Comprehensive benchmarking of single cell RNA sequencing technologies for characterizing cellular perturbation

ABSTRACTTechnological advances in transcriptome sequencing of single cells continues to provide an unprecedented view on tissue composition and cellular heterogeneity. While several studies have compared different single cell RNA-seq methods with respect to data quality and their ability to distinguish cell subpopulations, none of these studies investigated the heterogeneity of the cellular transcriptional response upon a chemical perturbation. In this study, we evaluated the transcriptional response of NGP neuroblastoma cells upon nutlin-3 treatment using the C1, ddSeq and Chromium single cell systems. These devices and library preparation methods are representative for the wide variety of platforms, ranging from microfluid chips to droplet-based systems and from full transcript sequencing to 3-prime end sequencing. In parallel, we used bulk RNA-seq for molecular characterization of the transcriptional response. Two complementary metrics to evaluate performance were applied: the first is the number and identity of differentially expressed genes as defined in consensus by two statistical models, and the second is the enrichment analysis of biological signals. Where relevant, to make the data more comparable, we downsampled sequencing library size, selected cell subpopulations based on specific RNA abundance features, or created pseudobulk samples. While the C1 detects the highest number of genes per cell and better resembles bulk RNA-seq, the Chromium identifies most differentially expressed genes, albeit still substantially fewer than bulk RNA-seq. Gene set enrichment analyses reveals that detection of a limited set of the most abundant genes in single cell RNA-seq experiments is sufficient for molecular phenotyping. Finally, single cell RNA-seq reveals a heterogeneous response of NGP neuroblastoma cells upon nutlin-3 treatment, revealing putative late-responder or resistant cells, both undetected in bulk RNA-seq experiments.

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Identification of Specific Cell Subpopulations and Marker Genes in Ovarian Cancer Using Single-Cell RNA Sequencing

BioMed Research International ◽

10.1155/2021/1005793 ◽

2021 ◽

Vol 2021 ◽

pp. 1-27

Author(s):

Yan Li ◽

Juan Wang ◽

Fang Wang ◽

Chengzhen Gao ◽

Yuanyuan Cao ◽

...

Keyword(s):

Ovarian Cancer ◽

Single Cell ◽

Differentially Expressed Genes ◽

Single Cells ◽

Differentially Expressed ◽

Ovarian Cancer Cells ◽

Marker Genes ◽

Specific Cell ◽

Rna Seq ◽

Cell Subpopulations

Objective. Ovarian cancer is the deadliest gynaecological cancer globally. In our study, we aimed to analyze specific cell subpopulations and marker genes among ovarian cancer cells by single-cell RNA sequencing (RNA-seq). Methods. Single-cell RNA-seq data of 66 high-grade serous ovarian cancer cells were employed from the Gene Expression Omnibus (GEO). Using the Seurat package, we performed quality control to remove cells with low quality. After normalization, we detected highly variable genes across the single cells. Then, principal component analysis (PCA) and cell clustering were performed. The marker genes in different cell clusters were detected. A total of 568 ovarian cancer samples and 8 normal ovarian samples were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes were identified according to ∣ log 2 fold change FC ∣ > 1 and adjusted p value <0.05. To explore potential biological processes and pathways, functional enrichment analyses were performed. Furthermore, survival analyses of differentially expressed marker genes were performed. Results. After normalization, 6000 highly variable genes were identified across the single cells. The cells were divided into 3 cell populations, including G1, G2M, and S cell cycles. A total of 1,124 differentially expressed genes were identified in ovarian cancer samples. These differentially expressed genes were enriched in several pathways associated with cancer, such as metabolic pathways, pathways in cancer, and PI3K-Akt signaling pathway. Furthermore, marker genes, STAT1, ANP32E, GPRC5A, and EGFL6, were highly expressed in ovarian cancer, while PMP22, FBXO21, and CYB5R3 were lowly expressed in ovarian cancer. These marker genes were positively associated with prognosis of ovarian cancer. Conclusion. Our findings revealed specific cell subpopulations and marker genes in ovarian cancer using single-cell RNA-seq, which provided a novel insight into the heterogeneity of ovarian cancer.

