QC steps in gene expression workflows

In gene expression analysis, RNA samples are processed for downstream detection and quantification. RNA molecules, especially mRNA and rRNA, are unstable, and are sensitive to heat and UV light exposure and RNase contamination. Furthermore, because transcriptomes vary depending on the species, genotype, cell and tissue types and environmental conditions, RNA samples are precious samples in terms of availability and information they convey.


Quality control
High-quality results depend on purification of RNA while preserving its integrity and keeping all fractions of interest. RNA samples must undergo as little degradation as possible before they are transcribed into much more stable cDNA, which is used for final qPCR or microarray analysis.

RNA quality control, along with results verification and validation, are critical to ensure that the results can be confidently converted into actionable insights.
Assays such as qPCR and microarrays can be costly, and the success and reproducibility of these assays rely on the quality of the input samples. Trying to make sense of results from gene expression analyses conducted using low-quality RNA can waste large amounts of time.

Next-generation spectrophotometry with the QIAxpert system provides quick and reliable nucleic acid quantification. With unique spectral content profiling capabilities, the method also provides information about the sample quality, differentiating RNA molecules from DNA and other absorbing contaminants.

Because RNA samples are much less stable than DNA, extra care is needed to prevent undesired sample alterations either by RNases, light or heat. Most gene expression workflows involve synthesizing cDNA from the RNA template by reverse transcription, and the resulting cDNA samples are analyzed in downstream assays. The success of the reverse transcription reaction and the quality of the resulting cDNA directly correlate with the quality of the input RNA.
Because producing high-quality cDNA requires a high-quality RNA template, it is important to assess the RNA quality and size distribution using a method such as electrophoresis. RNA integrity can be determined by analyzing the sample’s electrophoretic fingerprint. Automated algorithms such as the QIAxcel Advanced RNA Integrity Score (RIS) assess several parameters including the 28S and 18S peaks and attribute a score ranging from 1 (highly degraded) to 10 (mostly intact). For most applications, values ranging between 7–10 indicate an RNA quality suitable for downstream gene expression analysis. Objective assessment of RNA integrity using RIS improves comparison of samples, standardization and reproducibility of experiments.
Following PCR amplification of cDNA fragments, the specificity of the amplification can be confirmed by agarose gel or capillary electrophoresis. For highly accurate verification and validation, sequencing methods provide deeper insights into the sequence, especially for analysis of SNPs.


  • Pabinger, S. et al. 2014. A survey of tools for the analysis of quantitative PCR (qPCR) data. Biomol Detect and Quant. 1:1, 23–33.
  • Broeders, S. et al. 2014. Guidelines for validation of qualitative real-time PCR methods. Trends in Food Science & Tech. 37:2, 115–126.
  • Stanoszek, L.M. et al. 2013. Quality control methods for optimal BCR-ABL1 clinical testing in human whole blood samples. J Mol Diagn. 15:3, 391–400.
  • Bustin, S.A. et al. 2009. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 55:4, 611–622.

Find out more about QC checks in other laboratory workflows: