Gene expression analysis plays a pivotal role in a wide range of studies, including biomedical analysis and diagnostics. Of all the methods available for gene expression analysis, quantitative real-time PCR (qRT-PCR) is the most rapid, sensitive, and accurate to measure mRNA, and its use in clinical diagnostics is rising steadily.

RNA quality entails both purity and integrity. Both play a critical role in the accuracy, reproducibility and relevance of downstream analyses, and isolation of high quality RNA is therefore an essential prerequisite for the techniques used in gene expression and RNA analysis. This is even more so the case when you consider that diagnosis of certain diseases is based on measuring mRNA levels (e.g. certain cancers, kidney diseases).

In this article, we will look at what is meant by RNA quality, how it can be measured, challenges in working with RNA and how we can overcome these.

RNA Purity

RNA is typically extracted via phenol/chloroform precipitation or commercial RNA extraction kits, either manually or automated. Regardless of the method used, contaminants such genomic DNA (gDNA), DNases, RNases and proteases sometimes end up in the final extract, leaving you with an impure RNA extract, leading to some of the following problems:

  • gDNA: if not eliminated, it can be amplified along with cDNA during qRT-PCR, leading to over-estimation of the actual RNA concentration
  • DNases: can degrade cDNA for gene expression analysis
  • RNases: degrade RNA before you analyze it, leaving you with an unclear set of results
  • Proteases: can degrade enzymes used in downstream reactions (e.g. reverse transcriptase during cDNA synthesis)

It can be challenging to completely avoid contaminants in your sample, but a variety of post-extraction tools can be used to clean up your RNA. Genomic DNA can be removed by treating your samples with DNase prior to cDNA synthesis. Several RNA purification kits are also available on a commercial basis.

RNA Integrity

This refers to the quality or integrity of the RNA molecules themselves. Total RNA extracts usually contain rRNA subunits, mRNA, tRNA and small RNAs. For gene expression and transcriptome analyses, mRNA is usually the desired material. RNA is inherently susceptible to RNase degradation and it is a chemically unstable molecule. If you are working with RNA for the first time, it is a good idea to read up on RNA handling in advance, as it requires a lot more care than DNA.

Carrying out your experiments with substandard RNA can have dramatic effects on your results. Consider this for example: For qRT-PCR, mRNA is converted to cDNA by the reverse transcriptase enzyme. This enzyme catalyzes cDNA synthesis starting with the polyA tails on the individual mRNA molecules. If the polyA tails are damaged, the corresponding mRNA will not be converted to cDNA and this transcript will not be accounted for in the analysis. As you can imagine, this makes it impossible to compare gene expression levels across different genes, treatments or cell types, etc.

Challenges in Working with RNA – Factors That Affect Quality and the Consequences

Besides the few examples mentioned above, there are a lot of other steps where RNA quality can be compromised leading to a variety of problems in subsequent analyses. Fortunately, most of these can be addressed with careful planning, handling and standardization in the lab. Let’s have a closer look at where these challenges exist, their consequences and how we can minimize them (Table 1). Note: many of the sources and consequences overlap.

Table 1: Brief outline of problems and challenges faced when working with RNA





Contaminants e.g. RNases, DNAses

Via extraction and/or present in the workspace

Degradation of material, inhibition of cDNA synthesis/PCR reaction,
misrepresentation of RNA concentration and gene expression

Careful handling, RNA purification, use molecular biology grade tips etc.

Lengthy extraction time

Inefficient personnel /too many samples to deal with at once

Degradation, inconsistent RNA quality

Careful planning – handle a realistic number of samples, automate work if possible

Incorrect handling

Untrained/inefficient personnel

Degraded material

Standardized staff training
Ensure that every RNA preparation is rigorously assessed for quality

Incorrect & lengthy storage

Inconsistent freezer temperature, lack of storage space

Degraded material

Careful planning – try to use fresh RNA for your experiments

Source of RNA

Cell culture, biopsy, blood sample

Some sources are easier to work with than others

Careful planning – be up to date on the most suitable method for your material

Extraction technique

Manual extraction can introduce more variability than automated

Inconsistent RNA quality across sample set, degradation

Choose automated setup where possible, use robust homogenization techniques, freeze material in liquid nitrogen

How Can We Measure RNA Quality?

