Single cell transcriptomes and multiscale networks from persons with and without Alzheimer's disease.

The emergence of single nucleus RNA sequencing (snRNA-seq) offers to revolutionize the study of Alzheimer's disease (AD). Integration with complementary multiomics data such as genetics, proteomics and clinical data provides powerful opportunities to link cell subpopulations and molecular networks with a broader disease-relevant context. We report snRNA-seq profiles from superior frontal gyrus samples from 101 well characterized subjects from the Banner Brain and Body Donation Program in combination with whole genome sequences. We report findings that link common AD risk variants with CR1 expression in oligodendrocytes as well as alterations in hematological parameters. We observed an AD-associated CD83(+) microglial subtype with unique molecular networks and which is associated with immunoglobulin IgG4 production in the transverse colon. Our major observations were replicated in two additional, independent snRNA-seq data sets. These findings illustrate the power of multi-tissue molecular profiling to contextualize snRNA-seq brain transcriptomics and reveal disease biology.

Divergent landscapes of A-to-I editing in postmortem and living human brain.

Adenosine-to-inosine (A-to-I) editing is a prevalent post-transcriptional RNA modification within the brain. Yet, most research has relied on postmortem samples, assuming it is an accurate representation of RNA biology in the living brain. We challenge this assumption by comparing A-to-I editing between postmortem and living prefrontal cortical tissues. Major differences were found, with over 70,000 A-to-I sites showing higher editing levels in postmortem tissues. Increased A-to-I editing in postmortem tissues is linked to higher ADAR and ADARB1 expression, is more pronounced in non-neuronal cells, and indicative of postmortem activation of inflammation and hypoxia. Higher A-to-I editing in living tissues marks sites that are evolutionarily preserved, synaptic, developmentally timed, and disrupted in neurological conditions. Common genetic variants were also found to differentially affect A-to-I editing levels in living versus postmortem tissues. Collectively, these discoveries offer more nuanced and accurate insights into the regulatory mechanisms of RNA editing in the human brain.

Serum cytokine and inflammatory markers in individuals with heroin use disorder: potential biomarkers for diagnosis and disease severity.

Opioid use disorders cause major morbidity and mortality, and there is a pressing need for novel mechanistic targets and biomarkers for diagnosis and prognosis. Exposure to mu-opioid receptor (MOR) agonists causes changes in cytokine and inflammatory protein networks in peripheral blood, and also in brain glia and neurons. Individuals with heroin use disorder (iHUD) show dysregulated levels of several cytokines in blood. However, there is limited data on a comprehensive panel of such markers in iHUD versus healthy controls (HC), especially as a multi-target biomarker. We used a validated proximity extension assay for relative quantification of 92 cytokines and inflammatory proteins in serum of iHUD on medication assisted therapy (MAT; n=21), versus HC (n=24). Twenty-nine targets showed significant group differences (primarily iHUD>HC), surviving multiple comparison correction (p=0.05). This included 19 members of canonical cytokine families, including specific chemokines, interleukins, growth factors, and tumor necrosis factor (TNF)-related proteins. For dimensionality reduction, data from these 19 cytokines were entered into a principal component (PC) analysis, and PC1 scores were iHUD>HC (p<0.0001). A receiver-operating characteristic (ROC) curve analysis yielded an AUROC=91.7% (p<0.0001). This PC1 score remained a positive predictor of being in the HUD group in a multivariable logistic regression, which included demographic/clinical variables. Overall, this study shows a panel of cytokines that differ significantly between iHUD and HC, and provides a multi-target "cytokine biomarker score" for potential diagnostic purposes, and examination of disease severity.

Beyond memory impairment: the complex phenotypic landscape of Alzheimer's disease.

Neuropsychiatric symptoms (NPSs) in Alzheimer's disease (AD) constitute multifaceted behavioral manifestations that reflect processes of emotional regulation, thinking, and social behavior. They are as prevalent in AD as cognitive impairment and develop independently during the progression of neurodegeneration. As studying NPSs in AD is clinically challenging, most AD research to date has focused on cognitive decline. In this opinion article we summarize emerging literature on the prevalence, time course, and the underlying genetic, molecular, and pathological mechanisms related to NPSs in AD. Overall, we propose that NPSs constitute a cluster of core symptoms in AD, and understanding their neurobiology can lead to a more holistic approach to AD research, paving the way for more accurate diagnostic tests and personalized treatments embracing the goals of precision medicine.

