Multiplexed single-cell RNA-seq via transient barcoding for simultaneous expression profiling of various drug perturbations

The development of high-throughput single-cell RNA sequencing (scRNA-seq) has enabled access to information about gene expression in individual cells and insights into new biological areas. Although the interest in scRNA-seq has rapidly grown in recent years, the existing methods are plagued by many challenges when performing scRNA-seq on multiple samples. To simultaneously analyze multiple samples with scRNA-seq, we developed a universal sample barcoding method through transient transfection with short barcode oligonucleotides. By conducting a species-mixing experiment, we have validated the accuracy of our method and confirmed the ability to identify multiplets and negatives. Samples from a 48-plex drug treatment experiment were pooled and analyzed by a single run of Drop-Seq. This revealed unique transcriptome responses for each drug and target-specific gene expression signatures at the single-cell level. Our cost-effective method is widely applicable for the single-cell profiling of multiple experimental conditions, enabling the widespread adoption of scRNA-seq for various applications.

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Multiplexed single-cell RNA-seq via transient barcoding for drug screening

10.1101/359851 ◽

2018 ◽

Cited By ~ 4

Author(s):

Dongju Shin ◽

Wookjae Lee ◽

Ji Hyun Lee ◽

Duhee Bang

Keyword(s):

Single Cell ◽

Cost Effective ◽

Specific Gene ◽

Rna Seq ◽

Experimental Conditions ◽

Cost Effective Method ◽

Treatment Experiment ◽

Single Cell Profiling ◽

Multiple Samples ◽

Pooled Samples

AbstractTo simultaneously analyze multiple samples of various conditions with scRNA-seq, we developed a universal sample barcoding method through transient transfection of SBOs. A 48-plex drug treatment experiment of pooled samples analyzed by a single run of Drop-Seq revealed a unique transcriptome response for each drug and target-specific gene expression signatures at the single-cell level. Our cost-effective method is widely applicable for single-cell profiling of multiple experimental conditions.

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A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples

10.1101/2021.07.10.451910 ◽

2021 ◽

Author(s):

Wenpin Hou ◽

Zhicheng Ji ◽

Zeyu Chen ◽

E John Wherry ◽

Stephanie C Hicks ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Biological Processes ◽

Rna Seq ◽

Experimental Conditions ◽

Computational Framework ◽

Statistical Framework ◽

Gene Regulatory ◽

Multiple Samples ◽

False Discoveries

Pseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data has been widely used to study dynamic gene regulatory programs along continuous biological processes. While many computational methods have been developed to infer the pseudo-temporal trajectories of cells within a biological sample, methods that compare pseudo-temporal patterns with multiple samples (or replicates) across different experimental conditions are lacking. Lamian is a comprehensive and statistically-rigorous computational framework for differential multi-sample pseudotime analysis. It can be used to identify changes in a biological process associated with sample covariates, such as different biological conditions, and also to detect changes in gene expression, cell density, and topology of a pseudotemporal trajectory. Unlike existing methods that ignore sample variability, Lamian draws statistical inference after accounting for cross-sample variability and hence substantially reduces sample-specific false discoveries that are not generalizable to new samples. Using both simulations and real scRNA-seq data, including an analysis of differential immune response programs between COVID-19 patients with different disease severity levels, we demonstrate the advantages of Lamian in decoding cellular gene expression programs in continuous biological processes.

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SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references

Briefings in Bioinformatics ◽

10.1093/bib/bbz166 ◽

2020 ◽

Cited By ~ 13

Author(s):

Meichen Dong ◽

Aatish Thennavan ◽

Eugene Urrutia ◽

Yun Li ◽

Charles M Perou ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Specific Gene ◽

Rna Seq ◽

Cell Type ◽

Mixed Cell ◽

Single Cell Rna Sequencing

Abstract Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

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SAVER: Gene expression recovery for UMI-based single cell RNA sequencing

10.1101/138677 ◽

2017 ◽

Cited By ~ 15

Author(s):

Mo Huang ◽

Jingshu Wang ◽

Eduardo Torre ◽

Hannah Dueck ◽

Sydney Shaffer ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Single Cell Analysis ◽

Specific Gene ◽

Recovery Method ◽

Single Cell Rna Sequencing ◽

Cell Gene Expression ◽

Single Cell Profiling ◽

Downstream Analysis

AbstractRapid advances in massively parallel single cell RNA sequencing (scRNA-seq) is paving the way for high-resolution single cell profiling of biological samples. In most scRNA-seq studies, only a small fraction of the transcripts present in each cell are sequenced. The efficiency, that is, the proportion of transcripts in the cell that are sequenced, can be especially low in highly parallelized experiments where the number of reads allocated for each cell is small. This leads to unreliable quantification of lowly and moderately expressed genes, resulting in extremely sparse data and hindering downstream analysis. To address this challenge, we introduce SAVER (Single-cell Analysis Via Expression Recovery), an expression recovery method for scRNA-seq that borrows information across genes and cells to impute the zeros as well as to improve the expression estimates for all genes. We show, by comparison to RNA fluorescence in situ hybridization (FISH) and by data down-sampling experiments, that SAVER reliably recovers cell-specific gene expression concentrations, cross-cell gene expression distributions, and gene-to-gene and cell-to-cell correlations. This improves the power and accuracy of any downstream analysis involving genes with low to moderate expression.

