Leveraging single-cell ATAC-seq to identify disease-critical fetal and adult brain cell types

Prioritizing disease-critical cell types by integrating genome-wide association studies (GWAS) with functional data is a fundamental goal. Single-cell chromatin accessibility (scATAC-seq) and gene expression (scRNA-seq) have characterized cell types at high resolution, and early work on integrating GWAS with scRNA-seq has shown promise, but work on integrating GWAS with scATAC-seq has been limited. Here, we identify disease-critical fetal and adult brain cell types by integrating GWAS summary statistics from 28 brain-related diseases and traits (average N=298K) with 3.2 million scATAC-seq and scRNA-seq profiles from 83 cell types. We identified disease-critical fetal (resp. adult) brain cell types for 22 (resp. 23) of 28 traits using scATAC-seq data, and for 8 (resp. 17) of 28 traits using scRNA-seq data. Notable findings using scATAC-seq data included highly significant enrichments of fetal photoreceptor cells for major depressive disorder, fetal ganglion cells for BMI, fetal astrocytes for ADHD, and adult VGLUT2 excitatory neurons for schizophrenia. Our findings improve our understanding of brain-related diseases and traits, and inform future analyses of other diseases/traits.

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Single-cell epigenomic identification of inherited risk loci in Alzheimer’s and Parkinson’s disease

10.1101/2020.01.06.896159 ◽

2020 ◽

Cited By ~ 9

Author(s):

M. Ryan Corces ◽

Anna Shcherbina ◽

Soumya Kundu ◽

Michael J. Gloudemans ◽

Laure Frésard ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Single Cell ◽

Association Studies ◽

Regulatory Elements ◽

Brain Regions ◽

Chromatin Accessibility ◽

Adult Brain ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms

ABSTRACTGenome-wide association studies (GWAS) have identified thousands of variants associated with disease phenotypes. However, the majority of these variants do not alter coding sequences, making it difficult to assign their function. To this end, we present a multi-omic epigenetic atlas of the adult human brain through profiling of the chromatin accessibility landscapes and three-dimensional chromatin interactions of seven brain regions across a cohort of 39 cognitively healthy individuals. Single-cell chromatin accessibility profiling of 70,631 cells from six of these brain regions identifies 24 distinct cell clusters and 359,022 cell type-specific regulatory elements, capturing the regulatory diversity of the adult brain. We develop a machine learning classifier to integrate this multi-omic framework and predict dozens of functional single nucleotide polymorphisms (SNPs), nominating gene and cellular targets for previously orphaned GWAS loci. These predictions both inform well-studied disease-relevant genes, such as BIN1 in microglia for Alzheimer’s disease (AD) and reveal novel gene-disease associations, such as STAB1 in microglia and MAL in oligodendrocytes for Parkinson’s disease (PD). Moreover, we dissect the complex inverted haplotype of the MAPT (encoding tau) PD risk locus, identifying ectopic enhancer-gene contacts in neurons that increase MAPT expression and may mediate this disease association. This work greatly expands our understanding of inherited variation in AD and PD and provides a roadmap for the epigenomic dissection of noncoding regulatory variation in disease.

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Localization of migraine susceptibility genes in human brain by single-cell RNA sequencing

Cephalalgia ◽

10.1177/0333102418762476 ◽

2018 ◽

Vol 38 (13) ◽

pp. 1976-1983 ◽

Cited By ~ 5

Author(s):

William Renthal

Keyword(s):

Human Brain ◽

Single Cell ◽

Rna Sequencing ◽

Expression Profiles ◽

Cell Types ◽

Susceptibility Genes ◽

Brain Cell ◽

Cell Type ◽

Single Cell Rna Sequencing ◽

Brain Cell Types

Background Migraine is a debilitating disorder characterized by severe headaches and associated neurological symptoms. A key challenge to understanding migraine has been the cellular complexity of the human brain and the multiple cell types implicated in its pathophysiology. The present study leverages recent advances in single-cell transcriptomics to localize the specific human brain cell types in which putative migraine susceptibility genes are expressed. Methods The cell-type specific expression of both familial and common migraine-associated genes was determined bioinformatically using data from 2,039 individual human brain cells across two published single-cell RNA sequencing datasets. Enrichment of migraine-associated genes was determined for each brain cell type. Results Analysis of single-brain cell RNA sequencing data from five major subtypes of cells in the human cortex (neurons, oligodendrocytes, astrocytes, microglia, and endothelial cells) indicates that over 40% of known migraine-associated genes are enriched in the expression profiles of a specific brain cell type. Further analysis of neuronal migraine-associated genes demonstrated that approximately 70% were significantly enriched in inhibitory neurons and 30% in excitatory neurons. Conclusions This study takes the next step in understanding the human brain cell types in which putative migraine susceptibility genes are expressed. Both familial and common migraine may arise from dysfunction of discrete cell types within the neurovascular unit, and localization of the affected cell type(s) in an individual patient may provide insight into to their susceptibility to migraine.

