Molecular interrogation of hypothalamic organization reveals distinct dopamine neuronal subtypes

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

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

Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease

In the past decade, single-cell technologies have proliferated and improved from their technically challenging beginnings to become common laboratory methods capable of determining the expression of thousands of genes in thousands of cells simultaneously. The field has progressed by taking the CNS as a primary research subject - the cellular complexity and multiplicity of neuronal cell types provide fertile ground for the increasing power of single-cell methods. Current single-cell RNA sequencing methods can quantify gene expression with sufficient accuracy to finely resolve even subtle differences between cell types and states, thus providing a great tool for studying the molecular and cellular repertoire of the CNS and its disorders. However, single-cell RNA sequencing requires the dissociation of tissue samples, which means that the interrelationships between cells are lost. Spatial transcriptomic methods bypass tissue dissociation and retain this spatial information, thereby allowing gene expression to be assessed across thousands of cells within the context of tissue structural organization. Here, we discuss how single-cell and spatially resolved transcriptomics have been contributing to unravelling the pathomechanisms underlying brain disorders. We focus on three areas where we feel these new technologies have provided particularly useful insights: selective neuronal vulnerability, neuroimmune dysfunction and cell-type-specific treatment response. We also discuss the limitations and future directions of single-cell and spatial RNA sequencing technologies.

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

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

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

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

An optimized graph-based structure for single-cell RNA-seq cell-type classification based on non-linear dimension reduction

BACKGROUND

It is now possible to analyze cellular heterogeneity at the single-cell level thanks to the rapid developments in single-cell sequencing technologies. The clustering of cells is a fundamental and common step in heterogeneity analysis. Even so, accurate cell clustering remains a challenge due to the high levels of noise, the high dimensions, and the high sparsity of data.

RESULTS

Here, we present SCEA, a clustering approach for scRNA-seq data. Using two consecutive units, an encoder based on MLP and a graph attention auto-encoder, to obtain cell embedding and gene embedding, SCEA can simultaneously achieve cell low-dimensional representation and clustering performing various examinations to obtain the optimal value for each parameter, the presented result is in its most optimal form. To evaluate the performance of SCEA, we performed it on several real scRNA-seq datasets for clustering and visualization analysis.

CONCLUSIONS

The experimental results show that SCEA generally outperforms several popular single-cell analysis methods. As a result of using all available datasets, SCEA, in average, improves clustering accuracy by 4.4% in ARI Parameters over the well-known method scGAC. Also, the accuracy improvement of 11.65% is achieved by SCEA, compared to the Seurat model.

Somatostatin regulates central clock function and circadian responses to light

Daily and annual changes in light are processed by central clock circuits that control the timing of behavior and physiology. The suprachiasmatic nucleus (SCN) in the anterior hypothalamus processes daily photic inputs and encodes changes in day length (i.e., photoperiod), but the SCN circuits that regulate circadian and photoperiodic responses to light remain unclear. Somatostatin (SST) expression in the hypothalamus is modulated by photoperiod, but the role of SST in SCN responses to light has not been examined. Our results indicate that SST signaling regulates daily rhythms in behavior and SCN function in a manner influenced by sex. First, we use cell-fate mapping to provide evidence that SST in the SCN is regulated by light via de novo Sst activation. Next, we demonstrate that Sst  -/- mice display enhanced circadian responses to light, with increased behavioral plasticity to photoperiod, jetlag, and constant light conditions. Notably, lack of Sst  -/- eliminated sex differences in photic responses due to increased plasticity in males, suggesting that SST interacts with clock circuits that process light differently in each sex. Sst  -/- mice also displayed an increase in the number of retinorecipient neurons in the SCN core, which express a type of SST receptor capable of resetting the molecular clock. Last, we show that lack of SST signaling modulates central clock function by influencing SCN photoperiodic encoding, network after-effects, and intercellular synchrony in a sex-specific manner. Collectively, these results provide insight into peptide signaling mechanisms that regulate central clock function and its response to light.

Review of single-cell RNA-seq data clustering for cell-type identification and characterization

In recent years, the advances in single-cell RNA-seq techniques have enabled us to perform large-scale transcriptomic profiling at single-cell resolution in a high-throughput manner. Unsupervised learning such as data clustering has become the central component to identify and characterize novel cell types and gene expression patterns. In this study, we review the existing single-cell RNA-seq data clustering methods with critical insights into the related advantages and limitations. In addition, we also review the upstream single-cell RNA-seq data processing techniques such as quality control, normalization, and dimension reduction. We conduct performance comparison experiments to evaluate several popular single-cell RNA-seq clustering approaches on simulated and multiple single-cell transcriptomic data sets.

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.

Windows into stress: a glimpse at emerging roles for CRHPVN neurons

The corticotropin-releasing hormone cells in the paraventricular nucleus of the hypothalamus (CRH^PVN) control the slow endocrine response to stress. The synapses on these cells are exquisitely sensitive to acute stress, leveraging local signals to leave a lasting imprint on this system. Additionally, recent work indicates that these cells also play key roles in the control of distinct stress and survival behaviors. Here we review these observations and provide a perspective on the role of CRH^PVN neurons as integrative and malleable hubs for behavioral, physiological, and endocrine responses to stress.

