Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq.

By S. Islam, Una Kjällquist, A. Moliner, P. Zając, Jian-Bing Fan, P. Lönnerberg, S. Linnarsson. Genome Research, 2011

947 citations recorded by the source corpus.

Our understanding of the development and maintenance of tissues has been greatly aided by large-scale gene expression analysis. However, tissues are invariably complex, and expression analysis of a tissue confounds the true expression patterns of its constituent cell types. Here we describe a novel strategy to access such complex samples. Single-cell RNA-seq expression profiles were generated, and clustered to form a two-dimensional cell map onto which expression data were projected. The resulting cell map integrates three levels of organization: the whole population of cells, the functionally distinct subpopulations it contains, and the single cells themselves-all without need for known markers to classify cell types. The feasibility of the strategy was demonstrated by analyzing the transcriptomes of 85 single cells of two distinct types. We believe this strategy will enable the unbiased discovery and analysis of naturally occurring cell types during development, adult physiology, and disease.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1f52c8c4879a5c35121203d7975e4ad16f654a77

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Citations in the corpus

CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq (2016, methodology): “The STRT method introduced early barcoding at the reverse transcription stage [2], thereby enabling highlymultiplexed analyses, and adapted a template switching mechanism based on the ability of the reverse transcriptase to tag the end of the cDNA [3], eliminating the need for the polyadenylation re”
Genome-wide RNA Tomography in the zebrafish embryo. (2014, methodology): “in the Zebrafish Embryo”
Cell Type-Specific Transcriptome Analysis in the Drosophila Mushroom Body Reveals Memory-Related Changes in Gene Expression (2016, positive): “However, we found weaker correlations in low-expressing genes, consistent with the effects of noise in the PCR amplification (Brennecke et al., 2013; Efroni et al., 2015; Islam et al., 2011).”
The technology and biology of single-cell RNA sequencing. (2015, methodology): “To achieve this, all published methods (Hashimshony et al., 2012; Islam et al., 2011; Ramsköld et al., 2012), except designed primer-based RNA-sequencing, DP-Seq (Bhargava et al.”
Single cell RNA Seq reveals dynamic paracrine control of cellular variation (2014, result): “By comparing our single-cell RNA-Seq and RNA-FISH, we estimate that the transcript detection efficiency for our single-cell RNA-Seq is ~20%, consistent with previous reports 14,22 .”
Design and Analysis of Single-Cell Sequencing Experiments. (2015, methodology): “Over the last ﬁve years, a number of single-cell mRNA-sequencing methods with improved sensitivity and reduced technical noise have been introduced (Hashimshony et al., 2012; Islam et al., 2011, 2014; Picelli et al., 2013; Ramsko¨ld et al., 2012; Sasagawa et al., 2013).”
Defining cell types and states with single-cell genomics (2015, positive): “Generating cDNA libraries from picograms of RNA has required major technical innovations and remains an area of considerable development effort (Tang et al. 2009; Islam et al. 2011; Hashimshony et al. 2012; Ramsköld et al. 2012; Picelli et al. 2014).”