Performance comparison of reverse transcriptases for single-cell studies

Abstract

ABSTRACTBackgroundRecent technical advances allowing quantification of RNA from single cells are revolutionizing biology and medicine. Currently, almost all single-cell transcriptomic protocols rely on conversion of RNA to cDNA by reverse transcription (RT). However, RT is recognized as highly limiting step due to its inherent variability and suboptimal sensitivity, especially at minute amounts of RNA. Primary factor influencing RT outcome is reverse transcriptase (RTase). Recently, several new RTases with potential to decrease the loss of information during RT have been developed, but the thorough assessment of their performance is missing.MethodsWe have compared the performance of 11 RTases in RT-qPCR on single-cell and 100-cell bulk templates using two priming strategies: conventional mixture of random hexamers with oligo(dT)s and reduced concentration of oligo(dT)s mimicking common single-cell RNA-Seq library preparation protocols. Based on the performance, two RTases were further tested in high-throughput single-cell experiment.ResultsAll RTases tested reverse transcribed low-concentration templates with high accuracy (R2 > 0.9445) but variable reproducibility (median CVRT = 40.1 %). The most pronounced differences were found in the ability to capture rare transcripts (0 - 90% reaction positivity rate) as well as in the rate of RNA conversion to cDNA (7.3 - 124.5 % absolute yield). Finally, RTase performance and reproducibility across all tested parameters were compared using Z-scores and validity of obtained results was confirmed in a single-cell model experiment. The better performing RTase provided higher positive reaction rate and expression levels and improved resolution in clustering analysis.ConclusionsWe performed a comprehensive comparison of 11 RTases in low RNA concentration range and identified two best-performing enzymes (Maxima H-; SuperScript IV). We found that using better-performing enzyme (Maxima H-) over commonly-used below-average performer (SuperScript II) increases the sensitivity of single-cell experiment. Our results provide a reference for the improvement of current single-cell quantification protocols.