scholarly journals Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes

2020 ◽  
Author(s):  
Timothy J. Durham ◽  
Riza M. Daza ◽  
Louis Gevirtzman ◽  
Darren A. Cusanovich ◽  
William Stafford Noble ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Gene Expression Patterns ◽  
Rna Seq ◽  
Cell Type ◽  
Tissue Specific ◽  
C Elegans

AbstractRecently developed single cell technologies allow researchers to characterize cell states at ever greater resolution and scale. C. elegans is a particularly tractable system for studying development, and recent single cell RNA-seq studies characterized the gene expression patterns for nearly every cell type in the embryo and at the second larval stage (L2). Gene expression patterns are useful for learning about gene function and give insight into the biochemical state of different cell types; however, in order to understand these cell types, we must also determine how these gene expression levels are regulated. We present the first single cell ATAC-seq study in C. elegans. We collected data in L2 larvae to match the available single cell RNA-seq data set, and we identify tissue-specific chromatin accessibility patterns that align well with existing data, including the L2 single cell RNA-seq results. Using a novel implementation of the latent Dirichlet allocation algorithm, we leverage the single-cell resolution of the sci-ATAC-seq data to identify accessible loci at the level of individual cell types, providing new maps of putative cell type-specific gene regulatory sites, with promise for better understanding of cellular differentiation and gene regulation in the worm.

scholarly journals A single cell brain atlas in human Alzheimer’s disease

2019 ◽  
Author(s):  
Alexandra Grubman ◽  
Gabriel Chew ◽  
John F. Ouyang ◽  
Guizhi Sun ◽  
Xin Yi Choo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Single Cell ◽  
Cell Fate ◽  
Expression Patterns ◽  
Cell Types ◽  
Gene Expression Patterns ◽  
Cell Type ◽  
Web Resource ◽  
Cell Type Specific

AbstractAlzheimer’s disease (AD) is a heterogeneous disease that is largely dependent on the complex cellular microenvironment in the brain. This complexity impedes our understanding of how individual cell types contribute to disease progression and outcome. To characterize the molecular and functional cell diversity in the human AD brain we utilized single nuclei RNA- seq in AD and control patient brains in order to map the landscape of cellular heterogeneity in AD. We detail gene expression changes at the level of cells and cell subclusters, highlighting specific cellular contributions to global gene expression patterns between control and Alzheimer’s patient brains. We observed distinct cellular regulation of APOE which was repressed in oligodendrocyte progenitor cells (OPCs) and astrocyte AD subclusters, and highly enriched in a microglial AD subcluster. In addition, oligodendrocyte and microglia AD subclusters show discordant expression of APOE. Integration of transcription factor regulatory modules with downstream GWAS gene targets revealed subcluster-specific control of AD cell fate transitions. For example, this analysis uncovered that astrocyte diversity in AD was under the control of transcription factor EB (TFEB), a master regulator of lysosomal function and which initiated a regulatory cascade containing multiple AD GWAS genes. These results establish functional links between specific cellular sub-populations in AD, and provide new insights into the coordinated control of AD GWAS genes and their cell-type specific contribution to disease susceptibility. Finally, we created an interactive reference web resource which will facilitate brain and AD researchers to explore the molecular architecture of subtype and AD-specific cell identity, molecular and functional diversity at the single cell level.HighlightsWe generated the first human single cell transcriptome in AD patient brainsOur study unveiled 9 clusters of cell-type specific and common gene expression patterns between control and AD brains, including clusters of genes that present properties of different cell types (i.e. astrocytes and oligodendrocytes)Our analyses also uncovered functionally specialized sub-cellular clusters: 5 microglial clusters, 8 astrocyte clusters, 6 neuronal clusters, 6 oligodendrocyte clusters, 4 OPC and 2 endothelial clusters, each enriched for specific ontological gene categoriesOur analyses found manifold AD GWAS genes specifically associated with one cell-type, and sets of AD GWAS genes co-ordinately and differentially regulated between different brain cell-types in AD sub-cellular clustersWe mapped the regulatory landscape driving transcriptional changes in AD brain, and identified transcription factor networks which we predict to control cell fate transitions between control and AD sub-cellular clustersFinally, we provide an interactive web-resource that allows the user to further visualise and interrogate our dataset.Data resource web interface:http://adsn.ddnetbio.com

scholarly journals SAT-298 Integrative Single-Cell Transcriptomic and Epigenomic Landscape of Mouse Anterior Pituitary Cell Types

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Frederique Murielle Ruf-Zamojski ◽  
Michel A Zamojski ◽  
German Nudelman ◽  
Yongchao Ge ◽  
Natalia Mendelev ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Anterior Pituitary ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Pituitary Cell ◽  
Integrated Analysis ◽  
Pituitary Cells ◽  
Rna Seq ◽  
Cell Type