Transcriptome analysis reveals mRNAs and long non-coding RNAs associated with fecundity in the hypothalamus of high-and low-fecundity goat

As an important organ that coordinates the neuroendocrine system, the hypothalamus synthesizes and secretes reproductive hormones that act on the goat organism, thereby precisely regulating follicular development and reproductive processes in goats. However, it is still elusive to explore the mechanism of hypothalamic effects on goat fertility alone. Therefore, RNA-seq was used to analyze the gene expression in hypothalamic tissues of goats in high fertility group (HFG: litter size per litter ≥2) and low fertility group (LFG: litter size per litter = 1), and identified the differential lncRNAs and mRNAs and their associated pathways related to their fertility. The results showed that a total of 23 lncRNAs and 57 mRNAs were differentially expressed in the hypothalamic tissue of high and low fertility goats. GO terms and KEGG functional annotation suggest that DE lncRNAs and DE mRNAs were significantly enriched in hormone-related pathways regulating ovarian development, hormone synthesis and secretion, regulation of reproductive processes, Estrogen signaling pathway, Oxytocin signaling pathway and GnRH signaling pathway. And we constructed a co-expression network of lncRNAs and target genes, and identified reproduction-related genes such as NMUR2, FEZF1, and WT1. The sequencing results of the hypothalamic transcriptome have broadened our understanding of lncRNA and mRNA in goat hypothalamic tissue and provided some new insights into the molecular mechanisms of follicle development and regulation of its fertility in goats.

Inputs and Outputs of the Mammalian Circadian Clock

Circadian rhythms in mammals are coordinated by the central circadian pacemaker, the suprachiasmatic nucleus (SCN). Light and other environmental inputs change the timing of the SCN neural network oscillator, which, in turn, sends output signals that entrain daily behavioral and physiological rhythms. While much is known about the molecular, neuronal, and network properties of the SCN itself, the circuits linking the outside world to the SCN and the SCN to rhythmic outputs are understudied. In this article, we review our current understanding of the synaptic and non-synaptic inputs onto and outputs from the SCN. We propose that a more complete description of SCN connectivity is needed to better explain how rhythms in nearly all behaviors and physiological processes are generated and to determine how, mechanistically, these rhythms are disrupted by disease or lifestyle.

RNA regulation in brain function and disease 2022 (NeuroRNA): A conference report

Recent research integrates novel technologies and methods from the interface of RNA biology and neuroscience. This advancing integration of both fields creates new opportunities in neuroscience to deepen the understanding of gene expression programs and their regulation that underlies the cellular heterogeneity and physiology of the central nervous system. Currently, transcriptional heterogeneity can be studied in individual neural cell types in health and disease. Furthermore, there is an increasing interest in RNA technologies and their application in neurology. These aspects were discussed at an online conference that was shortly named NeuroRNA.

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

Intestinal microbes impact the health of the intestine and organs distal to the gut. Limosilactobacillus reuteri is a human intestinal microbe that promotes normal gut transit 1 , the anti-inflammatory immune system 2-4 , wound healing 5-7 , normal social behavior in mice 8-10 , and prevents bone reabsorption 11-17 . Each of these functions is impacted by oxytocin 18-22 , and oxytocin signaling is required for L. reuteri- mediated wound healing 5 and social behavior 9 ; however, the initiating events in the gut that lead to oxytocin stimulation and related beneficial functions remain unknown. Here we found evolutionarily conserved oxytocin production in the intestinal epithelium through analysis of single-cell RNA-Seq datasets and imaging of human and mouse intestinal tissues. Moreover, human intestinal organoids produce oxytocin, demonstrating that the intestinal epithelium is sufficient to produce oxytocin. We subsequently found that L. reuteri facilitates oxytocin secretion directly from human intestinal tissue and human intestinal organoids. Finally, we demonstrate that stimulation of oxytocin secretion by L. reuteri is dependent on the gut hormone secretin, which is produced in enteroendocrine cells 23 , while oxytocin itself is produced in enterocytes. Altogether, this work demonstrates that oxytocin is produced and secreted from enterocytes in the intestinal epithelium in response to secretin stimulated by L. reuteri . This work thereby identifies oxytocin as an intestinal hormone and provides mechanistic insight into avenues by which gut microbes promote host health.

Genome-wide gene by environment study of time spent in daylight and chronotype identifies emerging genetic architecture underlying light sensitivity

STUDY OBJECTIVES

Light is the primary stimulus for synchronizing the circadian clock in humans. There are very large interindividual differences in the sensitivity of the circadian clock to light. Little is currently known about the genetic basis for these interindividual differences.

METHODS

We performed a genome-wide gene-by-environment interaction study (GWIS) in 280 897 individuals from the UK Biobank cohort to identify genetic variants that moderate the effect of daytime light exposure on chronotype (individual time of day preference), acting as "light sensitivity" variants for the impact of daylight on the circadian system.

RESULTS

We identified a genome-wide significant SNP mapped to the ARL14EP gene (rs3847634; p < 5 × 10-8), where additional minor alleles were found to enhance the morningness effect of daytime light exposure (βGxE = -.03, SE = 0.005) and were associated with increased gene ARL14EP expression in brain and retinal tissues. Gene-property analysis showed light sensitivity loci were enriched for genes in the G protein-coupled glutamate receptor signaling pathway and genes expressed in Per2+ hypothalamic neurons. Linkage disequilibrium score regression identified Bonferroni significant genetic correlations of greater light sensitivity GWIS with later chronotype and shorter sleep duration. Greater light sensitivity was nominally genetically correlated with insomnia symptoms and risk for post-traumatic stress disorder (PTSD).

