Quantitative single-cell RNA-seq with unique molecular identifiers

By S. Islam, Amit Zeisel, S. Joost, G. L. Manno, P. Zając, M. Kasper, P. Lönnerberg, S. Linnarsson. Nature Methods, 2013

1,143 citations recorded by the source corpus.

Single-cell RNA sequencing (RNA-seq) is a powerful tool to reveal cellular heterogeneity, discover new cell types and characterize tumor microevolution. However, losses in cDNA synthesis and bias in cDNA amplification lead to severe quantitative errors. We show that molecular labels—random sequences that label individual molecules—can nearly eliminate amplification noise, and that microfluidic sample preparation and optimized reagents produce a fivefold improvement in mRNA capture efficiency.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/14e7a8484ba5e3a3b5dbfc7618b8b3e172c80298

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Citations in the corpus

Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model (2019, background): “However, newer protocols typically include unique molecular identifiers (UMIs) which enable computational removal of PCR duplicates [10], producing UMI counts.”
UMI-tools: Modelling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy (2016, methodology): “Islam et al (2014) previously identified the issue of sequencing errors and proposed removing UMIs whose counts fall below a threshold of 1% of the mean of all non-zero UMIs at the locus, a method we refer to as “ percentile ”.”
Single-cell transcriptome sequencing: recent advances and remaining challenges (2016, background): “Because only a small fraction of each cell’s transcript complement (approximately 10% for many protocols 9 ) is represented in the final sequencing libraries, scRNA-seq has limited sensitivity and is unable to reliably detect low-abundance transcripts 9,32,33 .”
Computational and analytical challenges in single-cell transcriptomics (2015, methodology): “For example, it has been observed that poor-quality cells are often enriched in the expression of mitochondrial genes 40 (perhaps because the cells are undergoing apoptosis), which can cause them to cluster separately.”
Genome-wide RNA Tomography in the zebrafish embryo. (2014, methodology): “The recent development of several RNA amplification strategies for single-cell RNA sequencing (Hashimshony et al., 2012; Islam et al., 2011, 2014; Picelli et al., 2013; Ramsköld et al., 2012; Tang et al., 2009) is now beginning to alleviate the restrictions caused by high input material…”
RNA-Seq methods for transcriptome analysis (2016, neutral): “In addition, barcoding is typically used to label cells, enabling simultaneous preparation of libraries from multiple cells.(40,42,93) On this note, recent high throughput single-cell methods have used oligonucleotide beads to deliver barcodes to cells and mRNAs at the same time (CytoSeq),(45) or ha”
Normalizing single-cell RNA sequencing data: Challenges and opportunities (2017, methodology): “UMI-based protocols in principle remove amplification and sequencing depth related biases, since multiple reads associated with the same UMI are collapsed into a unique count (Figure 1c).”
Classification of low quality cells from single-cell RNA-seq data (2016, methodology): “To understand how generalizable these features are across various cell types and protocols, we downloaded and processed gene expression data from over 5,000 single cells from published [8, 9, 13, 26, 36] and unpublished datasets comprising CD4+ T cells and mESCs.”
Interpretable dimensionality reduction of single cell transcriptome data with deep generative models (2017, methodology): “Specifically, we tested these variational influence neural networks and the model neural network layer combinations (the first number is the number of layers in the variational influence neural network, and the second number is the number of layers in the model neural network): (10, 1), (10, 3), (10”
A general and flexible method for signal extraction from single-cell RNA-seq data (2017, neutral): “The inclusion in the library preparation of unique molecular identifiers (UMIs) reduces amplification bias [9], but does not remove dropout events, nor the need for data normalization [10, 11].”