Assessing characteristics of RNA amplification methods for single cell RNA sequencing

Hannah R. Dueck, Rizi Ai, Adrian Camarena, Bo Ding, Reymundo Dominguez, Oleg V. Evgrafov, Jian Bing Fan, Stephen A. Fisher, Jennifer S. Herstein, Tae Kyung Kim, Jae Mun (Hugo) Kim, Ming Yi Lin, Rui Liu, William J. Mack, Sean McGroty, Joseph D. Nguyen, Neeraj Salathia, Jamie Shallcross, Tade Souaiaia, Jennifer M. SpaethlingChristopher P. Walker, Jinhui Wang, Kai Wang, Wei Wang, Andre Wildberg, Lina Zheng, Robert H. Chow, James Eberwine, James A. Knowles, Kun Zhang, Junhyong Kim

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

Background: Recently, measurement of RNA at single cell resolution has yielded surprising insights. Methods for single-cell RNA sequencing (scRNA-seq) have received considerable attention, but the broad reliability of single cell methods and the factors governing their performance are still poorly known. Results: Here, we conducted a large-scale control experiment to assess the transfer function of three scRNA-seq methods and factors modulating the function. All three methods detected greater than 70% of the expected number of genes and had a 50% probability of detecting genes with abundance greater than 2 to 4 molecules. Despite the small number of molecules, sequencing depth significantly affected gene detection. While biases in detection and quantification were qualitatively similar across methods, the degree of bias differed, consistent with differences in molecular protocol. Measurement reliability increased with expression level for all methods and we conservatively estimate measurements to be quantitative at an expression level greater than ~5-10 molecules. Conclusions: Based on these extensive control studies, we propose that RNA-seq of single cells has come of age, yielding quantitative biological information.

Original languageEnglish (US)
Article number966
JournalBMC genomics
Volume17
Issue number1
DOIs
StatePublished - Nov 24 2016
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Genetics

Keywords

  • Bioinformatics
  • Biotechnology
  • Genomics
  • Single-cell RNA-sequencing

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