CONCLUSIONS

This study is the first to assess light as an important exposure in the genomics of chronotype and is a critical first step in uncovering the genetic architecture of human circadian light sensitivity and its links to sleep and mental health.

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.

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.

Graph embedding and Gaussian mixture variational autoencoder network for end-to-end analysis of single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) is a revolutionary technology to determine the precise gene expression of individual cells and identify cell heterogeneity and subpopulations. However, technical limitations of scRNA-seq lead to heterogeneous and sparse data. Here, we present autoCell, a deep-learning approach for scRNA-seq dropout imputation and feature extraction. autoCell is a variational autoencoding network that combines graph embedding and a probabilistic depth Gaussian mixture model to infer the distribution of high-dimensional, sparse scRNA-seq data. We validate autoCell on simulated datasets and biologically relevant scRNA-seq. We show that interpolation of autoCell improves the performance of existing tools in identifying cell developmental trajectories of human preimplantation embryos. We identify disease-associated astrocytes (DAAs) and reconstruct DAA-specific molecular networks and ligand-receptor interactions involved in cell-cell communications using Alzheimer's disease as a prototypical example. autoCell provides a toolbox for end-to-end analysis of scRNA-seq data, including visualization, clustering, imputation, and disease-specific gene network identification.

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.

Imputation method for single-cell RNA-seq data using neural topic model

Single-cell RNA sequencing (scRNA-seq) technology studies transcriptome and cell-to-cell differences from higher single-cell resolution and different perspectives. Despite the advantage of high capture efficiency, downstream functional analysis of scRNA-seq data is made difficult by the excess of zero values (i.e., the dropout phenomenon). To effectively address this problem, we introduced scNTImpute, an imputation framework based on a neural topic model. A neural network encoder is used to extract underlying topic features of single-cell transcriptome data to infer high-quality cell similarity. At the same time, we determine which transcriptome data are affected by the dropout phenomenon according to the learning of the mixture model by the neural network. On the basis of stable cell similarity, the same gene information in other similar cells is borrowed to impute only the missing expression values. By evaluating the performance of real data, scNTImpute can accurately and efficiently identify the dropout values and imputes them accurately. In the meantime, the clustering of cell subsets is improved and the original biological information in cell clustering is solved, which is covered by technical noise. The source code for the scNTImpute module is available as open source at https://github.com/qiyueyang-7/scNTImpute.git.

scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data

Single cell RNA sequencing (scRNA-seq) enables researchers to characterize transcriptomic profiles at the single-cell resolution with increasingly high throughput. Clustering is a crucial step in single cell analysis. Clustering analysis of transcriptome profiled by scRNA-seq can reveal the heterogeneity and diversity of cells. However, single cell study still remains great challenges due to its high noise and dimension. Subspace clustering aims at discovering the intrinsic structure of data in unsupervised fashion. In this paper, we propose a deep sparse subspace clustering method scDSSC combining noise reduction and dimensionality reduction for scRNA-seq data, which simultaneously learns feature representation and clustering via explicit modelling of scRNA-seq data generation. Experiments on a variety of scRNA-seq datasets from thousands to tens of thousands of cells have shown that scDSSC can significantly improve clustering performance and facilitate the interpretability of clustering and downstream analysis. Compared to some popular scRNA-deq analysis methods, scDSSC outperformed state-of-the-art methods under various clustering performance metrics.

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.

Cardiometabolic Consequences of Deleting the Regulator of G protein Signaling-2 (Rgs2) From Cells Expressing Agouti-Related Peptide or the ANG (Angiotensin) II Type 1A Receptor in Mice

BACKGROUND

RGS (regulator of G protein signaling) family members catalyze the termination of G protein signaling cascades. Single nucleotide polymorphisms in the RGS2 gene in humans have been linked to hypertension, preeclampsia, and anxiety disorders. Mice deficient for Rgs2 (Rgs2Null) exhibit hypertension, anxiety, and altered adipose development and function.

METHODS

To study cell-specific functions of RGS2, a novel gene-targeted mouse harboring a conditional allele for the Rgs2 gene (Rgs2Flox) was developed. These mice were bred with mice expressing Cre-recombinase via the Agouti-related peptide locus (Agrp-Cre) to cause deletion of Rgs2 from all cells expressing Agrp (Rgs2Agrp-KO), or a novel transgenic mouse expressing Cre-recombinase via the ANG (angiotensin) type 1A receptor (Agtr1a/ AT1A) promoter encoded in a bacterial artificial chromosome (BAC-AT1A-Cre) to delete Rgs2 in all Agtr1a-expressing cells (Rgs2AT1A-KO).

RESULTS

Whereas Rgs2Flox, Rgs2Agrp-KO, and BAC-AT1A-Cre mice exhibited normal growth and survival, Rgs2AT1A-KO exhibited pre-weaning lethality. Relative to littermates, Rgs2Agrp-KO exhibited reduced fat gains when maintained on a high fat diet, associated with increased energy expenditure. Similarly, surviving adult Rgs2AT1A-KO mice also exhibited increased energy expenditure. Surprisingly, given the hypertensive phenotype previously reported for Rgs2Null mice and evidence supporting a role for RGS2 in terminating AT1A signaling in various cell types, Rgs2AT1A-KO mice exhibited normal blood pressure, ingestive behaviors, and renal functions, both before and after chronic infusion of ANG (490 ng/kg/min, sc).

