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RT-qPCR Diagnostics: The “Drosten” SARS-CoV-2 Assay Paradigm
The reverse transcription quantitative polymerase chain reaction (RT-qPCR) is an established tool for the diagnosis of RNA pathogens. Its potential for automation has caused it to be used as a presence/absence diagnostic tool even when RNA quantification is not required. This technology has been pushed to the forefront of public awareness by the COVID-19 pandemic, as its global application has enabled rapid and analytically sensitive mass testing, with the first assays targeting three viral genes published within days of the publication of the SARS-CoV-2 genomic sequence.
One of those, targeting the RNA-dependent RNA polymerase gene, has been heavily criticised for supposed scientific flaws at the molecular and methodological level, and this criticism has been extrapolated to doubts about the validity of RT-qPCR for COVID-19 testing in general. We have analysed this assay in detail, and our findings reveal some limitations but also highlight the robustness of the RT-qPCR methodology for SARS-CoV-2 detection.
Nevertheless, whilst our data show that some errors can be tolerated, it is always prudent to confirm that the primer and probe sequences complement their intended target, since, when errors do occur, they may result in a reduction in the analytical sensitivity. However, in this case, it is unlikely that a mismatch will result in poor specificity or a significant number of false-positive SARS-CoV-2 diagnoses, especially as this is routinely checked by diagnostic laboratories as part of their quality assurance.
Quantification of microRNA editing using two-tailed RT-qPCR for improved biomarker discovery
Even though microRNAs have been viewed as promising biomarkers for years, their clinical implementation is still lagging far behind. This is in part due to the lack of RT-qPCR technologies that can differentiate between microRNA isoforms.
For example, A-to-I editing of microRNAs through adenosine deaminase acting on RNA (ADAR) enzymes can affect their expression levels and functional roles, but editing isoform-specific assays are not commercially available. Here, we describe RT-qPCR assays that are specific for editing isoforms, using microRNA-379 (miR-379) as a model. The assays are based on two-tailed RT-qPCR, and we show them to be compatible both with SYBR Green and hydrolysis-based chemistries, as well as with both qPCR and digital PCR. The assays could readily detect different miR 379 editing isoforms in various human tissues as well as changes of editing levels in ADAR-overexpressing cell lines.
We found that the miR-379 editing frequency was higher in prostate cancer samples compared to benign prostatic hyperplasia samples. Furthermore, decreased expression of unedited miR-379, but not edited miR-379, was associated with treatment resistance, metastasis and shorter overall survival. Taken together, this study presents the first RT-qPCR assays that were demonstrated to distinguish A-to-I-edited microRNAs, and shows that they can be useful in the identification of biomarkers that previously have been masked by other isoforms.
Web-based LinRegPCR: application for the visualization and analysis of (RT)-qPCR amplification and melting data
Background: The analyses of amplification and melting curves have been shown to provide valuable information on the quality of the individual reactions in quantitative PCR (qPCR) experiments and to result in more reliable and reproducible quantitative results.
Implementation: The main steps in the amplification curve analysis are (1) a unique baseline subtraction, not using the ground phase cycles, (2) PCR efficiency determination from the exponential phase of the individual reactions, (3) setting a common quantification threshold and (4) calculation of the efficiency-corrected target quantity with the common threshold, efficiency per assay and Cq per reaction. The melting curve analysis encompasses smoothing of the observed fluorescence data, normalization to remove product-independent fluorescence loss, peak calling and assessment of the correct peak by comparing its melting temperature with the known melting temperature of the intended amplification product.
Results: The LinRegPCR web application provides visualization and analysis of a single qPCR run. The user interface displays the analysis results on the amplification curve analysis and melting curve analysis in tables and graphs in which deviant reactions are highlighted.
The annotated results in the tables can be exported for calculation of gene-expression ratios, fold-change between experimental conditions and further statistical analysis. Web-based LinRegPCR addresses two types of users, wet-lab scientists analyzing the amplification and melting curves of their own qPCR experiments and bioinformaticians creating pipelines for analysis of series of qPCR experiments by splitting its functionality into a stand-alone back-end RDML (Real-time PCR Data Markup Language) Python library and several companion applications for data visualization, analysis and interactive access. The use of the RDML data standard enables machine independent storage and exchange of qPCR data and the RDML-Tools assist with the import of qPCR data from the files exported by the qPCR instrument.
Conclusions: The combined implementation of these analyses in the newly developed web-based LinRegPCR ( https://www.gear-genomics.com/rdml-tools/ ) is platform independent and much faster than the original Windows-based versions of the LinRegPCR program. Moreover, web-based LinRegPCR includes a novel statistical outlier detection and the combination of amplification and melting curve analyses allows direct validation of the amplification product and reporting of reactions that amplify artefacts.
A comparison of the efficiency of RNA extraction from extracellular vesicles using the Qiagen RNeasy MinElute versus Enzymax LLC RNA Tini Spin columns and qPCR of miRNA
One consequence of the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is an interruption to the supply of laboratory consumables, particularly those used for RNA extraction. This category includes column-based RNA extraction kits designed to retain short RNA species (defined as having fewer than 200 nucleotides), from small sample volumes, e.g. exosomes or extracellular vesicles (EVs). Qiagen manufactures several kits for the extraction and enrichment of short RNA species, such as microRNA (miRNA), which contain silica-membrane columns called “RNeasy MinElute Spin Columns.”
These kits, which also contain buffers and collection tubes, range in price from USD380 to greater than USD1000 and have been subject to fulfillment delays. Scientists seeking to reduce single-use plastics and costs may wish to order the columns separately; however, Qiagen does not sell the RNeasy MinElute Spin Columns (in reasonable quantities) as an individual item.
Thus, we sought an alternative product and found RNA Tini Spin columns from Enzymax LLC. We conducted a systematic comparison of the efficiency of RNA extraction for miRNA quantitative real-time PCR (qPCR) using the Qiagen RNeasy MinElute Spin Columns and Enzymax LLC RNA Tini Spin columns and the Qiagen total RNA extraction protocol that enriches for short RNA species.
We compared the efficiency of extraction of five spike-in control miRNAs, six sample signal miRNAs, and nine low- to medium-abundance miRNAs by qPCR. The RNA was extracted from EV preparations purified from human plasma using CD81 immunoprecipitation. We report no statistically significant differences in extraction efficiencies between the two columns for any of the miRNAs examined. Therefore, we conclude that the Enzymax RNA Tini Spin columns are adequate substitutes for the Qiagen RNeasy MinElute Spin Columns for short RNA species enrichment and downstream qPCR from EVs in the miRNAs we examined.
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