Comparative analysis of single-cell RNA Sequencing Platforms and Methods

ABSTRACTSingle-cell RNA sequencing (scRNA-seq) offers great new opportunities for increasing our understanding of complex biological processes. In particular, development of an accurate Human Cell Atlas is largely dependent on the rapidly advancing technologies and molecular chemistries employed in scRNA-seq. These advances have already allowed an increase in throughput for scRNA-seq from 96 to 80,000 cells on a single instrument run by capturing cells within nano-liter droplets. While this increase in throughput is critical for many experimental questions, a thorough comparison between microfluidic-, plate-, and droplet-based technologies or between multiple available platforms utilizing these technologies is largely lacking. Here, we report scRNA-seq data from SUM149PT cells treated with the histone deacetylase inhibitor TSA vs. untreated controls across several scRNA-seq platforms (Fluidigm C1, WaferGen iCell8, 10X Genomics Chromium Controller, and Illumina/BioRad ddSEQ). The primary goal of this project was to demonstrate RNA sequencing (RNA-seq) methods for profiling the ultra-low amounts of RNA present in individual cells, and this report discusses the results of the study as well as technical challenges/lesson learned and present general guidelines for best practices in sample preparation and analysis.

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A systematic evaluation of single cell RNA-seq analysis pipelines

Nature Communications ◽

10.1038/s41467-019-12266-7 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 47

Author(s):

Beate Vieth ◽

Swati Parekh ◽

Christoph Ziegenhain ◽

Wolfgang Enard ◽

Ines Hellmann

Keyword(s):

Best Practices ◽

Sample Size ◽

Single Cell ◽

Differential Expression ◽

Rna Sequencing ◽

Systematic Evaluation ◽

Library Preparation ◽

Rna Seq ◽

Rapid Spread ◽

Single Cell Rna Sequencing

Abstract The recent rapid spread of single cell RNA sequencing (scRNA-seq) methods has created a large variety of experimental and computational pipelines for which best practices have not yet been established. Here, we use simulations based on five scRNA-seq library protocols in combination with nine realistic differential expression (DE) setups to systematically evaluate three mapping, four imputation, seven normalisation and four differential expression testing approaches resulting in ~3000 pipelines, allowing us to also assess interactions among pipeline steps. We find that choices of normalisation and library preparation protocols have the biggest impact on scRNA-seq analyses. Specifically, we find that library preparation determines the ability to detect symmetric expression differences, while normalisation dominates pipeline performance in asymmetric DE-setups. Finally, we illustrate the importance of informed choices by showing that a good scRNA-seq pipeline can have the same impact on detecting a biological signal as quadrupling the sample size.

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A Systematic Evaluation of Single Cell RNA-Seq Analysis Pipelines

10.1101/583013 ◽

2019 ◽

Cited By ~ 7

Author(s):

Beate Vieth ◽

Swati Parekh ◽

Christoph Ziegenhain ◽

Wolfgang Enard ◽

Ines Hellmann

Keyword(s):

Best Practices ◽

Sample Size ◽

Single Cell ◽

Differential Expression ◽

Rna Sequencing ◽

Systematic Evaluation ◽

Library Preparation ◽

Rna Seq ◽

Rapid Spread ◽

Single Cell Rna Sequencing

AbstractThe recent rapid spread of single cell RNA sequencing (scRNA-seq) methods has created a large variety of experimental and computational pipelines for which best practices have not been established, yet. Here, we use simulations based on five scRNA-seq library protocols in combination with nine realistic differential expression (DE) setups to systematically evaluate three mapping, four imputation, seven normalisation and four differential expression testing approaches resulting in ∼ 3,000 pipelines, allowing us to also assess interactions among pipeline steps. We find that choices of normalisation and library preparation protocols have the biggest impact on scRNA-seq analyses. Specifically, we find that library preparation determines the ability to detect symmetric expression differences, while normalisation dominates pipeline performance in asymmetric DE-setups. Finally, we illustrate the importance of informed choices by showing that a good scRNA-seq pipeline can have the same impact on detecting a biological signal as quadrupling the sample size.