Abstract The pituitary gland is a critical regulator of the neuroendocrine system. To further our understanding of the classification, cellular heterogeneity, and regulatory landscape of pituitary cell types, we performed and computationally integrated single cell (SC)/single nucleus (SN) resolution experiments capturing RNA expression, chromatin accessibility, and DNA methylation state from mouse dissociated whole pituitaries. Both SC and SN transcriptome analysis and promoter accessibility identified the five classical hormone-producing cell types (somatotropes, gonadotropes (GT), lactotropes, thyrotropes, and corticotropes). GT cells distinctively expressed transcripts for Cga, Fshb, Lhb, Nr5a1, and Gnrhr in SC RNA-seq and SN RNA-seq. This was matched in SN ATAC-seq with GTs specifically showing open chromatin at the promoter regions for the same genes. Similarly, the other classically defined anterior pituitary cells displayed transcript expression and chromatin accessibility patterns characteristic of their own cell type. This integrated analysis identified additional cell-types, such as a stem cell cluster expressing transcripts for Sox2, Sox9, Mia, and Rbpms, and a broadly accessible chromatin state. In addition, we performed bulk ATAC-seq in the LβT2b gonadotrope-like cell line. While the FSHB promoter region was closed in the cell line, we identified a region upstream of Fshb that became accessible by the synergistic actions of GnRH and activin A, and that corresponded to a conserved region identified by a polycystic ovary syndrome (PCOS) single nucleotide polymorphism (SNP). Although this locus appears closed in deep sequencing bulk ATAC-seq of dissociated mouse pituitary cells, SN ATAC-seq of the same preparation showed that this site was specifically open in mouse GT, but closed in 14 other pituitary cell type clusters. This discrepancy highlighted the detection limit of a bulk ATAC-seq experiment in a subpopulation, as GT represented ~5% of this dissociated anterior pituitary sample. These results identified this locus as a candidate for explaining the dual dependence of Fshb expression on GnRH and activin/TGFβ signaling, and potential new evidence for upstream regulation of Fshb. The pituitary epigenetic landscape provides a resource for improved cell type identification and for the investigation of the regulatory mechanisms driving cell-to-cell heterogeneity. Additional authors not listed due to abstract submission restrictions: N. Seenarine, M. Amper, N. Jain (ISMMS).

scholarly journals Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Dylan Kotliar ◽  
Adrian Veres ◽  
M Aurel Nagy ◽  
Shervin Tabrizi ◽  
Eran Hodis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Matrix Factorization ◽  
Cell Types ◽  
Environmental Cues ◽  
Rna Seq ◽  
Cell Type ◽  
Type Identity ◽  
Brain Organoid ◽  
Non Negative Matrix Factorization

Identifying gene expression programs underlying both cell-type identity and cellular activities (e.g. life-cycle processes, responses to environmental cues) is crucial for understanding the organization of cells and tissues. Although single-cell RNA-Seq (scRNA-Seq) can quantify transcripts in individual cells, each cell’s expression profile may be a mixture of both types of programs, making them difficult to disentangle. Here, we benchmark and enhance the use of matrix factorization to solve this problem. We show with simulations that a method we call consensus non-negative matrix factorization (cNMF) accurately infers identity and activity programs, including their relative contributions in each cell. To illustrate the insights this approach enables, we apply it to published brain organoid and visual cortex scRNA-Seq datasets; cNMF refines cell types and identifies both expected (e.g. cell cycle and hypoxia) and novel activity programs, including programs that may underlie a neurosecretory phenotype and synaptogenesis.

scholarly journals MarkerCount: A stable, count-based cell type identifier for single cell RNA-Seq experiments

2021 ◽  
Author(s):  
Hanbyeol Kim ◽  
Joongho Lee ◽  
Keunsoo Kang ◽  
Seokhyun Yoon
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Batch Effect ◽  
Expression Level ◽  
Rna Seq ◽  
Cell Type ◽  
Stable Performance ◽  
Downstream Analysis

Abstract Cell type identification is a key step to downstream analysis of single cell RNA-seq experiments. Indispensible information for this is gene expression, which is used to cluster cells, train the model and set rejection thresholds. Problem is they are subject to batch effect arising from different platforms and preprocessing. We present MarkerCount, which uses the number of markers expressed regardless of their expression level to initially identify cell types and, then, reassign cell type in cluster-basis. MarkerCount works both in reference and marker-based mode, where the latter utilizes only the existing lists of markers, while the former required pre-annotated dataset to train the model. The performance was evaluated and compared with the existing identifiers, both marker and reference-based, that can be customized with publicly available datasets and marker DB. The results show that MarkerCount provides a stable performance when comparing with other reference-based and marker-based cell type identifiers.

