The Challenges of RT

The Challenges of Reverse Transcription (RT)

For many years scientists have had to rely on platforms that use reverse transcriptase to convert RNA to cDNA in order to perform gene expression studies. Reverse transcription and amplification, however, come with challenges. The cDNA conversion and amplification steps can introduce variability into the data being generated. Additionally, other areas of the workflow such as sample prep and data analysis can also introduce variability into the data. The scientific community has begun recognizing these challenges and publications on the limitations of RT-based platforms have been emerging across the qPCR, microarray and NGS spaces.

 

Aird, D. et. al., “Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries.” Genome Biology  2011, 12:R18

Bustin, S. et. al., “Talking the talk but not walking the walk: RT-qPCR as a paradigm for the lack of reproducibility in molecular research.” Eur J Clin Invest 2017 Oct;47(10):756-774

Bustin, S. et. al., “The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments.” Clinical Chemistry 55:4 611-622 (2009)

Bustin, S. et. al., “Variability of the Reverse Transcription Step: Practical Implications.” Clinical Chemistry 61:1 000-000 (2015)

Casadevall, A. et. al., “Source of error in the retracted scientific literature.” FASEB J  2014 Sept; 28(9): 3847-3855

Dijkstra, J.R. et. al., “Critical appraisal of quantitative PCR results in colorectal cancer research: Can we ply on published qPCR results?” Mol Oncol 2014 Jun:8(4):813-8

Esteve-Codina, A. et. al., “A Comparison of RNA-Seq Results from Paired Formalin-Fixed Paraffin-Embedded and Fresh-Frozen Glioblastoma Tissue Samples.” PLOS. January 25, 2017

Head, S.R. et. al., “Library construction for next-generation sequencing: Overviews and challenges.” Biotechniques 2014 Feb 1: 56(2) 61-4

Northcott P.E. et al., “Rapid, Reliable, and Reproducible Molecular Sub-grouping of  Clinical Medulloblastoma Samples.” Acta Neuropathologica; November 16, 2011

Omolo, B. et. al., “Adaptation of a RAS pathway activation signature from FF to FFPE tissues in colorectal cancer.” BMC Medical Genomics 19 October 2016.

Raman, A.T. et. al., “Apparent bias towards long gene misregulation in MeCP2 syndromes disappears after controlling for baseline variations.” Nature Communications  13 August 2018 Article number: 3225 (2018)

Roy, S. et. al., “Standards and Guidelines for Validating Next-Generation Sequencing Bioinformatics Pipelines, A Joint Recommendation of the Association for Molecular Pathology and the College of American Pathologists.” The Journal of Molecular Diagnostics Vol. 20, No. 1, January 2018

Ruiz-Villalba, A. et. al., “Amplification of nonspecific products in quantitative polymerase chain reactions (qPCR).” Biomol Detect Quantif. 2017 Dec: 14:7-18.

Veldman-Jones, M.H. et. al., “Evaluating Robustness and Sensitivity of the NanoString Technologies nCounter Platform to Enable Multiplexed Gene Expression Analysis of Clinical Samples.” Cancer Res June 11, 2015: 75(13):2587-93

Challenges of Current Gene Expression Workflows

 

The Benefits of Counting RNA Directly

So what if you could bypass a cDNA conversion step and count RNA molecules directly? Direct RNA detection allows for robust, reproducible performance and unbiased transcript quantitation.

The nCounter platform offers numerous advantages for gene expression analysis:

 

NanoString no reverse transcription offerings

 

Interested in learning more? Your journey begins here

 

For Research Use Only. Not for use in diagnostic procedures.