scholarly journals Single cell transcriptome profiling of the human alcohol-dependent brain

2020 ◽  
Vol 29 (7) ◽  
pp. 1144-1153
Author(s):  
Eric Brenner ◽  
Gayatri R Tiwari ◽  
Manav Kapoor ◽  
Yunlong Liu ◽  
Amy Brock ◽  
...  
Keyword(s):  
Alcohol Dependence ◽  
Single Cell ◽  
Cell Types ◽  
Health Concern ◽  
Cell Type ◽  
Altered Expression ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
The Brain ◽  
Alcohol Dependent

Abstract Alcoholism remains a prevalent health concern throughout the world. Previous studies have identified transcriptomic patterns in the brain associated with alcohol dependence in both humans and animal models. But none of these studies have systematically investigated expression within the unique cell types present in the brain. We utilized single nucleus RNA sequencing (snRNA-seq) to examine the transcriptomes of over 16 000 nuclei isolated from the prefrontal cortex of alcoholic and control individuals. Each nucleus was assigned to one of seven major cell types by unsupervised clustering. Cell type enrichment patterns varied greatly among neuroinflammatory-related genes, which are known to play roles in alcohol dependence and neurodegeneration. Differential expression analysis identified cell type-specific genes with altered expression in alcoholics. The largest number of differentially expressed genes (DEGs), including both protein-coding and non-coding, were detected in astrocytes, oligodendrocytes and microglia. To our knowledge, this is the first single cell transcriptome analysis of alcohol-associated gene expression in any species and the first such analysis in humans for any addictive substance. These findings greatly advance the understanding of transcriptomic changes in the brain of alcohol-dependent individuals.

scholarly journals Single cell transcriptome profiling of the human alcohol-dependent brain

2019 ◽  
Author(s):  
Eric Brenner ◽  
Gayatri R. Tiwari ◽  
Yunlong Liu ◽  
Amy Brock ◽  
R. Dayne Mayfield
Keyword(s):  
Alcohol Dependence ◽  
Single Cell ◽  
Cell Types ◽  
Health Concern ◽  
Cell Type ◽  
Altered Expression ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
The Brain ◽  
Alcohol Dependent

AbstractBackgroundAlcoholism remains a prevalent health concern throughout the world. Previous studies have identified transcriptomic patterns in the brain associated with alcohol dependence in both humans and animal models. But none of these studies have systematically investigated expression within the unique cell types present in the brain.ResultsWe utilized single nucleus RNA sequencing (snRNA-seq) to examine the transcriptomes of over 16,000 nuclei isolated from prefrontal cortex of alcoholic and control individuals. Each nucleus was assigned to one of seven major cell types by unsupervised clustering. Cell type enrichment patterns varied greatly among neuroinflammatory-related genes, which are known to play roles in alcohol dependence and neurodegeneration. Differential expression analysis identified cell type-specific genes with altered expression in alcoholics. The largest number of differentially expressed genes (DEGs), including both protein-coding and non-coding, were detected in astrocytes, oligodendrocytes, and microglia.ConclusionsTo our knowledge, this is the first single cell transcriptome analysis of alcohol-associated gene expression in any species, and the first such analysis in humans for any addictive substance. These findings greatly advance understanding of transcriptomic changes in the brain of alcohol-dependent individuals.

scholarly journals Quantitative single-cell transcriptome-based ranking of engineered AAVs in human retinal explants

2021 ◽  
Author(s):  
Zhouhuan Xi ◽  
Bilge E. Ozturk ◽  
Molly E. Johnson ◽  
Leah C. Byrne
Keyword(s):  
Single Cell ◽  
Viral Vector ◽  
Cell Types ◽  
Adeno Associated Virus ◽  
Altered Expression ◽  
Gene Therapies ◽  
Retinal Tissue ◽  
Retinal Explants ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Gene therapy is a rapidly developing field, and adeno-associated virus (AAV) is a leading viral vector candidate for therapeutic gene delivery. Newly engineered AAVs with improved abilities are now entering the clinic. It has proven challenging, however, to predict the translational potential of gene therapies developed in animal models, due to cross-species differences. Human retinal explants are the only available model of fully developed human retinal tissue, and are thus important for the validation of candidate AAV vectors. In this study, we evaluated 18 wildtype and engineered AAV capsids in human retinal explants using a recently developed single-cell RNA-Seq AAV engineering pipeline (scAAVengr). Human retinal explants retained the same major cell types as fresh retina, with similar expression of cell-specific markers, except for a cone population with altered expression of cone-specific genes. The efficiency and tropism of AAVs in human explants were quantified, with single-cell resolution. The top performing serotypes, K91, K912, and 7m8, were further validated in non-human primate and human retinal explants. Together, this study provides detailed information about the transcriptome profiles of retinal explants, and quantifies the infectivity of leading AAV serotypes in human retina, accelerating the translation of retinal gene therapies to the clinic.

