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

Zhang, Xiannian; Li, Tianqi; Feng, Liang; Chen, Yaqi; Li, Zeyao; Huang, Yanyi; Wang, Jianbin

doi:10.1101/313130

Cited by 63 publications

(85 citation statements)

References 51 publications

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“…10x Genomics Chromium (10X), a widely used singlecell expression profiling platform, allows for the simultaneous transcriptome quantification of a large number of single cells. In spite of the skewness of poly-G enrichment [40] and low coverages, which might limit the application for detecting SNVs, 10X-derived scRNA-seq data could be useful for the investigation of variantcalling performances. Thus, we used different tools to call variants on scRNA-seq data of 78 cells sequenced by 10X in the Wang et al [39] dataset.…”

Section: Performance Evaluation Of Variant Callers In Different Datasetsmentioning

confidence: 99%

Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data

Liu

Zhang

et al. 2019

Genome Biol

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Background: Systematic interrogation of single-nucleotide variants (SNVs) is one of the most promising approaches to delineate the cellular heterogeneity and phylogenetic relationships at the single-cell level. While SNV detection from abundant single-cell RNA sequencing (scRNA-seq) data is applicable and cost-effective in identifying expressed variants, inferring sub-clones, and deciphering genotype-phenotype linkages, there is a lack of computational methods specifically developed for SNV calling in scRNA-seq. Although variant callers for bulk RNA-seq have been sporadically used in scRNA-seq, the performances of different tools have not been assessed.Results: Here, we perform a systematic comparison of seven tools including SAMtools, the GATK pipeline, CTAT, FreeBayes, MuTect2, Strelka2, and VarScan2, using both simulation and scRNA-seq datasets, and identify multiple elements influencing their performance. While the specificities are generally high, with sensitivities exceeding 90% for most tools when calling homozygous SNVs in high-confident coding regions with sufficient read depths, such sensitivities dramatically decrease when calling SNVs with low read depths, low variant allele frequencies, or in specific genomic contexts. SAMtools shows the highest sensitivity in most cases especially with low supporting reads, despite the relatively low specificity in introns or high-identity regions. Strelka2 shows consistently good performance when sufficient supporting reads are provided, while FreeBayes shows good performance in the cases of high variant allele frequencies.Conclusions: We recommend SAMtools, Strelka2, FreeBayes, or CTAT, depending on the specific conditions of usage. Our study provides the first benchmarking to evaluate the performances of different SNV detection tools for scRNA-seq data.

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Section: Performance Evaluation Of Variant Callers In Different Datasetsmentioning

confidence: 99%

Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data

Liu

Zhang

et al. 2019

Genome Biol

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“…We opted to build a fungal DROP-seq modified from the original approach presented in Macosko et al 43,44 , to address issues of cost and flexibility in comparison with commercial alternatives. In general, DROP-seq devices have been shown to be near equivalent to commercial systems 45 . In particular, here we built a printed circuit board to control microfluidic flow inspired by Stephenson et al 46 , and 3D printed plastic syringe pumps and cheap cameras inspired by Booeshaghi et al 47 ( Figure 1A ).…”

Section: Nanolitre Droplet-based Single Cell Rna-sequencing (Drop-seqmentioning

confidence: 99%

Candida albicans exhibits distinct cytoprotective responses to anti-fungal drugs that facilitate the evolution of drug resistance

Massahi

2020

Preprint

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We developed a modified protocol for nanolitre droplet-based single cell sequencing appropriate for fungal settings, and used it to transcriptionally profiled several thousands cells from a prototrophic Candida albicans population and several drug exposed colonies (incl. fluconazole, caspofungin and nystatin). Thousands of cells from each colony were profiled both at early and late time points post-treatment in order to infer survival trajectories from initial drug tolerance to drug resistance. We find that prototrophic C. albicans populations differentially and stochastically express cytoprotective epigenetic programs. For all drugs, there is evidence that tolerant individuals partition into distinct subpopulations, each with a unique survival strategy involving different regulatory programs. These responses are weakly related to changes in morphology (shift from white to opaque forms, or shift from yeast to filamentous forms). In turn, those subpopulations that successfully reach resistance each have a distinct multivariate epigenetic response that coordinates the expression of efflux pumps, chaperones, transport mechanisms, and cell wall maintenance. Live cell fluorescent imaging was used to validate predictions of which molecular responses most often led to survival after drug exposure. Together our findings provide evidence that C. albicans has a robust toolkit of short-term epigenetic cytoprotective responses designed to "buy time" and increase the chance of acquiring long-term resistance.

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“…Instead of using only nanowells, one can also coencapsulate barcoded polyT-beads and single cells in droplets, as implemented in the ''DropSeq'' and ''InDrop'' approaches (Klein et al, 2015;Macosko et al, 2015). This strategy is also exploited in the commercial 10X Genomics platform, which performed particularly well in terms of read and gene numbers per cell in the first comparative studies carried out (Lake et al, 2015(Lake et al, , 2018Zhang et al, 2019). However, it should be noted that ''home-made'' platforms such as DropSeq and InDrop offer a higher level of flexibility for experienced users, e.g., for implementing targeted sequencing of particular genes (Saikia et al, 2019;Zilionis et al, 2017).…”

Section: Geneticsmentioning

confidence: 99%

Microfluidics as an Emerging Precision Tool in Developmental Biology

Sonnen

Merten

2019

Developmental Cell

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Microfluidics has become a precision tool in modern biology. It enables omics data to be obtained from individual cells, as compared to averaged signals from cell populations, and it allows manipulation of biological specimens in entirely new ways. Cells and organisms can be perturbed at extraordinary spatiotemporal resolution, revealing mechanistic insights that would otherwise remain hidden. In this perspective article, we discuss the current and future impact of microfluidic technology in the field of developmental biology. In addition, we provide detailed information on how to start using this technology even without prior experience.

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

Cited by 63 publications

References 51 publications

Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data

Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data

Candida albicans exhibits distinct cytoprotective responses to anti-fungal drugs that facilitate the evolution of drug resistance

Microfluidics as an Emerging Precision Tool in Developmental Biology

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