Genetic regulation of cell type-specific chromatin accessibility shapes brain disease etiology.

Nucleotide variants in cell type-specific gene regulatory elements in the human brain are risk factors for human disease. We measured chromatin accessibility in 1932 aliquots of sorted neurons and non-neurons from 616 human postmortem brains and identified 34,539 open chromatin regions with chromatin accessibility quantitative trait loci (caQTLs). Only 10.4% of caQTLs are shared between neurons and non-neurons, which supports cell type-specific genetic regulation of the brain regulome. Incorporating allele-specific chromatin accessibility improves statistical fine-mapping and refines molecular mechanisms that underlie disease risk. Using massively parallel reporter assays in induced excitatory neurons, we screened 19,893 brain QTLs and identified the functional impact of 476 regulatory variants. Combined, this comprehensive resource captures variation in the human brain regulome and provides insights into disease etiology.

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

Sample-wise deconvolution methods estimate cell-type proportions and gene expressions in bulk tissue samples, yet their performance and biological applications remain unexplored, particularly in human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk tissue RNA sequencing (RNA-seq), single-cell/nuclei (sc/sn) RNA-seq, and immunohistochemistry. A total of 1,130,767 nuclei per 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 expressions. 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 did alone. Differential gene expressions associated with Alzheimer's disease, schizophrenia, and brain development were also examined using the deconvoluted data. Our findings, which were replicated in bulk tissue and single-cell data, provided insights into the biological applications of deconvoluted data in multiple brain disorders.

Single-cell multi-cohort dissection of the schizophrenia transcriptome.

The complexity and heterogeneity of schizophrenia have hindered mechanistic elucidation and the development of more effective therapies. Here, we performed single-cell dissection of schizophrenia-associated transcriptomic changes in the human prefrontal cortex across 140 individuals in two independent cohorts. Excitatory neurons were the most affected cell group, with transcriptional changes converging on neurodevelopment and synapse-related molecular pathways. Transcriptional alterations included known genetic risk factors, suggesting convergence of rare and common genomic variants on neuronal population-specific alterations in schizophrenia. Based on the magnitude of schizophrenia-associated transcriptional change, we identified two populations of individuals with schizophrenia marked by expression of specific excitatory and inhibitory neuronal cell states. This single-cell atlas links transcriptomic changes to etiological genetic risk factors, contextualizing established knowledge within the human cortical cytoarchitecture and facilitating mechanistic understanding of schizophrenia pathophysiology and heterogeneity.

Genome-Wide Association Study of Treatment-Resistant Depression: Shared Biology With Metabolic Traits.

OBJECTIVE: Treatment-resistant depression (TRD) occurs in roughly one-third of all individuals with major depressive disorder (MDD). Although research has suggested a significant common variant genetic component of liability to TRD, with heritability estimated at 8% when compared with non-treatment-resistant MDD, no replicated genetic loci have been identified, and the genetic architecture of TRD remains unclear. A key barrier to this work has been the paucity of adequately powered cohorts for investigation, largely because of the challenge in prospectively investigating this phenotype. The objective of this study was to perform a well-powered genetic study of TRD. METHODS: Using receipt of electroconvulsive therapy (ECT) as a surrogate for TRD, the authors applied standard machine learning methods to electronic health record data to derive predicted probabilities of receiving ECT. These probabilities were then applied as a quantitative trait in a genome-wide association study of 154,433 genotyped patients across four large biobanks. RESULTS: Heritability estimates ranged from 2% to 4.2%, and significant genetic overlap was observed with cognition, attention deficit hyperactivity disorder, schizophrenia, alcohol and smoking traits, and body mass index. Two genome-wide significant loci were identified, both previously implicated in metabolic traits, suggesting shared biology and potential pharmacological implications. CONCLUSIONS: This work provides support for the utility of estimation of disease probability for genomic investigation and provides insights into the genetic architecture and biology of TRD.

Divergent landscapes of A-to-I editing in postmortem and living human brain.

