Pitfalls of quantitative real-time reverse-transcription polymerase chain reaction

Abstract

Polymerase chain reaction (PCR)-based assays can target either DNA (the genome) or RNA (the transcriptome). Targeting the genome generates robust data that are informative and, most importantly, generally applicable. This is because the information contained within the genome is context-independent; i.e., generally, every normal cell contains the same DNA sequence--the same mutations and polymorphisms. The transcriptome, on the other hand, is context-dependent; i.e., the mRNA complement and level varies with physiology, pathology, or development. This makes the information contained within the transcriptome intrinsically flexible and variable. If this variability is combined with the technical limitations inherent in any reverse-transcription (RT)-PCR assay, it can be difficult to achieve not just a technically accurate but a biologically relevant result. Template quality, operator variability, the RT step itself, and subjectivity in data analysis and reporting are just a few technical aspects that make real-time RT-PCR appear to be a fragile assay that makes accurate data interpretation difficult. There can be little doubt that in the future, transcriptome-based analysis will become a routine technique. However, for the time being it remains a research tool, and it is important to recognize the considerable pitfalls associated with transcriptome analysis, with the successful application of RTPCR depending on careful experimental design, application, and validation.

FIGURE 1

RNA quality assessment. These plots and electropherogram show 12 RNA preparations extracted from fresh human colonic biopsies. The quality of these preparations, as judged by the absence of any bands other than the 28S, 18S, and 5S rRNA, is very high.

FIGURE 2

Importance of assaying high-quality RNA. A: RNA was extracted from 19 fresh colonic biopsies using Qiagen RNeasy columns (Crawley, UK) and analyzed on the Agilent Bioanalyzer using the RNA LabChip. The intact RNA preparation on the left shows the 18S and 28S rRNA peaks as well as a small amount of 5S RNA. Degradation of the RNA sample on the right produces a shift in the RNA size distribution toward smaller fragments and a decrease in fluorescence signal as dye intercalation sites are destroyed. Where such analysis revealed degradation, RNA was re-extracted from the same sample. Real-time RT-PCR assays were carried out for seven target genes and for each sample the copy number obtained from intact RNA was divided by the copy number obtained from the degraded RNA. If RNA quality was irrelevant, the ratio of the two would be expected to be close to 1, since these are identical samples. If RNA quality was important, the ratio should be greater than 1, since the copy number calculated from the intact RNA should be higher than the copy number from the degraded RNA. B: The result shows clearly that RNA quality does matter, for some genes (e.g., IGF-I) more than for others (IGF-IR). The anomalous result obtained for GH may be due to the secondary structure of its mRNA, which is reduced by degradation, thus making it more accessible to priming. GAPDH, Glyceraldehyde-3-phosphate dehydrogenase; PCNA, proliferating cell nuclear antigen; GH, growth hormone; IGF-1, insulin-like growth factor-1; 1α-OH, 1α-hydroxylase; IGF-1R, insulin-like growth factor-1receptor.

FIGURE 3

Assessment of inhibitors in RNA preparations. A test mastermix was prepared using the Brilliant 1-step qRT-PCR mastermix (Stratagene), to which a plant gene sense strand amplicon, primers (200 nM), and FAM-BHQ-labeled TaqMan probe (500 nM) (Applied Biosystems, Warrington, Cheshire, UK) were added. Total RNA was prepared from fresh colonic biopsies (5 mg) using Qiagen RNeasy columns and resuspended in 80 μL of Tris-Cl/EDTA buffer. RNA (100 ng) was added to the test mastermix and a one-tube RTPCR assay (final volume 25 μL, 10 hr RT at 50°C, 40 cycles of 30 min at 70°C) was carried out on a Stratagene MX-4000 real-time PCR instrument (white bars). Seven control amplifications containing water rather than RNA (black bars) were set up and run at the same time. Most of the RNA samples recorded the same C_t values as the water controls, and all but three were within 1 C_t of the control. However, one sample did record a significantly higher C_t, suggesting that this preparation contained a contaminant. Upon dilution, this sample recorded the same C_t as the control samples (not shown). C_t, threshold cycle.

FIGURE 4

Comparison of RT-PCR assays primed using random (A) or target-specific (B) primers. Both reactions were carried out using the Brilliant 2-step RT-PCR kit. The only difference was that assay A was primed using random nonamers primers (Applied Biosystems), whereas assay B was primed using a target-specific primer (Proligo, Paris, France). In each case, 100 ng of total RNA was subjected to 10-fold (A) or 5-fold (B) serial dilutions and reverse transcribed using standard RT conditions as specified by the manufacturer. One tenth of each cDNA preparation was then included in a PCR assay. dR, baseline-corrected raw fluorescence; dRn, baseline-corrected normalized fluorescence.

FIGURE 5

The wandering (drifting) baseline. A illustrates the problem of two amplification plots recording approximately the same ΔR_n values, yet only one crossing the threshold established by the default baseline setting of 3–15 cycles on a PRISM 7700. B shows how an upwards adjustment of the baseline corrects for the downward drift and establishes a level playing field, allowing the sample analyzed in the red well to be recorded as a positive. ΔR_n, baseline-corrected normalized fluorescence.

FIGURE 6

Altered thresholds alter results. A shows amplification plots from an assay that aims to detect a low copy number target and at the same time records very low ΔR_n values. An analysis of all wells places the threshold at 0.12, and leaves several amplification plots, that are clearly positive below that default threshold. One of these is a “no template control” (NTC). B shows how manual reduction of the threshold allows the recording of positive threshold cycles (C_t) for all samples, including the negative control. C shows how the use of an adaptive baseline also allows the recording of a positive C_t for the NTC.