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SSCC: a novel computational framework for rapid and accurate clustering large single cell RNA-seq data

10.1101/344242 ◽

2018 ◽

Cited By ~ 2

Author(s):

Xianwen Ren ◽

Liangtao Zheng ◽

Zemin Zhang

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Random Projection ◽

Rna Seq ◽

Sequencing Data ◽

Computational Framework ◽

Human Blood Cells ◽

Single Cell Rna Sequencing ◽

Data Volume

ABSTRACTClustering is a prevalent analytical means to analyze single cell RNA sequencing data but the rapidly expanding data volume can make this process computational challenging. New methods for both accurate and efficient clustering are of pressing needs. Here we proposed a new clustering framework based on random projection and feature construction for large scale single-cell RNA sequencing data, which greatly improves clustering accuracy, robustness and computational efficacy for various state-of-the-art algorithms benchmarked on multiple real datasets. On a dataset with 68,578 human blood cells, our method reached 20% improvements for clustering accuracy and 50-fold acceleration but only consumed 66% memory usage compared to the widely-used software package SC3. Compared to k-means, the accuracy improvement can reach 3-fold depending on the concrete dataset. An R implementation of the framework is available from https://github.com/Japrin/sscClust.

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Ultra-high throughput single-cell RNA sequencing by combinatorial fluidic indexing

10.1101/2019.12.17.879304 ◽

2019 ◽

Cited By ~ 4

Author(s):

Paul Datlinger ◽

André F Rendeiro ◽

Thorina Boenke ◽

Thomas Krausgruber ◽

Daniele Barreca ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Population Genomics ◽

Cost Effective ◽

Mouse Cell ◽

Droplet Microfluidics ◽

Rna Seq ◽

Single Cell Rna Sequencing ◽

Massive Scale ◽

Tcr Activation

AbstractCell atlas projects and single-cell CRISPR screens hit the limits of current technology, as they require cost-effective profiling for millions of individual cells. To satisfy these enormous throughput requirements, we developed “single-cell combinatorial fluidic indexing” (scifi) and applied it to single-cell RNA sequencing. The resulting scifi-RNA-seq assay combines one-step combinatorial pre-indexing of single-cell transcriptomes with subsequent single-cell RNA-seq using widely available droplet microfluidics. Pre-indexing allows us to load multiple cells per droplet, which increases the throughput of droplet-based single-cell RNA-seq up to 15-fold, and it provides a straightforward way of multiplexing hundreds of samples in a single scifi-RNA-seq experiment. Compared to multi-round combinatorial indexing, scifi-RNA-seq provides an easier, faster, and more efficient workflow, thereby enabling massive-scale scRNA-seq experiments for a broad range of applications ranging from population genomics to drug screens with scRNA-seq readout. We benchmarked scifi-RNA-seq on various human and mouse cell lines, and we demonstrated its feasibility for human primary material by profiling TCR activation in T cells.

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A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies

Genes ◽

10.3390/genes12121947 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1947

Author(s):

Samarendra Das ◽

Anil Rai ◽

Michael L. Merchant ◽

Matthew C. Cave ◽

Shesh N. Rai

Keyword(s):

Single Cell ◽

Differential Expression ◽

Rna Sequencing ◽

High Throughput Sequencing ◽

Performance Metrics ◽

Differential Expression Analysis ◽

Individual Performance ◽

Rna Seq ◽

Gene Expressions ◽

Single Cell Rna Sequencing

Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput sequencing technique for studying gene expressions at the cell level. Differential Expression (DE) analysis is a major downstream analysis of scRNA-seq data. DE analysis the in presence of noises from different sources remains a key challenge in scRNA-seq. Earlier practices for addressing this involved borrowing methods from bulk RNA-seq, which are based on non-zero differences in average expressions of genes across cell populations. Later, several methods specifically designed for scRNA-seq were developed. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to comprehensively study the performance of DE analysis methods. Here, we provide a review and classification of different DE approaches adapted from bulk RNA-seq practice as well as those specifically designed for scRNA-seq. We also evaluate the performance of 19 widely used methods in terms of 13 performance metrics on 11 real scRNA-seq datasets. Our findings suggest that some bulk RNA-seq methods are quite competitive with the single-cell methods and their performance depends on the underlying models, DE test statistic(s), and data characteristics. Further, it is difficult to obtain the method which will be best-performing globally through individual performance criterion. However, the multi-criteria and combined-data analysis indicates that DECENT and EBSeq are the best options for DE analysis. The results also reveal the similarities among the tested methods in terms of detecting common DE genes. Our evaluation provides proper guidelines for selecting the proper tool which performs best under particular experimental settings in the context of the scRNA-seq.

