendritic compartment or by axon collaterals to other brain regions. Because these peptides are crucial for several physiological processes, including fluid homoeostasis and reproduction, it is of great importance to map the HNS connectome in its entirety in order to understand its functions. In recent years, advances in imaging technologies have provided considerable new information about the HNS. These approaches include the use of reporter proteins under the control of specific promoters, viral tracers, brain-clearing methods, genetically encoded indicators, sniffer cells, mass spectrometry imaging, and spatially resolved transcriptomics. In this review, we illustrate how these latest approaches have enhanced our understanding of the structure and function of the HNS and how they might contribute further in the coming years.

A Poisson reduced-rank regression model for association mapping in sequencing data

BACKGROUND

Single-cell RNA-sequencing (scRNA-seq) technologies allow for the study of gene expression in individual cells. Often, it is of interest to understand how transcriptional activity is associated with cell-specific covariates, such as cell type, genotype, or measures of cell health. Traditional approaches for this type of association mapping assume independence between the outcome variables (or genes), and perform a separate regression for each. However, these methods are computationally costly and ignore the substantial correlation structure of gene expression. Furthermore, count-based scRNA-seq data pose challenges for traditional models based on Gaussian assumptions.

RESULTS

We aim to resolve these issues by developing a reduced-rank regression model that identifies low-dimensional linear associations between a large number of cell-specific covariates and high-dimensional gene expression readouts. Our probabilistic model uses a Poisson likelihood in order to account for the unique structure of scRNA-seq counts. We demonstrate the performance of our model using simulations, and we apply our model to a scRNA-seq dataset, a spatial gene expression dataset, and a bulk RNA-seq dataset to show its behavior in three distinct analyses.

CONCLUSION

We show that our statistical modeling approach, which is based on reduced-rank regression, captures associations between gene expression and cell- and sample-specific covariates by leveraging low-dimensional representations of transcriptional states.

Understanding the aging hypothalamus, one cell at a time

The hypothalamus is a brain region that integrates signals from the periphery and the environment to maintain organismal homeostasis. To do so, specialized hypothalamic neuropeptidergic neurons control a range of processes, such as sleep, feeding, the stress response, and hormone release. These processes are altered with age, which can affect longevity and contribute to disease status. Technological advances, such as single-cell RNA sequencing, are upending assumptions about the transcriptional identity of cell types in the hypothalamus and revealing how distinct cell types change with age. In this review, we summarize current knowledge about the contribution of hypothalamic functions to aging. We highlight recent single-cell studies interrogating distinct cell types of the mouse hypothalamus and suggest ways in which single-cell 'omics technologies can be used to further understand the aging hypothalamus and its role in longevity.

Systematic investigation of imprinted gene expression and enrichment in the mouse brain explored at single-cell resolution

BACKGROUND

Although a number of imprinted genes are known to be highly expressed in the brain, and in certain brain regions in particular, whether they are truly over-represented in the brain has never been formally tested. Using thirteen single-cell RNA sequencing datasets we systematically investigated imprinted gene over-representation at the organ, brain region, and cell-specific levels.

RESULTS

We established that imprinted genes are indeed over-represented in the adult brain, and in neurons particularly compared to other brain cell-types. We then examined brain-wide datasets to test enrichment within distinct brain regions and neuron subpopulations and demonstrated over-representation of imprinted genes in the hypothalamus, ventral midbrain, pons and medulla. Finally, using datasets focusing on these regions of enrichment, we identified hypothalamic neuroendocrine populations and the monoaminergic hindbrain neurons as specific hotspots of imprinted gene expression.

CONCLUSIONS

These analyses provide the first robust assessment of the neural systems on which imprinted genes converge. Moreover, the unbiased approach, with each analysis informed by the findings of the previous level, permits highly informed inferences about the functions on which imprinted gene expression converges. Our findings indicate the neuronal regulation of motivated behaviours such as feeding and sleep, alongside the regulation of pituitary function, as functional hotspots for imprinting. This adds statistical rigour to prior assumptions and provides testable predictions for novel neural and behavioural phenotypes associated with specific genes and imprinted gene networks. In turn, this work sheds further light on the potential evolutionary drivers of genomic imprinting in the brain.

Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis

Single-cell RNA-sequencing has become a powerful tool to study biologically significant characteristics at explicitly high resolution. However, its application on emerging data is currently limited by its intrinsic techniques. Here, we introduce Tissue-AdaPtive autoEncoder (TAPE), a deep learning method connecting bulk RNA-seq and single-cell RNA-seq to achieve precise deconvolution in a short time. By constructing an interpretable decoder and training under a unique scheme, TAPE can predict cell-type fractions and cell-type-specific gene expression tissue-adaptively. Compared with popular methods on several datasets, TAPE has a better overall performance and comparable accuracy at cell type level. Additionally, it is more robust among different cell types, faster, and sensitive to provide biologically meaningful predictions. Moreover, through the analysis of clinical data, TAPE shows its ability to predict cell-type-specific gene expression profiles with biological significance. We believe that TAPE will enable and accelerate the precise analysis of high-throughput clinical data in a wide range.

Magel2 knockdown in hypothalamic POMC neurons innervating the medial amygdala reduces susceptibility to diet-induced obesity

Hyperphagia and obesity profoundly affect the health of children with Prader-Willi syndrome (PWS). The Magel2 gene among the genes in the Prader-Willi syndrome deletion region is expressed in proopiomelanocortin (POMC) neurons in the arcuate nucleus of the hypothalamus (ARC). Knockout of the Magel2 gene disrupts POMC neuronal circuits and functions. Here, we report that loss of the Magel2 gene exclusively in ARC^POMC neurons innervating the medial amygdala (MeA) causes a reduction in body weight in both male and female mice fed with a high-fat diet. This anti-obesity effect is associated with an increased locomotor activity. There are no significant differences in glucose and insulin tolerance in mice without the Magel2 gene in ARC^POMC neurons innervating the MeA. Plasma estrogen levels are higher in female mutant mice than in controls. Blockade of the G protein-coupled estrogen receptor (GPER), but not estrogen receptor-α (ER-α), reduces locomotor activity in female mutant mice. Hence, our study provides evidence that knockdown of the Magel2 gene in ARC^POMC neurons innervating the MeA reduces susceptibility to diet-induced obesity with increased locomotor activity through activation of central GPER.

De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution

Uncovering the tissue molecular architecture at single-cell resolution could help better understand organisms' biological and pathological processes. However, bulk RNA-seq can only measure gene expression in cell mixtures, without revealing the transcriptional heterogeneity and spatial patterns of single cells. Herein, we introduce Bulk2Space ( https://github.com/ZJUFanLab/bulk2space ), a deep learning framework-based spatial deconvolution algorithm that can simultaneously disclose the spatial and cellular heterogeneity of bulk RNA-seq data using existing single-cell and spatial transcriptomics references. The use of bulk transcriptomics to validate Bulk2Space unveils, in particular, the spatial variance of immune cells in different tumor regions, the molecular and spatial heterogeneity of tissues during inflammation-induced tumorigenesis, and spatial patterns of novel genes in different cell types. Moreover, Bulk2Space is utilized to perform spatial deconvolution analysis on bulk transcriptome data from two different mouse brain regions derived from our in-house developed sequencing approach termed Spatial-seq. We have not only reconstructed the hierarchical structure of the mouse isocortex but also further annotated cell types that were not identified by original methods in the mouse hypothalamus.

Zebrafish as a model to investigate the CRH axis and interactions with DISC1

Release of corticotropin-releasing hormone (CRH) from CRH neurons activates the hypothalamo-pituitary-adrenal (HPA) axis, one of the main physiological stress response systems. Complex feedback loops operate in the HPA axis and understanding the neurobiological mechanisms regulating CRH neurons is of great importance in the context of stress disorders. In this article, we review how in vivo studies in zebrafish have advanced knowledge of the neurobiology of CRH neurons. Disrupted-in-schizophrenia 1 (DISC1) mutant zebrafish have blunted stress responses and can be used to model human stress disorders. We propose that DISC1 influences the development and functioning of CRH neurons as a mechanism linking DISC1 to psychiatric disorders.

Hypothalamic effective connectivity at rest is associated with body weight and energy homeostasis

Hunger and satiety drive eating behaviours via changes in brain function. The hypothalamus is a central component of the brain networks that regulate food intake. Animal research parsed the roles of the lateral hypothalamus (LH) and medial hypothalamus (MH) in hunger and satiety, respectively. Here, we examined how hunger and satiety change information flow between human LH and MH brain networks, and how these interactions are influenced by body mass index (BMI). Forty participants (16 overweight/obese) underwent two resting-state functional MRI scans while being fasted and sated. The excitatory/inhibitory influence of information flow between the MH and LH was modelled using spectral dynamic causal modelling. Our results revealed two core networks interacting across homeostatic state and weight: subcortical bidirectional connections between the LH, MH and the substantia nigra pars compacta (prSN), and cortical top-down inhibition from fronto-parietal and temporal areas. During fasting, we found higher inhibition between the LH and prSN, whereas the prSN received greater top-down inhibition from across the cortex. Individuals with higher BMI showed that these network dynamics occur irrespective of homeostatic state. Our findings reveal fasting affects brain dynamics over a distributed hypothalamic-midbrain-cortical network. This network is less sensitive to state-related fluctuations among people with obesity.

HypoMap-a unified single-cell gene expression atlas of the murine hypothalamus

The hypothalamus plays a key role in coordinating fundamental body functions. Despite recent progress in single-cell technologies, a unified catalog and molecular characterization of the heterogeneous cell types and, specifically, neuronal subtypes in this brain region are still lacking. Here, we present an integrated reference atlas, 'HypoMap,' of the murine hypothalamus, consisting of 384,925 cells, with the ability to incorporate new additional experiments. We validate HypoMap by comparing data collected from Smart-Seq+Fluidigm C1 and bulk RNA sequencing of selected neuronal cell types with different degrees of cellular heterogeneity. Finally, via HypoMap, we identify classes of neurons expressing glucagon-like peptide-1 receptor (Glp1r) and prepronociceptin (Pnoc), and validate them using single-molecule in situ hybridization. Collectively, HypoMap provides a unified framework for the systematic functional annotation of murine hypothalamic cell types, and it can serve as an important platform to unravel the functional organization of hypothalamic neurocircuits and to identify druggable targets for treating metabolic disorders.

Single cell and spatial transcriptomic analyses reveal microglia-plasma cell crosstalk in the brain during Trypanosoma brucei infection

Human African trypanosomiasis, or sleeping sickness, is caused by the protozoan parasite Trypanosoma brucei and induces profound reactivity of glial cells and neuroinflammation when the parasites colonise the central nervous system. However, the transcriptional and functional responses of the brain to chronic T. brucei infection remain poorly understood. By integrating single cell and spatial transcriptomics of the mouse brain, we identify that glial responses triggered by infection are readily detected in the proximity to the circumventricular organs, including the lateral and 3rd ventricle. This coincides with the spatial localisation of both slender and stumpy forms of T. brucei. Furthermore, in silico predictions and functional validations led us to identify a previously unknown crosstalk between homeostatic microglia and Cd138+ plasma cells mediated by IL-10 and B cell activating factor (BAFF) signalling. This study provides important insights and resources to improve understanding of the molecular and cellular responses in the brain during infection with African trypanosomes.