Adenosine-to-inosine (A-to-I) editing is a prevalent post-transcriptional RNA modification within the brain. Yet, most research has relied on postmortem samples, assuming it is an accurate representation of RNA biology in the living brain. We challenge this assumption by comparing A-to-I editing between postmortem and living prefrontal cortical tissues. Major differences were found, with over 70,000 A-to-I sites showing higher editing levels in postmortem tissues. Increased A-to-I editing in postmortem tissues is linked to higher ADAR1 and ADARB1 expression, is more pronounced in non-neuronal cells, and indicative of postmortem activation of inflammation and hypoxia. Higher A-to-I editing in living tissues marks sites that are evolutionarily preserved, synaptic, developmentally timed, and disrupted in neurological conditions. Common genetic variants were also found to differentially affect A-to-I editing levels in living versus postmortem tissues. Collectively, these discoveries illuminate the nuanced functions and intricate regulatory mechanisms of RNA editing within the human brain.

Sex differences in brain cell-type specific chromatin accessibility in schizophrenia.

Our understanding of the sex-specific role of the non-coding genome in serious mental illness remains largely incomplete. To address this gap, we explored sex differences in 1,393 chromatin accessibility profiles, derived from neuronal and non-neuronal nuclei of two distinct cortical regions from 234 cases with serious mental illness and 235 controls. We identified sex-specific enhancer-promoter interactions and showed that they regulate genes involved in X-chromosome inactivation (XCI). Examining chromosomal conformation allowed us to identify sex-specific cis- and trans-regulatory domains (CRDs and TRDs). Co-localization of sex-specific TRDs with schizophrenia common risk variants pinpointed male-specific regulatory regions controlling a number of metabolic pathways. Additionally, enhancers from female-specific TRDs were found to regulate two genes known to escape XCI, (XIST and JPX), underlying the importance of TRDs in deciphering sex differences in schizophrenia. Overall, these findings provide extensive characterization of sex differences in the brain epigenome and disease-associated regulomes.

Alzheimer's disease transcriptional landscape in ex-vivo human microglia.

Microglia are resident immune cells of the brain and are implicated in the etiology of Alzheimer's Disease (AD) and other diseases. Yet the cellular and molecular processes regulating their function throughout the course of the disease are poorly understood. Here, we present the transcriptional landscape of primary microglia from 189 human postmortem brains, including 58 healthy aging individuals and 131 with a range of disease phenotypes, including 63 patients representing the full spectrum of clinical and pathological severity of AD. We identified transcriptional changes associated with multiple AD phenotypes, capturing the severity of dementia and neuropathological lesions. Transcript-level analyses identified additional genes with heterogeneous isoform usage and AD phenotypes. We identified changes in gene-gene coordination in AD, dysregulation of co-expression modules, and disease subtypes with distinct gene expression. Taken together, these data further our understanding of the key role of microglia in AD biology and nominate candidates for therapeutic intervention.

Convergence of the dysregulated regulome in schizophrenia with polygenic risk and evolutionarily constrained enhancers.

Enhancers play an essential role in the etiology of schizophrenia; however, the dysregulation of enhancer activity and its impact on the regulome in schizophrenia remains understudied. To address this gap in our knowledge, we assessed enhancer and gene expression in 1,382 brain samples comprising cases with schizophrenia and unaffected controls. Dysregulation of enhancer expression was concordant with changes in gene expression, and was more closely associated with schizophrenia polygenic risk, suggesting that enhancer dysregulation is proximal to the genetic etiology of the disease. Modeling the shared variance of cis-coordinated genes and enhancers revealed a gene regulatory program that was highly associated with genetic vulnerability to schizophrenia. By integrating coordinated factors with evolutionary constraints, we found that enhancers acquired during human evolution are more likely to regulate genes that are implicated in neuropsychiatric disorders and, thus, hold potential as therapeutic targets. Our analysis provides a systematic view of regulome dysregulation in schizophrenia and highlights its convergence with schizophrenia polygenic risk and human-gained enhancers.

Activity-Dependent Transcriptional Program in NGN2+ Neurons Enriched for Genetic Risk for Brain-Related Disorders.