 To avoid wasting manpower, time and money running experiments with substandard RNA, it is imperative to rigorously assess your RNA before use. Several methods exist to do this, which are outlined below. A more detailed description of these methods can be found elsewhere (1).

1) Agarose gel electrophoresis

  • Agarose gels stained with SYBR Green dye or ethidium bromide allow visualization of RNA integrity. High quality RNA will appear as sharp distinct 28S and 18S rRNA bands, with the 28S band approximately twice as intense as the 18S band. Smearing in the lanes is a sign of partially degraded RNA while low molecular weight smears indicate complete degradation.
  • To monitor RNA accurately, you should include an RNA molecular weight marker when running your gel. Beware that you won’t see defined mRNA bands on the gel as it makes up only 1–5 % of total RNA. You might see a smear throughout the lanes, which is most intense between 1.5 and 2 KB, and this contains the mRNA.
  • Although effective, agarose gels provide only a qualitative assessment of RNA quality, are low-throughput, and demand a lot of RNA (which can be precious!). Depending on your situation, you might opt for something with quantitative output, such as the methods outlined below.

2) Nanodrop quantification

  • The NanoDrop essentially works like a spectrophotometer, quantifying the RNA concentration by measuring its OD (optical density) at 260 nm.
  • The nanodrop requires only 1–2 µl, and can measure a range covering 400–750 nm, providing more information about RNA integrity and contaminants.
  • To get an accurate measurement, pure RNA preparations should be used, so it is a good idea to purify/DNAse-treat your RNA prior to quantification. Alternatively, you can use the nanodrop in conjunction with RNA RiboGreen dye, which only measures polymeric nucleic acids.

3) Bioanalyzer analysis

  • The bioanaylzer uses lab-on-a-chip technology to provide highly accurate information on RNA quantity and quality.
  • RNA samples are electrophoretically resolved on a micro-fabricated chip and subsequently detected via laser induced fluorescence detection. This technique can detect down to 200 pg total RNA.
  • Including an RNA ladder with each run allows the estimation of the RNA band sizes. RNA integrity may be assessed by visualization of the 18S and 28S ribosomal RNA bands.
  • In contrast to the other techniques described here, RNA measurements obtained by the bioanalyzer are relatively uninfluenced by contamination.

Techniques such as northern blotting, cDNA library construction and cDNA microarray analysis require long RNA fragments (>1kb), which are inherently susceptible to degradation. Since these techniques can be expensive, labor intensive and time-consuming, they should be approached with the highest quality RNA. RT-PCR and ribonuclease protection assays usually analyze shorter regions of RNA, and are therefore slightly more tolerant to partially degraded RNA. However, you should strive for high quality RNA in all your experiments. For many RNA-based applications (e.g. qRT-PCR, microarray and others), the effect that variability in downstream steps such as sample preparation, cDNA synthesis and the PCR reaction itself has on the accuracy of results is not fully understood.

As outlined here, both endogenous and exogenous factors, from contaminants to sloppy personnel, can have significant and detrimental impacts on RNA quality and the accuracy of downstream analysis. Despite observations that certain techniques (e.g. qRT-PCR with small amplicons) can tolerate partially degraded RNA, there are likely to be many other factors at play, so obtaining reliable data with substandard RNA is never a given. Efforts to determine the effect of RNA quality on the outcome of qRT-PCR has been described elsewhere (2).

Take home message: To be on the safe side, and to reduce uncertainty in your data, always start out with high quality RNA.

Additional Reading:

  1. Fleige S, Pfaffl MW (2006) RNA integrity and the effect on the real-time qRT-PCR performance. Mol Aspects Med 27:126–139.
  2. Vermeulen J, De Preter K, Lefever S, Nuytens J, De Vloed F, Derveaux S, et al. (2011) Measurable impact of RNA quality on gene expression results from quantitative PCR. Nuc. Acids Res. 39:e63.

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One Comment

  1. Hi there, I would like to know if it’s okay to use 2 different RNA extraction kits across samples (eg, leaves of different maturity) prior downstream analyses. How do we know if the total RNA of both the samples is consistent in terms of quality between the two kits?

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