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SCDC: Bulk Gene Expression Deconvolution by Multiple Single-Cell RNA Sequencing References

10.1101/743591 ◽

2019 ◽

Cited By ~ 1

Author(s):

Meichen Dong ◽

Aatish Thennavan ◽

Eugene Urrutia ◽

Yun Li ◽

Charles M. Perou ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Specific Gene ◽

Rna Seq ◽

Cell Type ◽

Mixed Cell ◽

Single Cell Rna Sequencing

AbstractRecent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

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Single-Cell RNA-Seq of Mouse Decidual Leukocytes Reveals Intriguing Gestational Changes in the Immune Cell Landscape and Effects of SRC-1/-2 Double-Deficiency

Journal of the Endocrine Society ◽

10.1210/jendso/bvab048.1532 ◽

2021 ◽

Vol 5 (Supplement_1) ◽

pp. A753-A754

Author(s):

Jingfei Chen ◽

Jialei Duan ◽

Alina Montalbano ◽

Boxun Li ◽

Gary Hon ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Immune Cell ◽

Expression Patterns ◽

Critical Role ◽

Fetal Lung ◽

Specific Gene ◽

Rna Seq ◽

Cell Clusters ◽

Pregnant Mice

Abstract Single-cell RNA-seq of Mouse Decidual Leukocytes Reveals Intriguing Gestational Changes in the Immune Cell Landscape and Effects of SRC-1/-2 Double-DeficiencyOur previous findings suggest that the fetus signals the mother when it is ready to be born through secretion of surfactant components/immune modulators, surfactant protein A (SP-A) and platelet-activating factor (PAF) by the fetal lung into amniotic fluid (AF). This occurs with increased proinflammatory cytokine expression by fetal AF macrophages (Mϕ), increased myometrial NF-κB activation and contractile (CAP) gene expression. Steroid receptor coactivator (Src)-1 and Src-2 are critical for developmental induction SP-A and PAF by the fetal lung. The finding that pregnant wild-type (WT) mice carrying Src-1 and Src-2 double-deficient fetuses (Src-1/-2d/d) manifested a marked delay (~38h) in parturition, further suggests that signals for parturition arise from the fetus and that Src-1 and Src-2 serve a critical role. Infiltrating leukocytes at the maternal-fetal interface (MFI)/decidua are known to play a central role in pregnancy maintenance and parturition timing. However, there is limited knowledge regarding gestational changes in immune cell composition toward term. To analyze gestational changes in the composition of immune cells within decidua and effects of Src-1/-2d/d, we conducted single-cell RNA-seq of ~17,000 decidual leukocytes (CD45+) from pregnant mice at 15.5 and 18.5 dpc carrying WT or Src-1/-2d/d fetuses. Unsupervised clustering identified 22 distinct decidual immune cell clusters, comprised of Mϕ, B cells, natural killer cells, neutrophils, dendritic cells and monocytes. Significant differences in cell type composition and transcriptional profiles were found across study groups (WT @ 15.5 dpc vs. 18.5 dpc; Src-1/-2d/dvs. WT @15.5 dpc and 18.5 dpc). Interestingly, in deciduas of pregnant mice carrying WT fetuses, Mϕ and B cell clusters markedly increased between 15.5 and 18.5 dpc, whereas, neutrophils declined; however, these gestational changes did not occur in pregnant mice carrying Src-1/-2d/d fetuses. Differential gene expression and gene ontology enrichment analyses revealed specific gene expression patterns distinguishing these immune cell subtypes to uncover their putative functions. These findings highlight the complexity and dynamics of the decidual immune cell landscape during the transition from myometrial quiescence to contractility, and the fetal-maternal interactions leading to parturition. Support: NIH grants R01-HL050022 (CRM) and P01-HD087150 (CRM), Burroughs Wellcome Preterm Birth Grant #1019823 (CRM).

