Comparative analysis of droplet-based ultra-high-throughput single-cell RNA-seq systems

SummarySince its establishment in 2009, single-cell RNA-seq has been a major driver behind progress in biomedical research. In developmental biology and stem cell studies, the ability to profile single cells confers particular benefits. While most studies still focus on individual tissues or organs, the recent development of ultra-high-throughput single-cell RNA-seq has demonstrated potential power in characterizing more complex systems or even the entire body. However, although multiple ultra-high-throughput single-cell RNA-seq systems have attracted attention, no systematic comparison of these systems has been performed. Here, we focus on three widely used droplet-based ultra-high-throughput single-cell RNA-seq systems, inDrop, Drop-seq, and 10X Genomics Chromium. While each system is capable of profiling single-cell transcriptomes, their detailed comparison revealed the distinguishing features and suitable applications for each system.

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Comparative Analysis of Droplet-Based Ultra-High-Throughput Single-Cell RNA-Seq Systems

Molecular Cell ◽

10.1016/j.molcel.2018.10.020 ◽

2019 ◽

Vol 73 (1) ◽

pp. 130-142.e5 ◽

Cited By ~ 79

Author(s):

Xiannian Zhang ◽

Tianqi Li ◽

Feng Liu ◽

Yaqi Chen ◽

Jiacheng Yao ◽

...

Keyword(s):

Comparative Analysis ◽

Single Cell ◽

High Throughput ◽

Rna Seq

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Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

BMC Genomics ◽

10.1186/s12864-020-07358-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Tracy M. Yamawaki ◽

Daniel R. Lu ◽

Daniel C. Ellwanger ◽

Dev Bhatt ◽

Paolo Manzanillo ◽

...

Keyword(s):

Single Cell ◽

High Throughput ◽

Immune Cell ◽

Cell Types ◽

Data Interpretation ◽

Detection Sensitivity ◽

Rna Seq ◽

Cell Recovery

Abstract Background Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluated methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5′ v1 and 3′ v3 methods. We demonstrate that these methods have fewer dropout events, which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures. Conclusion Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

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High-Throughput Single-Cell RNA-Seq of Large Cells and Nuclei

Genetic Engineering & Biotechnology News ◽

10.1089/gen.37.17.06 ◽

2017 ◽

Vol 37 (17) ◽

pp. 12-13

Author(s):

Jennifer Chew ◽

Adam Bemis ◽

Ronald Lebofsky ◽

Anna Quinlan ◽

Kelly Kaihara

Keyword(s):

Single Cell ◽

High Throughput ◽

Rna Seq

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A web server for comparative analysis of single-cell RNA-seq data

Nature Communications ◽

10.1038/s41467-018-07165-2 ◽

2018 ◽

Vol 9 (1) ◽

Cited By ~ 19

Author(s):

Amir Alavi ◽

Matthew Ruffalo ◽

Aiyappa Parvangada ◽

Zhilin Huang ◽

Ziv Bar-Joseph

Keyword(s):

Comparative Analysis ◽

Single Cell ◽

Web Server ◽

Rna Seq

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Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNA-seq

10.1101/2020.11.16.384222 ◽

2020 ◽

Author(s):

Viacheslav Mylka ◽

Jeroen Aerts ◽

Irina Matetovici ◽

Suresh Poovathingal ◽

Niels Vandamme ◽

...

Keyword(s):