CONCLUSIONS

These results demonstrate the development of a novel mouse with conditional expression of Rgs2 and illustrate the role of Rgs2 within selected cell types for cardiometabolic control.

Food-induced dopamine signaling in AgRP neurons promotes feeding

Obesity comorbidities such as diabetes and cardiovascular disease are pressing public health concerns. Overconsumption of calories leads to weight gain; however, neural mechanisms underlying excessive food consumption are poorly understood. Here, we demonstrate that dopamine receptor D1 (Drd1) expressed in the agouti-related peptide/neuropeptide Y (AgRP/NPY) neurons of the arcuate hypothalamus is required for appropriate responses to a high-fat diet (HFD). Stimulation of Drd1 and AgRP/NPY co-expressing arcuate neurons is sufficient to induce voracious feeding. Delivery of a HFD after food deprivation acutely induces dopamine (DA) release in the ARC, whereas animals that lack Drd1 expression in ARC^AgRP/NPY neurons (Drd1^AgRP-KO) exhibit attenuated foraging and refeeding of HFD. These results define a role for the DA input to the ARC that encodes acute responses to food and position Drd1 signaling in the ARC^AgRP/NPY neurons as an integrator of the hedonic and homeostatic neuronal feeding circuits.

NetTDP: permutation-based true discovery proportions for differential co-expression network analysis

Existing methods for differential network analysis could only infer whether two networks of interest have differences between two groups of samples, but could not quantify and localize network differences. In this work, a novel method, permutation-based Network True Discovery Proportions (NetTDP), is proposed to quantify the number of edges (correlations) or nodes (genes) for which the co-expression networks are different. In the NetTDP method, we propose an edge-level statistic and a node-level statistic, and detect true discoveries of edges and nodes in the sense of differential co-expression network, respectively, by the permutation-based sumSome method. Furthermore, the NetTDP method could further localize the differences by inferring the TDPs for edge or gene subsets of interest, which can be selected post hoc. Our NetTDP method allows inference on data-driven modules or biology-driven gene sets, and remains valid even when these sub-networks are optimized using the same data. Experimental results on both simulation data sets and five real data sets show the effectiveness of the proposed method in inferring the quantification and localization of differential co-expression networks. The R code is available at https://github.com/LiminLi-xjtu/NetTDP.

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.

A Spacetime Odyssey of Neural Progenitors to Generate Neuronal Diversity

To understand how the nervous system develops from a small pool of progenitors during early embryonic development, it is fundamentally important to identify the diversity of neuronal subtypes, decode the origin of neuronal diversity, and uncover the principles governing neuronal specification across different regions. Recent single-cell analyses have systematically identified neuronal diversity at unprecedented scale and speed, leaving the deconstruction of spatiotemporal mechanisms for generating neuronal diversity an imperative and paramount challenge. In this review, we highlight three distinct strategies deployed by neural progenitors to produce diverse neuronal subtypes, including predetermined, stochastic, and cascade diversifying models, and elaborate how these strategies are implemented in distinct regions such as the neocortex, spinal cord, retina, and hypothalamus. Importantly, the identity of neural progenitors is defined by their spatial position and temporal patterning factors, and each type of progenitor cell gives rise to distinguishable cohorts of neuronal subtypes. Microenvironmental cues, spontaneous activity, and connectional pattern further reshape and diversify the fate of unspecialized neurons in particular regions. The illumination of how neuronal diversity is generated will pave the way for producing specific brain organoids to model human disease and desired neuronal subtypes for cell therapy, as well as understanding the organization of functional neural circuits and the evolution of the nervous system.

A hypothalamic dopamine locus for psychostimulant-induced hyperlocomotion in mice

The lateral septum (LS) has been implicated in the regulation of locomotion. Nevertheless, the neurons synchronizing LS activity with the brain’s clock in the suprachiasmatic nucleus (SCN) remain unknown. By interrogating the molecular, anatomical and physiological heterogeneity of dopamine neurons of the periventricular nucleus (PeVN; A14 catecholaminergic group), we find that Th⁺/Dat1⁺ cells from its anterior subdivision innervate the LS in mice. These dopamine neurons receive dense neuropeptidergic innervation from the SCN. Reciprocal viral tracing in combination with optogenetic stimulation ex vivo identified somatostatin-containing neurons in the LS as preferred synaptic targets of extrahypothalamic A14 efferents. In vivo chemogenetic manipulation of anterior A14 neurons impacted locomotion. Moreover, chemogenetic inhibition of dopamine output from the anterior PeVN normalized amphetamine-induced hyperlocomotion, particularly during sedentary periods. Cumulatively, our findings identify a hypothalamic locus for the diurnal control of locomotion and pinpoint a midbrain-independent cellular target of psychostimulants.

The psychostimulant-sensitive neural mechanism linking the circadian clock to locomotion is unknown. Here, hypothalamic A14 neurons are shown to time diurnal activity by entraining the lateral septum, and their activity is shown to be sensitive to amphetamine.