Lipid biosynthesis enzyme Agpat5 in AgRP-neurons is required for insulin-induced hypoglycemia sensing and glucagon secretion

The counterregulatory response to hypoglycemia that restores normal blood glucose levels is an essential physiological function. It is initiated, in large part, by incompletely characterized brain hypoglycemia sensing neurons that trigger the secretion of counterregulatory hormones, in particular glucagon, to stimulate hepatic glucose production. In a genetic screen of recombinant inbred BXD mice we previously identified Agpat5 as a candidate regulator of hypoglycemia-induced glucagon secretion. Here, using genetic mouse models, we demonstrate that Agpat5 expressed in agouti-related peptide neurons is required for their activation by hypoglycemia, for hypoglycemia-induced vagal nerve activity, and glucagon secretion. We find that inactivation of Agpat5 leads to increased fatty acid oxidation and ATP production and that suppressing Cpt1a-dependent fatty acid import into mitochondria restores hypoglycemia sensing. Collectively, our data show that AgRP neurons are involved in the control of glucagon secretion and that Agpat5, by partitioning fatty acyl-CoAs away from mitochondrial fatty acid oxidation and ATP generation, ensures that the fall in intracellular ATP, which triggers neuronal firing, faithfully reflects changes in glycemia.

During hypoglycemia, glucagon secretion is part of the mechanism needed to restore normal blood glucose levels. Here, Strembitska et al. report that sensing of hypoglycemia by AgRP neurons requires Agpat5, an enzyme which prevents fatty acids from entering the mitochondria for ATP production, ensuring correct neuronal activation and glucagon secretion.

Microfluidics for Neuronal Cell and Circuit Engineering

The widespread adoption of microfluidic devices among the neuroscience and neurobiology communities has enabled addressing a broad range of questions at the molecular, cellular, circuit, and system levels. Here, we review biomedical engineering approaches that harness the power of microfluidics for bottom-up generation of neuronal cell types and for the assembly and analysis of neural circuits. Microfluidics-based approaches are instrumental to generate the knowledge necessary for the derivation of diverse neuronal cell types from human pluripotent stem cells, as they enable the isolation and subsequent examination of individual neurons of interest. Moreover, microfluidic devices allow to engineer neural circuits with specific orientations and directionality by providing control over neuronal cell polarity and permitting the isolation of axons in individual microchannels. Similarly, the use of microfluidic chips enables the construction not only of 2D but also of 3D brain, retinal, and peripheral nervous system model circuits. Such brain-on-a-chip and organoid-on-a-chip technologies are promising platforms for studying these organs as they closely recapitulate some aspects of in vivo biological processes. Microfluidic 3D neuronal models, together with 2D in vitro systems, are widely used in many applications ranging from drug development and toxicology studies to neurological disease modeling and personalized medicine. Altogether, microfluidics provide researchers with powerful systems that complement and partially replace animal models.

The limitations of investigating appetite through circuit manipulations: are we biting off more than we can chew?

Disordered eating can underpin a number of debilitating and prevalent chronic diseases, such as obesity. Broader advances in psychopharmacology and biology have motivated some neuroscientists to address diet-induced obesity through reductionist, pre-clinical eating investigations on the rodent brain. Specifically, chemogenetic and optogenetic methods developed in the 21st century allow neuroscientists to perform in vivo, region-specific/projection-specific/promoter-specific circuit manipulations and immediately assess the impact of these manipulations on rodent feeding. These studies are able to rigorously conclude whether a specific neuronal population regulates feeding behaviour in the hope of eventually developing a mechanistic neuroanatomical map of appetite regulation. However, an artificially stimulated/inhibited rodent neuronal population that changes feeding behaviour does not necessarily represent a pharmacological target for treating eating disorders in humans. Chemogenetic/optogenetic findings must therefore be triangulated with the array of theories that contribute to our understanding of appetite. The objective of this review is to provide a wide-ranging discussion of the limitations of chemogenetic/optogenetic circuit manipulation experiments in rodents that are used to investigate appetite. Stepping into and outside of medical science epistemologies, this paper draws on philosophy of science, nutrition, addiction biology and neurophilosophy to prompt more integrative, transdisciplinary interpretations of chemogenetic/optogenetic appetite data. Through discussing the various technical and epistemological limitations of these data, we provide both an overview of chemogenetics and optogenetics accessible to non-neuroscientist obesity researchers, as well as a resource for neuroscientists to expand the number of lenses through which they interpret their circuit manipulation findings.

