mRNA-Seq whole-transcriptome analysis of a single cell

By K. Lao, Fuchou Tang, Catalin C. Barbacioru, Yangzhou Wang, Ellen Nordman, Clarence C Lee, Nanlan Xu, D. Wang, B. Tuch, J. Bodeau and 2 more. Nature Methods, 2009

3,557 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3dfdef00e10cf010cda6ef4e5b10b8170b75f51f

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Citations in the corpus

Optimization of de novo transcriptome assembly from next-generation sequencing data. (2010, background): “…isolation of fast-evolving genes (Montoya-Burgos et al. 2010), the detection of new alternative splice variants (Carninci 2008; Gibbons et al. 2009; Tang et al. 2009), allele-specific gene expression (Main et al. 2009), SNP discovery in genes (Barbazuk et al. 2007), or epigenetic gene regulation…”
Transcriptomics technologies (2017, background): “UMIs are particularly well-suited to single-cell RNA-Seq transcriptomics, in which the amount of input RNA is restricted and extended amplification of the sample is required [68] [69][70].”
Single-cell RNA sequencing technologies and bioinformatics pipelines (2018, methodology): “The ﬁ rst description of single-cell transcriptome analysis based on a next-generation sequencing platform was published in 2009, and it described the characterization of cells from early developmental stages 12 .”
Inhibition of Inhibition in Visual Cortex: The Logic of Connections Between Molecularly Distinct Interneurons (2013, positive): “Systematic whole-transcriptome analysis[50] of single cells, by vastly increasing the number of detected genes, will not only help refining our categorization criteria but eventually provide a causal link between the molecular expression patterns and the various functional and morphological…”
Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq (2015, neutral): “…in patch-clamp mode 13 , which is restricted to only a small number (up to ~50) 14 of prespecified genes, or on spotted cDNA array 15 , which has a limited dynamic range, sensitivity and specificity compared to sequencing-based approaches and cannot detect novel transcripts or splice variants 7 .”
Design and Analysis of Single-Cell Sequencing Experiments. (2015, methodology): “Theﬁrstprotocol forsingle-cell sequencing waspub-lished in2009 by theSurani laboratory (Tang et al., 2009) andwas subsequently used to trace the derivation of mouse embryonic stem cells from the inner cell mass with single-cell resolution (Tang et al., 2010).”
DEGseq: an R package for identifying differentially expressed genes from RNA-seq data (2010, background): “Up to now, there are few handy programs for comparing RNAseq data and identifying differentially expressed genes from the data, although some recent publications have described their methods for this task (Marioni et al., 2008; Bloom et al., 2009; Tang et al., 2009).”
CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq (2016, methodology): “[1], using a polyT primer with an anchor sequence to select for the cell’s mRNA.”
An Abundance of Ubiquitously Expressed Genes Revealed by Tissue Transcriptome Sequence Data (2009, background): “When single-cell transcriptomes (like [32]) are available for multiple cell types, it will be possible to identify the core set of genes expressed in every mammalian cell.”