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  • 1
    In: The Lancet Regional Health - Western Pacific, Elsevier BV, Vol. 38 ( 2023-09), p. 100846-
    Type of Medium: Online Resource
    ISSN: 2666-6065
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
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  • 2
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 384, No. 6698 ( 2024-05-24)
    Abstract: Single-cell genomics offers a powerful method to understand how variants influence gene expression, especially across the numerous cell types in the human brain. Moreover, it can potentially refine our understanding of the regulatory mechanisms underlying brain-related traits. However, population-scale cohorts with a wide range of brain phenotypes are needed to infer key associations among variants and to develop models of regulation at the single-cell scale. RATIONALE To address this, the PsychENCODE Consortium performed many single-cell experiments [single-nucleus RNA sequencing (snRNA-seq), snATAC-seq (ATAC, assay for transposase-accessible chromatin), and snMultiome plus genotyping] and computational analyses on prefrontal-cortex samples of adults with a range of brain-related disorders such as schizophrenia, autism spectrum disorder, bipolar disorder, and Alzheimer’s disease, as well as controls. RESULTS We developed a uniformly processed resource comprising 〉 2.8 million nuclei from 388 individuals ( brainscope.psychencode.org ). The resource is based on harmonized cell typing, with 28 neuronal and non-neuronal cell types (registered against BICCN). Partitioning the expression variation within these types revealed higher cell-type variability than interindividual variability; this pattern was amplified in neurotransmitter and neurorelated drug-target genes such as CNR1 . Integration of expression and genotype data revealed 〉 1.4 million single-cell expression quantitative trait loci (eQTLs), many of which were not seen in bulk gene-expression datasets and a subset of which involved variants related to brain disorders. Moreover, we found that expression patterns across cell types recapitulated the spatial relationships of excitatory neurons across cortical layers and enabled the identification of “dynamic eQTLs,” with smooth changes in regulatory effect across cortical layers. The chromatin datasets in the resource allowed for identification of 〉 550,000 single-cell cis-regulatory elements, which were enriched at loci linked to brain-related traits. Combining expression, chromatin, and eQTL datasets, we built cell type–specific gene regulatory networks. In these, information-flow bottleneck genes tended to be specific to particular cell types, in contrast to hubs. We also developed cell-to-cell communication networks, which highlighted differences in signaling pathways in disorders, including altered Wnt signaling in schizophrenia and bipolar disorder. We developed an integrative deep-learning model with embedded layers for genotypes, eQTLs, and regulatory and cell-to-cell communications networks. The model allowed for accurate imputation of cell type–specific expression and phenotype from genotype. It prioritized 〉 250 risk genes and drug targets for brain-related disorders along with associated cell types. Simulated perturbation of individual genes led to predicted expression changes mirroring those for disease cases, suggesting drug targets. Lastly, we constructed predictive models for aging and Alzheimer’s disease, showing, for instance, that expression and chromatin in specific neurons were highly predictive of an individual’s age. CONCLUSION Our population-scale single-cell resource for the human brain can help facilitate precision-medicine approaches for neuropsychiatric disorders, especially by prioritizing follow-up genes and drug targets linked to cell types. brainSCOPE resource. snRNA-seq and snATAC-seq from 388 individuals allowed assessment of regulatory elements (scCREs), single-cell eQTLs (scQTLs), and gene regulatory networks across cell types. These were integrated into a model (LNCTP, Linear Network of Cell Type Phenotypes) to predict phenotypes and prioritize genes and cell types.