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Discrete Distributional Differential Expression (D3E) - A Tool for Gene Expression Analysis of Single-cell RNA-seq Data

10.1101/020735 ◽

2015 ◽

Cited By ~ 2

Author(s):

Mihails Delmans ◽

Martin Hemberg

Keyword(s):

Gene Expression ◽

Single Cell ◽

Burst Size ◽

Synthetic Data ◽

Driving Mechanism ◽

Rna Seq ◽

Experimental Conditions ◽

Distributional Method ◽

Average Burst Size ◽

Differential Gene

The advent of high throughput RNA-seq at the single-cell level has opened up new opportunities to elucidate the heterogeneity of gene expression. One of the most widespread applications of RNA-seq is to identify genes which are differentially expressed (DE) between two experimental conditions. Here, we present a discrete, distributional method for differential gene expression (D3E), a novel algorithm specifically designed for single-cell RNA-seq data. We use synthetic data to evaluate D3E, demonstrating that it can detect changes in expression, even when the mean level remains unchanged. D3E is based on an analytically tractable stochastic model, and thus it provides additional biological insights by quantifying biologically meaningful properties, such as the average burst size and frequency. We use D3E to investigate experimental data, and with the help of the underlying model, we directly test hypotheses about the driving mechanism behind changes in gene expression.

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Finding cell-specific expression patterns in the early Ciona embryo with single-cell RNA-seq

10.1101/197699 ◽

2017 ◽

Author(s):

Garth R. Ilsley ◽

Ritsuko Suyama ◽

Takeshi Noda ◽

Nori Satoh ◽

Nicholas M. Luscombe

Keyword(s):

Gene Expression ◽

Single Cell ◽

Expression Patterns ◽

Cell Types ◽

Specific Gene ◽

Rna Seq ◽

Cell Stage ◽

Specific Expression ◽

Temporal Gene Expression

AbstractSingle-cell RNA-seq has been established as a reliable and accessible technique enabling new types of analyses, such as identifying cell types and studying spatial and temporal gene expression variation and change at single-cell resolution. Recently, single-cell RNA-seq has been applied to developing embryos, which offers great potential for finding and characterising genes controlling the course of development along with their expression patterns. In this study, we applied single-cell RNA-seq to the 16-cell stage of the Ciona embryo, a marine chordate and performed a computational search for cell-specific gene expression patterns. We recovered many known expression patterns from our single-cell RNA-seq data and despite extensive previous screens, we succeeded in finding new cell-specific patterns, which we validated by in situ and single-cell qPCR.

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Sources of Variation in Cell-Type RNA-Seq Profiles

10.21203/rs.2.23415/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Johan Gustafsson ◽

Felix Held ◽

Jonathan Robinson ◽

Elias Björnson ◽

Rebecka Jörnsten ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Specific Gene ◽

Rna Seq ◽

Cell Type ◽

Specific Gene Expression ◽

Cell Type Specific ◽

Technical Factors

Abstract Background Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. Results We evaluated different normalization methods, quantified the magnitude of variation introduced by different sources, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We applied methods such as random forest regression to a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is of the same magnitude as the biological variation across cell types. Tissue of origin and cell subtype are less important but still substantial factors, while the difference between individuals is relatively small. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample.Conclusions Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples.

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Single cell RNA-seq study of wild type and Hox9,10,11 mutant developing uterus

10.1101/395574 ◽

2018 ◽

Cited By ~ 1

Author(s):

Michael L. Mucenski ◽

Robert Mahoney ◽

Mike Adam ◽

Andrew S. Potter ◽

S. Steven Potter

Keyword(s):

Gene Expression ◽

Single Cell ◽

Hox Genes ◽

Expression Patterns ◽

Wild Type Mouse ◽

Cell Types ◽

Specific Gene ◽

Rna Seq ◽

Wild Type ◽

Mouse Uterus

AbstractThe uterus is a remarkable organ that must guard against infections while maintaining the ability to support growth of a fetus without rejection. The Hoxa10 and Hoxa11 genes have previously been shown to play essential roles in uterus development and function. In this report we show that the Hoxc9,10,11 genes play a redundant role in the formation of uterine glands. In addition, we use single cell RNA-seq to create a high resolution gene expression atlas of the developing wild type mouse uterus. Cell types and subtypes are defined, for example dividing endothelial cells into arterial, venous, capillary, and lymphatic, while epithelial cells separate into luminal and glandular subtypes. Further, a surprising heterogeneity of stromal and myocyte cell types are identified. Transcription factor codes and ligand/receptor interactions are characterized. We also used single cell RNA-seq to globally define the altered gene expression patterns in all developing uterus cell types for two Hox mutants, with 8 or 9 mutant Hox genes. The mutants show a striking disruption of Wnt signaling as well as the Cxcl12/Cxcr4 ligand/receptor axis.Summary statementA single cell RNA-seq study of the developing mouse uterus defines cellular heterogeneities, lineage specific gene expression programs and perturbed pathways in Hox9,10,11 mutants.

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