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Inferring relevant tissues and cell types for complex traits in genome-wide association studies

10.1101/2021.06.09.447805 ◽

2021 ◽

Author(s):

Rujin Wang ◽

Danyu Lin ◽

Yuchao Jiang

Keyword(s):

Single Cell ◽

Complex Traits ◽

Association Studies ◽

Cell Types ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Cell Type ◽

Disease Etiology ◽

Genome Wide ◽

Cell Type Specific

More than a decade of genome-wide association studies (GWASs) have identified genetic risk variants that are significantly associated with complex traits. Emerging evidence suggests that the function of trait-associated variants likely acts in a tissue- or cell-type-specific fashion. Yet, it remains challenging to prioritize trait-relevant tissues or cell types to elucidate disease etiology. Here, we present EPIC (cEll tyPe enrIChment), a statistical framework that relates large-scale GWAS summary statistics to cell-type-specific omics measurements from single-cell sequencing. We derive powerful gene-level test statistics for common and rare variants, separately and jointly, and adopt generalized least squares to prioritize trait-relevant tissues or cell types while accounting for the correlation structures both within and between genes. Using enrichment of loci associated with four lipid traits in the liver and enrichment of loci associated with three neurological disorders in the brain as ground truths, we show that EPIC outperforms existing methods. We extend our framework to single-cell transcriptomic data and identify cell types underlying type 2 diabetes and schizophrenia. The enrichment is replicated using independent GWAS and single-cell datasets and further validated using PubMed search and existing bulk case-control testing results.

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Brain Microenvironment Heterogeneity: Potential Value for Brain Tumors

Frontiers in Oncology ◽

10.3389/fonc.2021.714428 ◽

2021 ◽

Vol 11 ◽

Author(s):

Laura Álvaro-Espinosa ◽

Ana de Pablos-Aragoneses ◽

Manuel Valiente ◽

Neibla Priego

Keyword(s):

Brain Tumors ◽

Single Cell ◽

Brain Injuries ◽

Clinical Work ◽

Cell Types ◽

Brain Cell ◽

Brain Disorders ◽

Brain Connectome ◽

The Brain ◽

Brain Cell Types

Uncovering the complexity of the microenvironment that emerges in brain disorders is key to identify potential vulnerabilities that might help challenging diseases affecting this organ. Recently, genomic and proteomic analyses, especially at the single cell level, have reported previously unrecognized diversity within brain cell types. The complexity of the brain microenvironment increases during disease partly due to the immune infiltration from the periphery that contributes to redefine the brain connectome by establishing a new crosstalk with resident brain cell types. Within the rewired brain ecosystem, glial cell subpopulations are emerging hubs modulating the dialogue between the Immune System and the Central Nervous System with important consequences in the progression of brain tumors and other disorders. Single cell technologies are crucial not only to define and track the origin of disease-associated cell types, but also to identify their molecular similarities and differences that might be linked to specific brain injuries. These altered molecular patterns derived from reprogramming the healthy brain into an injured organ, might provide a new generation of therapeutic targets to challenge highly prevalent and lethal brain disorders that remain incurable with unprecedented specificity and limited toxicities. In this perspective, we present the most relevant clinical and pre-clinical work regarding the characterization of the heterogeneity within different components of the microenvironment in the healthy and injured brain with a special interest on single cell analysis. Finally, we discuss how understanding the diversity of the brain microenvironment could be exploited for translational purposes, particularly in primary and secondary tumors affecting the brain.

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Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics

10.1101/2021.03.19.436212 ◽

2021 ◽

Author(s):

Karthik A. Jagadeesh ◽

Kushal K Dey ◽

Daniel T. Montoro ◽

Steven Gazal ◽

Jesse M Engreitz ◽

...

Keyword(s):