Genetic Variation ◽

Comparative Analysis ◽

Single Cell ◽

Cell Lines ◽

Clinical Studies ◽

Clinical Samples ◽

Rna Seq ◽

Batch Effects ◽

Single Cell Sequencing ◽

Single Nucleus

ABSTRACTMultiplexing of samples in single-cell RNA-seq studies allows significant reduction of experimental costs, straightforward identification of doublets, increased cell throughput, and reduction of sample-specific batch effects. Recently published multiplexing techniques using oligo-conjugated antibodies or - lipids allow barcoding sample-specific cells, a process called ‘hashing’. Here, we compare the hashing performance of TotalSeq-A and -C antibodies, custom synthesized lipids and MULTI-seq lipid hashes in four cell lines, both for single-cell RNA-seq and single-nucleus RNA-seq. Hashing efficiency was evaluated using the intrinsic genetic variation of the cell lines. Benchmarking of different hashing strategies and computational pipelines indicates that correct demultiplexing can be achieved with both lipid- and antibody-hashed human cells and nuclei, with MULTISeqDemux as the preferred demultiplexing function and antibody-based hashing as the most efficient protocol on cells. Antibody hashing was further evaluated on clinical samples using PBMCs from healthy and SARS-CoV-2 infected patients, where we demonstrate a more affordable approach for large single-cell sequencing clinical studies, while simultaneously reducing batch effects.

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Sensitive high-throughput single-cell RNA-seq reveals within-clonal transcript correlations in yeast populations

Nature Microbiology ◽

10.1038/s41564-018-0346-9 ◽

2019 ◽

Vol 4 (4) ◽

pp. 683-692 ◽

Cited By ~ 17

Author(s):

Mariona Nadal-Ribelles ◽

Saiful Islam ◽

Wu Wei ◽

Pablo Latorre ◽

Michelle Nguyen ◽

...

Keyword(s):

Single Cell ◽

High Throughput ◽

Rna Seq

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Systematic Comparison of High-Throughput Single-Cell RNAseq Methods for Immune Cell Profiling

10.21203/rs.3.rs-72851/v1 ◽

2020 ◽

Author(s):

Chi-Ming Kevin Li ◽

Tracy M Yamawaki ◽

Daniel R Lu ◽

Daniel C Ellwanger ◽

Dev Bhatt ◽

...

Keyword(s):

Single Cell ◽

High Throughput ◽

Immune Cell ◽

Cell Types ◽

Drop Out ◽

Detection Sensitivity ◽

Rna Seq ◽

Cell Recovery

Abstract Background: Elucidation of immune populations with single-cell RNA-seq has greatly benefited the fieldof immunology by deepening the characterization of immune heterogeneity and leading to thediscovery of new subtypes. However, single-cell methods inherently suffer from limitations in therecovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropoutevents. This issue is often compounded by limited sample availability and limited prior knowledge ofheterogeneity, which can confound data interpretation.Results: Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. Weprepared 21 libraries under identical conditions of a defined mixture of two human and two murinelymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluatemethods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expressionsignatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5’v1 and 3’ v3 methods. We demonstrate that these methods have fewer drop-out events whichfacilitates the identification of differentially-expressed genes and improves the concordance of singlecellprofiles to immune bulk RNA-seq signatures.Conclusion: Overall, our characterization of immune cell mixtures provides useful metrics, which canguide selection of a high-throughput single-cell RNA-seq method for profiling more complex immunecellheterogeneity usually found in vivo.

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Power in Numbers: Single-Cell RNA-Seq Strategies to Dissect Complex Tissues

Annual Review of Genetics ◽

10.1146/annurev-genet-120417-031247 ◽

2018 ◽

Vol 52 (1) ◽

pp. 203-221 ◽

Cited By ~ 29

Author(s):

Kenneth D. Birnbaum

Keyword(s):

Single Cell ◽

High Throughput ◽

Genetic Screening ◽

Developmental Trajectories ◽

Fluorescent Microscopy ◽

The Novel ◽

Rna Seq ◽

Single Cell Rna Sequencing ◽

Developmental Dynamics

The growing scale and declining cost of single-cell RNA-sequencing (RNA-seq) now permit a repetition of cell sampling that increases the power to detect rare cell states, reconstruct developmental trajectories, and measure phenotype in new terms such as cellular variance. The characterization of anatomy and developmental dynamics has not had an equivalent breakthrough since groundbreaking advances in live fluorescent microscopy. The new resolution obtained by single-cell RNA-seq is a boon to genetics because the novel description of phenotype offers the opportunity to refine gene function and dissect pleiotropy. In addition, the recent pairing of high-throughput genetic perturbation with single-cell RNA-seq has made practical a scale of genetic screening not previously possible.

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