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
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    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2024
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  • 3
    In: Molecular Omics, Royal Society of Chemistry (RSC), Vol. 15, No. 2 ( 2019), p. 150-163
    Type of Medium: Online Resource
    ISSN: 2515-4184
    Language: English
    Publisher: Royal Society of Chemistry (RSC)
    Publication Date: 2019
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  • 4
    In: Echocardiography, Wiley, Vol. 40, No. 6 ( 2023-06), p. 537-549
    Abstract: To evaluate the left ventricular energy loss (EL), energy loss reserve (EL‐r), and energy loss reserve rate in patients with mild coronary artery stenosis by using vector flow mapping (VFM) combined with exercise stress echocardiography. Methods A total of 34 patients (case group) with mild coronary artery stenosis and 36 sex and age matched patients (control group) without coronary artery stenosis according to coronary angiogram were prospectively enrolled. The total energy loss (ELt), basal segment energy loss (ELb), middle segment energy loss (ELm), apical segment energy loss (ELa), energy loss reserve (EL‐r), and energy loss reserve rate were recorded in the isovolumic systolic period (S1), rapid ejection period (S2), slow ejection period (S3), isovolumic diastolic period (D1), rapid filling period (D2), slow filling period (D3), and atrial contraction period (D4). Results Compared with the control group, some of the EL in the resting case group were higher; some of the EL in the case group were lower after exercise, and those during D1 ELb and D3 ELb were higher. Compared with the resting state, the total EL and the EL within the time segment in the control group were higher after exercise, except during D2 ELb. In the case group, except for during D1 ELt, ELb and D2 ELb, the total and segmental EL of each phase was mostly higher after exercise ( p   〈  .05). Compared with the control group, most of the EL‐r and EL reserve rates in the case group were lower ( p   〈  .05). Conclusion The EL, EL‐r, and energy loss reserve rate have a certain value in the evaluation of cardiac function in patients with mild coronary artery stenosis.
    Type of Medium: Online Resource
    ISSN: 0742-2822 , 1540-8175
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2023
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  • 5
    In: Science Translational Medicine, American Association for the Advancement of Science (AAAS), Vol. 16, No. 749 ( 2024-05-29)
    Abstract: Xia et al. analyzed transcriptomics data from 2160 postmortem adult brain samples from patients with schizophrenia, bipolar disorder, or autism spectrum disorder. They found that females exhibited a higher burden of transcriptomic dysfunction and greater connectivity variability compared with males. They report enrichment for genes associated with immune and synaptic-related pathways such as SCN2A , FGF14 , and C3 in the transcriptomic burden of females compared with males. Understanding this sex-specific difference may lead to better treatments for psychiatric disorders. —Orla Smith
    Type of Medium: Online Resource
    ISSN: 1946-6234 , 1946-6242
    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2024
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  • 6
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 384, No. 6698 ( 2024-05-24)
    Abstract: Schizophrenia population genomics has identified strong germline genetic associations for this highly heritable disorder, and molecular investigation of postmortem brain samples has yielded evidence of transcriptomic and epigenomic alterations associated with this disease. However, identifying molecular and cellular pathophysiological processes linking etiological risk factors and clinical presentation remains a challenge, due in part to the complex cellular architecture of the brain. RATIONALE Past work has implicated specific populations of excitatory and inhibitory neurons in the pathophysiology of schizophrenia, but existing large transcriptomic datasets of bulk tissue samples cannot directly assess cell type–specific contributions to disease. Single-cell RNA sequencing technologies allow measurement of genome-wide gene expression in individual cells with high-throughput, moving beyond bulk tissue measures to map disease-associated transcriptional changes in discrete cellular populations without bias toward preselected cell types. Investigating disease-associated phenotypic changes across the myriad cellular populations of the human brain can produce new insights into neuropsychiatric disease biology. RESULTS Using multiplexed single-nucleus RNA sequencing, we developed a single-cell resolution transcriptomic atlas of the prefrontal cortex across subjects with and without schizophrenia and present data from 468,727 nuclei