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CHARTS: A web application for characterizing and comparing tumor subpopulations in publicly available single-cell RNA-seq datasets

10.1101/2020.09.23.310441 ◽

2020 ◽

Author(s):

Matthew N. Bernstein ◽

Zijian Ni ◽

Michael Collins ◽

Mark E. Burkard ◽

Christina Kendziorski ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Web Application ◽

Cellular Heterogeneity ◽

Rna Seq ◽

Individual Gene ◽

Genome Wide ◽

Progression Of Disease ◽

Cell Subpopulations ◽

Available Information

AbstractBackgroundSingle-cell RNA-seq (scRNA-seq) enables the profiling of genome-wide gene expression at the single-cell level and in so doing facilitates insight into and information about cellular heterogeneity within a tissue. Perhaps nowhere is this more important than in cancer, where tumor and tumor microenvironment heterogeneity directly impact development, maintenance, and progression of disease. While publicly available scRNA-seq cancer datasets offer unprecedented opportunity to better understand the mechanisms underlying tumor progression, metastasis, drug resistance, and immune evasion, much of the available information has been underutilized, in part, due to the lack of tools available for aggregating and analysing these data.ResultsWe present CHARacterizing Tumor Subpopulations (CHARTS), a computational pipeline and web application for analyzing, characterizing, and integrating publicly available scRNA-seq cancer datasets. CHARTS enables the exploration of individual gene expression, cell type, malignancy-status, differentially expressed genes, and gene set enrichment results in subpopulations of cells across multiple tumors and datasets.ConclusionCHARTS is an easy to use, comprehensive platform for exploring single-cell subpopulations within tumors across the ever-growing collection of public scRNA-seq cancer datasets. CHARTS is freely available at charts.morgridge.org.

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Single-cell mapper (scMappR): using scRNA-seq to infer the cell-type specificities of differentially expressed genes

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab011 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Dustin J Sokolowski ◽

Mariela Faykoo-Martinez ◽

Lauren Erdman ◽

Huayun Hou ◽

Cadia Chan ◽

...

Keyword(s):

Single Cell ◽

Differentially Expressed Genes ◽

Cell Types ◽

Differentially Expressed ◽

Rna Seq ◽

Kidney Regeneration ◽

Cell Type ◽

Cell Type Specificity ◽

Cost Constraints ◽

Mouse Tissues

Abstract RNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell-types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by leveraging cell-type expression data generated by scRNA-seq and existing deconvolution methods. After evaluating scMappR with simulated RNA-seq data and benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small population of immune cells. While scMappR can work with user-supplied scRNA-seq data, we curated scRNA-seq expression matrices for ∼100 human and mouse tissues to facilitate its stand-alone use with bulk RNA-seq data from these species. Overall, scMappR is a user-friendly R package that complements traditional differential gene expression analysis of bulk RNA-seq data.

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RNA-seq Reveals Differentially Expressed Genes between Two indica Inbred Rice Genotypes Associated with Drought-Yield QTLs

Agronomy ◽

10.3390/agronomy10050621 ◽

2020 ◽

Vol 10 (5) ◽

pp. 621 ◽

Cited By ~ 2

Author(s):

Nelzo C. Ereful ◽

Li-yu Liu ◽

Andy Greenland ◽

Wayne Powell ◽

Ian Mackay ◽

...

Keyword(s):

Water Stress ◽

Differentially Expressed Genes ◽

Transcriptional Response ◽

Differentially Expressed ◽

Yield Response ◽

Oxygen Binding ◽

Rna Seq ◽

Binding Function ◽

Significant Enrichment ◽

Pathway Analyses

Two indica inbred rice lines, IR64, a drought-sensitive, and Apo, a moderately drought-tolerant genotype, were exposed to non- (control or unstressed) and water-stress treatments. Leaf samples collected at an early flowering stage were sequenced by RNA-seq. Reads generated were analyzed for differential expression (DE) implementing various models in baySeq to capture differences in genome-wide transcriptional response under contrasting water regimes. IR64, the drought-sensitive variety consistently exhibited a broader transcriptional response while Apo showed relatively modest transcriptional changes under water-stress conditions across all models implemented. Gene ontology (GO) and KEGG pathway analyses of genes revealed that IR64 showed enhancement of functions associated with signal transduction, protein binding and receptor activity. Apo uniquely showed significant enrichment of genes associated with an oxygen binding function and peroxisome pathway. In general, IR64 exhibited more extensive molecular re-programming, presumably, a highly energy-demanding route to deal with the abiotic stress. Several of these differentially expressed genes (DEGs) were found to co-localize with QTL marker regions previously identified to be associated with drought-yield response, thus, are the most promising candidate genes for further studies.

