Quantitative assessment of single-cell RNA-sequencing methods

By A. Wu, N. Neff, T. Kalisky, Piero Dalerba, B. Treutlein, M. Rothenberg, Francis M. Mburu, Gary L. Mantalas, Sopheak K. Sim, M. Clarke and 1 more. Nature Methods, 2013

709 citations recorded by the source corpus.

Interest in single-cell whole-transcriptome analysis is growing rapidly, especially for profiling rare or heterogeneous populations of cells. We compared commercially available single-cell RNA amplification methods with both microliter and nanoliter volumes, using sequence from bulk total RNA and multiplexed quantitative PCR as benchmarks to systematically evaluate the sensitivity and accuracy of various single-cell RNA-seq approaches. We show that single-cell RNA-seq can be used to perform accurate quantitative transcriptome measurement in individual cells with a relatively small number of sequencing reads and that sequencing large numbers of single cells can recapitulate bulk transcriptome complexity.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a8c900ab67a1f9111445f98fb4d8eaac95e6ae17

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In Molecular Biology

Citations in the corpus

Defining cell types and states with single-cell genomics (2015, methodology): “Single-cell versions of the RNA-seq protocol isolate individual cells in microfluidic capillaries (Wu et al. 2013), by serial dilution, or via flow”
Single cell RNA Seq reveals dynamic paracrine control of cellular variation (2014, methodology): “We sequenced each sample to an average depth of 4.5±3.0 million read pairs, since single-cell expression estimates stabilized at low read-depths 13,19 (Extended Fig.”
Estimating Mutual Information for Discrete-Continuous Mixtures (2017, background): “Gene expressions form a rich source of data from which to infer gene regulatory networks; it is now possible to sequence gene expression data from single cells using a technology called single-cell RNA-sequencing [52].”
The SH-SY5Y cell line in Parkinson’s disease research: a systematic review (2017, background): “Next to the more conventional “ensemble” approaches, such as western blotting, transcriptomics or proteomics, the use of novel single-cell microscopy and single-cell RNAseq approaches will become instrumental to provide more detailed phenotypic profiling of the differentiated cell population [47, 48”
Design and computational analysis of single-cell RNA-sequencing experiments (2016, background): “In fact, the majority of genes seem to be detected at 500,000 reads; and over half are typically detected at 250,000 reads [13, 14].”
Droplet barcoding for single cell transcriptomics applied to embryonic stem cells (2015, positive): “Microfluidic chips containing hundreds of valves can trap, lyse and assay biomolecules from single cells with higher precision and often with better efficiencies than micro-titer plates (Streets et al. 2014; Wu et al. 2014).”
The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): Illuminating the Functional Diversity of Eukaryotic Life in the Oceans through Transcriptome Sequencing (2014, background): “Methodologies for population [25–27] and single-cell genomics and transcriptomics are advancing rapidly [4,28–30], transitioning from technological feats to something we should expect to work routinely.”
Single-Cell Transcriptomics Reveals a Population of Dormant Neural Stem Cells that Become Activated upon Brain Injury. (2015, positive): “Consistent with previous studies (Treutlein et al., 2014; Wu et al., 2014), RNA spike-in standards that were included to control for technical noise showed high correlation across transcript abundances that spanned approximately five orders of magnitude (Figure S2).”