A survey of human brain transcriptome diversity at the single cell level

Neural cell isolation from adult macaques for high-throughput analyses and neurosphere cultures

The low number of neural progenitor cells (NPCs) present in the adult and aged primate brains represents a challenge for generating high-yield and viable in vitro cultures of primary brain cells. Here we report a step-by-step approach for the fast and reproducible isolation of high-yield and viable primary brain cells, including mature neurons, immature cells and NPCs, from adult and aged macaques. We describe the anesthesia, transcardial perfusion and brain tissue preparation; the subsequent microdissection of the regions of interest and their enzymatic dissociation, leading to the separation of single cells. The cell isolation steps of our protocol can also be used for routine cell culturing, in particular for NPC expansion and differentiation, suitable for studies of hippocampal neurogenesis in the adult macaque brain. The purified primary brain cells are largely free from myelin debris and erythrocytes, paving the way for multiple downstream applications in vitro and in vivo. When combined with single-cell profiling techniques, this approach allows an unbiased and comprehensive mapping of cell states in the adult and aged macaque brain, which is needed to advance our understanding of human cognitive and neurological diseases. The neural cell isolation protocol requires 4 h and a team of four to six users with expertize in primary brain cell isolation to avoid tissue hypoxia during the time-sensitive steps of the procedure.

Inhibition of PINK1-Mediated Mitophagy Contributes to Postoperative Cognitive Dysfunction through Activation of Caspase-3/GSDME-Dependent Pyroptosis

PTEN-induced kinase 1 (PINK1)-mediated mitophagy and caspase-1/gasdermin D canonical pyroptosis pathways have been implicated in the pathogenesis of postoperative cognitive dysfunction (POCD). However, gasdermin E (GSDME), another recently identified executioner of pyroptosis that can be specifically cleaved by caspase-3, is highly expressed in the brain and neurons. This study aimed to ascertain whether PINK1-dependent mitophagy governs postoperative cognitive capacity through caspase-3/GSDME. Twelve month old male Sprague-Dawley rats underwent exploratory laparotomy under isoflurane anesthesia. Lipopolysaccharide (LPS)-primed SH-SY5Y cells were used to mimic postsurgical neuroinflammation. For the interventional study, rats were administered with adeno-associated virus serotype 9 (AAV9)-mediated silencing of Pink1 and/or caspase-3 inhibitor Ac-DEVD-CHO (Ac-DC). SH-SY5Y cells were treated with siPINK1 and/or Ac-DC. Cognitive performance was assessed using the Morris water maze test. The mitophagy- and pyroptosis-related parameters were determined in the hippocampus and SH-SY5Y cells. Anesthesia/surgery and LPS caused defective PINK1-mediated mitophagy and activation of caspase-3/GSDME-dependent pyroptosis. AAV-9 mediated Pink1 overexpression mitigated cognitive impairment and caspase-3/GSDME-dependent pyroptosis. Conversely, inhibition of PINK1 aggravates POCD and overactivates neuronal pyroptosis. These abnormalities were rescued by Ac-DC treatment. Collectively, PINK1-mediated mitophagy regulates anesthesia and surgery-induced cognitive impairment by negatively affecting the caspase-3/GSDME pyroptosis pathway, which provides a promising therapeutic target for POCD.

Genome-wide association study of cerebellar white matter microstructure and genetic overlap with common brain disorders

BACKGROUND

The cerebellum is recognized as being involved in neurocognitive and motor functions with communication with extra-cerebellar regions relying on the white matter integrity of the cerebellar peduncles. However, the genetic determinants of cerebellar white matter integrity remain largely unknown.

METHODS

We conducted a genome-wide association analysis of cerebellar white matter microstructure using diffusion tensor imaging data from 25,415 individuals from UK Biobank. The integrity of cerebellar white matter microstructure was measured as fractional anisotropy (FA) and mean diffusivity (MD). Identification of independent genomic loci, functional annotation, and tissue and cell-type analysis were conducted with FUMA. The linkage disequilibrium score regression (LDSC) was used to calculate genetic correlations between cerebellar white matter microstructure and regional brain volumes and brain-related traits. Furthermore, the conditional/conjunctional false discovery rate (condFDR/conjFDR) framework was employed to identify the shared genetic basis between cerebellar white matter microstructure and common brain disorders.

RESULTS

We identified 11 genetic loci (P < 8.3 × 10^-9) and 86 genes associated with cerebellar white matter microstructure. Further functional enrichment analysis implicated the involvement of GABAergic neurons and cholinergic pathways. Significant polygenetic overlap between cerebellar white matter tracts and their anatomically connected or adjacent brain regions was detected. In addition, we report the overall genetic correlation and specific loci shared between cerebellar white matter microstructural integrity and brain-related traits, including movement, cognitive, psychiatric, and cerebrovascular categories.

CONCLUSIONS

Collectively, this study represents a step forward in understanding the genetics of cerebellar white matter microstructure and its shared genetic etiology with common brain disorders.

KGLRR: A low-rank representation K-means with graph regularization constraint method for Single-cell type identification

Single-cell RNA sequencing technology provides a tremendous opportunity for studying disease mechanisms at the single-cell level. Cell type identification is a key step in the research of disease mechanisms. Many clustering algorithms have been proposed to identify cell types. Most clustering algorithms perform similarity calculation before cell clustering. Because clustering and similarity calculation are independent, a low-rank matrix obtained only by similarity calculation may be unable to fully reveal the patterns in single-cell data. In this study, to capture accurate single-cell clustering information, we propose a novel method based on a low-rank representation model, called KGLRR, that combines the low-rank representation approach with K-means clustering. The cluster centroid is updated as the cell dimension decreases to better from new clusters and improve the quality of clustering information. In addition, the low-rank representation model ignores local geometric information, so the graph regularization constraint is introduced. KGLRR is tested on both simulated and real single-cell datasets to validate the effectiveness of the new method. The experimental results show that KGLRR is more robust and accurate in cell type identification than other advanced algorithms.

De novo mutations disturb early brain development more frequently than common variants in schizophrenia

Investigating functional, temporal, and cell-type expression features of mutations is important for understanding a complex disease. Here, we collected and analyzed common variants and de novo mutations (DNMs) in schizophrenia (SCZ). We collected 2,636 missense and loss-of-function (LoF) DNMs in 2,263 genes across 3,477 SCZ patients (SCZ-DNMs). We curated three gene lists: (a) SCZ-neuroGenes (159 genes), which are intolerant to LoF and missense DNMs and are neurologically important, (b) SCZ-moduleGenes (52 genes), which were derived from network analyses of SCZ-DNMs, and (c) SCZ-commonGenes (120 genes) from a recent GWAS as reference. To compare temporal gene expression, we used the BrainSpan dataset. We defined a fetal effect score (FES) to quantify the involvement of each gene in prenatal brain development. We further employed the specificity indexes (SIs) to evaluate cell-type expression specificity from single-cell expression data in cerebral cortices of humans and mice. Compared with SCZ-commonGenes, SCZ-neuroGenes and SCZ-moduleGenes were highly expressed in the prenatal stage, had higher FESs, and had higher SIs in fetal replicating cells and undifferentiated cell types. Our results suggested that gene expression patterns in specific cell types in early fetal stages might have impacts on the risk of SCZ during adulthood.

