Your reverse transcription may not be optimal

Reverse transcription (RT) is a critical step in a real-time PCR. There are several commercially available RT kits which differ mainly in the kind of enzyme they use, bacterial (rTth) or viral origin (e.g., M-MuLV or AMV), and the kind of approach used for RT (random hexamers, oligo-dT, or specific reverse primers). Viral origin enzymes can work at lower temperatures (35–50 °C) than bacterial enzymes (60-72 °C), and bacterial enzymes work better with GC-rich templates and templates that present particularly complex secondary structures. According to the kit/method chosen, you can use a one-step approach—in which RT and PCR amplification are performed in the same tube—or a two-step approach—in which RT and PCR are performed separately.

Poor RT performance could be because:

• The input amount of RNA is not optimal for the size of RT reaction
• There are RT inhibitors present in your RNA

RT efficiency depends on the following:

Quality of the starting template

In the gene expression workflow, it is of fundamental importance not only to stabilize the RNA, in order to prevent RNA degradation by RNases, but also to select the extraction method that can assure a high yield and purity of nucleic acid and a complete removal of PCR inhibitors. The extraction method you select should take into account not only the starting material (blood, tissue, biopsies, etc.), but also the relative RNase activity and nucleic acid content. Ambion RNA Isolation kits from Applied Biosystems assure maximum yield and efficiency in the recovery of RNA and complete removal of PCR inhibitors.

Quantitation of starting RNA

It is fundamental to accurately quantitate the starting template (RNA) to be reverse transcribed, in order to avoid a decrease of efficiency in the RT step, due to an excess of starting template. You can perform quantitation by spectrophotometry, fluorometry, etc. However, this allows only RNA quantitation by evaluating the absorbance at 260nm and the absence of proteins and salt by evaluating the absorbance ratios A260/A280 and A260/A230. Instead, make an assessment of RNA integrity by determining the RIN index (RNA Integrity Number).

Type of reverse transcriptase and priming

Home brew methods or commercially available kits can use different approaches (random hexamers, oligo-dT, or specific reverse primers). Use commercially available kits that perform reverse-transcription by viral origin enzymes (M-MuLV), with either random hexamers or a mixture of random hexamers and oligo-dTs to maximize RT efficiency. The Applied Biosystems High Capacity cDNA Reverse Transcription Kits demonstrate good linearity across many different targets, with increasing template inputs, and high performance for capacity and efficiency.

For more information about how to evaluate and select reverse transcription chemistries, refer to page 20 of the Guide to Performing Relative Quantitation of Gene Expression using Quantitative Real Time PCR.

How much RNA should be added to the RT reactions?

Several factors influence the range of acceptable RNA input concentrations, including sample quality (which in turn is affected by the origin/type of sample, as well as the RNA isolation protocol used), expression levels of the genes being assayed in the experiment and the RT kit itself.

All RT kits recommend a maximum concentration of RNA. Such guidelines, while providing a good starting point, are not always completely reliable in a quantitative experiment. The upper limit may in fact be lower for certain sample types and/or RNA isolation protocols. Also, higher expressed genes—and in particular normalizer genes (endogenous controls) such as 18S—have less tolerance for pushing the upper limits of RNA concentrations in the RT reaction.

One result of lowered RT efficiency is poor conversion of RNA to cDNA, leading to wasted sample. Less RNA, in some instances, can prove just as optimal, if not more optimal, as more RNA.

In the figure below, a dilution series of RNA was converted to cDNA using individual RT reactions, before performing real time PCR. All reactions were performed in triplicate.

Linear reverse transcription is observed for the lowest four starting concentrations of RNA. However, the effect of RNA input beyond RT capacity is seen for the highest point. In this instance, reduction in the reverse transcription appears to be equal for both genes.

The concentrated RNA sample contains significant levels of a compound that is inhibiting RT. Dilute the sample (along with the inhibitor) to prevent this inhibition. The target and the control genes appear to undergo RT suppression at equal rates; therefore, the only bad result is that less cDNA is extracted from more RNA. However, final fold change calculations are not likely to be compromised.

A greater problem, shown in the figure below, is when RT of the normalizer gene occurs at a progressively lower rate, in the presence of higher concentrations of RNA, while the RT efficiency of the lower-expressed target gene(s) remains unchanged.

Linear reverse transcription.  RT efficiency is linear for both genes at the lower four concentrations of RNA. However, at the highest concentration of RNA, RT of the normalizer gene is affected to a much greater degree than that of the target gene

When this type of disparity occurs, final sample-to-sample gene expression data are inevitably inaccurate. This is because the relative amounts of control and target cDNA being amplified from sample-to-sample will vary as a result of RT efficiency differences. In short, final fold-change data may not be reliable. The main issue is that you may not know this is happening, since the amplification curves on the TaqMan array look perfectly normal.

Solution:

1. Do not use too much RNA—run RNA dilution curves to determine the appropriate amount of RNA to use.
2. Use one of the samples for the RNA dilution curves—it is important to use a sample that has been isolated in a manner consistent with all of the other samples.
3. Determine the optimal RNA input range in the context of the sample preparation method, the genes of interest, and the RT kit. The best way to perform the validation is to first take the representative RNA and dilute it serially in nuclease-free water.
4. Convert the dilution series of RNA to cDNA using individual RT reactions, using the same reaction volume for each RT (figure below).
   
   

   

   Dilution series of RNA converted to cDNA. A dilution series of a representative RNA sample is prepared. Next, each dilution is individually converted to cDNA using the same reverse transcription volume for each reaction.       

5. Amplify the resulting cDNAs to assay for specific generated curves from which you can determine the consistency of RT efficiency across the entire range of RNA input amounts.
6. Set up the experiment in the SDS software using the Standard Curve (Absolute Quantification) template, not the Relative Quantification (ΔΔCt) template—this allows the software to generate dilution curves for each gene during the analysis, enabling you to assess the linearity of RT efficiency for all assays.

 An example of good linearity is shown below:

Linear reverse transcription for all starting concentrations of RNA

In this situation, both the control and target genes have linear RT efficiencies across all starting concentrations of RNA. This means that in the future, using this same sample type or sample preparation approach, the procedure can be started with a concentration of RNA in the RT reactions that corresponds to the undiluted sample or the 1:10 and 1:100 dilutions.

If, however, the curves are not linear across all dilution points, then use less RNA, such that the starting amount falls within a range where all genes, control plus targets, show linearity.