Performance Comparison of Reverse Transcriptases for Single-Cell Studies
Language English Country Great Britain, England Media print
Document type Comparative Study, Journal Article, Research Support, Non-U.S. Gov't
PubMed
31699702
DOI
10.1373/clinchem.2019.307835
PII: clinchem.2019.307835
Knihovny.cz E-resources
- MeSH
- Single-Cell Analysis MeSH
- DNA Primers metabolism MeSH
- Real-Time Polymerase Chain Reaction methods MeSH
- Humans MeSH
- Reverse Transcriptase Polymerase Chain Reaction methods MeSH
- Reproducibility of Results MeSH
- RNA-Directed DNA Polymerase metabolism MeSH
- RNA metabolism MeSH
- Superoxide Dismutase-1 genetics MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Comparative Study MeSH
- Names of Substances
- DNA Primers MeSH
- RNA-Directed DNA Polymerase MeSH
- RNA MeSH
- Superoxide Dismutase-1 MeSH
BACKGROUND: Recent advances allowing quantification of RNA from single cells are revolutionizing biology and medicine. Currently, almost all single-cell transcriptomic protocols rely on reverse transcription (RT). However, RT is recognized as a known source of variability, particularly with low amounts of RNA. Recently, several new reverse transcriptases (RTases) with the potential to decrease the loss of information have been developed, but knowledge of their performance is limited. METHODS: We compared the performance of 11 RTases in quantitative reverse transcription PCR (RT-qPCR) on single-cell and 100-cell bulk templates, using 2 priming strategies: a conventional mixture of random hexamers with oligo(dT)s and a reduced concentration of oligo(dT)s mimicking common single-cell RNA-sequencing protocols. Depending on their performance, 2 RTases were further tested in a high-throughput single-cell experiment. RESULTS: All tested RTases demonstrated high precision (R2 > 0.9445). The most pronounced differences were found in their ability to capture rare transcripts (0%-90% reaction positivity rate) and in their absolute reaction yield (7.3%-137.9%). RTase performance and reproducibility were compared with Z scores. The 2 best-performing enzymes were Maxima H- and SuperScript IV. The validity of the obtained results was confirmed in a follow-up single-cell model experiment. The better-performing enzyme (Maxima H-) increased the sensitivity of the single-cell experiment and improved resolution in the clustering analysis over the commonly used RTase (SuperScript II). CONCLUSIONS: Our comprehensive comparison of 11 RTases in low RNA input conditions identified 2 best-performing enzymes. Our results provide a point of reference for the improvement of current single-cell quantification protocols.
Faculty of Science Charles University Prague Czech Republic
Laboratory of Gene Expression Institute of Biotechnology CAS Vestec Czech Republic
Laboratory of Growth Regulators Faculty of Science Palacky University Olomouc Czech Republic
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