Pharmacological FGF21 signals to glutamatergic neurons to enhance leptin action and lower body weight during obesity

OBJECTIVE

Fibroblast growth factor 21 (FGF21) is a peripherally-derived endocrine hormone that acts on the central nervous system (CNS) to regulate whole body energy homeostasis. Pharmacological administration of FGF21 promotes weight loss in obese animal models and human subjects with obesity. However, the central targets mediating these effects are incompletely defined.

METHODS

To explore the mechanism for FGF21's effects to lower body weight, we pharmacologically administer FGF21 to genetic animal models lacking the obligate FGF21 co-receptor, β-klotho (KLB), in either glutamatergic (Vglut2-Cre) or GABAergic (Vgat-Cre) neurons. In addition, we abolish FGF21 signaling to leptin receptor (LepR-Cre) positive cells. Finally, we examine the synergistic effects of FGF21 and leptin to lower body weight and explore the importance of physiological leptin levels in FGF21-mediated regulation of body weight.

RESULTS

Here we show that FGF21 signaling to glutamatergic neurons is required for FGF21 to modulate energy expenditure and promote weight loss. In addition, we demonstrate that FGF21 signals to leptin receptor-expressing cells to regulate body weight, and that central leptin signaling is required for FGF21 to fully stimulate body weight loss during obesity. Interestingly, co-administration of FGF21 and leptin synergistically leads to robust weight loss.

CONCLUSIONS

These data reveal an important endocrine crosstalk between liver- and adipose-derived signals which integrate in the CNS to modulate energy homeostasis and body weight regulation.

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.

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.

scMTD: a statistical multidimensional imputation method for single-cell RNA-seq data leveraging transcriptome dynamic information

Background

Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to capture transcriptomes at single-cell resolution. However, dropout events distort the gene expression levels and underlying biological signals, misleading the downstream analysis of scRNA-seq data.

Results

We develop a statistical model-based multidimensional imputation algorithm, scMTD, that identifies local cell neighbors and specific gene co-expression networks based on the pseudo-time of cells, leveraging information on cell-level, gene-level, and transcriptome dynamic to recover scRNA-seq data. Compared with the state-of-the-art imputation methods through several real-data-based analytical experiments, scMTD effectively recovers biological signals of transcriptomes and consistently outperforms the other algorithms in improving FISH validation, trajectory inference, differential expression analysis, clustering analysis, and identification of cell types.

Conclusions

scMTD maintains the gene expression characteristics, enhances the clustering of cell subpopulations, assists the study of gene expression dynamics, contributes to the discovery of rare cell types, and applies to both UMI-based and non-UMI-based data. Overall, scMTD’s reliability, applicability, and scalability make it a promising imputation approach for scRNA-seq data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13578-022-00886-4.

Seasonal changes in day length induce multisynaptic neurotransmitter switching to regulate hypothalamic network activity and behavior

Seasonal changes in day length (photoperiod) affect numerous physiological functions. The suprachiasmatic nucleus (SCN)-paraventricular nucleus (PVN) axis plays a key role in processing photoperiod-related information. Seasonal variations in SCN and PVN neurotransmitter expression have been observed in humans and animal models. However, the molecular mechanisms by which the SCN-PVN network responds to altered photoperiod is unknown. Here, we show in mice that neuromedin S (NMS) and vasoactive intestinal polypeptide (VIP) neurons in the SCN display photoperiod-induced neurotransmitter plasticity. In vivo recording of calcium dynamics revealed that NMS neurons alter PVN network activity in response to winter-like photoperiod. Chronic manipulation of NMS neurons is sufficient to induce neurotransmitter switching in PVN neurons and affects locomotor activity. Our findings reveal previously unidentified molecular adaptations of the SCN-PVN network in response to seasonality and the role for NMS neurons in adjusting hypothalamic function to day length via a coordinated multisynaptic neurotransmitter switching affecting behavior.

Molecular diversity and evolution of neuron types in the amniote brain

The existence of evolutionarily conserved regions in the vertebrate brain is well established. The rules and constraints underlying the evolution of neuron types, however, remain poorly understood. To compare neuron types across brain regions and species, we generated a cell type atlas of the brain of a bearded dragon and compared it with mouse datasets. Conserved classes of neurons could be identified from the expression of hundreds of genes, including homeodomain-type transcription factors and genes involved in connectivity. Within these classes, however, there are both conserved and divergent neuron types, precluding a simple categorization of the brain into ancestral and novel areas. In the thalamus, neuronal diversification correlates with the evolution of the cortex, suggesting that developmental origin and circuit allocation are drivers of neuronal identity and evolution.

Cell type-specific inference of differential expression in spatial transcriptomics

A central problem in spatial transcriptomics is detecting differentially expressed (DE) genes within cell types across tissue context. Challenges to learning DE include changing cell type composition across space and measurement pixels detecting transcripts from multiple cell types. Here, we introduce a statistical method, cell type-specific inference of differential expression (C-SIDE), that identifies cell type-specific DE in spatial transcriptomics, accounting for localization of other cell types. We model gene expression as an additive mixture across cell types of log-linear cell type-specific expression functions. C-SIDE's framework applies to many contexts: DE due to pathology, anatomical regions, cell-to-cell interactions and cellular microenvironment. Furthermore, C-SIDE enables statistical inference across multiple/replicates. Simulations and validation experiments on Slide-seq, MERFISH and Visium datasets demonstrate that C-SIDE accurately identifies DE with valid uncertainty quantification. Last, we apply C-SIDE to identify plaque-dependent immune activity in Alzheimer's disease and cellular interactions between tumor and immune cells. We distribute C-SIDE within the R package https://github.com/dmcable/spacexr .