BACKGROUND: Converging evidence from large-scale genetic and postmortem studies highlights the role of aberrant neurotransmission and genetic regulation in brain-related disorders. However, identifying neuronal activity-regulated transcriptional programs in the human brain and understanding how changes contribute to disease remain challenging. METHODS: To better understand how the activity-dependent regulome contributes to risk for brain-related disorders, we profiled the transcriptomic and epigenomic changes following neuronal depolarization in human induced pluripotent stem cell-derived glutamatergic neurons (NGN2) from 6 patients with schizophrenia and 5 control participants. RESULTS: Multiomic data integration associated global patterns of chromatin accessibility with gene expression and identified enhancer-promoter interactions in glutamatergic neurons. Within 1 hour of potassium chloride-induced depolarization, independent of diagnosis, glutamatergic neurons displayed substantial activity-dependent changes in the expression of genes regulating synaptic function. Depolarization-induced changes in the regulome revealed significant heritability enrichment for schizophrenia and Parkinson's disease, adding to mounting evidence that sequence variation within activation-dependent regulatory elements contributes to the genetic risk for brain-related disorders. Gene coexpression network analysis elucidated interactions among activity-dependent and disease-associated genes and pointed to a key driver (NAV3) that interacted with multiple genes involved in axon guidance. CONCLUSIONS: Overall, we demonstrated that deciphering the activity-dependent regulome in glutamatergic neurons reveals novel targets for advanced diagnosis and therapy.

Long-read RNA-seq atlas of novel microglia isoforms elucidates disease-associated genetic regulation of splicing.

Microglia, the innate immune cells of the central nervous system, have been genetically implicated in multiple neurodegenerative diseases. We previously mapped the genetic regulation of gene expression and mRNA splicing in human microglia, identifying several loci where common genetic variants in microglia-specific regulatory elements explain disease risk loci identified by GWAS. However, identifying genetic effects on splicing has been challenging due to the use of short sequencing reads to identify causal isoforms. Here we present the isoform-centric microglia genomic atlas (isoMiGA) which leverages the power of long-read RNA-seq to identify 35,879 novel microglia isoforms. We show that the novel microglia isoforms are involved in stimulation response and brain region specificity. We then quantified the expression of both known and novel isoforms in a multi-ethnic meta-analysis of 555 human microglia short-read RNA-seq samples from 391 donors, the largest to date, and found associations with genetic risk loci in Alzheimer's disease and Parkinson's disease. We nominate several loci that may act through complex changes in isoform and splice site usage.

QuadST: A Powerful and Robust Approach for Identifying Cell-Cell Interaction-Changed Genes on Spatially Resolved Transcriptomics.

Spatially resolved transcriptomics (SRT) have enabled profiling spatial organization of cells and their transcriptome in situ. Various analytical methods have been developed to uncover cell-cell interaction processes using SRT data. To improve upon existing efforts, we developed a novel statistical framework called QuadST for the robust and powerful identification of interaction-changed genes (ICGs) for cell-type-pair specific interactions on a single-cell SRT dataset. QuadST is motivated by the idea that in the presence of cell-cell interaction, gene expression level can vary with cell-cell distance between cell type pairs, which can be particularly pronounced within and in the vicinity of cell-cell interaction distance. Specifically, QuadST infers ICGs in a specific cell type pair's interaction based on a quantile regression model, which allows us to assess the strength of distance-expression association across entire distance quantiles conditioned on gene expression level. To identify ICGs, QuadST performs a hypothesis testing with an empirically estimated FDR, whose upper bound is determined by the ratio of cumulative associations at symmetrically smaller and larger distance quantiles simultaneously across all genes. Simulation studies illustrate that QuadST provides consistent FDR control and better power performance than other compared methods. Its application on SRT datasets profiled from mouse brains demonstrates that QuadST can identify ICGs presumed to play a role in specific cell type pair interactions (e.g., synaptic pathway genes among excitatory neuron cell interactions). These results suggest that QuadST can be a useful tool to discover genes and regulatory processes involved in specific cell type pair interactions.

A public resource of single cell transcriptomes and multiscale networks from persons with and without Alzheimer's disease.

