Quantitative single-cell RNA-seq with unique molecular identifiers

  title={Quantitative single-cell RNA-seq with unique molecular identifiers},
  author={Saiful Islam and Amit Zeisel and Simon Joost and Gioele La Manno and Paweł Zając and Maria Kasper and Peter L{\"o}nnerberg and Sten Linnarsson},
  journal={Nature Methods},
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. 

Single-Cell RNA-Seq by Multiple Annealing and Tailing-Based Quantitative Single-Cell RNA-Seq (MATQ-Seq).

Multiple annealing and dC-tailing-based quantitative single-cell RNA-seq with ~90% capture efficiency is described, and MATQ-seq is a total RNA assay allowing for detection of nonpolyadenylated transcripts.

Single‐Cell RNA‐seq: Introduction to Bioinformatics Analysis

This unit presents a bioinformatics workflow for analyzing single‐cell RNA‐seq data with a few current publicly available computational tools focused on the interpretation of the heterogeneity from single‐ cell transcriptomes as well as the identification of cell clusters and genes that are differentially expressed between clusters.

Quantitative single-cell transcriptomics

The basic principles underlying the different experimental protocols and how to benchmark them are reviewed, and the essential methods to process scRNA-seq data from mapping, filtering, normalization and batch corrections to basic differential expression analysis are compared.

Effective detection of variation in single-cell transcriptomes using MATQ-seq

It is shown that MATQ-seq captures genuine biological variation between whole transcriptomes of single cells, a highly sensitive and quantitative method for single-cell sequencing of total RNA.

Computational and analytical challenges in single-cell transcriptomics

The development of high-throughput RNA sequencing (RNA-seq) at the single-cell level has already led to profound new discoveries in biology, ranging from the identification of novel cell types to the

Normalizing single-cell RNA sequencing data: challenges and opportunities

Single-cell transcriptomics is becoming an important component of the molecular biologist's toolkit. A critical step when analyzing data generated using this technology is normalization. However,

Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads

Improvements in the reaction steps make it possible to effectively convert initial reads to UMI counts, at a rate of 30–50%, and detect more genes in high-throughput single-cell RNA-seq methods.

Experimental design for single-cell RNA sequencing

General considerations in experimental design and the two most popular approaches, plate-based Smart-Seq2 and microdroplet-based scRNA-seq at the example of 10x Chromium are discussed.

Revealing allele-specific gene expression by single-cell transcriptomics.

Single-Cell Transcriptomic Analysis.

Single-cell sequencing measures the sequence information from individual cells using optimized single-cell isolation protocols and next-generation sequencing technologies. Recent advancement in



Smart-seq2 for sensitive full-length transcriptome profiling in single cells

Smart-seq2 with improved reverse transcription, template switching and preamplification to increase both yield and length of cDNA libraries generated from individual cells to improve detection, coverage, bias and accuracy.

Counting absolute numbers of molecules using unique molecular identifiers

Unique molecular identifiers (UMIs), which make each molecule in a population distinct, are applied to genome-scale human karyotyping and mRNA sequencing in Drosophila melanogaster to improve accuracy of almost any next-generation sequencing method.

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

Applying Smart-Seq to circulating tumor cells from melanomas, it is 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.

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

This strategy will enable the unbiased discovery and analysis of naturally occurring cell types during development, adult physiology, and disease and be demonstrated by analyzing the transcriptomes of 85 single cells of two distinct types.

Stem cell transcriptome profiling via massive-scale mRNA sequencing

A massive-scale RNA sequencing protocol, short quantitative random RNA libraries or SQRL, is developed, highlighting how SQRL can be used to characterize transcriptome content and dynamics in a quantitative and reproducible manner, and suggesting that the understanding of transcriptional complexity is far from complete.

Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

This study uses single-cell RNA sequencing to investigate heterogeneity in the response of mouse bone-marrow-derived dendritic cells (BMDCs) to lipopolysaccharide, and finds extensive, and previously unobserved, bimodal variation in messenger RNA abundance and splicing patterns.

Highly multiplexed and strand-specific single-cell RNA 5′ end sequencing

This work presents a detailed protocol for quantitative gene expression analysis in single cells, by the sequencing of mRNA 5′ ends, more suitable for large-scale quantitative analysis, as well as for the characterization of transcription start sites, but it is unsuitable for the detection of alternatively spliced transcripts.

Digital RNA sequencing minimizes sequence-dependent bias and amplification noise with optimized single-molecule barcodes

This method allows counting with single-copy resolution despite sequence-dependent bias and PCR-amplification noise, and is analogous to digital PCR but amendable to quantifying a whole transcriptome.

mRNA-Seq whole-transcriptome analysis of a single cell

A single-cell digital gene expression profiling assay with only a single mouse blastomere is described, which detected the expression of 75% more genes than microarray techniques and identified 1,753 previously unknown splice junctions called by at least 5 reads.