scholarly journals Identifying Gene Expression Programs of Cell-type Identity and Cellular Activity with Single-Cell RNA-Seq

2018 ◽  
Author(s):  
Dylan Kotliar ◽  
Adrian Veres ◽  
M. Aurel Nagy ◽  
Shervin Tabrizi ◽  
Eran Hodis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Matrix Factorization ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Relative Contribution ◽  
Neuronal Synapses ◽  
Type Identity ◽  
Brain Organoid

AbstractIdentifying gene expression programs underlying both cell-type identity and cellular activities (e.g. life-cycle processes, responses to environmental cues) is crucial for understanding the organization of cells and tissues. Although single-cell RNA-Seq (scRNA-Seq) can quantify transcripts in individual cells, each cell’s expression profile may be a mixture of both types of programs, making them difficult to disentangle. Here we illustrate and enhance the use of matrix factorization as a solution to this problem. We show with simulations that a method that we call consensus non-negative matrix factorization (cNMF) accurately infers identity and activity programs, including the relative contribution of programs in each cell. Applied to published brain organoid and visual cortex scRNA-Seq datasets, cNMF refines the hierarchy of cell types and identifies both expected (e.g. cell cycle and hypoxia) and intriguing novel activity programs. We propose that one of the novel programs may reflect a neurosecretory phenotype and a second may underlie the formation of neuronal synapses. We make cNMF available to the community and illustrate how this approach can provide key insights into gene expression variation within and between cell types.

scholarly journals Comprehensive integration of single cell data

2018 ◽  
Author(s):  
Tim Stuart ◽  
Andrew Butler ◽  
Paul Hoffman ◽  
Christoph Hafemeister ◽  
Efthymia Papalexi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Substantial Improvement ◽  
Single Cell Protein ◽  
Cell Protein ◽  
Spatial Positioning ◽  
Cell Data

Single cell transcriptomics (scRNA-seq) has transformed our ability to discover and annotate cell types and states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, including high-dimensional immunophenotypes, chromatin accessibility, and spatial positioning, a key analytical challenge is to integrate these datasets into a harmonized atlas that can be used to better understand cellular identity and function. Here, we develop a computational strategy to “anchor” diverse datasets together, enabling us to integrate and compare single cell measurements not only across scRNA-seq technologies, but different modalities as well. After demonstrating substantial improvement over existing methods for data integration, we anchor scRNA-seq experiments with scATAC-seq datasets to explore chromatin differences in closely related interneuron subsets, and project single cell protein measurements onto a human bone marrow atlas to annotate and characterize lymphocyte populations. Lastly, we demonstrate how anchoring can harmonize in-situ gene expression and scRNA-seq datasets, allowing for the transcriptome-wide imputation of spatial gene expression patterns, and the identification of spatial relationships between mapped cell types in the visual cortex. Our work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets.Availability: Installation instructions, documentation, and tutorials are available at: https://www.satijalab.org/seurat

scholarly journals Tissue-specific profiling reveals distinctive regulatory architectures for ubiquitous, germline and somatic genes

2020 ◽  
Author(s):  
Jacques Serizay ◽  
Yan Dong ◽  
Jürgen Jänes ◽  
Michael Chesney ◽  
Chiara Cerrato ◽  
...  
Keyword(s):  
Gene Expression ◽  
Core Promoter ◽  
Regulatory Element ◽  
Expression Patterns ◽  
Somatic Tissue ◽  
Gene Expression Patterns ◽  
Specific Gene ◽  
Detailed Knowledge ◽  
Tissue Specific ◽  
C Elegans

AbstractDespite increasingly detailed knowledge of gene expression patterns, the regulatory architectures that drive them are not well understood. To address this, we compared transcriptional and regulatory element activities across five adult tissues of C. elegans, covering ∼90% of cells, and defined regulatory grammars associated with ubiquitous, germline and somatic tissue-specific gene expression patterns. We find architectural features that distinguish two major promoter types. Germline-specific and ubiquitously-active promoters have well positioned +1 and −1 nucleosomes associated with a periodic 10-bp WW signal. Somatic tissue-specific promoters lack these features, have wider nucleosome depleted regions, and are more enriched for core promoter elements, which surprisingly differ between tissues. A 10-bp periodic WW signal is also associated with +1 nucleosomes of ubiquitous promoters in fly and zebrafish but is not detected in mouse and human. Our results demonstrate fundamental differences in regulatory architectures of germline-active and somatic tissue-specific genes and provide a key resource for future studies.