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Sparsely-Connected Autoencoder (SCA) for single cell RNAseq data mining

10.1101/2020.05.26.117705 ◽

2020 ◽

Author(s):

L Alessandri ◽

F Cordero ◽

M Beccuti ◽

N Licheri ◽

M Arigoni ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

Medical Personnel ◽

Cellular Heterogeneity ◽

Biological Information ◽

The Neural Network ◽

Rnaseq Data ◽

Functional Features ◽

Cell Subpopulations

AbstractSingle-cell RNA sequencing (scRNAseq) is an essential tool to investigate cellular heterogeneity. Although scRNAseq has some technical challenges, it would be of great interest being able to disclose biological information out of cell subpopulations, which can be defined by cluster analysis of scRNAseq data. In this manuscript, we evaluated the efficacy of sparsely-connected autoencoder (SCA) as tool for the functional mining of single cells clusters. We show that SCA can be uses as tool to uncover hidden features associated to scRNAseq data. Our approach is strengthened by two metrics, QCF and QCM, which respectively allow to evaluate the ability of SCA to reconstruct a cells cluster and to evaluate the overall quality of the neural network model. Our data indicate that SCA encoded spaces, derived by different experimentally validated data (TFs targets, miRNAs targets, Kinases targets, and cancer-related immune signatures), can be used to grasp single cell cluster-specific functional features. In our implementation, SCA efficacy comes from its ability to reconstruct only specific clusters, thus indicating only those clusters where the SCA encoding space is a key element for cells aggregation. SCA analysis is implemented as module in rCASC framework and it is supported by a GUI to simplify it usage for biologists and medical personnel.

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CCSN: Single Cell RNA Sequencing Data Analysis by Conditional Cell-specific Network

10.1101/2020.01.25.919829 ◽

2020 ◽

Author(s):

Lin Li ◽

Hao Dai ◽

Zhaoyuan Fang ◽

Luonan Chen

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Network Flow ◽

Single Cells ◽

Cellular Heterogeneity ◽

Rna Seq ◽

Sequencing Data ◽

Cell Clustering ◽

A Cell

AbstractThe rapid advancement of single cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. However, compared with bulk RNA sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq) suffers from higher noise and lower coverage, which brings new computational difficulties. Based on statistical independence, cell-specific network (CSN) is able to quantify the overall associations between genes for each cell, yet suffering from a problem of overestimation related to indirect effects. To overcome this problem, we propose the “conditional cell-specific network” (CCSN) method, which can measure the direct associations between genes by eliminating the indirect associations. CCSN can be used for cell clustering and dimension reduction on a network basis of single cells. Intuitively, each CCSN can be viewed as the transformation from less “reliable” gene expression to more “reliable” gene-gene associations in a cell. Based on CCSN, we further design network flow entropy (NFE) to estimate the differentiation potency of a single cell. A number of scRNA-seq datasets were used to demonstrate the advantages of our approach: (1) one direct association network for one cell; (2) most existing scRNA-seq methods designed for gene expression matrices are also applicable to CCSN-transformed degree matrices; (3) CCSN-based NFE helps resolving the direction of differentiation trajectories by quantifying the potency of each cell. CCSN is publicly available at http://sysbio.sibcb.ac.cn/cb/chenlab/soft/CCSN.zip.

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powsimR: Power analysis for bulk and single cell RNA-seq experiments

10.1101/117150 ◽

2017 ◽

Cited By ~ 5

Author(s):

Beate Vieth ◽

Christoph Ziegenhain ◽

Swati Parekh ◽

Wolfgang Enard ◽

Ines Hellmann

Keyword(s):

Single Cell ◽

Differential Expression ◽

Differentially Expressed Genes ◽

Power Analysis ◽

A Priori ◽

Differentially Expressed ◽

Rna Seq ◽

Flexible Tool ◽

Power Analyses

AbstractPower analysis is essential to optimize the design of RNA-seq experiments and to assess and compare the power to detect differentially expressed genes in RNA-seq data. PowsimR is a flexible tool to simulate and evaluate differential expression from bulk and especially single-cell RNA-seq data making it suitable for a priori and posterior power analyses.

