Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity

Sasagawa, Yohei; Nikaido, Itoshi; Hayashi, Tetsutaro; Danno, Hiroki; Uno, Kenichiro D.; Imai, Takeshi; Ueda, Hiroki R.

doi:10.1186/gb-2013-14-4-r31

Cited by 411 publications

(372 citation statements)

References 29 publications

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“…These results give additional confidence that the variance estimates are accurately inferred. Finally, we repeated the validation of scLVM using a second previously published data set of 35 mESCs staged for the cell cycle, but prepared for sequencing with an alternative protocol (Quartz-Seq) 32 and cultured under different media conditions that are known to induce reduced variability in expression of cell-cycle genes 33 . Again, direct comparison of variance estimates from scLVM with the gold standard derived from the staging information of individual cells yielded good agreement ( Supplementary Figs.…”

Section: A N a Ly S I Smentioning

confidence: 99%

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Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

et al. 2015

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Recent technical developments have enabled the transcriptomes of hundreds of cells to be assayed in an unbiased manner, opening up the possibility that new subpopulations of cells can be found. However, the effects of potential confounding factors, such as the cell cycle, on the heterogeneity of gene expression and therefore on the ability to robustly identify subpopulations remain unclear. We present and validate a computational approach that uses latent variable models to account for such hidden factors. We show that our single-cell latent variable model (scLVM) allows the identification of otherwise undetectable subpopulations of cells that correspond to different stages during the differentiation of naive T cells into T helper 2 cells. Our approach can be used not only to identify cellular subpopulations but also to tease apart different sources of gene expression heterogeneity in single-cell transcriptomes.Single-cell measurements of gene expression, using imaging techniques such as RNA-FiSH (fluorescence in situ hybridization), have provided important insights into the kinetics of transcription and cell-to-cell variation in gene expression [1][2][3] . However, such approaches can examine the expression of only a small number of genes in each experiment, thus restricting our ability to examine co-expression patterns and to robustly identify subpopulations of cells. Protocols have been developed to overcome these limitations by amplifying small quantities of mRNA 4,5 , which, in combination with microfluidics approaches for isolating individual cells 6,7 , have been used to analyze the co-expression of tens to hundreds of genes in single cells 8,9 . These protocols also allow the entire transcriptome of large numbers of single cells to be assayed in an unbiased way. This was initially done using microarrays 10,11 but is more often now done using next-generation sequencing [12][13][14][15] . Such approaches have been used to model early embryogenesis in the mouse 16 and to investigate bimodality in gene expression patterns of differentiating immune cell types 17 .After the generation of single-cell RNA-sequencing (RNA-seq) profiles from hundreds of cells, one goal to identify subpopulations that share a common gene-expression profile. Some of these subpopulations may represent previously unidentified cell types. Additionally, by studying patterns of gene expression in different single cells, insights into the regulatory landscape of each cell population can be obtained.However, methods for identifying subpopulations of cells and modeling their gene regulatory landscapes are only now beginning to emerge 18,19 . To fully exploit single-cell RNA-seq data, we have to account for the random noise inherent to such data sets 20 and, equally important, to account for different hidden factors that might result in gene expression heterogeneity. Although the importance of accounting for unobserved factors is well established in bulk RNA-seq studies [21][22][23] , robust approaches to detect and account for confounding f...

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Section: A N a Ly S I Smentioning

confidence: 99%

“…We used the normalized data and counts from the primary publication 32 . These data consist of gene expression level estimates, obtained using the Quartz-Seq protocol, for 35 mESCs, where the cell-cycle state of each cell is known a priori (7 S, 8 G2M and 20 G1 cells).…”

Section: Supplementary Data 1)mentioning

confidence: 99%

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

et al. 2015

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“…In the past 6 years, five main methods have been developed and optimized to reverse transcribe the mRNA and amplify the cDNA from one single cell to achieve a better coverage and a lower cost per cell6, 7, 8, 9, 10, 11, 12, 13, 14 (Table 2). A parallel development of multiple algorithms has taken place in order to deal with the huge amount of data these new experiments have produced 15.…”

Section: Recent Development Of Single‐cell Techniquesmentioning

confidence: 99%

Single‐cell technologies to study the immune system

Proserpio

Mahata

2015

Immunology

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SummaryThe immune system is composed of a variety of cells that act in a coordinated fashion to protect the organism against a multitude of different pathogens. The great variability of existing pathogens corresponds to a similar high heterogeneity of the immune cells. The study of individual immune cells, the fundamental unit of immunity, has recently transformed from a qualitative microscopic imaging to a nearly complete quantitative transcriptomic analysis. This shift has been driven by the rapid development of multiple single‐cell technologies. These new advances are expected to boost the detection of less frequent cell types and transient or intermediate cell states. They will highlight the individuality of each single cell and greatly expand the resolution of current available classifications and differentiation trajectories. In this review we discuss the recent advancement and application of single‐cell technologies, their limitations and future applications to study the immune system.

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“…The time efficiency of SDEAP is discussed in the 115 Supplementary Materials. To further demonstrate the utility of SDEAP in real biological applications, the DTE genes predicted by SDEAP were used as biomarkers to classify different cancer subtypes, cell types and cell-cycle phases on several recently published RNA-Seq datasets (Eswaran et al, 2012;Sasagawa et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Finding DTE genes that could be used as transcriptomic biomarkers to identify subtypes of such diseases could be important for the design of clinical trials to investigate targeted treatments. Moreover, the expression patterns of transcripts in individual cells of different cell types or cell-cycle phases, which can be revealed by the SC RNA-Seq tech-5 nology nowadays, are fundamental to studies on alternative cellular functions during the development of a tissue or an organ (Sasagawa et al, 2013;Trapnell, 2015). Our real data experiments demonstrate that the classification of RNA-Seq samples using the ASEs from SDEAP is much more consistent with the real biological condi-10 tions (i.e.…”

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confidence: 99%

SDEAP: a splice graph based differential transcript expression analysis tool for population data

Yang

Jiang

2016

Bioinformatics

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Motivation: Differential transcript expression (DTE) analysis without predefined conditions is critical to biological studies. For example, it can be used to discover biomarkers to classify cancer samples into previously unknown subtypes such that better diagnosis and therapy methods can be developed for the subtypes. Although several DTE tools for population data, i.e. data without 20 known biological conditions, have been published, these tools either assume binary conditions in the input population or require the number of conditions as a part of the input. Fixing the number of conditions to binary is unrealistic and may distort the results of a DTE analysis. Estimating the correct number of conditions in a population could also be challenging for a routine user. Moreover, the existing tools only provide differential usages of exons, which may be insufficient to 25 interpret the patterns of alternative splicing across samples and restrains the applications of the tools from many biology studies.Results: We propose a novel DTE analysis algorithm, called SDEAP, that estimates the number of conditions directly from the input samples using a Dirichlet mixture model and discovers alternative splicing events using a new graph modular decomposition algorithm. By taking advantage of the above 30 technical improvement, SDEAP was able to outperform the other DTE analysis methods in our extensive experiments on simulated data and real data with qPCR validation. The prediction of SDEAP also allowed us to classify the samples of cancer subtypes and cell-cycle phases more accurately.

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Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity

Cited by 411 publications

References 29 publications

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

Single‐cell technologies to study the immune system

SDEAP: a splice graph based differential transcript expression analysis tool for population data

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