scholarly journals DSAVE: Detection of misclassified cells in single-cell RNA-Seq data

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243360
Author(s):  
Johan Gustafsson ◽  
Jonathan Robinson ◽  
Juan S. Inda-Díaz ◽  
Elias Björnson ◽  
Rebecka Jörnsten ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Log Likelihood ◽  
Single Cell Rna Sequencing ◽  
Cell Transcriptome ◽  
Average Gene ◽  
Single Cell Transcriptome

Single-cell RNA sequencing has become a valuable tool for investigating cell types in complex tissues, where clustering of cells enables the identification and comparison of cell populations. Although many studies have sought to develop and compare different clustering approaches, a deeper investigation into the properties of the resulting populations is lacking. Specifically, the presence of misclassified cells can influence downstream analyses, highlighting the need to assess subpopulation purity and to detect such cells. We developed DSAVE (Down-SAmpling based Variation Estimation), a method to evaluate the purity of single-cell transcriptome clusters and to identify misclassified cells. The method utilizes down-sampling to eliminate differences in sampling noise and uses a log-likelihood based metric to help identify misclassified cells. In addition, DSAVE estimates the number of cells needed in a population to achieve a stable average gene expression profile within a certain gene expression range. We show that DSAVE can be used to find potentially misclassified cells that are not detectable by similar tools and reveal the cause of their divergence from the other cells, such as differing cell state or cell type. With the growing use of single-cell RNA-seq, we foresee that DSAVE will be an increasingly useful tool for comparing and purifying subpopulations in single-cell RNA-Seq datasets.

scholarly journals Single-cell regulatory landscape and disease vulnerability map of adult Macaque cortex

2020 ◽  
Author(s):  
Ying Lei ◽  
Mengnan Cheng ◽  
Zihao Li ◽  
Zhenkun Zhuang ◽  
Liang Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Primary Motor Cortex ◽  
Neurological Diseases ◽  
Single Cells ◽  
Cell Types ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Nucleotide Polymorphisms ◽  
Cell Type

Non-human primates (NHP) provide a unique opportunity to study human neurological diseases, yet detailed characterization of the cell types and transcriptional regulatory features in the NHP brain is lacking. We applied a combinatorial indexing assay, sci-ATAC-seq, as well as single-nuclei RNA-seq, to profile chromatin accessibility in 43,793 single cells and transcriptomics in 11,477 cells, respectively, from prefrontal cortex, primary motor cortex and the primary visual cortex of adult cynomolgus monkey Macaca fascularis. Integrative analysis of these two datasets, resolved regulatory elements and transcription factors that specify cell type distinctions, and discovered area-specific diversity in chromatin accessibility and gene expression within excitatory neurons. We also constructed the dynamic landscape of chromatin accessibility and gene expression of oligodendrocyte maturation to characterize adult remyelination. Furthermore, we identified cell type-specific enrichment of differentially spliced gene isoforms and disease-associated single nucleotide polymorphisms. Our datasets permit integrative exploration of complex regulatory dynamics in macaque brain tissue at single-cell resolution.

scholarly journals Single-cell deconvolution of 3,000 post-mortem brain samples for eQTL and GWAS dissection in mental disorders

2021 ◽  
Author(s):  
Yongjin Park ◽  
Liang He ◽  
Jose Davila-Velderrain ◽  
Lei Hou ◽  
Shahin Mohammadi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Genetic Variants ◽  
Cell Types ◽  
Brain Regions ◽  
Post Mortem ◽  
Rna Seq ◽  
Cell Type ◽  
Post Mortem Brain ◽  
Cell Type Specific

AbstractThousands of genetic variants acting in multiple cell types underlie complex disorders, yet most gene expression studies profile only bulk tissues, making it hard to resolve where genetic and non-genetic contributors act. This is particularly important for psychiatric and neurodegenerative disorders that impact multiple brain cell types with highly-distinct gene expression patterns and proportions. To address this challenge, we develop a new framework, SPLITR, that integrates single-nucleus and bulk RNA-seq data, enabling phenotype-aware deconvolution and correcting for systematic discrepancies between bulk and single-cell data. We deconvolved 3,387 post-mortem brain samples across 1,127 individuals and in multiple brain regions. We find that cell proportion varies across brain regions, individuals, disease status, and genotype, including genetic variants in TMEM106B that impact inhibitory neuron fraction and 4,757 cell-type-specific eQTLs. Our results demonstrate the power of jointly analyzing bulk and single-cell RNA-seq to provide insights into cell-type-specific mechanisms for complex brain disorders.

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