Single Cell ◽

Disease Progression ◽

Genetic Variants ◽

Complex Traits ◽

Disease Risk ◽

Association Studies ◽

Cell Types ◽

Genome Wide Association Studies ◽

Cascade Process ◽

Cellular Processes

Cellular dysfunction is a hallmark of disease. Genome-wide association studies (GWAS) have provided a powerful means to identify loci and genes contributing to disease risk, but in many cases the related cell types/states through which genes confer disease risk remain unknown. Deciphering such relationships is important both for our understanding of disease, and for developing therapeutic interventions. Here, we introduce a framework for integrating single-cell RNA-seq (scRNA-seq), epigenomic maps and GWAS summary statistics to infer the underlying cell types and processes by which genetic variants influence disease. We analyzed 1.6 million scRNA-seq profiles from 209 individuals spanning 11 tissue types and 6 disease conditions, and constructed gene programs capturing cell types, disease progression in cell types, and cellular processes both within and across cell types. We evaluated these gene programs for disease enrichment by transforming them to SNP annotations with tissue-specific epigenomic maps and computing enrichment scores across 60 diseases and complex traits (average N=297K). The inferred disease enrichments recapitulated known biology and highlighted novel relationships for different conditions, including GABAergic neurons in major depressive disorder (MDD), disease progression programs in M cells in ulcerative colitis, and a disease-specific complement cascade process in multiple sclerosis. Our framework provides a powerful approach for identifying the cell types and cellular processes by which genetic variants influence disease.

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Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration

Nature Communications ◽

10.1038/s41467-019-12780-8 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 32

Author(s):

Madhvi Menon ◽

Shahin Mohammadi ◽

Jose Davila-Velderrain ◽

Brittany A. Goods ◽

Tanina D. Cadwell ◽

...

Keyword(s):

Macular Degeneration ◽

Single Cell ◽

Association Studies ◽

Enrichment Analysis ◽

Cell Types ◽

Age Related Macular Degeneration ◽

Retinal Cell ◽

Genome Wide Association Studies ◽

Human Retina ◽

Age Related

Abstract Genome-wide association studies (GWAS) have identified genetic variants associated with age-related macular degeneration (AMD), one of the leading causes of blindness in the elderly. However, it has been challenging to identify the cell types associated with AMD given the genetic complexity of the disease. Here we perform massively parallel single-cell RNA sequencing (scRNA-seq) of human retinas using two independent platforms, and report the first single-cell transcriptomic atlas of the human retina. Using a multi-resolution network-based analysis, we identify all major retinal cell types, and their corresponding gene expression signatures. Heterogeneity is observed within macroglia, suggesting that human retinal glia are more diverse than previously thought. Finally, GWAS-based enrichment analysis identifies glia, vascular cells, and cone photoreceptors to be associated with the risk of AMD. These data provide a detailed analysis of the human retina, and show how scRNA-seq can provide insight into cell types involved in complex, inflammatory genetic diseases.

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EpiRegio: analysis and retrieval of regulatory elements linked to genes

Nucleic Acids Research ◽

10.1093/nar/gkaa382 ◽

2020 ◽

Vol 48 (W1) ◽

pp. W193-W199 ◽

Cited By ~ 4

Author(s):

Nina Baumgarten ◽

Dennis Hecker ◽

Sivarajan Karunanithi ◽

Florian Schmidt ◽

Markus List ◽

...

Keyword(s):

Gene Expression ◽

Target Genes ◽

Association Studies ◽

Web Server ◽

Cell Types ◽

Regulatory Elements ◽

Chromatin Accessibility ◽

Genome Wide Association Studies ◽

Coding Regions ◽

Genomic Regions

Abstract A current challenge in genomics is to interpret non-coding regions and their role in transcriptional regulation of possibly distant target genes. Genome-wide association studies show that a large part of genomic variants are found in those non-coding regions, but their mechanisms of gene regulation are often unknown. An additional challenge is to reliably identify the target genes of the regulatory regions, which is an essential step in understanding their impact on gene expression. Here we present the EpiRegio web server, a resource of regulatory elements (REMs). REMs are genomic regions that exhibit variations in their chromatin accessibility profile associated with changes in expression of their target genes. EpiRegio incorporates both epigenomic and gene expression data for various human primary cell types and tissues, providing an integrated view of REMs in the genome. Our web server allows the analysis of genes and their associated REMs, including the REM’s activity and its estimated cell type-specific contribution to its target gene’s expression. Further, it is possible to explore genomic regions for their regulatory potential, investigate overlapping REMs and by that the dissection of regions of large epigenomic complexity. EpiRegio allows programmatic access through a REST API and is freely available at https://epiregio.de/.

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A global overview of single-cell type selectivity and pleiotropy in complex diseases and traits

10.1101/2020.11.18.388488 ◽

2020 ◽

Author(s):

Chao Xue ◽

Lin Jiang ◽

Qihan Long ◽

Ying Chen ◽

Xiangyi Li ◽

...