Oxytocin, Vasopressin, and Social Behavior: From Neural Circuits to Clinical Opportunities

Oxytocin and vasopressin are peptide hormones secreted from the pituitary that are well known for their peripheral endocrine effects on childbirth/nursing and blood pressure/urine concentration, respectively. However, both peptides are also released in the brain, where they modulate several aspects of social behaviors. Oxytocin promotes maternal nurturing and bonding, enhances social reward, and increases the salience of social stimuli. Vasopressin modulates social communication, social investigation, territorial behavior, and aggression, predominantly in males. Both peptides facilitate social memory and pair bonding behaviors in monogamous species. Here we review the latest research delineating the neural circuitry of the brain oxytocin and vasopressin systems and summarize recent investigations into the circuit-based mechanisms modulating social behaviors. We highlight research using modern molecular genetic technologies to map, monitor activity of, or manipulate neuropeptide circuits. Species diversity in oxytocin and vasopressin effects on social behaviors are also discussed. We conclude with a discussion of the translational implications of oxytocin and vasopressin for improving social functioning in disorders with social impairments, such as autism spectrum disorder.

Interactome overlap between risk genes of epilepsy and targets of anti-epileptic drugs

Aanti-epileptic drugs have been used for treating epilepsy for decades, meanwhile, more than one hundred genes have been identified to be associated with risk of epilepsy; however, the interaction mechanism between anti-epileptic drugs and risk genes of epilepsy was still not clearly understood. In this study, we systematically explored the interaction of epilepsy risk genes and anti-epileptic drug targets through a network-based approach. Our results revealed that anti-epileptic drug targets were significantly over-represented in risk genes of epilepsy with 17 overlapping genes and P-value = 2.2 ×10 −16. We identified a significantly localized PPI network with 55 epileptic risk genes and 94 anti-epileptic drug target genes, and network overlap analysis showed significant interactome overlap between risk genes and drug targets with P-value = 0.04. Besides, genes from PPI network were significantly enriched in the co-expression network of epilepsy with 22 enriched genes and P-value = 1.3 ×10 −15; meanwhile, cell type enrichment analysis indicated genes in this network were significantly enriched in 4 brain cell types (Interneuron, Medium Spiny Neuron, CA1 pyramidal Neuron, and Somatosensory pyramidal Neuron). These results provide evidence for significant interactions between epilepsy risk genes and anti-epileptic drug targets from the perspective of network biology.

Oxytocin-based therapies for treatment of Prader-Willi and Schaaf-Yang syndromes: evidence, disappointments, and future research strategies

The prosocial neuropeptide oxytocin is being developed as a potential treatment for various neuropsychiatric disorders including autism spectrum disorder (ASD). Early studies using intranasal oxytocin in patients with ASD yielded encouraging results and for some time, scientists and affected families placed high hopes on the use of intranasal oxytocin for behavioral therapy in ASD. However, a recent Phase III trial obtained negative results using intranasal oxytocin for the treatment of behavioral symptoms in children with ASD. Given the frequently observed autism-like behavioral phenotypes in Prader-Willi and Schaaf-Yang syndromes, it is unclear whether oxytocin treatment represents a viable option to treat behavioral symptoms in these diseases. Here we review the latest findings on intranasal OT treatment, Prader-Willi and Schaaf-Yang syndromes, and propose novel research strategies for tailored oxytocin-based therapies for affected individuals. Finally, we propose the critical period theory, which could explain why oxytocin-based treatment seems to be most efficient in infants, but not adolescents.

Ablation of Growth Hormone Receptor in GABAergic Neurons Leads to Increased Pulsatile Growth Hormone Secretion

Growth hormone (GH) acts in several hypothalamic neuronal populations to modulate metabolism and the autoregulation of GH secretion via negative-feedback loops. However, few studies have investigated whether GH receptor (GHR) expression in specific neuronal populations is required for the homeostatic control of GH secretion and energy homeostasis. In the present study, we investigated the consequences of the specific GHR ablation in GABAergic (VGAT-expressing) or glutamatergic (VGLUT2-expressing) cells. GHR ablation in GABAergic neurons led to increased GH secretion, lean mass, and body growth in male and female mice. VGAT-specific GHR knockout (KO) male mice also showed increased serum insulin-like growth factor-1, hypothalamic Ghrh, and hepatic Igf1 messenger RNA levels. In contrast, normal GH secretion, but reduced lean body mass, was observed in mice carrying GHR ablation in glutamatergic neurons. GHR ablation in GABAergic cells increased weight loss and led to decreased blood glucose levels during food restriction, whereas VGLUT2-specific GHR KO mice showed blunted feeding response to 2-deoxy-D-glucose both in males and females, and increased relative food intake, oxygen consumption, and serum leptin levels in male mice. Of note, VGLUT2-cre female mice, independently of GHR ablation, exhibited a previously unreported phenotype of mild reduction in body weight without further metabolic alterations. The autoregulation of GH secretion via negative-feedback loops requires GHR expression in GABAergic cells. Furthermore, GHR ablation in GABAergic and glutamatergic neuronal populations leads to distinct metabolic alterations. These findings contribute to the understanding of the neuronal populations responsible for mediating the neuroendocrine and metabolic effects of GH.