Dysregulation of Long Intergenic Non-Coding RNA Expression in the Schizophrenia Brain

BACKGROUND

Transcriptomic studies of the brains of schizophrenia (SZ) patients have produced abundant but largely inconsistent findings about the disorders pathophysiology. These inconsistencies might stem not only from the heterogeneous nature of the disorder, but also from the unbalanced focus on particular cortical regions and protein-coding genes. Compared to protein-coding transcripts, long intergenic non-coding RNA (lincRNA) display substantially greater brain region and disease response specificity, positioning them as prospective indicators of SZ-associated alterations. Further, a growing understanding of the systemic character of the disorder calls for a more systematic screening involving multiple diverse brain regions.

AIM

We aimed to identify and interpret alterations of the lincRNA expression profiles in SZ by examining the transcriptomes of 35 brain regions.

METHODS

We measured the transcriptome of 35 brain regions dissected from eight adult brain specimens, four SZ patients, and four healthy controls, using high-throughput RNA sequencing. Analysis of these data yielded 861 annotated human lincRNAs passing the detection threshold.

RESULTS

Of the 861 detected lincRNA, 135 showed significant region-dependent expression alterations in SZ (two-way ANOVA, BH-adjusted p 0.05) and 37 additionally showed significant differential expression between HC and SZ individuals in at least one region (post hoc Tukey test, p 0.05). For these 37 differentially expressed lincRNAs (DELs), 88% of the differences occurred in a cluster of brain regions containing axon-rich brain regions and cerebellum. Functional annotation of the DEL targets further revealed stark enrichment in neurons and synaptic transmission terms and pathways.

CONCLUSION

Our study highlights the utility of a systematic brain transcriptome analysis relying on the expression profiles measured across multiple brain regions and singles out white matter regions as a prospective target for further SZ research.

Cell Features Reconstruction from Gene Association Network of Single Cell

Gene expression as an unstable form of cell characterization has been widely used for single-cell analyses. Although there are cell-specific networks (CSN) to explore stable gene associations within a single cell, the amount of information in CSN is huge and there is no method to measure the interaction level between genes. Therefore, this paper presents a two-level approach to reconstructing single-cell features, which transforms the original gene expression feature into the gene ontology feature and gene interaction feature. Specifically, we first squeeze all CSNs into a cell network feature matrix (CNFM) by fusing the global position and neighborhood influence of genes. Next, we propose a computational method of gene gravitation based on CNFM to quantify the extent of gene-gene interaction, and we can construct a gene gravitation network for single cells. Finally, we further design a novel index of gene gravitation entropy to quantitatively evaluate the level of single-cell differentiation. The experiments on eight different scRNA-seq datasets show the effectiveness and broad application prospects of our method.

Gene panel selection for targeted spatial transcriptomics

Targeted spatial transcriptomics hold particular promise in analysis of complex tissues. Most such methods, however, measure only a limited panel of transcripts, which need to be selected in advance to inform on the cell types or processes being studied. A limitation of existing gene selection methods is that they rely on scRNA-seq data, ignoring platform effects between technologies. Here we describe gpsFISH, a computational method to perform gene selection through optimizing detection of known cell types. By modeling and adjusting for platform effects, gpsFISH outperforms other methods. Furthermore, gpsFISH can incorporate cell type hierarchies and custom gene preferences to accommodate diverse design requirements.

Human cortical spheroids with a high diversity of innately developing brain cell types

BACKGROUND

Three-dimensional (3D) human brain spheroids are instrumental to study central nervous system (CNS) development and (dys)function. Yet, in current brain spheroid models the limited variety of cell types hampers an integrated exploration of CNS (disease) mechanisms.

METHODS

Here we report a 5-month culture protocol that reproducibly generates H9 embryonic stem cell-derived human cortical spheroids (hCSs) with a large cell-type variety.

RESULTS

We established the presence of not only neuroectoderm-derived neural progenitor populations, mature excitatory and inhibitory neurons, astrocytes and oligodendrocyte (precursor) cells, but also mesoderm-derived microglia and endothelial cell populations in the hCSs via RNA-sequencing, qPCR, immunocytochemistry and transmission electron microscopy. Transcriptomic analysis revealed resemblance between the 5-months-old hCSs and dorsal frontal rather than inferior regions of human fetal brains of 19-26 weeks of gestational age. Pro-inflammatory stimulation of the generated hCSs induced a neuroinflammatory response, offering a proof-of-principle of the applicability of the spheroids.

CONCLUSIONS

Our protocol provides a 3D human brain cell model containing a wide variety of innately developing neuroectoderm- as well as mesoderm-derived cell types, furnishing a versatile platform for comprehensive examination of intercellular CNS communication and neurological disease mechanisms.

Proteomic Analysis of Huntington's Disease Medium Spiny Neurons Identifies Alterations in Lipid Droplets

Huntington's disease (HD) is a neurodegenerative disease caused by a CAG repeat expansion in the Huntingtin (HTT) gene. The resulting polyglutamine (polyQ) tract alters the function of the HTT protein. Although HTT is expressed in different tissues, the medium spiny projection neurons (MSNs) in the striatum are particularly vulnerable in HD. Thus, we sought to define the proteome of human HD patient-derived MSNs. We differentiated HD72 induced pluripotent stem cells and isogenic controls into MSNs and carried out quantitative proteomic analysis. Using data-dependent acquisitions with FAIMS for label-free quantification on the Orbitrap Lumos mass spectrometer, we identified 6,323 proteins with at least two unique peptides. Of these, 901 proteins were altered significantly more in the HD72-MSNs than in isogenic controls. Functional enrichment analysis of upregulated proteins demonstrated extracellular matrix and DNA signaling (DNA replication pathway, double-strand break repair, G1/S transition) with the highest significance. Conversely, processes associated with the downregulated proteins included neurogenesis-axogenesis, the brain-derived neurotrophic factor-signaling pathway, Ephrin-A: EphA pathway, regulation of synaptic plasticity, triglyceride homeostasis cholesterol, plasmid lipoprotein particle immune response, interferon-γ signaling, immune system major histocompatibility complex, lipid metabolism and cellular response to stimulus. Moreover, proteins involved in the formation and maintenance of axons, dendrites, and synapses (e.g., Septin protein members) were dysregulated in HD72-MSNs. Importantly, lipid metabolism pathways were altered, and using quantitative image, we found analysis that lipid droplets accumulated in the HD72-MSN, suggesting a deficit in the turnover of lipids possibly through lipophagy. Our proteomics analysis of HD72-MSNs identified relevant pathways that are altered in MSNs and confirm current and new therapeutic targets for HD.

scGGAN: single-cell RNA-seq imputation by graph-based generative adversarial network

Single-cell RNA sequencing (scRNA-seq) data are typically with a large number of missing values, which often results in the loss of critical gene signaling information and seriously limit the downstream analysis. Deep learning-based imputation methods often can better handle scRNA-seq data than shallow ones, but most of them do not consider the inherent relations between genes, and the expression of a gene is often regulated by other genes. Therefore, it is essential to impute scRNA-seq data by considering the regional gene-to-gene relations. We propose a novel model (named scGGAN) to impute scRNA-seq data that learns the gene-to-gene relations by Graph Convolutional Networks (GCN) and global scRNA-seq data distribution by Generative Adversarial Networks (GAN). scGGAN first leverages single-cell and bulk genomics data to explore inherent relations between genes and builds a more compact gene relation network to jointly capture the homogeneous and heterogeneous information. Then, it constructs a GCN-based GAN model to integrate the scRNA-seq, gene sequencing data and gene relation network for generating scRNA-seq data, and trains the model through adversarial learning. Finally, it utilizes data generated by the trained GCN-based GAN model to impute scRNA-seq data. Experiments on simulated and real scRNA-seq datasets show that scGGAN can effectively identify dropout events, recover the biologically meaningful expressions, determine subcellular states and types, improve the differential expression analysis and temporal dynamics analysis. Ablation experiments confirm that both the gene relation network and gene sequence data help the imputation of scRNA-seq data.

Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data

Sample-wise deconvolution methods have been developed to estimate cell-type proportions and gene expressions in bulk-tissue samples. However, the performance of these methods and their biological applications has not been evaluated, particularly on human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk-tissue RNAseq, single-cell/nuclei (sc/sn) RNAseq, and immunohistochemistry. A total of 1,130,767 nuclei/cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expression. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk-tissue or single-cell eQTLs alone. Differential gene expression associated with multiple phenotypes were also examined using the deconvoluted data. Our findings, which were replicated in bulk-tissue RNAseq and sc/snRNAseq data, provided new insights into the biological applications of deconvoluted data.

Astrocyte adaptation in Alzheimer's disease: a focus on astrocytic P2X7R

Astrocytes are key homeostatic and defensive cells of the central nervous system (CNS). They undertake numerous functions during development and in adulthood to support and protect the brain through finely regulated communication with other cellular elements of the nervous tissue. In Alzheimer's disease (AD), astrocytes undergo heterogeneous morphological, molecular and functional alterations represented by reactive remodelling, asthenia and loss of function. Reactive astrocytes closely associate with amyloid β (Aβ) plaques and neurofibrillary tangles in advanced AD. The specific contribution of astrocytes to AD could potentially evolve along the disease process and includes alterations in their signalling, interactions with pathological protein aggregates, metabolic and synaptic impairments. In this review, we focus on the purinergic receptor, P2X7R, and discuss the evidence that P2X7R activation contributes to altered astrocyte functions in AD. Expression of P2X7R is increased in AD brain relative to non-demented controls, and animal studies have shown that P2X7R antagonism improves cognitive and synaptic impairments in models of amyloidosis and tauopathy. While P2X7R activation can induce inflammatory signalling pathways, particularly in microglia, we focus here specifically on the contributions of astrocytic P2X7R to synaptic changes and protein aggregate clearance in AD, highlighting cell-specific roles of this purinoceptor activation that could be targeted to slow disease progression.

Natural variation in gene expression and viral susceptibility revealed by neural progenitor cell villages

Human genome variation contributes to diversity in neurodevelopmental outcomes and vulnerabilities; recognizing the underlying molecular and cellular mechanisms will require scalable approaches. Here, we describe a "cell village" experimental platform we used to analyze genetic, molecular, and phenotypic heterogeneity across neural progenitor cells from 44 human donors cultured in a shared in vitro environment using algorithms (Dropulation and Census-seq) to assign cells and phenotypes to individual donors. Through rapid induction of human stem cell-derived neural progenitor cells, measurements of natural genetic variation, and CRISPR-Cas9 genetic perturbations, we identified a common variant that regulates antiviral IFITM3 expression and explains most inter-individual variation in susceptibility to the Zika virus. We also detected expression QTLs corresponding to GWAS loci for brain traits and discovered novel disease-relevant regulators of progenitor proliferation and differentiation such as CACHD1. This approach provides scalable ways to elucidate the effects of genes and genetic variation on cellular phenotypes.

Quantitative Evaluation of Stem-like Markers of Human Glioblastoma Using Single-Cell RNA Sequencing Datasets

Targeting glioblastoma (GBM) stem-like cells (GSCs) is a common interest in both the laboratory investigation and clinical treatment of GBM. Most of the currently applied GBM stem-like markers lack validation and comparison with common standards regarding their efficiency and feasibility in various targeting methods. Using single-cell RNA sequencing datasets from 37 GBM patients, we obtained a large pool of 2173 GBM stem-like marker candidates. To evaluate and select these candidates quantitatively, we characterized the efficiency of the candidate markers in targeting the GBM stem-like cells by their frequencies and significance of being the stem-like cluster markers. This was followed by further selection based on either their differential expression in GBM stem-like cells compared with normal brain cells or their relative expression level compared with other expressed genes. The cellular location of the translated protein was also considered. Different combinations of selection criteria highlight different markers for different application scenarios. By comparing the commonly used GSCs marker CD133 (PROM1) with markers selected by our method regarding their universality, significance, and abundance, we revealed the limitations of CD133 as a GBM stem-like marker. Overall, we propose BCAN, PTPRZ1, SOX4, etc. for laboratory-based assays with samples free of normal cells. For in vivo targeting applications that require high efficiency in targeting the stem-like subtype, the ability to distinguish GSCs from normal brain cells, and a high expression level, we recommend the intracellular marker TUBB3 and the surface markers PTPRS and GPR56.

scHOIS: Determining Cell Heterogeneity Through Hierarchical Clustering Based on Optimal Imputation Strategy

Advances in single-cell RNA sequencing (scRNA-seq) technology provide an unbiased and high-throughput analysis of each cell at single-cell resolution, and further facilitate the development of cellular heterogeneity analysis. Despite the promise of scRNA-seq, the data generated by this method are sparse and noisy because of the presence of dropout events, which can greatly impact downstream analyses such as differential gene expression, cell type annotation, and linage trajectory reconstruction. The development of effective and robust computational methods to address both dropout and clustering are thus urgently needed. In this study, we propose a flexible, accurate two-stage algorithm for single cell heterogeneity analysis via hierarchical clustering based on an optimal imputation strategy, called scHOIS. At the first stage, masked non-negative matrix factorization is applied to approximate the original observed scRNA-seq data, with optimal rank determined by variance analysis. At the second stage, hierarchical clustering is applied to group the imputed cells using Pearson correlation to measure similarity, with the optimal number of clusters determined by integrating three classical indexes. We performed extensive experiments on real-world datasets, which showed that scHOIS effectively and robustly distinguished cellular differences and that the clustering performance of this algorithm was superior to that of other state-of-the-art methods.

The genetic overlap between Alzheimer’s disease, amyotrophic lateral sclerosis, Lewy body dementia, and Parkinson’s disease

Neurodegenerative diseases are a group of disorders characterized by neuronal cell death causing a variety of physical and mental problems. While these disorders can be characterized by their phenotypic presentation within the nervous system, their aetiologies differ to varying degrees. The majority of previous genetic evidence for overlap between neurodegenerative diseases has been pairwise. In this study, we aimed to identify overlap between the 4 investigated neurodegenerative disorders (Alzheimer's disease, amyotrophic lateral sclerosis, Lewy body dementia, and Parkinson's disease) at the variant, gene, genomic locus, gene-set, cell, or tissue level, with specific interest in overlap between 3 or more diseases. Using local genetic correlation, we found 2 loci (TMEM175 and HLA) that were shared across 3 disorders. We also highlighted genes, genomic loci, gene sets, cell types, and tissue types which may be important to 2 or more disorders by analyzing the association of variants with a common factor estimated from the 4 disorders. Our study successfully highlighted genetic loci and tissues associated with 2 or more neurodegenerative diseases.

scMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studies

Single-Cell RNA sequencing (scRNA-seq) has provided unprecedented opportunities for exploring gene expression and thus uncovering regulatory relationships between genes at the single-cell level. However, scRNA-seq relies on isolating cells from tissues. Therefore, the spatial context of the regulatory processes is lost. A recent technological innovation, spatial transcriptomics, allows for the measurement of gene expression while preserving spatial information. An initial step in the spatial transcriptomic analysis is to identify the cell type, which requires a careful selection of cell-specific marker genes. For this purpose, currently, scRNA-seq data is used to select a limited number of marker genes from among all genes that distinguish cell types from each other. This study proposes scMAGS (single-cell MArker Gene Selection), a novel method for marker gene selection from scRNA-seq data for spatial transcriptomics studies. scMAGS uses a filtering step in which the candidate genes are identified before the marker gene selection step. For the selection of marker genes, cluster validity indices, the Silhouette index, or the Calinski-Harabasz index (for large datasets) are utilized. Experimental results showed that, in comparison to the existing methods, scMAGS is scalable, fast, and accurate. Even for large datasets with millions of cells, scMAGS could find the required number of marker genes in a reasonable amount of time with fewer memory requirements. scMAGS is made freely available at https://github.com/doganlab/scmags and can be downloaded from the Python Package Directory (PyPI) software repository with the command pip install scmags.

Fundamental Neurochemistry Review: Incorporating a greater diversity of cell types, including microglia, in brain organoid cultures improves clinical translation

Brain organoids have the potential to improve clinical translation, with the added benefit of reducing any extraneous use of experimental animals. As brain organoids are three-dimensional in vitro constructs that emulate the human brain, they bridge in vitro and in vivo studies more appropriately than monocultures. Although many factors contribute to the failure of extrapolating monoculture-based information to animal-based experiments and clinical trials, for the purpose of this review, we will focus on glia (non-neuronal brain cells), whose functions and transcriptome are particularly abnormal in monocultures. As discussed herein, glia require signals from-and contact with-other cell types to exist in their homeostatic state, which likely contributes to some of the differences between data derived from monocultures and data derived from brain organoids and even two-dimensional co-cultures. Furthermore, we highlight transcriptomic differences between humans and mice in regard to aging and Alzheimer's disease, emphasizing need for a model using the human genome-again, a benefit of brain organoids-to complement data derived from animals. We also identify an urgency for guidelines to improve the reporting and transparency of research using organoids. The lack of reporting standards creates challenges for the comparison and discussion of data from different articles. Importantly, brain organoids mark the first human model enabling the study of brain cytoarchitecture and development.

Genome-wide association and multi-omics studies identify MGMT as a novel risk gene for Alzheimer's disease among women

INTRODUCTION

Variants in the tau gene (MAPT) region are associated with breast cancer in women and Alzheimer's disease (AD) among persons lacking apolipoprotein E ε4 (ε4-).

METHODS

To identify novel genes associated with tau-related pathology, we conducted two genome-wide association studies (GWAS) for AD, one among 10,340 ε4- women in the Alzheimer's Disease Genetics Consortium (ADGC) and another in 31 members (22 women) of a consanguineous Hutterite kindred.

RESULTS

We identified novel associations of AD with MGMT variants in the ADGC (rs12775171, odds ratio [OR] = 1.4, P = 4.9 × 10-8) and Hutterite (rs12256016 and rs2803456, OR = 2.0, P = 1.9 × 10-14) datasets. Multi-omics analyses showed that the most significant and largest number of associations among the single nucleotide polymorphisms (SNPs), DNA-methylated CpGs, MGMT expression, and AD-related neuropathological traits were observed among women. Furthermore, promoter capture Hi-C analyses revealed long-range interactions of the MGMT promoter with MGMT SNPs and CpG sites.

DISCUSSION

These findings suggest that epigenetically regulated MGMT expression is involved in AD pathogenesis, especially in women.

Revealing the neurobiology underlying interpersonal neural synchronization with multimodal data fusion

Humans synchronize with one another to foster successful interactions. Here, we use a multimodal data fusion approach with the aim of elucidating the neurobiological mechanisms by which interpersonal neural synchronization (INS) occurs. Our meta-analysis of 22 functional magnetic resonance imaging and 69 near-infrared spectroscopy hyperscanning experiments (740 and 3721 subjects) revealed robust brain regional correlates of INS in the right temporoparietal junction and left ventral prefrontal cortex. Integrating this meta-analytic information with public databases, biobehavioral and brain-functional association analyses suggested that INS involves sensory-integrative hubs with functional connections to mentalizing and attention networks. On the molecular and genetic levels, we found INS to be associated with GABAergic neurotransmission and layer IV/V neuronal circuits, protracted developmental gene expression patterns, and disorders of neurodevelopment. Although limited by the indirect nature of phenotypic-molecular association analyses, our findings generate new testable hypotheses on the neurobiological basis of INS.

A genome and single-nucleus cerebral cortex transcriptome atlas of the short-finned pilot whale Globicephala macrorhynchus

Cetaceans (dolphins, whales, and porpoises) have large and anatomically sophisticated brains. To expand our understanding of the cellular makeup of cetacean brains and the similarities and divergence between the brains of cetaceans and terrestrial mammals, we report a short-finned pilot whale (Globicephala macrorhynchus) single-nucleus transcriptome atlas. To achieve this goal, we assembled a chromosome-scale reference genome spanning 2.25 Gb on 22 chromosomes and profiled the gene expression of five major anatomical cortical regions of the short-finned pilot whale by single-nucleus RNA-sequencing (snRNA-seq). We identified six major cell lineages in the cerebral cortex (excitatory neurons, inhibitory neurons, oligodendrocytes, oligodendrocyte precursor cells, astrocytes, and endothelial cells), eight molecularly distinct subclusters of excitatory neurons, and four subclusters of inhibitory neurons. Finally, a comparison of snRNA-seq data from the short-finned pilot whale, human, and rhesus macaque revealed a broadly conserved cellular makeup of brain cell types. Our study provides genomic resources and molecular insights into cetacean brain evolution.

An intersectional genetic approach for simultaneous cell type-specific labelling and gene knockout in the mouse

Precise genome manipulation in specific cell types and subtypes in vivo is crucial for neurobiological research because of the cellular heterogeneity of the brain. Site-specific recombinase systems in the mouse, such as Cre-loxP, improve cell type-specific genome manipulation; however, undesirable expression of cell type-specific Cre can occur. This could be due to transient expression during early development, natural expression in more than one cell type, kinetics of recombinases, sensitivity of the Cre reporter, and disruption in cis-regulatory elements by transgene insertion. Moreover, cell subtypes cannot be distinguished in cell type-specific Cre mice. To address these issues, we applied an intersectional genetic approach in mouse using triple recombination systems (Cre-loxP, Flp-FRT and Dre-rox). As a proof of principle, we labelled heterogeneous cell subtypes and deleted target genes within given cell subtypes by labelling neuropeptide Y (NPY)-, calretinin (calbindin 2) (CR)- and cholecystokinin (CCK)-expressing GABAergic neurons in the brain followed by deletion of RNA-binding Fox-1 homolog 3 (Rbfox3) in our engineered mice. Together, our study applies an intersectional genetic approach in vivo to generate engineered mice serving dual purposes of simultaneous cell subtype-specific labelling and gene knockout.