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Splatter: simulation of single-cell RNA sequencing data

10.1101/133173 ◽

2017 ◽

Cited By ~ 8

Author(s):

Luke Zappia ◽

Belinda Phipson ◽

Alicia Oshlack

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Real Data ◽

Cell Types ◽

Rna Seq ◽

Sequencing Data ◽

Sequencing Technologies ◽

Simulation Based ◽

Single Cell Rna Sequencing ◽

Multiple Cell

AbstractAs single-cell RNA sequencing technologies have rapidly developed, so have analysis methods. Many methods have been tested, developed and validated using simulated datasets. Unfortunately, current simulations are often poorly documented, their similarity to real data is not demonstrated, or reproducible code is not available.Here we present the Splatter Bioconductor package for simple, reproducible and well-documented simulation of single-cell RNA-seq data. Splatter provides an interface to multiple simulation methods including Splat, our own simulation, based on a gamma-Poisson distribution. Splat can simulate single populations of cells, populations with multiple cell types or differentiation paths.

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Myeloid heterogeneity in kidney disease as revealed through single cell RNA sequencing

Kidney360 ◽

10.34067/kid.0003682021 ◽

2021 ◽

pp. 10.34067/KID.0003682021

Author(s):

Rachel M B Bell ◽

Laura Denby

Keyword(s):

Kidney Disease ◽

Single Cell ◽

Rna Sequencing ◽

Single Cells ◽

Myeloid Cells ◽

Rna Seq ◽

Cellular Compartment ◽

Single Cell Rna Sequencing ◽

Health And Disease ◽

Diseased Kidney

Kidney disease represents a global health burden of increasing prevalence and is an independent risk factor for cardiovascular disease. Myeloid cells are a major cellular compartment of the immune system; they are found in the healthy kidney and in increased numbers in the damaged and/or diseased kidney, where they act as key players in the progression of injury, inflammation and fibrosis. They possess enormous plasticity and heterogeneity, adopting different phenotypic and functional characteristics in response to stimuli in the local milieu. Though this inherent complexity remains to be fully understood in the kidney, advances in single-cell genomics promises to change this. Specifically, single-cell RNA sequencing (scRNA-seq) has had a transformative effect on kidney research, enabling the profiling and analysis of the transcriptomes of single cells at unprecedented resolution and throughput, and subsequent generation of cell atlases. Moving forward, combining scRNA- and single-nuclear RNA-seq with greater resolution spatial transcriptomics will allow spatial mapping of kidney disease of varying aetiology to further reveal the patterning of immune cells and non-immune renal cells. This review summarises the roles of myeloid cells in kidney health and disease, the experimental workflow in currently available scRNA-seq technologies and published findings using scRNA-seq in the context of myeloid cells and the kidney.

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DOP23 Single-cell RNA sequencing identifies an important role for class I histone-deacetylase enzymes in intestinal myofibroblasts from patients with Crohn’s Disease strictures

Journal of Crohn s and Colitis ◽

10.1093/ecco-jcc/jjab073.062 ◽

2021 ◽

Vol 15 (Supplement_1) ◽

pp. S062-S062

Author(s):