Genome-wide meta-analysis of insomnia prioritizes genes associated with metabolic and psychiatric pathways

Insomnia is a heritable, highly prevalent sleep disorder for which no sufficient treatment currently exists. Previous genome-wide association studies with up to 1.3 million subjects identified over 200 associated loci. This extreme polygenicity suggested that many more loci remain to be discovered. The current study almost doubled the sample size to 593,724 cases and 1,771,286 controls, thereby increasing statistical power, and identified 554 risk loci (including 364 novel loci). To capitalize on this large number of loci, we propose a novel strategy to prioritize genes using external biological resources and functional interactions between genes across risk loci. Of all 3,898 genes naively implicated from the risk loci, we prioritize 289 and find brain-tissue expression specificity and enrichment in specific gene sets of synaptic signaling functions and neuronal differentiation. We show that this novel gene prioritization strategy yields specific hypotheses on underlying mechanisms of insomnia that would have been missed by traditional approaches.

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.

Vasopressin neurons in the paraventricular hypothalamus promote wakefulness via lateral hypothalamic orexin neurons

The sleep-wakefulness cycle is regulated by complicated neural networks that include many different populations of neurons throughout the brain. Arginine vasopressin neurons in the paraventricular nucleus of the hypothalamus (PVH^AVP) regulate various physiological events and behaviors, such as body-fluid homeostasis, blood pressure, stress response, social interaction, and feeding. Changes in arousal level often accompany these PVH^AVP-mediated adaptive responses. However, the contribution of PVH^AVP neurons to sleep-wakefulness regulation has remained unknown. Here, we report the involvement of PVH^AVP neurons in arousal promotion. Optogenetic stimulation of PVH^AVP neurons rapidly induced transitions to wakefulness from both NREM and REM sleep. This arousal effect was dependent on AVP expression in these neurons. Similarly, chemogenetic activation of PVH^AVP neurons increased wakefulness and reduced NREM and REM sleep, whereas chemogenetic inhibition of these neurons significantly reduced wakefulness and increased NREM sleep. We observed dense projections of PVH^AVP neurons in the lateral hypothalamus with potential connections to orexin/hypocretin (LH^Orx) neurons. Optogenetic stimulation of PVH^AVP neuronal fibers in the LH immediately induced wakefulness, whereas blocking orexin receptors attenuated the arousal effect of PVH^AVP neuronal activation drastically. Monosynaptic rabies-virus tracing revealed that PVH^AVP neurons receive inputs from multiple brain regions involved in sleep-wakefulness regulation, as well as those involved in stress response and energy metabolism. Moreover, PVH^AVP neurons mediated the arousal induced by novelty stress and a melanocortin receptor agonist melanotan-II. Thus, our data suggested that PVH^AVP neurons promote wakefulness via LH^Orx neurons in the basal sleep-wakefulness and some stressful conditions.

What is a cell type and how to define it?

Cell types are the basic functional units of an organism. Cell types exhibit diverse phenotypic properties at multiple levels, making them challenging to define, categorize, and understand. This review provides an overview of the basic principles of cell types rooted in evolution and development and discusses approaches to characterize and classify cell types and investigate how they contribute to the organism's function, using the mammalian brain as a primary example. I propose a roadmap toward a conceptual framework and knowledge base of cell types that will enable a deeper understanding of the dynamic changes of cellular function under healthy and diseased conditions.

GABAergic and Glutamatergic Phenotypes of Neurons Expressing Calcium-Binding Proteins in the Preoptic Area of the Guinea Pig

The mammalian preoptic area (POA) has large populations of calbindin (CB), calretinin (CR) and parvalbumin (PV) neurons, but phenotypes of these cells are unknown. Therefore, the question is whether neurons expressing CB, CR, and/or PV are GABAergic or glutamatergic. Double-immunofluorescence staining followed by epifluorescence and confocal microscopy was used to determine the coexpression patterns of CB, CR and PV expressing neurons with vesicular GABA transporters (VGAT) as specific markers of GABAergic neurons and vesicular glutamate transporters (VGLUT 2) as specific markers of glutamatergic neurons. The guinea pig was adopted as, like humans, it has a reproductive cycle with a true luteal phase and a long gestation period. The results demonstrated that in the guinea pig POA of both sexes, ~80% of CB+ and ~90% of CR+ neurons coexpress VGAT; however, one-fifth of CB+ neurons and one-third of CR+ cells coexpress VGLUT. About two-thirds of PV+ neurons express VGAT, and similar proportion of them coexpress VGLUT. Thus, many CB+, CR+ and PV+ neurons may be exclusively GABAergic (VGAT-expressing cells) or glutamatergic (VGLUT-expressing cells); however, at least a small fraction of CR+ cells and at least one-third of PV+ cells are likely neurons with a dual GABA/glutamate phenotype that may coexpress both transporters.