Keyword(s):

Single Cell ◽

Human Genetics ◽

Expression Profiles ◽

Association Studies ◽

Cell Types ◽

Genome Wide Association Studies ◽

Cell Type ◽

Cell Type Specificity ◽

Complex Phenotypes ◽

Single Cell Type

AbstractAfter centuries of genetic studies, one of the most fundamental questions, i.e. in what cell types do DNA mutations regulate a phenotype, remains unanswered for most complex phenotypes. The current availability of hundreds of genome-wide association studies (GWASs) and single-cell RNA sequencing (scRNA-seq) of millions of cells provides a unique opportunity to address the question. In the present study, we firstly constructed an association landscape between over 20,000 single cell clusters and 997 complex phenotypes by a cross annotation framework with scRNA-seq expression profiles and GWAS summary statistics. We then performed an extensive overview of cell-type specificity and pleiotropy in human phenotypes and found most phenotypes (>90%) were moderately selectively associated with a limited number of cell types while a small fraction cell types (<10%) had strong pleiotropy in multiple phenotypes (~100). Moreover, we identified three cell type-phenotype mutual pleiotropy blocks in the landscape. The application of the single cell type-phenotype cross annotation framework (named SPA) also explained the T cell biased lymphopenia and suggested important supporting genes in severe COVID-19 from human genetics angle. All the cell type-phenotype association results can be queried and visualized at http://pmglab.top/spa.

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Motif-Raptor: A Cell Type-Specific and Transcription Factor Centric Approach for Post-GWAS Prioritization of Causal Regulators

Bioinformatics ◽

10.1093/bioinformatics/btab072 ◽

2021 ◽

Author(s):

Qiuming Yao ◽

Paolo Ferragina ◽

Yakir Reshef ◽

Guillaume Lettre ◽

Daniel E Bauer ◽

...

Keyword(s):

Gene Expression ◽

Genetic Variants ◽

Complex Traits ◽

Association Studies ◽

Cell Types ◽

Chromatin Accessibility ◽

Supplementary Information ◽

Genome Wide Association Studies ◽

Systematic Understanding ◽

Coding Variants

Abstract Motivation Genome-wide association studies (GWAS) have identified thousands of common trait-associated genetic variants but interpretation of their function remains challenging. These genetic variants can overlap the binding sites of transcription factors (TFs) and therefore could alter gene expression. However, we currently lack a systematic understanding on how this mechanism contributes to phenotype. Results We present Motif-Raptor, a TF-centric computational tool that integrates sequence-based predictive models, chromatin accessibility, gene expression datasets and GWAS summary statistics to systematically investigate how TF function is affected by genetic variants. Given trait associated non-coding variants, Motif-Raptor can recover relevant cell types and critical TFs to drive hypotheses regarding their mechanism of action. We tested Motif-Raptor on complex traits such as rheumatoid arthritis and red blood cell count and demonstrated its ability to prioritize relevant cell types, potential regulatory TFs and non-coding SNPs which have been previously characterized and validated. Availability Motif-Raptor is freely available as a Python package at: https://github.com/pinellolab/MotifRaptor. Supplementary information Supplementary data are available at Bioinformatics online.

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Ascertaining cells' synaptic connections and RNA expression simultaneously with massively barcoded rabies virus libraries

10.1101/2021.09.06.459177 ◽

2021 ◽

Author(s):

Arpiar Saunders ◽

Kee Wui Huang ◽

Cassandra Vondrak ◽

Christina Hughes ◽

Karina Smolyar ◽

...

Keyword(s):

Single Cell ◽

Rabies Virus ◽

Cell Types ◽

Brain Cell ◽

Host Cells ◽

Neural Function ◽

Rna Expression ◽

Synaptic Organization ◽

Single Experiment ◽

Brain Cell Types

Brain function depends on forming and maintaining connections between neurons of specific types, ensuring neural function while allowing the plasticity necessary for cellular and behavioral dynamics. However, systematic descriptions of how brain cell types organize into synaptic networks and which molecules instruct these relationships are not readily available. Here, we introduce SBARRO (Synaptic Barcode Analysis by Retrograde Rabies ReadOut), a method that uses single-cell RNA sequencing to reveal directional, monosynaptic relationships based on the paths of a barcoded rabies virus from its "starter" postsynaptic cell to that cell's presynaptic partners1. Thousands of these partner relationships can be ascertained in a single experiment, alongside genome-wide RNA profiles - and thus cell identities and molecular states - of each host cell. We used SBARRO to describe synaptic networks formed by diverse mouse brain cell types in vitro, leveraging a system similar to those used to identify synaptogenic molecules. We found that the molecular identity (cell type/subtype) of the starter cell predicted the number and types of cells that had synapsed onto it. Rabies transmission tended to occur into cells with RNA-expression signatures related to developmental maturation and synaptic transmission. The estimated size of a cell's presynaptic network, relative to that of other cells of the same type, associated with increased expression of Arpp21 and Cdh13. By tracking individual virions and their clonal progeny as they travel among host cells, single-cell, single-virion genomic technologies offer new opportunities to map the synaptic organization of neural circuits in health and disease.

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