An ependymal cell census identifies heterogeneous and ongoing cell maturation in the adult mouse spinal cord that changes dynamically on injury

The adult spinal cord stem cell potential resides within the ependymal cell population and declines with age. Ependymal cells are, however, heterogeneous, and the biological diversity this represents and how it changes with age remain unknown. Here, we present a single-cell transcriptomic census of spinal cord ependymal cells from adult and aged mice, identifying not only all known ependymal cell subtypes but also immature as well as mature cell states. By comparing transcriptomes of spinal cord and brain ependymal cells, which lack stem cell abilities, we identify immature cells as potential spinal cord stem cells. Following spinal cord injury, these cells re-enter the cell cycle, which is accompanied by a short-lived reversal of ependymal cell maturation. We further analyze ependymal cells in the human spinal cord and identify widespread cell maturation and altered cell identities. This in-depth characterization of spinal cord ependymal cells provides insight into their biology and informs strategies for spinal cord repair.

Astrocytic Yin Yang 1 is critical for murine brain development and protection against apoptosis, oxidative stress, and inflammation

The transcription factor Yin Yang 1 (YY1) is ubiquitously expressed in mammalian cells, regulating the expression of a variety of genes involved in proliferation, differentiation, and apoptosis in a context-dependent manner. While it is well-established that global YY1 knockout (KO) leads to embryonic death in mice and that YY1 deletion in neurons or oligodendrocytes induces impaired brain function, the role of astrocytic YY1 in the brain remains unknown. We investigated the role of astrocytic YY1 in the brain using a glial fibrillary acidic protein (GFAP)-specific YY1 conditional KO (YY1 cKO) mouse model to delete astrocytic YY1. Astrocytic YY1 cKO mice were tested for behavioral phenotypes, such as locomotor activity, coordination, and cognition, followed by an assessment of relevant biological pathways using RNA-sequencing analysis, immunoblotting, and immunohistochemistry in the cortex, midbrain, and cerebellum. YY1 cKO mice showed abnormal phenotypes, movement deficits, and cognitive dysfunction. At the molecular level, astrocytic YY1 deletion altered the expression of genes associated with proliferation and differentiation, p53/caspase apoptotic pathways, oxidative stress response, and inflammatory signaling including NF-κB, STAT, and IRF in all regions. Astrocytic YY1 deletion significantly increased the expression of GFAP as astrocytic activation and Iba1 as microglial activation, indicating astrocytic YY1 deletion activated microglia as well. Accordingly, multiple inflammatory cytokines and chemokines including TNF-α and CXCL10 were elevated. Combined, these novel findings suggest that astrocytic YY1 is a critical transcription factor for normal brain development and locomotor activity, motor coordination, and cognition. Astrocytic YY1 is also essential in preventing pathological oxidative stress, apoptosis, and inflammation.

Defining the pig microglial transcriptome reveals its core signature, regional heterogeneity, and similarity with human and rodent microglia

Microglia play key roles in brain homeostasis as well as responses to neurodegeneration and neuroinflammatory processes caused by physical disease and psychosocial stress. The pig is a physiologically relevant model species for studying human neurological disorders, many of which are associated with microglial dysfunction. Furthermore, pigs are an important agricultural species, and there is a need to understand how microglial function affects their welfare. As a basis for improved understanding to enhance biomedical and agricultural research, we sought to characterize pig microglial identity at genome-wide scale and conduct inter-species comparisons. We isolated pig hippocampal tissue and microglia from frontal cortex, hippocampus, and cerebellum, as well as alveolar macrophages from the lungs and conducted RNA-sequencing (RNAseq). By comparing the transcriptomic profiles between microglia, macrophages, and hippocampal tissue, we derived a set of 239 highly enriched genes defining the porcine core microglial signature. We found brain regional heterogeneity based on 150 genes showing significant (adjusted p < 0.01) regional variations and that cerebellar microglia were most distinct. We compared normalized gene expression for microglia from human, mice and pigs using microglia signature gene lists derived from each species and demonstrated that a core microglial marker gene signature is conserved across species, but that species-specific expression subsets also exist. Our data provide a valuable resource defining the pig microglial transcriptome signature that validates and highlights pigs as a useful large animal species bridging between rodents and humans in which to study the role of microglia during homeostasis and disease.

Neurophysiological signatures of cortical micro-architecture

Systematic spatial variation in micro-architecture is observed across the cortex. These micro-architectural gradients are reflected in neural activity, which can be captured by neurophysiological time-series. How spontaneous neurophysiological dynamics are organized across the cortex and how they arise from heterogeneous cortical micro-architecture remains unknown. Here we extensively profile regional neurophysiological dynamics across the human brain by estimating over 6 800 timeseries features from the resting state magnetoencephalography (MEG) signal. We then map regional time-series profiles to a comprehensive multi-modal, multi-scale atlas of cortical micro-architecture, including microstructure, metabolism, neurotransmitter receptors, cell types and laminar differentiation. We find that the dominant axis of neurophysiological dynamics reflects characteristics of power spectrum density and linear correlation structure of the signal, emphasizing the importance of conventional features of electromagnetic dynamics while identifying additional informative features that have traditionally received less attention. Moreover, spatial variation in neurophysiological dynamics is colocalized with multiple micro-architectural features, including genomic gradients, intracortical myelin, neurotransmitter receptors and transporters, and oxygen and glucose metabolism. Collectively, this work opens new avenues for studying the anatomical basis of neural activity.

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.

Molecular Logic of Synaptic Diversity Between Drosophila Tonic and Phasic Motoneurons

Although neuronal subtypes display unique synaptic organization and function, the underlying transcriptional differences that establish these features is poorly understood. To identify molecular pathways that contribute to synaptic diversity, single neuron PatchSeq RNA profiling was performed on Drosophila tonic and phasic glutamatergic motoneurons. Tonic motoneurons form weaker facilitating synapses onto single muscles, while phasic motoneurons form stronger depressing synapses onto multiple muscles. Super-resolution microscopy and in vivo imaging demonstrated synaptic active zones in phasic motoneurons are more compact and display enhanced Ca 2+ influx compared to their tonic counterparts. Genetic analysis identified unique synaptic properties that mapped onto gene expression differences for several cellular pathways, including distinct signaling ligands, post-translational modifications and intracellular Ca 2+ buffers. These findings provide insights into how unique transcriptomes drive functional and morphological differences between neuronal subtypes.

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.

SimCH: simulation of single-cell RNA sequencing data by modeling cellular heterogeneity at gene expression level

Single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) has been a powerful technology for transcriptome analysis. However, the systematic validation of diverse computational tools used in scRNA-seq analysis remains challenging. Here, we propose a novel simulation tool, termed as Simulation of Cellular Heterogeneity (SimCH), for the flexible and comprehensive assessment of scRNA-seq computational methods. The Gaussian Copula framework is recruited to retain gene coexpression of experimental data shown to be associated with cellular heterogeneity. The synthetic count matrices generated by suitable SimCH modes closely match experimental data originating from either homogeneous or heterogeneous cell populations and either unique molecular identifier (UMI)-based or non-UMI-based techniques. We demonstrate how SimCH can benchmark several types of computational methods, including cell clustering, discovery of differentially expressed genes, trajectory inference, batch correction and imputation. Moreover, we show how SimCH can be used to conduct power evaluation of cell clustering methods. Given these merits, we believe that SimCH can accelerate single-cell research.