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Single-cell RNA sequencing reveals the mechanism of sonodynamic therapy combined with a RAS inhibitor in the setting of hepatocellular carcinoma

Journal of Nanobiotechnology ◽

10.1186/s12951-021-00923-3 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Bolin Wu ◽

Yanchi Yuan ◽

Jiayin Liu ◽

Haitao Shang ◽

Jing Dong ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Single Cell ◽

Differentially Expressed Genes ◽

Therapeutic Strategy ◽

Single Cell Analysis ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Cellular Heterogeneity ◽

Sonodynamic Therapy ◽

Frequent Event

Abstract Background Ras activation is a frequent event in hepatocellular carcinoma (HCC). Combining a RAS inhibitor with traditional clinical therapeutics might be hampered by a variety of side effects, thus hindering further clinical translation. Herein, we report on integrating an IR820 nanocapsule-augmented sonodynamic therapy (SDT) with the RAS inhibitor farnesyl-thiosalicylic acid (FTS). Using cellular and tumor models, we demonstrate that combined nanocapsule-augmented SDT with FTS induces an anti-tumor effect, which not only inhibits tumor progression, and enables fluorescence imaging. To dissect the mechanism of a combined tumoricidal therapeutic strategy, we investigated the scRNA-seq transcriptional profiles of an HCC xenograft following treatment. Results Integrative single-cell analysis identified several clusters that defined many corresponding differentially expressed genes, which provided a global view of cellular heterogeneity in HCC after combined SDT/FTS treatment. We conclude that the combination treatment suppressed HCC, and did so by inhibiting endothelial cells and a modulated immunity. Moreover, hepatic stellate secretes hepatocyte growth factor, which plays a key role in treating SDT combined FTS. By contrast, enrichment analysis estimated the functional roles of differentially expressed genes. The Gene Ontology terms “cadherin binding” and “cell adhesion molecule binding” and KEGG pathway “pathway in cancer” were significantly enriched by differentially expressed genes after combined SDT/FTS therapy. Conclusions Thus, some undefined mechanisms were revealed by scRNA-seq analysis. This report provides a novel proof-of-concept for combinatorial HCC-targeted therapeutics that is based on a non-invasive anti-tumor therapeutic strategy and a RAS inhibitor.

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Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes

10.1101/2020.08.24.265298 ◽

2020 ◽

Author(s):

Dustin J. Sokolowski ◽

Mariela Faykoo-Martinez ◽

Lauren Erdman ◽

Huayun Hou ◽

Cadia Chan ◽

...

Keyword(s):

Single Cell ◽

Differential Expression ◽

Differentially Expressed Genes ◽

Cell Types ◽

R Package ◽

Differentially Expressed ◽

Rna Seq ◽

Kidney Regeneration ◽

Cell Type ◽

User Friendly

AbstractRNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by integrating cell-type expression data generated by scRNA-seq and existing deconvolution methods. After benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. We found that scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small proportion of immune cells. While scMappR can work with any user supplied scRNA-seq data, we curated scRNA-seq expression matrices for ∼100 human and mouse tissues to facilitate its use with bulk RNA-seq data alone. Overall, scMappR is a user-friendly R package that complements traditional differential expression analysis available at CRAN.HighlightsscMappR integrates scRNA-seq and bulk RNA-seq to re-calibrate bulk differentially expressed genes (DEGs).scMappR correctly identified immune-cell expressed DEGs from a bulk RNA-seq analysis of mouse kidney regeneration.scMappR is deployed as a user-friendly R package available at CRAN.

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Dynamic Analysis of Alternative Polyadenylation from Single-Cell RNA-Seq (scDaPars) Reveals Cell Subpopulations Invisible to Gene Expression Analysis

10.1101/2020.09.23.310649 ◽

2020 ◽

Author(s):

Yipeng Gao ◽

Lei Li ◽

Christopher I. Amos ◽

Wei Li

Keyword(s):

Gene Expression ◽

Single Cell ◽

Expression Analysis ◽

Gene Expression Analysis ◽

Single Cells ◽

Single Gene ◽

Simulated Data ◽

Alternative Polyadenylation ◽

Cellular Heterogeneity ◽

Cell Subpopulations

AbstractAlternative polyadenylation (APA) is a major mechanism of post-transcriptional regulation in various cellular processes including cell proliferation and differentiation, but the APA heterogeneity among single cells remains largely unknown. Single-cell RNA sequencing (scRNA-seq) has been extensively used to define cell subpopulations at the transcription level. Yet, most scRNA-seq data have not been analyzed in an “APA-aware” manner. Here, we introduce scDaPars, a bioinformatics algorithm to accurately quantify APA events at both single-cell and single-gene resolution using standard scRNA-seq data. Validations in both real and simulated data indicate that scDaPars can robustly recover missing APA events caused by the low amounts of mRNA sequenced in single cells. When applied to cancer and human endoderm differentiation data, scDaPars not only revealed cell-type-specific APA regulation but also identified cell subpopulations that are otherwise invisible to conventional gene expression analysis. Thus, scDaPars will enable us to understand cellular heterogeneity at the post-transcriptional APA level.

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