Full-Length mRNA-Seq from single cell levels of RNA and individual circulating tumor cells

By Daniel Ramsköld, Shujun J Luo, Yu-Chieh Wang, Robin Li, Q. Deng, O. Faridani, G. Daniels, I. Khrebtukova, J. Loring, L. Laurent and 2 more. Nature Biotechnology, 2012

1,537 citations recorded by the source corpus.

Genome-wide transcriptome analyses are routinely used to monitor tissue-, disease- and cell type–specific gene expression, but it has been technically challenging to generate expression profiles from single cells. Here we describe a robust mRNA-Seq protocol (Smart-Seq) that is applicable down to single cell levels. Compared with existing methods, Smart-Seq has improved read coverage across transcripts, which enhances detailed analyses of alternative transcript isoforms and identification of single-nucleotide polymorphisms. We determined the sensitivity and quantitative accuracy of Smart-Seq for single-cell transcriptomics by evaluating it on total RNA dilution series. We found that although gene expression estimates from single cells have increased noise, hundreds of differentially expressed genes could be identified using few cells per cell type. Applying Smart-Seq to circulating tumor cells from melanomas, we identified distinct gene expression patterns, including candidate biomarkers for melanoma circulating tumor cells. Our protocol will be useful for addressing fundamental biological problems requiring genome-wide transcriptome profiling in rare cells.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3b159f4698c347b09664cc6eaa0fe881726a5179

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

In Molecular Biology

Citations in the corpus

Single-cell Genomics Unveils Critical Regulators of Th17 cell Pathogenicity (2015, methodology): “We prepared mRNA SMART-Seq libraries using microfluidic chips (Fluidigm C1), followed by transposon-based library construction.”
Mapping the Mouse Cell Atlas by Microwell-Seq. (2018, positive): “…2011; Guo et al., 2010; Han et al., 2017), single-cell mass cytometry (Bendall et al., 2011), and singlecell mRNA-seq (Hashimshony et al., 2012; Ramsköld et al., 2012; Shalek et al., 2013; Tang et al., 2009; Treutlein et al., 2014) allow for dissection of cell heterogeneity at high resolution.”
Identification of cell types from single-cell transcriptomes using a novel clustering method (2015, methodology): “Since technical variability in the measurements of gene expression levels becomes pronounced for lowly expressed genes due to random loss of transcripts (Ramsko¨ld et al. , 2012), excluding such genes before analysis could enhance the reliability of results.”
Exponential scaling of single-cell RNA-seq in the past decade (2017, methodology): “Early single cell experiments were motivated by the prospect of an in depth analysis of gene expression in a few precious cells (Brouilette et al., 2012; Ramsköld et al., 2012; Tang et al., 2009, 2010, 2011).”
Design and computational analysis of single-cell RNA-sequencing experiments (2016, methodology): “To identify genes that have expression differences across conditions in an scRNA-seq study, investigators often use methods from bulk RNA-seq that test for shifts in uni-modal distributions across conditions [3, 31, 56, 67, 74, 75].”
The Human Cell Atlas (2017, methodology): “Some may take a census of mRNA species by focusing on 3’- or 5’-ends (Islam et al., 2014; Macosko et al., 2015), while others assess mRNA structure and splicing by collecting near-full-length sequence (Hashimshony et al., 2012; Ramskold et al., 2012).”
Classification of low quality cells from single-cell RNA-seq data (2016, methodology): “Libraries for these data were prepared using the Smart-Seq [25], SmartSeq2 [24], or modified Smart-Seq with UMIs [22].”