HMGCS2-Induced Autophagic Degradation of Tau Involves Ketone Body and ANKRD24

BACKGROUND

Accumulation of hyperphosphorylated Tau (pTau) contributes to the formation of neurofibrillary tangles in Alzheimer's disease (AD), and targeting Tau/pTau metabolism has emerged as a therapeutic approach. We have previously reported that mitochondrial 3-hydroxy-3-methylglutaryl-COA synthase 2 (HMGCS2) is involved in AD by promoting autophagic clearance of amyloid-β protein precursor via ketone body-associated mechanism, whether HMGCS2 may also regulate Tau metabolism remains elusive.

OBJECTIVE

The present study was to investigate the role of HMGCS2 in Tau/p degradation.

METHODS

The protein levels of Tau and pTau including pT217 and pT181, as well as autophagic markers LAMP1 and LC3-II were assessed by western blotting. The differentially regulated genes by HMGCS2 were analyzed by RNA sequencing. Autophagosomes were assessed by transmission electron microscopy.

RESULTS

HMGCS2 significantly decreased Tau/pTau levels, which was paralleled by enhanced formation of autophagic vacuoles and prevented by autophagic regulators chloroquine, bafilomycin A1, 3-methyladenine, and rapamycin. Moreover, HMGCS2-induced alterations of LAMP1/LC3-II and Tau/pTau levels were mimicked by ketone body acetoacetate or β-hydroxybutyrate. Further RNA-sequencing identified ankyrin repeat domain 24 (ANKRD24) as a target gene of HMGCS2, and silencing of ANKRD24 reduced LAMP1/LC3-II levels, which was accompanied by the altered formation of autophagic vacuoles, and diminished the effect of HMGCS2 on Tau/pTau.

CONCLUSION

HMGCS2 promoted autophagic clearance of Tau/pTau, in which ketone body and ANKRD24 played an important role.

Integrative transcriptomic analysis of the amyotrophic lateral sclerosis spinal cord implicates glial activation and suggests new risk genes

Amyotrophic lateral sclerosis (ALS) is a progressively fatal neurodegenerative disease affecting motor neurons in the brain and spinal cord. In this study, we investigated gene expression changes in ALS via RNA sequencing in 380 postmortem samples from cervical, thoracic and lumbar spinal cord segments from 154 individuals with ALS and 49 control individuals. We observed an increase in microglia and astrocyte gene expression, accompanied by a decrease in oligodendrocyte gene expression. By creating a gene co-expression network in the ALS samples, we identified several activated microglia modules that negatively correlate with retrospective disease duration. We mapped molecular quantitative trait loci and found several potential ALS risk loci that may act through gene expression or splicing in the spinal cord and assign putative cell types for FNBP1, ACSL5, SH3RF1 and NFASC. Finally, we outline how common genetic variants associated with splicing of C9orf72 act as proxies for the well-known repeat expansion, and we use the same mechanism to suggest ATXN3 as a putative risk gene.

Single-cell RNA sequencing in the context of neuropathic pain: progress, challenges, and prospects

Neuropathic pain, characterized by persistent or intermittent spontaneous pain as well as some unpleasant abnormal sensations, is one of the most prevalent health problems in the world. Ectopic nerve activity, central and peripheral nociceptive sensitization and many other potential mechanisms may participate in neuropathic pain. The complexity and ambiguity of neuropathic pain mechanisms result in difficulties in pain management, and existing treatment plans provide less-than-satisfactory relief. In recent years, single-cell RNA sequencing (scRNA-seq) has been increasingly applied and has become a powerful means for biological researchers to explore the complexity of neurobiology. This technique can be used to perform unbiased, high-throughput and high-resolution transcriptional analyses of neuropathic pain-associated cells, improving the understanding of neuropathic pain mechanisms and enabling individualized pain management. To date, scRNA-seq has been preliminarily used in neuropathic pain research for applications such as compiling a dorsal root ganglion atlas, identifying new cell types and discovering gene regulatory networks associated with neuropathic pain. Although scRNA-seq is a relatively new technique in the neuropathic pain field, there have been several studies based on animal models. However, because of the various differences between animals and humans, more attention should be given to translational medicine research. With the aid of scRNA-seq, researchers can further explore the mechanism of neuropathic pain to improve the clinical understanding of the diagnosis, treatment and management of neuropathic pain.

Cell type characterization of spatiotemporal gene co-expression modules in Down syndrome brain

Down syndrome (DS) is the most common genetic cause of intellectual disability and increases the risk of other brain-related dysfunctions, like seizures, early-onset Alzheimer's disease, and autism. To reveal the molecular profiles of DS-associated brain phenotypes, we performed a meta-data analysis of the developmental DS brain transcriptome at cell type and co-expression module levels. In the DS brain, astrocyte-, microglia-, and endothelial cell-associated genes show upregulated patterns, whereas neuron- and oligodendrocyte-associated genes show downregulated patterns. Weighted gene co-expression network analysis identified cell type-enriched co-expressed gene modules. We present eight representative cell-type modules for neurons, astrocytes, oligodendrocytes, and microglia. We classified the neuron modules into glutamatergic and GABAergic neurons and associated them with detailed subtypes. Cell type modules were interpreted by analyzing spatiotemporal expression patterns, functional annotations, and co-expression networks of the modules. This study provides insight into the mechanisms underlying brain abnormalities in DS and related disorders.

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.

Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysis

In the analysis of single-cell RNA-sequencing (scRNA-seq) data, how to effectively and accurately identify cell clusters from a large number of cell mixtures is still a challenge. Low-rank representation (LRR) method has achieved excellent results in subspace clustering. But in previous studies, most LRR-based methods usually choose the original data matrix as the dictionary. In addition, the methods based on LRR usually use spectral clustering algorithm to complete cell clustering. Therefore, there is a matching problem between the spectral clustering method and the affinity matrix, which is difficult to ensure the optimal effect of clustering. Considering the above two points, we propose the DLNLRR method to better identify the cell type. First, DLNLRR can update the dictionary during the optimization process instead of using the predefined fixed dictionary, so it can realize dictionary learning and LRR learning at the same time. Second, DLNLRR can realize subspace clustering without relying on spectral clustering algorithm, that is, we can perform clustering directly based on the low-rank matrix. Finally, we carry out a large number of experiments on real single-cell datasets and experimental results show that DLNLRR is superior to other scRNA-seq data analysis algorithms in cell type identification.

Improving the performance of single-cell RNA-seq data mining based on relative expression orderings

The advent of single-cell RNA-sequencing (scRNA-seq) provides an unprecedented opportunity to explore gene expression profiles at the single-cell level. However, gene expression values vary over time and under different conditions even within the same cell. There is an urgent need for more stable and reliable feature variables at the single-cell level to depict cell heterogeneity. Thus, we construct a new feature matrix called the delta rank matrix (DRM) from scRNA-seq data by integrating an a priori gene interaction network, which transforms the unreliable gene expression value into a stable gene interaction/edge value on a single-cell basis. This is the first time that a gene-level feature has been transformed into an interaction/edge-level for scRNA-seq data analysis based on relative expression orderings. Experiments on various scRNA-seq datasets have demonstrated that DRM performs better than the original gene expression matrix in cell clustering, cell identification and pseudo-trajectory reconstruction. More importantly, the DRM really achieves the fusion of gene expressions and gene interactions and provides a method of measuring gene interactions at the single-cell level. Thus, the DRM can be used to find changes in gene interactions among different cell types, which may open up a new way to analyze scRNA-seq data from an interaction perspective. In addition, DRM provides a new method to construct a cell-specific network for each single cell instead of a group of cells as in traditional network construction methods. DRM's exceptional performance is due to its extraction of rich gene-association information on biological systems and stable characterization of cells.

