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Evaluation of Relative Quantification of Alternatively Spliced Transcripts Using Droplet Digital PCR

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Evaluation of Relative Quantification of Alternatively Spliced Transcripts Using Droplet Digital PCR

Mattias Van Heetvelde et al. Biomol Detect Quantif.

Abstract

Introduction: For the relative quantification of isoform expression, RT-qPCR has been the gold standard for over a decade. More recently, digital PCR is becoming widely implemented, as it is promised to be more accurate, sensitive and less affected by inhibitors, without the need for standard curves. In this study we evaluated RT-qPCR versus RT-droplet digital PCR (ddPCR) for the relative quantification of isoforms in controls and carriers of the splice site mutation BRCA1 c.212+3A>G, associated with increased expression of several isoforms.

Materials and methods: RNA was extracted from EBV cell lines of controls and heterozygous BRCA1 c.212+3A>G carriers. Transcript-specific plasmids were available to determine the efficiency, precision, reproducibility and accuracy of each method.

Results: Both ddPCR and RT-qPCR were able to accurately quantify all targets and showed the same LOB, LOD and LOQ; also precision and reproducibility were similar. Both techniques have the same dynamic range and linearity at biologically relevant template concentrations. However, a significantly higher cost and workload was required for ddPCR experiments.

Conclusions: Our study recognizes the potential and validity of digital PCR but shows the value of a highly optimized qPCR for the relative quantification of isoforms. Cost efficiency and simplicity turned out to be better for RT-qPCR.

Keywords: Alternative splicing; Droplet digital PCR; Reverse transcriptase polymerase chain reaction.

Figures

Fig. 1
Fig. 1
Sashimi plot of targeted RNA sequencing results. For the relative transcript quantification, the relative abundance of the three relevant transcripts was calculated as the number of reads covering the transcript-specific splice site divided by the total number of reads spanning the 3 splice sites (junctions 5–7). RNA positions are based on reference sequence NM_007294.3 starting at the A of the translation initiation codon (ATG) as position +1. Genomic positions are derived from hg19 reference sequence. 13 unique junctions were identified in controls (depicted in orange) and/or BRCA1 c.212+3A>G carriers (grey). At the boundary between exon 6 and 7 (junction 1), 59,606 and 72,822 reads were identified for the controls and the carriers respectively. As expected, three highly abundant transcripts are found on the other end of exon 6, connecting it to either exon 5 (junctions 5 and 6) or exon 3 (junction 7). For junction 5 (BRCA1-ex5FL), junction 6 (BRCA1-Δ22ntex5; c.191_212del22) and junction 7 (BRCA1-Δex5; c.135_212del78) the sum of the number of identified reads for these junctions is 59,486 and 69,525, which equals 99.80% and 87.01% of the number of reads at junction 1 for controls and the carriers respectively. In carriers the 13% difference is not explained by the novel junctions (junctions 3, 4 and 9), therefore we assume that the difference is caused by a percentage of prematurely terminated reads. The extremely low coverage also suggests that all junctions other than 1, 5, 6, 7 and 11 are artefacts, introduced by PCR or bioinformatics, rather than relevant isoforms. In BRCA1 c.212+3A>G carriers there is almost a five-fold relative difference in expression for BRCA1-Δ22ntex5 compared to controls, while BRCA1-Δex5 expression ratios are similar between carriers and controls.
Fig. 2
Fig. 2
Linearity and dynamic range was tested on for all assays with both quantitative PCR methods, using dilution series containing transcript-specific plasmids ((a) and (b)) or cDNA from EBV samples (non-carriers ((c) and (d)) and carriers ((e) and (f)). Measured relative template quantity (RQ) is depicted on the y-axis in function of theoretical template concentration on the x-axis. Almost all assay-sample combinations show excellent linearity with both techniques (R2 > 0.98). Linearity was only worse for BRCA1-Δex5 in dilutions from EBV controls with ddPCR (R2 = 0.81). An important difference is that the dynamic range of qPCR across the dilution series was larger for several sample-target combinations, especially when quantifying transcript-specific plasmids. From these data we also estimated the LOB, LOD and LOQ values. For both methods LOB was equal to 0 (none of the no template controls yielded any amplification) and the LOQ and LOD for both methods reaches the theoretical minimum of 1 copy per reaction in transcript-specific plasmid dilution series. When using EBV derived samples the LOQ for both qPCR and ddPCR was found to range between 100 pg and 6.25 ng of cDNA depending on the abundance of a certain transcript in each sample.
Fig. 3
Fig. 3
Boxplot of coefficients of variation for all cDNA-assay combinations over all time points of both quantitative PCR techniques. We observed an inverse correlation between the CV and the relative abundance of each isoform; CVs are consistently higher for isoforms with lower expression. CVs for each transcript range within the same order of magnitude and all experiments show very small standard errors, leading us to conclude that precision, reproducibility and repeatability are not an issue with either quantitative PCR technique.
Fig. 4
Fig. 4
(a) and (b) show relative expression values for all relevant isoforms at all time points of extraction for qPCR and ddPCR. Data points from carrier samples and control samples are shown in yellow and grey respectively. In accordance with the literature and our NGS data, BRCA1-Δ22ntex5 is highly overexpressed in carriers in comparison to controls. (c) and (d) show the quantification of sample 1, 2 and 3. Sample 1 contained 33%, 33% and 34% of plasmid containing BRCA1-Δ22ntex5, BRCA1-ex5FL and BRCA1-Δex5 respectively. For samples 2 and 3 this was 57%/28%/15% and 30%/38%/32% respectively. The theoretical relative amount of each transcript in each sample (depicted with white dots in each bar) was calculated based on the volume of plasmid stock that was added, the measured amount of plasmid (ng/μL) in the stock solution and the theoretical molecular weight of each plasmid (based on the plasmid sequence).

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