Single-cell analysis of the aging female mouse hypothalamus

Alterations in metabolism, sleep patterns, body composition, and hormone status are all key features of aging. While the hypothalamus is a well-conserved brain region that controls these homeostatic and survival-related behaviors, little is known about the intrinsic features of hypothalamic aging. Here, we perform single nuclei RNA-sequencing of 40,064 hypothalamic nuclei from young and aged female mice. We identify cell type-specific signatures of aging in neuronal subtypes as well as astrocytes and microglia. We uncover changes in cell types critical for metabolic regulation and body composition, and in an area of the hypothalamus linked to cognition. Our analysis also reveals an unexpected female-specific feature of hypothalamic aging: the master regulator of X-inactivation, Xist, is elevated with age, particularly in hypothalamic neurons. Moreover, using machine learning, we show that levels of X-chromosome genes, and Xist itself, can accurately predict cellular age. This study identifies critical cell-specific changes of the aging hypothalamus in mammals, and uncovers a potential marker of neuronal aging in females.

Melanocortin MC4R receptor is required for energy expenditure but not blood pressure effects of angiotensin II within the mouse brain

The brain renin-angiotensin system (RAS) is implicated in control of blood pressure (BP), fluid intake, and energy expenditure (EE). Angiotensin II (ANG II) within the arcuate nucleus of the hypothalamus contributes to control of resting metabolic rate (RMR) and thereby EE through its actions on Agouti-related peptide (AgRP) neurons, which also contribute to EE control by leptin. First, we determined that although leptin stimulates EE in control littermates, mice with transgenic activation of the brain RAS (sRA) exhibit increased EE and leptin has no additive effect to exaggerate EE in these mice. These findings led us to hypothesize that leptin and ANG II in the brain stimulate EE through a shared mechanism. Because AgRP signaling to the melanocortin MC4R receptor contributes to the metabolic effects of leptin, we performed a series of studies examining RMR, fluid intake, and BP responses to ANG II in mice rendered deficient for expression of MC4R via a transcriptional block (Mc4r-TB). These mice were resistant to stimulation of RMR in response to activation of the endogenous brain RAS via chronic deoxycorticosterone acetate (DOCA)-salt treatment, whereas fluid and electrolyte effects remained intact. These mice were also resistant to stimulation of RMR via acute intracerebroventricular (ICV) injection of ANG II, whereas BP responses to ICV ANG II remained intact. Collectively, these data demonstrate that the effects of ANG II within the brain to control RMR and EE are dependent on MC4R signaling, whereas fluid homeostasis and BP responses are independent of MC4R signaling.

scMAGIC: accurately annotating single cells using two rounds of reference-based classification

Here, we introduce scMAGIC (Single Cell annotation using MArker Genes Identification and two rounds of reference-based Classification [RBC]), a novel method that uses well-annotated single-cell RNA sequencing (scRNA-seq) data as the reference to assist in the classification of query scRNA-seq data. A key innovation in scMAGIC is the introduction of a second-round RBC in which those query cells whose cell identities are confidently validated in the first round are used as a new reference to again classify query cells, therefore eliminating the batch effects between the reference and the query data. scMAGIC significantly outperforms 13 competing RBC methods with their optimal parameter settings across 86 benchmark tests, especially when the cell types in the query dataset are not completely covered by the reference dataset and when there exist significant batch effects between the reference and the query datasets. Moreover, when no reference dataset is available, scMAGIC can annotate query cells with reasonably high accuracy by using an atlas dataset as the reference.

One Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data

Integrative analysis of large-scale single-cell RNA sequencing (scRNA-seq) datasets can aggregate complementary biological information from different datasets. However, most existing methods fail to efficiently integrate multiple large-scale scRNA-seq datasets. We propose OCAT, One Cell At a Time, a machine learning method that sparsely encodes single-cell gene expression to integrate data from multiple sources without highly variable gene selection or explicit batch effect correction. We demonstrate that OCAT efficiently integrates multiple scRNA-seq datasets and achieves the state-of-the-art performance in cell type clustering, especially in challenging scenarios of non-overlapping cell types. In addition, OCAT can efficaciously facilitate a variety of downstream analyses.

scGAC: a graph attentional architecture for clustering single-cell RNA-seq data

MOTIVATION

Emerging single-cell RNA sequencing (scRNA-seq) technology empowers biological research at cellular level. One of the most crucial scRNA-seq data analyses is clustering single cells into subpopulations. However, the high variability, high sparsity and high dimensionality of scRNA-seq data pose lots of challenges for clustering analysis. Although many single-cell clustering methods have been recently developed, few of them fully exploit latent relationship among cells, thus leading to suboptimal clustering results.

RESULTS

Here, we propose a novel unsupervised clustering method, scGAC (single-cell Graph Attentional Clustering), for scRNA-seq data. scGAC firstly constructs a cell graph and refines it by network denoising. Then, it learns clustering-friendly representation of cells through a graph attentional autoencoder, which propagates information across cells with different weights and captures latent relationship among cells. Finally, scGAC adopts a self-optimizing method to obtain the cell clusters. Experiments on 16 real scRNA-seq datasets show that scGAC achieves excellent performance and outperforms existing state-of-art single-cell clustering methods.

AVAILABILITY AND IMPLEMENTATION

Python implementation of scGAC is available at Github (https://github.com/Joye9285/scGAC) and Figshare (https://figshare.com/articles/software/scGAC/19091348).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.