Changes in aggression and locomotor behaviors in response to zinc is accompanied by brain cell heterogeneity and metabolic and circadian dysregulation of the brain-liver axis

Zinc is an essential nutrient for life, but over-accumulation can result in toxicity. Anthropogenic activities can increase zinc concentrations in aquatic environments (e.g., to ∼0.46-1.00 mg/L), which are above the safe level of 0.1 mg/L. We investigated the behavior and physiology of zebrafish (Danio rerio) in response to environment-related exposure to zinc chloride at 0.0 (Ctrl), 1.0 (ZnCl₂-low) and 1.5 (ZnCl₂-high) mg/L for 6 weeks (the zinc conversion ratio of zinc chloride is ∼0.48 and the nominal (measured) values were: Ctrl, 0 (∼0.01); ZnCl₂-low, 0.48 (∼0.51); ZnCl₂-high, 0.72 (∼0.69) mg/L). Low-zinc exposure resulted in significantly increased locomotion and fast moving behaviors, while high-zinc exposure resulted in significantly increased aggression and freezing frequency. Single cell RNA-seq of neurons, astrocytes, and oligodendrocytes of the brain revealed expression of genes related to ion transport, neuron generation, and immunomodulation that were heterogeneously regulated by zinc exposure. Astrocyte-induced central nervous system inflammation potentially integrated neurotoxicity and behavior. Integrated analyses of brain and hepatic transcriptional signatures showed that genes (and pathways) dysregulated by zinc were associated with sensory functions, circadian rhythm, glucose and lipid metabolism, and amyloid β-protein clearance. Our results showed that environment-related zinc contamination can be heterogeneously toxic to brain cells and can disturb coordination of brain-liver physiology. This may disrupt neurobehavior and cause a neurodegeneration-like syndrome in adult zebrafish.

Neuroendocrinology of the lung revealed by single-cell RNA sequencing

Pulmonary neuroendocrine cells (PNECs) are sensory epithelial cells that transmit airway status to the brain via sensory neurons and locally via calcitonin gene-related peptide (CGRP) and γ- aminobutyric acid (GABA). Several other neuropeptides and neurotransmitters have been detected in various species, but the number, targets, functions, and conservation of PNEC signals are largely unknown. We used scRNAseq to profile hundreds of the rare mouse and human PNECs. This revealed over 40 PNEC neuropeptide and peptide hormone genes, most cells expressing unique combinations of 5-18 genes. Peptides are packaged in separate vesicles, their release presumably regulated by the distinct, multimodal combinations of sensors we show are expressed by each PNEC. Expression of the peptide receptors predicts an array of local cell targets, and we show the new PNEC signal angiotensin directly activates one subtype of innervating sensory neuron. Many signals lack lung targets so may have endocrine activity like those of PNEC-derived carcinoid tumors. PNECs are an extraordinarily rich and diverse signaling hub rivaling the enteroendocrine system.

The long noncoding RNA FEDORA is a cell type- and sex-specific regulator of depression

Women suffer from depression at twice the rate of men, but the underlying molecular mechanisms are poorly understood. Here, we identify marked baseline sex differences in the expression of long noncoding RNAs (lncRNAs), a class of regulatory transcripts, in human postmortem brain tissue that are profoundly lost in depression. One such human lncRNA, RP11-298D21.1 (which we termed FEDORA), is enriched in oligodendrocytes and neurons and up-regulated in the prefrontal cortex (PFC) of depressed females only. We found that virally expressing FEDORA selectively either in neurons or in oligodendrocytes of PFC promoted depression-like behavioral abnormalities in female mice only, changes associated with cell type-specific regulation of synaptic properties, myelin thickness, and gene expression. We also found that blood FEDORA levels have diagnostic implications for depressed women and are associated with clinical response to ketamine. These findings demonstrate the important role played by lncRNAs, and FEDORA in particular, in shaping the sex-specific landscape of the brain and contributing to sex differences in depression.

Multiregion transcriptomic profiling of the primate brain reveals signatures of aging and the social environment

Aging is accompanied by a host of social and biological changes that correlate with behavior, cognitive health and susceptibility to neurodegenerative disease. To understand trajectories of brain aging in a primate, we generated a multiregion bulk (N = 527 samples) and single-nucleus (N = 24 samples) brain transcriptional dataset encompassing 15 brain regions and both sexes in a unique population of free-ranging, behaviorally phenotyped rhesus macaques. We demonstrate that age-related changes in the level and variance of gene expression occur in genes associated with neural functions and neurological diseases, including Alzheimer's disease. Further, we show that higher social status in females is associated with younger relative transcriptional ages, providing a link between the social environment and aging in the brain. Our findings lend insight into biological mechanisms underlying brain aging in a nonhuman primate model of human behavior, cognition and health.

An improved Fuzzy based GWO algorithm for predicting the potential host receptor of COVID-19 infection

Coronavirus disease (COVID-19) is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and has infected millions worldwide. SARS-CoV-2 spike protein uses Angiotensin-converting enzyme 2 (ACE2) and Transmembrane serine protease 2 (TMPRSS2) for entering and fusing the host cell membrane. However, interaction with spike protein receptors and protease processing are not the only factors determining coronaviruses' entry. Several proteases mediate the entry of SARS-CoV-2 virus into the host cell. Identifying receptor factors helps understand tropism, transmission, and pathogenesis of COVID-19 infection in humans. The paper aims to identify novel viral receptor or membrane proteins that are transcriptionally and biologically similar to ACE2 and TMPRSS2 through a fuzzy clustering technique that employs the Grey wolf optimizer (GWO) algorithm for finding the optimal cluster center. The exploratory and exploitation capability of GWO algorithm is improved by hybridizing mutation and crossover operators of the evolutionary algorithm. Also, the genetic diversity of the grey wolf population is enhanced by eliminating weak individuals from the population. The proposed clustering algorithm's effectiveness is shown by detecting novel viral receptors and membrane proteins associated with the pathogenesis of SARS-CoV-2 infection. The expression profiles of ACE2 protein and its co-receptor factor are analyzed and compared with single-cell transcriptomics profiling using the Seurat R toolkit, mass spectrometry (MS), and immunohistochemistry (IHC). Our advanced clustering method infers that cell that expresses high ACE2 level are more affected by SARS-CoV-infection. So, SARS-CoV-2 virus affects lung, intestine, testis, heart, kidney, and liver more severely than brain, bone marrow, skin, spleen, etc. We have identified 58 novel viral receptors and 816 membrane proteins, and their role in the pathogenicity mechanism of SARS-CoV-2 infection has been studied. Besides, our study confirmed that Neuropilins (NRP1), G protein-coupled receptor 78 (GPR78), C-type lectin domain family 4 member M (CLEC4M), Kringle containing transmembrane protein 1 (KREMEN1), Asialoglycoprotein receptor 1 (ASGR1), A Disintegrin and metalloprotease 17 (ADAM17), Furin, Neuregulin-1,(NRG1), Basigin or CD147 and Poliovirus receptor (PVR) are the potential co-receptors of SARS-CoV-2 virus. A significant finding is that heparin derivative glycosaminoglycans could block the replication of SARS-CoV-2 virus inside the host cytoplasm. The membrane protein N-Deacetylase/N-Sulfotransferase-2 (NDST2), Extostosin protein (EXT1, EXT2, and EXT3), Glucuronic acid epimerase (GLCE), and Xylosyltransferase I, II (XYLT1, XYLT2) could act as the therapeutic target for inhibiting the spread of SARS-CoV-2 infection. Drugs such as carboplatin and gemcitabine are effective in such situations.