isolated from 140 individuals across two well-defined and independently assayed cohorts. We identified expression profiles of brain cell types and neuronal subpopulations and systematically characterized the transcriptional changes associated with schizophrenia in each. For completeness, we report independent, cohort-specific analyses and joint meta-analysis of differential expression across 25 cell types. Using these data, we identified highly cell type–specific and reproducible expression changes, with 6634 differential expression events affecting 2455 genes and favoring down-regulated gene expression within excitatory neuronal populations. We found significant overlap with previously reported bulk cortex expression changes, primarily for excitatory neuronal populations, whereas changes in lower-abundance cell types were less efficiently captured in tissue-level profiling. Differentially expressed genes enrich neurodevelopmental and synapse-related molecular pathways and point to a regulatory core of coexpressed transcription factors linked to genetic risk variants for schizophrenia and developmental delay. Transcription factor targeting of schizophrenia differentially expressed genes in neuronal populations was validated with CUT & Tag in neuronal nuclei isolated from human prefrontal cortex. Furthermore, both transcriptional changes and putative upstream regulatory factors were enriched with genes harboring common and rare risk variants for schizophrenia, presenting evidence that genetic risk variants across the population frequency spectrum tend to target genes with measurable expression alterations in the excitatory neurons of patients with schizophrenia. Finally, the magnitude of schizophrenia-associated transcriptomic change segregated two populations of schizophrenia subjects. Transcriptomic heterogeneity within the cohorts was associated with specific cellular states shared across multiple neuronal populations, marked by genes related to synaptic function and one-carbon metabolism, suggesting genes characterizing distinct molecular phenotypes of schizophrenia. CONCLUSION Our results provide a valuable resource to investigate the molecular pathophysiology of schizophrenia at single-cell resolution, offering insights into preferential dysregulation of specific neuronal populations and their potential role in mediating genetic risk. Together, they suggest convergence of etiological genetic risk factors, neuronal transcriptional dysregulation, and symptomatic manifestation in schizophrenia. Single-cell schizophrenia transcriptomics. Single-nucleus RNA sequencing (snRNA-seq) identified cell type–specific differentially expressed genes (DEGs) in 25 cell types. DEG sets enrich disease-relevant biological pathways, implicate a coherently expressed transcription factors module, and are associated with schizophrenia genetic risk variants. Magnitude of transcriptional change identified neuronal cell state–associated subgroups. SZ, schizophrenia; CON, control; McL, McLean; MSSM, Mount Sinai School of Medicine.
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
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    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2024
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  • 7
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 22, No. 2 ( 2021-03-22), p. 1929-1939
    Abstract: Long noncoding RNAs (lncRNAs) have been proven to play important roles in transcriptional processes and biological functions. With the increasing study of human diseases and biological processes, information in human H3K27ac ChIP-seq, ATAC-seq and DNase-seq datasets is accumulating rapidly, resulting in an urgent need to collect and process data to identify transcriptional regulatory regions of lncRNAs. We therefore developed a comprehensive database for human regulatory information of lncRNAs (TRlnc, http://bio.licpathway.net/TRlnc), which aimed to collect available resources of transcriptional regulatory regions of lncRNAs and to annotate and illustrate their potential roles in the regulation of lncRNAs in a cell type-specific manner. The current version of TRlnc contains 8 683 028 typical enhancers/super-enhancers and 32 348 244 chromatin accessibility regions associated with 91 906 human lncRNAs. These regions are identified from over 900 human H3K27ac ChIP-seq, ATAC-seq and DNase-seq samples. Furthermore, TRlnc provides the detailed genetic and epigenetic annotation information within transcriptional regulatory regions (promoter, enhancer/super-enhancer and chromatin accessibility regions) of lncRNAs, including common SNPs, risk SNPs, eQTLs, linkage disequilibrium SNPs, transcription factors, methylation sites, histone modifications and 3D chromatin interactions. It is anticipated that the use of TRlnc will help users to gain in-depth and useful insights into the transcriptional regulatory mechanisms of lncRNAs.