Central FGF21 production regulates memory but not peripheral metabolism

Fibroblast growth factor 21 (FGF21) is a liver-derived endocrine hormone that functions to regulate energy homeostasis and macronutrient intake. Recently, FGF21 was reported to be produced and secreted from hypothalamic tanycytes, to regulate peripheral lipid metabolism; however, rigorous investigation of FGF21 expression in the brain has yet to be accomplished. Using a mouse model that drives CRE recombinase in FGF21-expressing cells, we demonstrate that FGF21 is not expressed in the hypothalamus, but instead is produced from the retrosplenial cortex (RSC), an essential brain region for spatial learning and memory. Furthermore, we find that central FGF21 produced in the RSC enhances spatial memory but does not regulate energy homeostasis or sugar intake. Finally, our data demonstrate that administration of FGF21 prolongs the duration of long-term potentiation in the hippocampus and enhances activation of hippocampal neurons. Thus, endogenous and pharmacological FGF21 appear to function in the hippocampus to enhance spatial memory.

Zhou et al. reveal that the endocrine hormone FGF21 is expressed in the brain. Central FGF21 expression occurs in distinct areas, including the retrosplenial cortex, but not the hypothalamus. Interestingly, brain-derived FGF21 regulates spatial memory formation, but not metabolism, and the converse is true for liver-derived FGF21.

Single-nucleus chromatin accessibility and RNA sequencing reveal impaired brain development in prenatally e-cigarette exposed neonatal rats

Although emerging evidence reveals that vaping alters the function of the central nervous system, the effects of maternal vaping on offspring brain development remain elusive. Using a well-established in utero exposure model, we performed single-nucleus ATAC-seq (snATAC-seq) and RNA sequencing (snRNA-seq) on prenatally e-cigarette-exposed rat brains. We found that maternal vaping distorted neuronal lineage differentiation in the neonatal brain by promoting excitatory neurons and inhibiting lateral ganglionic eminence-derived inhibitory neuronal differentiation. Moreover, maternal vaping disrupted calcium homeostasis, induced microglia cell death, and elevated susceptibility to cerebral ischemic injury in the developing brain of offspring. Our results suggest that the aberrant calcium signaling, diminished microglial population, and impaired microglia-neuron interaction may all contribute to the underlying mechanisms by which prenatal e-cigarette exposure impairs neonatal rat brain development. Our findings raise the concern that maternal vaping may cause adverse long-term brain damage to the offspring.

Single-cell transcriptomics and cell-specific proteomics reveals molecular signatures of sleep

Every day, we sleep for a third of the day. Sleep is important for cognition, brain waste clearance, metabolism, and immune responses. The molecular mechanisms governing sleep are largely unknown. Here, we used a combination of single-cell RNA sequencing and cell-type-specific proteomics to interrogate the molecular underpinnings of sleep. Different cell types in three important brain regions for sleep (brainstem, cortex, and hypothalamus) exhibited diverse transcriptional responses to sleep need. Sleep restriction modulates astrocyte-neuron crosstalk and sleep need enhances expression of specific sets of transcription factors in different brain regions. In cortex, we also interrogated the proteome of two major cell types: astrocytes and neurons. Sleep deprivation differentially alters the expression of proteins in astrocytes and neurons. Similarly, phosphoproteomics revealed large shifts in cell-type-specific protein phosphorylation. Our results indicate that sleep need regulates transcriptional, translational, and post-translational responses in a cell-specific manner.