Sterol dysregulation in Smith-Lemli-Opitz syndrome causes astrocyte immune reactivity through microglia crosstalk

Owing to the need for de novo cholesterol synthesis and cholesterol-enriched structures within the nervous system, cholesterol homeostasis is critical to neurodevelopment. Diseases caused by genetic disruption of cholesterol biosynthesis, such as Smith-Lemli-Opitz syndrome, which is caused by mutations in 7-dehydrocholesterol reductase (DHCR7), frequently result in broad neurological deficits. Although astrocytes regulate multiple neural processes ranging from cell migration to network-level communication, immunological activation of astrocytes is a hallmark pathology in many diseases. However, the impact of DHCR7 on astrocyte function and immune activation remains unknown. We demonstrate that astrocytes from Dhcr7 mutant mice display hallmark signs of reactivity, including increased expression of glial fibrillary acidic protein (GFAP) and cellular hypertrophy. Transcript analyses demonstrate extensive Dhcr7 astrocyte immune activation, hyper-responsiveness to glutamate stimulation and altered calcium flux. We further determine that the impacts of Dhcr7 are not astrocyte intrinsic but result from non-cell-autonomous effects of microglia. Our data suggest that astrocyte-microglia crosstalk likely contributes to the neurological phenotypes observed in disorders of cholesterol biosynthesis. Additionally, these data further elucidate a role for cholesterol metabolism within the astrocyte-microglia immune axis, with possible implications in other neurological diseases.

A high-efficiency differential expression method for cancer heterogeneity using large-scale single-cell RNA-sequencing data

Colorectal cancer is a highly heterogeneous disease. Tumor heterogeneity limits the efficacy of cancer treatment. Single-cell RNA-sequencing technology (scRNA-seq) is a powerful tool for studying cancer heterogeneity at cellular resolution. The sparsity, heterogeneous diversity, and fast-growing scale of scRNA-seq data pose challenges to the flexibility, accuracy, and computing efficiency of the differential expression (DE) methods. We proposed HEART (high-efficiency and robust test), a statistical combination test that can detect DE genes with various sources of differences beyond mean expression changes. To validate the performance of HEART, we compared HEART and the other six popular DE methods on various simulation datasets with different settings by two simulation data generation mechanisms. HEART had high accuracy (F1 score &gt;0.75) and brilliant computational efficiency (less than 2 min) on multiple simulation datasets in various experimental settings. HEART performed well on DE genes detection for the PBMC68K dataset quantified by UMI counts and the human brain single-cell dataset quantified by read counts (F1 score = 0.79, 0.65). By applying HEART to the single-cell dataset of a colorectal cancer patient, we found several potential blood-based biomarkers (CTTN, S100A4, S100A6, UBA52, FAU, and VIM) associated with colorectal cancer metastasis and validated them on additional spatial transcriptomic data of other colorectal cancer patients.

Identification of glioblastoma-specific antigens expressed in patient-derived tumor cells as candidate targets for chimeric antigen receptor T cell therapy

Background

New therapies for glioblastoma (GBM) are urgently needed because the disease prognosis is poor. Chimeric antigen receptor (CAR)-T cell therapy that targets GBM-specific cell surface antigens is a promising therapeutic strategy. However, extensive transcriptome analyses have uncovered few GBM-specific target antigens.

Methods

We established a library of monoclonal antibodies (mAbs) against a tumor cell line derived from a patient with GBM. We identified mAbs that reacted with tumor cell lines from patients with GBM but not with nonmalignant human brain cells. We then detected the antigens they recognized using expression cloning. CAR-T cells derived from a candidate mAb were generated and tested in vitro and in vivo.

Results

We detected 507 mAbs that bound to tumor cell lines from patients with GBM. Among them, E61 and A13 reacted with tumor cell lines from most patients with GBM, but not with nonmalignant human brain cells. We found that B7-H3 was the antigen recognized but E61. CAR-T cells were established using the antigen-recognition domain of E61-secreted cytokines and exerted cytotoxicity in co-culture with tumor cells from patients with GBM.

Conclusions

Cancer-specific targets for CAR-T cells were identified using a mAb library raised against primary GBM tumor cells from a patient. We identified a GBM-specific mAb and its antigen. More mAbs against various GBM samples and novel target antigens are expected to be identified using this strategy.

Tissue dissociation for single-cell and single-nuclei RNA sequencing for low amounts of input material

Background

Recent technological advances opened the opportunity to simultaneously study gene expression for thousands of individual cells on a genome-wide scale. The experimental accessibility of such single-cell RNA sequencing (scRNAseq) approaches allowed gaining insights into the cell type composition of heterogeneous tissue samples of animal model systems and emerging models alike. A major prerequisite for a successful application of the method is the dissociation of complex tissues into individual cells, which often requires large amounts of input material and harsh mechanical, chemical and temperature conditions. However, the availability of tissue material may be limited for small animals, specific organs, certain developmental stages or if samples need to be acquired from collected specimens. Therefore, we evaluated different dissociation protocols to obtain single cells from small tissue samples of Drosophila melanogaster eye-antennal imaginal discs.

Results

We show that a combination of mechanical and chemical dissociation resulted in sufficient high-quality cells. As an alternative, we tested protocols for the isolation of single nuclei, which turned out to be highly efficient for fresh and frozen tissue samples. Eventually, we performed scRNAseq and single-nuclei RNA sequencing (snRNAseq) to show that the best protocols for both methods successfully identified relevant cell types. At the same time, snRNAseq resulted in less artificial gene expression that is caused by rather harsh dissociation conditions needed to obtain single cells for scRNAseq. A direct comparison of scRNAseq and snRNAseq data revealed that both datasets share biologically relevant genes among the most variable genes, and we showed differences in the relative contribution of the two approaches to identified cell types.

Conclusion

We present two dissociation protocols that allow isolating single cells and single nuclei, respectively, from low input material. Both protocols resulted in extraction of high-quality RNA for subsequent scRNAseq or snRNAseq applications. If tissue availability is limited, we recommend the snRNAseq procedure of fresh or frozen tissue samples as it is perfectly suited to obtain thorough insights into cellular diversity of complex tissue.

Human prefrontal cortex gene regulatory dynamics from gestation to adulthood at single-cell resolution

Human brain development is underpinned by cellular and molecular reconfigurations continuing into the third decade of life. To reveal cell dynamics orchestrating neural maturation, we profiled human prefrontal cortex gene expression and chromatin accessibility at single-cell resolution from gestation to adulthood. Integrative analyses define the dynamic trajectories of each cell type, revealing major gene expression reconfiguration at the prenatal-to-postnatal transition in all cell types followed by continuous reconfiguration into adulthood and identifying regulatory networks guiding cellular developmental programs, states, and functions. We uncover links between expression dynamics and developmental milestones, characterize the diverse timing of when cells acquire adult-like states, and identify molecular convergence from distinct developmental origins. We further reveal cellular dynamics and their regulators implicated in neurological disorders. Finally, using this reference, we benchmark cell identities and maturation states in organoid models. Together, this captures the dynamic regulatory landscape of human cortical development.

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.