    Type of Medium: Online Resource
    ISSN: 1477-4054
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
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  • 8
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 384, No. 6698 ( 2024-05-24)
    Abstract: Genome-wide association studies (GWASs) have identified thousands of loci associated with neurodevelopmental and psychiatric disorders, yet our lack of understanding of the target genes and biological mechanisms underlying these associations remains a major challenge. GWAS signals for many neuropsychiatric disorders, including autism spectrum disorder, schizophrenia, and bipolar disorder, are particularly enriched for gene-regulatory elements active during human brain development. However, the lack of a unified population-scale, ancestrally diverse gene-regulatory atlas of human brain development has been a major obstacle for the functional assessment of top loci and post-GWAS integrative analyses. RATIONALE To address this critical gap in knowledge, we have uniformly processed and systematically characterized gene, isoform, and splicing quantitative trait loci (cumulatively referred to as xQTLs) in the developing human brain across 672 unique samples from 4 to 39 postconception weeks spanning European, African-American, and Latino/admixed American ancestries). With this expanded atlas, we sought to specifically localize the timing and molecular features mediating the greatest proportion of neuropsychiatric GWAS heritability, to prioritize candidate risk genes and mechanisms for top loci, and to compare with analogous results using larger adult brain functional genomic reference panels. RESULTS In total, we identified 15,752 genes harboring a gene, isoform and/or splicing cis -xQTL, including 49 genes associated with four large, recurrent inversions. Highly concordant effect sizes were observed across populations, and our diverse reference panel improved resolution to fine-map underlying candidate causal regulatory variants. Substantially more genes were found to harbor QTLs in the first versus second trimester of brain development, with a notable drop in gene expression and splicing heritability observed from 10 to 18 weeks coinciding with a period of rapidly increasing cellular heterogeneity in the developing brain. Isoform-level regulation, particularly in the second trimester, mediated a greater proportion of heritability across multiple psychiatric GWASs compared with gene expression regulation. Through colocalization and transcriptome-wide association studies, we prioritized biological mechanisms for ~60% of GWAS loci across five neuropsychiatric disorders, with 〉 2-fold more colocalizations observed compared with larger adult brain functional genomic reference panels. We observed convergence between common and rare-variant associations, including a cryptic splicing event in the high-confidence schizophrenia risk gene SP4 . Finally, we constructed a comprehensive set of developmentally regulated gene and isoform coexpression networks harboring unique cell-type specificity and genetic enrichments. Leveraging this cell-type specificity, we identified 〉 8000 module interaction QTLs, many of which exhibited additional GWAS colocalizations. Overall, neuropsychiatric GWASs and rare variant signals localized more strongly within maturing excitatory- and interneuron-associated modules compared with those enriched for neural progenitor cell types. Results can be visualized at devbrainhub.gandallab.org . CONCLUSION We have generated a large-scale, cross-population resource of gene, isoform, and splicing regulation in the developing human brain, providing comprehensive developmental and cell-type-informed mechanistic insights into the genetic underpinnings of complex neurodevelopmental and psychiatric disorders. A comprehensive transcriptome regulatory atlas of the developing human neocortex. RNA-sequencing and single-nucleotide polymorphism genotypes were uniformly integrated within a diverse set of 672 samples of the developing human neocortex. Gene regulation was systematically assessed across the gene, isoform expression, and local splicing levels, yielding 15,752 genes harboring a significant xQTL. Gene regulation was highly dynamic, with a substantial drop observed in gene expression heritability over development. Integrative analyses with neuropsychiatric GWASs uncovered hundreds of candidate risk genes and mechanisms, providing insights into the cellular, molecular, and developmental specificity underlying disease-associated genetic variation.
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
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    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2024
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  • 9
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 384, No. 6698 ( 2024-05-24)
    Abstract: The cortical layers of the human neocortex were classically defined by histological distinction of cell types according to size, shape, and density. However, emerging single-cell and spatially resolved transcriptomic technologies have facilitated the identification of molecularly defined cell populations and spatial domains that move beyond classic cell type definitions and cytoarchitectural boundaries. RATIONALE Given the close relationship between brain structure and function, assigning gene expression to distinct anatomical subdivisions and cell populations within the human brain improves our understanding of these highly specialized regions and how they contribute to brain disorders. We sought to create a data-driven molecular neuroanatomical map of the human dorsolateral prefrontal cortex (DLPFC) at cellular resolution using unsupervised transcriptomic approaches to identify spatial domains associated with neuropsychiatric and neurodevelopmental disorders. RESULTS We generated complementary single-cell and spatial transcriptomic data from 10 adult, neurotypical control donors across the anterior-posterior axis of the DLPFC. Unsupervised spatial clustering revealed fine-resolution