Cell-type-specific epigenetic effects of early life stress on the brain

Early life stress (ELS) induces long-term phenotypic adaptations that contribute to increased vulnerability to a host of neuropsychiatric disorders. Epigenetic mechanisms, including DNA methylation, histone modifications and non-coding RNA, are a proposed link between environmental stressors, alterations in gene expression, and phenotypes. Epigenetic modifications play a primary role in shaping functional differences between cell types and can be modified by environmental perturbations, especially in early development. Together with contributions from genetic variation, epigenetic mechanisms orchestrate patterns of gene expression within specific cell types that contribute to phenotypic variation between individuals. To date, many studies have provided insights into epigenetic changes resulting from ELS. However, most of these studies have examined heterogenous brain tissue, despite evidence of cell-type-specific epigenetic modifications in phenotypes associated with ELS. In this review, we focus on rodent and human studies that have examined epigenetic modifications induced by ELS in select cell types isolated from the brain or associated with genes that have cell-type-restricted expression in neurons, microglia, astrocytes, and oligodendrocytes. Although significant challenges remain, future studies using these approaches can enable important mechanistic insight into the role of epigenetic variation in the effects of ELS on brain function.

Molecular profile and response to energy deficit of leptin-receptor neurons in the lateral hypothalamus

Leptin exerts its effects on energy balance by inhibiting food intake and increasing energy expenditure via leptin receptors in the hypothalamus. While LepR neurons in the arcuate nucleus of the hypothalamus, the primary target of leptin, have been extensively studied, LepR neurons in other hypothalamic nuclei remain understudied. LepR neurons in the lateral hypothalamus contribute to leptin's effects on food intake and reward, but due to the low abundance of this population it has been difficult to study their molecular profile and responses to energy deficit. We here explore the transcriptome of LepR neurons in the LH and their response to energy deficit. Male LepR-Cre mice were injected in the LH with an AAV carrying Cre-dependent L10:GFP. Few weeks later the hypothalami from fed and food-restricted (24-h) mice were dissected and the TRAP protocol was performed, for the isolation of translating mRNAs from LepR cells in the LH, followed by RNA sequencing. After mapping and normalization, differential expression analysis was performed with DESeq2. We confirm that the isolated mRNA is enriched in LepR transcripts and other known neuropeptide markers of LepR^LH neurons, of which we investigate the localization patterns in the LH. We identified novel markers of LepR^LH neurons with association to energy balance and metabolic disease, such as Acvr1c, Npy1r, Itgb1, and genes that are differentially regulated by food deprivation, such as Fam46a and Rrad. Our dataset provides a reliable and extensive resource of the molecular makeup of LH LepR neurons and their response to food deprivation.

Ontogenetic rules for the molecular diversification of hypothalamic neurons

The hypothalamus is an evolutionarily conserved endocrine interface that, among other roles, links central homeostatic control to adaptive bodily responses by releasing hormones and neuropeptides from its many neuronal subtypes. In its preoptic, anterior, tuberal and mammillary subdivisions, a kaleidoscope of magnocellular and parvocellular neuroendocrine command neurons, local-circuit neurons, and neurons that project to extrahypothalamic areas are intermingled in partially overlapping patches of nuclei. Molecular fingerprinting has produced data of unprecedented mass and depth to distinguish and even to predict the synaptic and endocrine competences, connectivity and stimulus selectivity of many neuronal modalities. These new insights support eminent studies from the past century but challenge others on the molecular rules that shape the developmental segregation of hypothalamic neuronal subtypes and their use of morphogenic cues for terminal differentiation. Here, we integrate single-cell RNA sequencing studies with those of mouse genetics and endocrinology to describe key stages of hypothalamus development, including local neurogenesis, the direct terminal differentiation of glutamatergic neurons, transition cascades for GABAergic and GABAergic cell-derived dopamine cells, waves of local neuronal migration, and sequential enrichment in neuropeptides and hormones. We particularly emphasize how transcription factors determine neuronal identity and, consequently, circuit architecture, and whether their deviations triggered by environmental factors and hormones provoke neuroendocrine illnesses.