Disclosing quantitative RT-PCR raw data during manuscript submission: a call for action.

Accuracy and transparency of scientific data are becoming more and more relevant with the increasing concern regarding the evaluation of data reproducibility in many research areas. This concern is also true for quantifying coding and noncoding RNAs, with the remarkable increase in publications reporting RNA profiling and sequencing studies. To address the problem, we propose the following recommendations: (a) accurate documentation of experimental procedures in Materials and methods (and not only in the supplementary information, as many journals have a strict mandate for making Materials and methods as visible as possible in the main text); (b) submission of RT-qPCR raw data for all experiments reported; and (c) adoption of a unified, simple format for submitted RT-qPCR raw data. The Real-time PCR Data Essential Spreadsheet Format (RDES) was created for this purpose.

Evaluation of efficiency and sensitivity of 1D and 2D sample pooling strategies for SARS-CoV-2 RT-qPCR screening purposes.

To increase the throughput, lower the cost, and save scarce test reagents, laboratories can pool patient samples before SARS-CoV-2 RT-qPCR testing. While different sample pooling methods have been proposed and effectively implemented in some laboratories, no systematic and large-scale evaluations exist using real-life quantitative data gathered throughout the different epidemiological stages. Here, we use anonymous data from 9673 positive cases to model, simulate and compare 1D and 2D pooling strategies. We show that the optimal choice of pooling method and pool size is an intricate decision with a testing population-dependent efficiency-sensitivity trade-off and present an online tool to provide the reader with custom real-time 1D pooling strategy recommendations.

Reliable and Scalable SARS-CoV-2 qPCR Testing at a High Sample Throughput: Lessons Learned from the Belgian Initiative.

We present our approach to rapidly establishing a standardized, multi-site, nation-wide COVID-19 screening program in Belgium. Under auspices of a federal government Task Force responsible for upscaling the country's testing capacity, we were able to set up a national testing initiative with readily available resources, putting in place a robust, validated, high-throughput, and decentralized qPCR molecular testing platform with embedded proficiency testing. We demonstrate how during an acute scarcity of equipment, kits, reagents, personnel, protective equipment, and sterile plastic supplies, we introduced an approach to rapidly build a reliable, validated, high-volume, high-confidence workflow based on heterogeneous instrumentation and diverse assays, assay components, and protocols. The workflow was set up with continuous quality control monitoring, tied together through a clinical-grade information management platform for automated data analysis, real-time result reporting across different participating sites, qc monitoring, and making result data available to the requesting physician and the patient. In this overview, we address challenges in optimizing high-throughput cross-laboratory workflows with minimal manual intervention through software, instrument and assay validation and standardization, and a process for harmonized result reporting and nation-level infection statistics monitoring across the disparate testing methodologies and workflows, necessitated by a rapid scale-up as a response to the pandemic.

Consensus guidelines for the validation of qRT-PCR assays in clinical research by the CardioRNA consortium.

Despite promising findings, quantitative PCR (qPCR)-based tests for RNA quantification have experienced serious limitations in their clinical application. The noticeable lack of technical standardization remains a huge obstacle in the translation of qPCR-based tests. The incorporation of qPCR-based tests into the clinic will benefit from guidelines for clinical research assay validation. This will ultimately impact the clinical management of the patient, including diagnosis, prognosis, prediction, monitoring of the therapeutic response, and evaluation of toxicity. However, clear assay validation protocols for biomarker investigation in clinical trials using molecular assays are currently lacking. Here, we will focus on the necessary steps, including sample acquisition, processing and storage, RNA purification, target selection, assay design, and experimental design, that need to be taken toward the appropriate validation of qRT-PCR assays in clinical research. These recommendations can fill the gap between research use only (RUO) and in vitro diagnostics (IVD). Our contribution provides a tool for basic and clinical research for the development of validated assays in the intermediate steps of biomarker research. These guidelines are based on the current understanding and consensus within the EU-CardioRNA COST Action consortium (www.cardiorna.eu). Their applicability encompasses all clinical areas.

Evaluating Diagnostic Accuracy of Saliva Sampling Methods for Severe Acute Respiratory Syndrome Coronavirus 2 Reveals Differential Sensitivity and Association with Viral Load.

