scholarly journals Performance comparison of reverse transcriptases for single-cell studies

2019 ◽  
Author(s):  
Zucha Daniel ◽  
Androvic Peter ◽  
Kubista Mikael ◽  
Valihrach Lukas
Keyword(s):  
Single Cell ◽  
Cell Model ◽  
Single Cells ◽  
Performance Comparison ◽  
Reverse Transcriptases ◽  
Single Cell Model ◽  
Cell Experiment ◽  
Z Scores ◽  
Technical Advances ◽  
Almost All

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.

scholarly journals Performance Comparison of Reverse Transcriptases for Single-Cell Studies

2019 ◽  
Vol 66 (1) ◽  
pp. 217-228 ◽  
Author(s):  
Daniel Zucha ◽  
Peter Androvic ◽  
Mikael Kubista ◽  
Lukas Valihrach
Keyword(s):  
Single Cell ◽  
Reverse Transcription ◽  
Cell Model ◽  
Single Cells ◽  
Performance Comparison ◽  
Reaction Yield ◽  
Reverse Transcriptases ◽  
Reverse Transcription Pcr ◽  
Cell Experiment ◽  
Almost All

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.

scholarly journals Direct observation of frequency modulated transcription in single cells using light activation

eLife ◽  
2013 ◽  
Vol 2 ◽  
Author(s):  
Daniel R Larson ◽  
Christoph Fritzsch ◽  
Liang Sun ◽  
Xiuhau Meng ◽  
David S Lawrence ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Molecule ◽  
Single Cell Analysis ◽  
Cell Model ◽  
Single Cells ◽  
Single Gene ◽  
Gene Activation ◽  
Light Activation ◽  
Dynamic Synthesis ◽  
Single Cell Model

Single-cell analysis has revealed that transcription is dynamic and stochastic, but tools are lacking that can determine the mechanism operating at a single gene. Here we utilize single-molecule observations of RNA in fixed and living cells to develop a single-cell model of steroid-receptor mediated gene activation. We determine that steroids drive mRNA synthesis by frequency modulation of transcription. This digital behavior in single cells gives rise to the well-known analog dose response across the population. To test this model, we developed a light-activation technology to turn on a single steroid-responsive gene and follow dynamic synthesis of RNA from the activated locus.

Calibration of single-cell model parameters based on membrane resistance improves the accuracy of cardiac tissue simulations

2021 ◽  
pp. 101375
Author(s):  
Elnaz Pouranbarani ◽  
Lucas Arantes Berg ◽  
Rafael Sachetto Oliveira ◽  
Rodrigo Weber dos Santos ◽  
Anders Nygren
Keyword(s):  
Single Cell ◽  
Cardiac Tissue ◽  
Cell Model ◽  
Membrane Resistance ◽  
Model Parameters ◽  
Single Cell Model

scholarly journals Functional analysis of the methyltransferase SMYD in the single-cell model organism Tetrahymena thermophila

2020 ◽  
Vol 2 (2) ◽  
pp. 109-122
Author(s):  
Xiaolu Zhao ◽  
Yuan Li ◽  
Lili Duan ◽  
Xiao Chen ◽  
Fengbiao Mao ◽  
...  
Keyword(s):  
Functional Analysis ◽  
Single Cell ◽  
Tetrahymena Thermophila ◽  
Cell Model ◽  
Model Organism ◽  
Single Cell Model

scholarly journals Complete Transcriptome of the Soybean Root Hair Cell, a Single-Cell Model, and Its Alteration in Response to Bradyrhizobium japonicum Infection

2009 ◽  
Vol 152 (2) ◽  
pp. 541-552 ◽  
Author(s):  
Marc Libault ◽  
Andrew Farmer ◽  
Laurent Brechenmacher ◽  
Jenny Drnevich ◽  
Raymond J. Langley ◽  
...  
Keyword(s):  
Hair Cell ◽  
Single Cell ◽  
Root Hair ◽  
Bradyrhizobium Japonicum ◽  
Cell Model ◽  
Soybean Root ◽  
Root Hair Cell ◽  
Single Cell Model

scholarly journals Small-on-Large Fractional Derivative–Based Single-Cell Model Incorporating Cytoskeleton Prestretch

2017 ◽  
Vol 143 (5) ◽  
Author(s):  
M. Fraldi ◽  
A. Cugno ◽  
A. R. Carotenuto ◽  
A. Cutolo ◽  
N. M. Pugno ◽  
...  
Keyword(s):  
Fractional Derivative ◽  
Single Cell ◽  
Cell Model ◽  
Single Cell Model ◽  
Small On Large
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