A Lewis ◽

B Pan-Castillo ◽

G Berti ◽

C Felice ◽

H Gordon ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cell Line ◽

Rna Sequencing ◽

Histone Deacetylase ◽

Chromatin Accessibility ◽

Collagen I ◽

Class I ◽

Rna Seq ◽

Single Cell Rna Sequencing

Abstract Background Histone-deacetylase (HDAC) enzymes are a broad class of ubiquitously expressed enzymes that modulate histone acetylation, chromatin accessibility and gene expression. In models of Inflammatory bowel disease (IBD), HDAC inhibitors, such as Valproic acid (VPA) are proven anti-inflammatory agents and evidence suggests that they also inhibit fibrosis in non-intestinal organs. However, the role of HDAC enzymes in stricturing Crohn’s disease (CD) has not been characterised; this is key to understanding the molecular mechanism and developing novel therapies. Methods To evaluate HDAC expression in the intestine of SCD patients, we performed unbiased single-cell RNA sequencing (sc-RNA-seq) of over 10,000 cells isolated from full-thickness surgical resection specimens of non-SCD (NSCD; n=2) and SCD intestine (n=3). Approximately, 1000 fibroblasts were identified for further analysis, including a distinct cluster of myofibroblasts. Changes in gene expression were compared between myofibroblasts and other resident intestinal fibroblasts using the sc-RNA-seq analysis pipeline in Partek. Changes in HDAC expression and markers of HDAC activity (H3K27ac) were confirmed by immunohistochemistry in FFPE tissue from patient matched NSCD and SCD intestine (n=14 pairs). The function of HDACs in intestinal fibroblasts in the CCD-18co cell line and primary CD myofibroblast cultures (n=16 cultures) was assessed using VPA, a class I HDAC inhibitor. Cells were analysed using a variety of molecular techniques including ATAC-seq, gene expression arrays, qPCR, western blot and immunofluorescent protein analysis. Results Class I HDAC (HDAC1, p= 2.11E-11; HDAC2, p= 4.28E-11; HDAC3, p= 1.60E-07; and HDAC8, p= 2.67E-03) expression was increased in myofibroblasts compared to other intestinal fibroblasts subtypes. IHC also showed an increase in the percentage of stromal HDAC2 positive cells, coupled with a decrease in the percentage of H3K27ac positive cells, in the mucosa overlying SCD intestine relative to matched NSCD areas. In the CCD-18co cell line and primary myofibroblast cultures, VPA reduced chromatin accessibility at Collagen-I gene promoters and suppressed their transcription. VPA also inhibited TGFB-induced up-regulation of Collagen-I, in part by inhibiting TGFB1|1/SMAD4 signalling. TGFB1|1 was identified as a mesenchymal specific target of VPA and siRNA knockdown of TGFB1|1 was sufficient suppress TGFB-induced up-regulation of Collagen-I. Conclusion In SCD patients, class I HDAC expression is increased in myofibroblasts. Class I HDACs inhibitors impair TGFB-signalling and inhibit Collagen-I expression. Selective targeting of TGFB1|1 offers the opportunity to increase treatment specificity by selectively targeting meschenymal cells.

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SPsimSeq: semi-parametric simulation of bulk and single cell RNA sequencing data

10.1101/677740 ◽

2019 ◽

Cited By ~ 1

Author(s):

Alemu Takele Assefa ◽

Jo Vandesompele ◽

Olivier Thas

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Empirical Distribution ◽

Supplementary Information ◽

Rna Seq ◽

Sequencing Data ◽

Actual Distribution ◽

Wide Range ◽

Single Cell Rna Sequencing

SummarySPsimSeq is a semi-parametric simulation method for bulk and single cell RNA sequencing data. It simulates data from a good estimate of the actual distribution of a given real RNA-seq dataset. In contrast to existing approaches that assume a particular data distribution, our method constructs an empirical distribution of gene expression data from a given source RNA-seq experiment to faithfully capture the data characteristics of real data. Importantly, our method can be used to simulate a wide range of scenarios, such as single or multiple biological groups, systematic variations (e.g. confounding batch effects), and different sample sizes. It can also be used to simulate different gene expression units resulting from different library preparation protocols, such as read counts or UMI counts.Availability and implementationThe R package and associated documentation is available from https://github.com/CenterForStatistics-UGent/SPsimSeq.Supplementary informationSupplementary data are available at bioRχiv online.

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Detection and removal of barcode swapping in single-cell RNA-seq data

10.1101/177048 ◽

2017 ◽

Cited By ~ 5

Author(s):

Jonathan A. Griffiths ◽

Arianne C. Richard ◽

Karsten Bach ◽

Aaron T.L. Lun ◽

John C Marioni

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Flow Cell ◽

Rna Seq ◽

Genomic Assays ◽

Single Cell Rna Sequencing ◽

Sequencing Studies ◽

Continued Use ◽

Statistical Approaches ◽

Transcriptomic Studies

AbstractBarcode swapping results in the mislabeling of sequencing reads between multiplexed samples on the new patterned flow cell Illumina sequencing machines. This may compromise the validity of numerous genomic assays, especially for single-cell studies where many samples are routinely multiplexed together. The severity and consequences of barcode swapping for single-cell transcriptomic studies remain poorly understood. We have used two statistical approaches to robustly quantify the fraction of swapped reads in each of two plate-based single-cell RNA sequencing datasets. We found that approximately 2.5% of reads were mislabeled between samples on the HiSeq 4000 machine, which is lower than previous reports. We observed no correlation between the swapped fraction of reads and the concentration of free barcode across plates. Furthermore, we have demonstrated that barcode swapping may generate complex but artefactual cell libraries in droplet-based single-cell RNA sequencing studies. To eliminate these artefacts, we have developed an algorithm to exclude individual molecules that have swapped between samples in 10X Genomics experiments, exploiting the combinatorial complexity present in the data. This permits the continued use of cutting-edge sequencing machines for droplet-based experiments while avoiding the confounding effects of barcode swapping.

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