data-driven spatial domains with distinct molecular signatures, including deep cortical sublayers and a vasculature-enriched meninges layer. Cell type clustering of single-nucleus RNA-sequencing (snRNA-seq) data revealed 29 distinct populations across seven broad neuronal and glial cell types, including 15 excitatory subpopulations. To add cellular resolution to our data-driven molecular atlas, we took two complementary approaches to integrate single-cell and spatial transcriptomics data. First, we used our previously developed spatial registration framework to map the paired snRNA-seq data to specific unsupervised spatial domains, providing anatomy-based laminar identities to excitatory neuron subpopulations. Second, we used three existing spot-level deconvolution tools to computationally predict the cell type composition of spatial domains on the basis of the paired snRNA-seq reference data. These tools were rigorously benchmarked against a newly generated gold-standard reference dataset acquired with the Visium Spatial Proteogenomics assay, which enabled us to label and quantify four broad cell types across the DLPFC on the basis of protein marker expression, including neurons, oligodendrocytes, astrocytes, and microglia. Using these approaches, we identified the proportion of cell types in each spatial domain and showed that these proportions were consistent across individuals and the DLPFC anterior-posterior axis. We demonstrated the clinical relevance of our highly integrated molecular atlas using cell-cell communication analyses to spatially map cell type–specific ligand-receptor interactions associated with genetic risk for schizophrenia (SCZ). For example, we mapped the interaction between ephrin ligand EFNA5 and ephrin receptor EPHA5 to deep-layer excitatory neuron subtypes and spatial domains. To leverage the rich single-cell data generated by PsychENCODE Consortium companion studies, we spatially registered eight DLPFC snRNA-seq datasets collected across the consortium in the context of different neuropsychiatric disorders and demonstrated a convergence of excitatory, inhibitory, and non-neuronal cell types in relevant spatial domains. Using PsychENCODE Consortium and other publicly available gene sets, we further demonstrated the clinical relevance of our data-driven molecular atlas by mapping the enrichment of cell types and genes associated with neuropsychiatric disorders—including autism spectrum disorder, posttraumatic stress disorder, and major depressive disorder—to discrete spatial domains. CONCLUSION Our study identified high-resolution, data-driven spatial domains across the human DLPFC, providing anatomical context for cell type–specific gene expression changes associated with neurodevelopmental disorders and psychiatric illness. We provide a roadmap for the implementation and biological validation of unsupervised spatial clustering approaches in other regions of the human brain. We share interactive data resources for the scientific community to further interrogate molecular mechanisms associated with complex brain disorders. Data-driven molecular anatomy of the human DLPFC. Integrated single-nucleus and spatial transcriptomics data were generated across the anterior-posterior axis of the human DLPFC from 10 neurotypical control donors to create a data-driven molecular neuroanatomical atlas of the neocortex identifying spatial domains. Integrative analyses revealed distinct cell type compositions, cell-cell interactions, and colocalization of ligand-receptor pairs linked to schizophrenia genetic risk. t-SNE, t -distributed stochastic neighbor embedding. [Created with BioRender.com .]
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
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    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2024
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    detail.hit.zdb_id: 2066996-3
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  • 10
    In: International Journal of Stroke, SAGE Publications
    Abstract: Proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors enable an additional 54% to 75% reduction in low-density lipoprotein cholesterol (LDL-C) in statin-treated patients, demonstrating plaque regression in coronary artery disease. However, the impact of achieving an extremely low level of LDL-C with PCSK9 inhibitors (e.g., evolocumabEvolocumab) on symptomatic intracranial atherosclerosis remains unexplored. Aim and hypothesis To determine if combining evolocumabEvolocumab and statins achieves a more significant symptomatic intracranial plaque reduction than statin therapy solely. Sample size estimates With a sample size of 1000 subjects, a two-sided of 0.05, and 20% lost to follow-up, the study will have 83.3% power to detect the difference in intracranial plaque burden. Methods and design This is an investigator-initiated multicenter, randomized, open-label, outcome assessor–blinded trial, evaluating the impact of evolocumabEvolocumab on intracranial plaque burden assessed by high-resolution magnetic resonance imaging at baseline in patients undergoing a clinically indicated acute stroke or transient ischemic attack due to intracranial artery stenosis, and after 24 weeks of treatment. Subjects (n = 1000) will be randomized 1:1 into two groups to receive either evolocumabEvolocumab 140 mg every two weeks with statin therapy or solely statin therapy. Study outcomes The primary endpoint is the change in plaque burden assessed by high-resolution magnetic resonance imaging, performed at baseline and the end of the 24-week treatment period. Discussion This trial will explore whether more significant plaque regression is achievable with treatment after combining statins and PCSK9 inhibitors, providing information about important efficacy, mechanism, and safety data. Trial registration number: ChiCTR2300068868; https://www.chictr.org.cn/
    Type of Medium: Online Resource
    ISSN: 1747-4930 , 1747-4949
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2024
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