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Performance Comparison of Reverse Transcriptases for Single-Cell Studies



Performance Comparison of Reverse Transcriptases for Single-Cell Studies. Daniel Zucha, Peter Androvic, Mikael Kubista, Lukas Valihrach. DOI: 10.1373/clinchem.2019.307835 Published November 2019.

Abstract

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.

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