The emergence of technologies that can support high-throughput profiling of single cell transcriptomes offers to revolutionize the study of brain tissue from persons with and without Alzheimer's disease (AD). Integration of these data with additional complementary multiomics data such as genetics, proteomics and clinical data provides powerful opportunities to link observed cell subpopulations and molecular network features within a broader disease-relevant context. We report here single nucleus RNA sequencing (snRNA-seq) profiles generated from superior frontal gyrus cortical tissue samples from 101 exceptionally well characterized, aged subjects from the Banner Brain and Body Donation Program in combination with whole genome sequences. We report findings that link common AD risk variants with CR1 expression in oligodendrocytes as well as alterations in peripheral hematological lab parameters, with these observations replicated in an independent, prospective cohort study of ageing and dementia. We also observed an AD-associated CD83(+) microglial subtype with unique molecular networks that encompass many known regulators of AD-relevant microglial biology, and which are associated with immunoglobulin IgG4 production in the transverse colon. These findings illustrate the power of multi-tissue molecular profiling to contextualize snRNA-seq brain transcriptomics and reveal novel disease biology. The transcriptomic, genetic, phenotypic, and network data resources described within this study are available for access and utilization by the scientific community.

The neuronal chromatin landscape in adult schizophrenia brains is linked to early fetal development.

Non-coding variants increase risk of neuropsychiatric disease. However, our understanding of the cell-type specific role of the non-coding genome in disease is incomplete. We performed population scale (N=1,393) chromatin accessibility profiling of neurons and non-neurons from two neocortical brain regions: the anterior cingulate cortex and dorsolateral prefrontal cortex. Across both regions, we observed notable differences in neuronal chromatin accessibility between schizophrenia cases and controls. A per-sample disease pseudotime was positively associated with genetic liability for schizophrenia. Organizing chromatin into cis- and trans-regulatory domains, identified a prominent neuronal trans-regulatory domain (TRD1) active in immature glutamatergic neurons during fetal development. Polygenic risk score analysis using genetic variants within chromatin accessibility of TRD1 successfully predicted susceptibility to schizophrenia in the Million Veteran Program cohort. Overall, we present the most extensive resource to date of chromatin accessibility in the human cortex, yielding insights into the cell-type specific etiology of schizophrenia.

DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction.

BACKGROUND: Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied for phenotype prediction at different scales, but due to the black-box nature of machine learning, integrating these modalities and interpreting biological mechanisms can be challenging. Additionally, the partial availability of these multimodal data presents a challenge in developing these predictive models. METHOD: To address these challenges, we developed DeepGAMI, an interpretable neural network model to improve genotype-phenotype prediction from multimodal data. DeepGAMI leverages functional genomic information, such as eQTLs and gene regulation, to guide neural network connections. Additionally, it includes an auxiliary learning layer for cross-modal imputation allowing the imputation of latent features of missing modalities and thus predicting phenotypes from a single modality. Finally, DeepGAMI uses integrated gradient to prioritize multimodal features for various phenotypes. RESULTS: We applied DeepGAMI to several multimodal datasets including genotype and bulk and cell-type gene expression data in brain diseases, and gene expression and electrophysiology data of mouse neuronal cells. Using cross-validation and independent validation, DeepGAMI outperformed existing methods for classifying disease types, and cellular and clinical phenotypes, even using single modalities (e.g., AUC score of 0.79 for Schizophrenia and 0.73 for cognitive impairment in Alzheimer's disease). CONCLUSION: We demonstrated that DeepGAMI improves phenotype prediction and prioritizes phenotypic features and networks in multiple multimodal datasets in complex brains and brain diseases. Also, it prioritized disease-associated variants, genes, and regulatory networks linked to different phenotypes, providing novel insights into the interpretation of gene regulatory mechanisms. DeepGAMI is open-source and available for general use.

Multi-omic profiling of the developing human cerebral cortex at the single-cell level.

The cellular complexity of the human brain is established via dynamic changes in gene expression throughout development that is mediated, in part, by the spatiotemporal activity of cis-regulatory elements (CREs). We simultaneously profiled gene expression and chromatin accessibility in 45,549 cortical nuclei across six broad developmental time points from fetus to adult. We identified cell type-specific domains in which chromatin accessibility is highly correlated with gene expression. Differentiation pseudotime trajectory analysis indicates that chromatin accessibility at CREs precedes transcription and that dynamic changes in chromatin structure play a critical role in neuronal lineage commitment. In addition, we mapped cell type-specific and temporally specific genetic loci implicated in neuropsychiatric traits, including schizophrenia and bipolar disorder. Together, our results describe the complex regulation of cell composition at critical stages in lineage determination and shed light on the impact of spatiotemporal alterations in gene expression on neuropsychiatric disease.