scholarly journals A Single-Cell Transcriptome Atlas for Zebrafish Development

2019 ◽  
Author(s):  
Dylan R. Farnsworth ◽  
Lauren Saunders ◽  
Adam C. Miller
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Developmental Time ◽  
Cell Type ◽  
Specific Expression ◽  
Transcriptional Profiles ◽  
Larval Stages ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

ABSTRACTThe ability to define cell types and how they change during organogenesis is central to our understanding of animal development and human disease. Despite the crucial nature of this knowledge, we have yet to fully characterize all distinct cell types and the gene expression differences that generate cell types during development. To address this knowledge gap, we produced an Atlas using single-cell RNA-sequencing methods to investigate gene expression from the pharyngula to early larval stages in developing zebrafish. Our single-cell transcriptome Atlas encompasses transcriptional profiles from 44,102 cells across four days of development using duplicate experiments that confirmed high reproducibility. We annotated 220 identified clusters and highlighted several strategies for interrogating changes in gene expression associated with the development of zebrafish embryos at single-cell resolution. Furthermore, we highlight the power of this analysis to assign new cell-type or developmental stage-specific expression information to many genes, including those that are currently known only by sequence and/or that lack expression information altogether. The resulting Atlas is a resource of biologists to generate hypotheses for genetic (mutant) or functional analysis, to launch an effort to define the diversity of cell-types during zebrafish organogenesis, and to examine the transcriptional profiles that produce each cell type over developmental time.

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 A single-cell catalogue of regulatory states in the ageing Drosophila brain

2017 ◽  
Author(s):  
Kristofer Davie ◽  
Jasper Janssens ◽  
Duygu Koldere ◽  
Uli Pech ◽  
Sara Aibar ◽  
...  
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Neuronal Cell ◽  
Cell Types ◽  
Disease Mutations ◽  
Drosophila Brain ◽  
Gene Regulatory ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
The Brain

SummaryThe diversity of cell types and regulatory states in the brain, and how these change during ageing, remains largely unknown. Here, we present a single-cell transcriptome catalogue of the entire adult Drosophila melanogaster brain sampled across its lifespan. Both neurons and glia age through a process of “regulatory erosion”, characterized by a strong decline of RNA content, and accompanied by increasing transcriptional and chromatin noise. We identify more than 50 cell types by specific transcription factors and their downstream gene regulatory networks. In addition to neurotransmitter types and neuroblast lineages, we find a novel neuronal cell state driven by datilografo and prospero. This state relates to neuronal birth order, the metabolic profile, and the activity of a neuron. Our single-cell brain catalogue reveals extensive regulatory heterogeneity linked to ageing and brain function and will serve as a reference for future studies of genetic variation and disease mutations.

scholarly journals In search of a core cellular network in single cell transcriptome data

2021 ◽  
Author(s):  
Ming Yang ◽  
Benjamin R. Harrison ◽  
Daniel E.L. Promislow
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cellular Network ◽  
Cell Types ◽  
Specific Gene ◽  
Transcriptome Data ◽  
Cell Type ◽  
Cell Type Specific ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractBackgroundAlong with specialized functions, cells of multicellular organisms also perform essential functions common to most if not all cells. Whether diverse cells do this by using the same set of genes, interacting in a fixed coordinated fashion to execute essential functions, remains a central question in biology. Single-cell RNA-sequencing (scRNA-seq) measures gene expression of individual cells, enabling researchers to discover gene expression patterns that contribute to the diversity of cell functions. Current analyses focus primarily on identifying differentially expressed genes across cells. However, patterns of co-expression between genes are probably more indicative of biological processes than are the expression of individual genes. Using single cell transcriptome data from the fly brain, here we focus on gene co-expression to search for a core cellular network.ResultsIn this study, we constructed cell type-specific gene co-expression networks using single cell transcriptome data of brains from the fruit fly, Drosophila melanogaster. We detected a set of highly coordinated genes preserved across cell types in fly brains and defined this set as the core cellular network. This core is very small compared with cell type-specific gene co-expression networks and shows dense connectivity. Modules within this core are enriched for basic cellular functions, such as translation and ATP metabolic processes, and gene members of these modules have distinct evolutionary signatures.ConclusionsOverall, we demonstrated that a core cellular network exists in diverse cell types of fly brains and this core exhibits unique topological, structural, functional and evolutionary properties.

