Cell type purification by single-cell transcriptome-trained 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.

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A Single-Cell Transcriptome Atlas for Zebrafish Development

10.1101/738344 ◽

2019 ◽

Cited By ~ 1

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.

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

PLoS ONE ◽

10.1371/journal.pone.0243360 ◽

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.

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Single cell transcriptome profiling of the human alcohol-dependent brain

10.1101/780304 ◽

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.

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

10.1101/2021.09.19.460857 ◽

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.

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

10.1101/638254 ◽

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.

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Single cell transcriptome profiling of the human alcohol-dependent brain

Human Molecular Genetics ◽

10.1093/hmg/ddaa038 ◽

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.

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

10.1101/2021.06.07.447430 ◽

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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A computational method to aid the design and analysis of single cell RNA-seq experiments for cell type identification

10.1101/247114 ◽

2018 ◽

Cited By ~ 1

Author(s):

Douglas Abrams ◽

Parveen Kumar ◽

R. Krishna Murthy Karuturi ◽

Joshy George

Keyword(s):

Experimental Design ◽

Single Cell ◽

Single Cells ◽

Cell Types ◽

Cell Number ◽

Fold Change ◽

Computational Method ◽

Marker Genes ◽

Cell Type ◽

Estimate Sample Size

AbstractBackgroundThe advent of single cell RNA sequencing (scRNA-seq) enabled researchers to study transcriptomic activity within individual cells and identify inherent cell types in the sample. Although numerous computational tools have been developed to analyze single cell transcriptomes, there are no published studies and analytical packages available to guide experimental design and to devise suitable analysis procedure for cell type identification.ResultsWe have developed an empirical methodology to address this important gap in single cell experimental design and analysis into an easy-to-use tool called SCEED (Single Cell Empirical Experimental Design and analysis). With SCEED, user can choose a variety of combinations of tools for analysis, conduct performance analysis of analytical procedures and choose the best procedure, and estimate sample size (number of cells to be profiled) required for a given analytical procedure at varying levels of cell type rarity and other experimental parameters. Using SCEED, we examined 3 single cell algorithms using 48 simulated single cell datasets that were generated for varying number of cell types and their proportions, number of genes expressed per cell, number of marker genes and their fold change, and number of single cells successfully profiled in the experiment.ConclusionsBased on our study, we found that when marker genes are expressed at fold change of 4 or more than the rest of the genes, either Seurat or Simlr algorithm can be used to analyze single cell dataset for any number of single cells isolated (minimum 1000 single cells were tested). However, when marker genes are expected to be only up to fC 2 upregulated, choice of the single cell algorithm is dependent on the number of single cells isolated and proportion of rare cell type to be identified. In conclusion, our work allows the assessment of various single cell methods and also aids in examining the single cell experimental design.

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