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Quantitative single-cell RNA-seq with unique molecular identiers
npg 201 4 Nature America, Inc. All rights reserved.
Saiful Islam1, Amit Zeisel1, Simon Joost2, Gioele La Manno1, Pawel Zajac1, Maria Kasper2, Peter Lnnerberg1 & Sten Linnarsson1
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 amplication lead to severe quantitative errors. We show that molecular labelsrandom sequences that label individual moleculescan nearly eliminate amplication noise, and that microuidic sample preparation and optimized reagents produce a vefold improvement in mRNA capture efciency.
RNA-seq has become the method of choice for transcriptome analysis in tissues13 and in single cells47. The two main challenges in single-cell RNA-seq are the efficiency of cDNA synthesis (which sets the limit of detection) and the amplification bias (which reduces quantitative accuracy). Published protocols have been reported to have limits of detection of between five and ten mRNA molecules57, corresponding to a capture efficiency of around 10%, and all current methods use amplification, either by PCR or by in vitro transcription.
To correct for amplification bias, we8 and others911 have described how molecules can be directly counted through the use of unique molecular identifiers (UMIs). For single-cell RNA-seq, UMIs have been used as an internal validation control12 but have not yet been explored as a direct, quantitative measure of gene expression. Molecule counting corrects for PCR-induced artifacts (Supplementary Fig. 1) and provides an absolute scale of measurement with a defined zero level. In contrast, standard RNA-seq uses relative measures such as reads per kilobase per million reads (RPKM), which mask differences in total mRNA content. For example, a gene may be upregulated in terms of RPKM and have a decrease in absolute expression level if the total mRNA content also changes. Thus an absolute scale of measurement is crucial for interpreting transcriptional dynamics in single cells.
We applied molecule counting to mouse embryonic stem (ES) cells and used spike-in controls to monitor technical performance (similar results were obtained in independent experiments
1Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden. 2Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden. Correspondence should be addressed to S.L. ([email protected]).
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