scholarly journals Single-cell Transcriptome Mapping Identifies Common and Cell-type Specific Genes Affected by Acute Delta9-tetrahydrocannabinol in Humans

2019 ◽  
Author(s):  
Ying Hu ◽  
Mohini Ranganathan ◽  
Chang Shu ◽  
Xiaoyu Liang ◽  
Suhas Ganesh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Response ◽  
Single Cell ◽  
Immune Cells ◽  
Cell Types ◽  
Gene Set Enrichment Analysis ◽  
Cell Type ◽  
Cell Type Specific ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractDelta 9-tetrahydrocannabinol (THC), the principal psychoactive constituent of cannabis, is also known to modulate immune response in peripheral cells. The mechanisms of THC’s effects on gene expression in human immune cells remains poorly understood. Combining a within-subject design with single cell transcriptome mapping, we report that administration of THC acutely alters gene expression in 15,973 human blood immune cells. Controlled for high inter-individual transcriptomic variability, we identified 294 transcriptome-wide significant genes among eight cell types including 69 common genes and 225 cell-type specific genes affected by acute THC administration, including those genes involving not only in immune response, cytokine production, but signal transduction, and cell proliferation and apoptosis. We revealed distinct transcriptomic sub-clusters affected by THC in major immune cell types where THC perturbed cell type-specific intracellular gene expression correlations. Gene set enrichment analysis further supports the findings of THC’s common and cell-type specific effects on immune response and cell toxicity. We found that THC alters the correlation of cannabinoid receptor gene, CNR2, with other genes in B cells, in which CNR2 showed the highest level of expression. This comprehensive cell-specific transcriptomic profiling identified novel genes regulated by THC and provides important insights into THC’s acute effects on immune function that may have important medical implications.

scholarly journals Single cell transcriptome atlas of the Drosophila larval brain

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Clarisse Brunet Avalos ◽  
G Larisa Maier ◽  
Rémy Bruggmann ◽  
Simon G Sprecher
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Neural Cells ◽  
Larval Brain ◽  
Cell Diversity ◽  
Transcriptional Changes ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
The Brain

Cell diversity of the brain and how it is affected by starvation, remains largely unknown. Here, we introduce a single cell transcriptome atlas of the entire Drosophila first instar larval brain. We first assigned cell-type identity based on known marker genes, distinguishing five major groups: neural progenitors, differentiated neurons, glia, undifferentiated neurons and non-neural cells. All major classes were further subdivided into multiple subtypes, revealing biological features of various cell-types. We further assessed transcriptional changes in response to starvation at the single-cell level. While after starvation the composition of the brain remains unaffected, transcriptional profile of several cell clusters changed. Intriguingly, different cell-types show very distinct responses to starvation, suggesting the presence of cell-specific programs for nutrition availability. Establishing a single-cell transcriptome atlas of the larval brain provides a powerful tool to explore cell diversity and assess genetic profiles from developmental, functional and behavioral perspectives.

scholarly journals Cell type purification by single-cell transcriptome-trained sorting

2018 ◽  
Author(s):  
Chloé S Baron ◽  
Aditya Barve ◽  
Mauro J Muraro ◽  
Gitanjali Dharmadhikari ◽  
Reinier van der Linden ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Sorting ◽  
Cell Types ◽  
Computational Method ◽  
Human Pancreas ◽  
Cell Type ◽  
Specific Antibodies ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Index Sorting

Traditional cell type enrichment using fluorescence activated cell sorting (FACS) relies on methods that specifically label the cell type of interest. Here we propose GateID, a computational method that combines single-cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without resorting to specific antibodies or transgenes.

scholarly journals Super-cells untangle large and complex single-cell transcriptome networks

2021 ◽  
Author(s):  
Mariia Bilous ◽  
Loc Tran ◽  
Chiara Cianciaruso ◽  
Santiago J Carmona ◽  
Mikael J Pittet ◽  
...  
Keyword(s):  
Single Cell ◽  
Coarse Graining ◽  
Cell Populations ◽  
Cell Type ◽  
Large Numbers ◽  
Single Cell Rna Sequencing ◽  
Gene Correlation ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Single-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells represent an important hurdle for downstream analyses, interpretation and visualization. Here we develop a network-based coarse-graining framework where highly similar cells are merged into super-cells. We demonstrate that super-cells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, super-cells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop.

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