Nasopharyngeal swabs are considered the preferential collection method for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) diagnostics. Less invasive and simpler alternative sampling procedures, such as saliva collection, are desirable. We compared saliva specimens and nasopharyngeal (NP) swabs with respect to sensitivity in detecting SARS-CoV-2. A nasopharyngeal and two saliva specimens (collected by spitting or oral swabbing) were obtained from >2500 individuals. All samples were tested by RT-qPCR, detecting RNA of SARS-CoV-2. The test sensitivity was compared on the two saliva collections with the nasopharyngeal specimen for all subjects and stratified by symptom status and viral load. Of the 2850 patients for whom all three samples were available, 105 were positive on NP swab, whereas 32 and 23 were also positive on saliva spitting and saliva swabbing samples, respectively. The sensitivity of the RT-qPCR to detect SARS-CoV-2 among NP-positive patients was 30.5% (95% CI, 1.9%-40.2%) for saliva spitting and 21.9% (95% CI, 14.4%-31.0%) for saliva swabbing. However, when focusing on subjects with medium to high viral load, sensitivity on saliva increased substantially: 93.9% (95% CI, 79.8%-99.3%) and 76.9% (95% CI, 56.4%-91.0%) for spitting and swabbing, respectively, regardless of symptomatic status. Our results suggest that saliva cannot readily replace nasopharyngeal sampling for SARS-CoV-2 diagnostics but may enable identification of the most contagious cases with medium to high viral loads.

A Year in the Life of the EU-CardioRNA COST Action: CA17129 Catalysing Transcriptomics Research in Cardiovascular Disease.

The EU-CardioRNA Cooperation in Science and Technology (COST) Action is a European-wide consortium established in 2018 with 31 European country members and four associate member countries to build bridges between translational researchers from academia and industry who conduct research on non-coding RNAs, cardiovascular diseases and similar research areas. EU-CardioRNA comprises four core working groups (WG1-4). In the first year since its launch, EU-CardioRNA met biannually to exchange and discuss recent findings in related fields of scientific research, with scientific sessions broadly divided up according to WG. These meetings are also an opportunity to establish interdisciplinary discussion groups, brainstorm ideas and make plans to apply for joint research grants and conduct other scientific activities, including knowledge transfer. Following its launch in Brussels in 2018, three WG meetings have taken place. The first of these in Lisbon, Portugal, the second in Istanbul, Turkey, and the most recent in Maastricht, The Netherlands. Each meeting includes a scientific session from each WG. This meeting report briefly describes the highlights and key take-home messages from each WG session in this first successful year of the EU-CardioRNA COST Action.

Catalyzing Transcriptomics Research in Cardiovascular Disease: The CardioRNA COST Action CA17129.

Cardiovascular disease (CVD) remains the leading cause of death worldwide and, despite continuous advances, better diagnostic and prognostic tools, as well as therapy, are needed. The human transcriptome, which is the set of all RNA produced in a cell, is much more complex than previously thought and the lack of dialogue between researchers and industrials and consensus on guidelines to generate data make it harder to compare and reproduce results. This European Cooperation in Science and Technology (COST) Action aims to accelerate the understanding of transcriptomics in CVD and further the translation of experimental data into usable applications to improve personalized medicine in this field by creating an interdisciplinary network. It aims to provide opportunities for collaboration between stakeholders from complementary backgrounds, allowing the functions of different RNAs and their interactions to be more rapidly deciphered in the cardiovascular context for translation into the clinic, thus fostering personalized medicine and meeting a current public health challenge. Thus, this Action will advance studies on cardiovascular transcriptomics, generate innovative projects, and consolidate the leadership of European research groups in the field.COST (European Cooperation in Science and Technology) is a funding organization for research and innovation networks (www.cost.eu).

Cost-effective and robust genotyping using double-mismatch allele-specific quantitative PCR.

For a wide range of diseases, SNPs in the genome are the underlying mechanism of dysfunction. Therefore, targeted detection of these variations is of high importance for early diagnosis and (familial) screenings. While allele-specific PCR has been around for many years, its adoption for SNP genotyping or somatic mutation detection has been hampered by its low discriminating power and high costs. To tackle this, we developed a cost-effective qPCR based method, able to detect SNPs in a robust and specific manner. This study describes how to combine the basic principles of allele-specific PCR (the combination of a wild type and variant primer) with the straightforward readout of DNA-binding dye based qPCR technology. To enhance the robustness and discriminating power, an artificial mismatch in the allele-specific primer was introduced. The resulting method, called double-mismatch allele-specific qPCR (DMAS-qPCR), was successfully validated using 12 SNPs and 15 clinically relevant somatic mutations on 48 cancer cell lines. It is easy to use, does not require labeled probes and is characterized by high analytical sensitivity and specificity. DMAS-qPCR comes with a complimentary online assay design tool, available for the whole scientific community, enabling researchers to design custom assays and implement those as a diagnostic test.

High-throughput PCR assay design for targeted resequencing using primerXL.

BACKGROUND: Although the sequencing landscape is rapidly evolving and sequencing costs are continuously decreasing, whole genome sequencing is still too expensive for use on a routine basis. Targeted resequencing of only the regions of interest decreases both costs and the complexity of the downstream data-analysis. Various target enrichment strategies are available, but none of them obtain the degree of coverage uniformity, flexibility and specificity of PCR-based enrichment. On the other hand, the biggest limitation of target enrichment by PCR is the need to design large numbers of partially overlapping assays to cover the target. RESULTS: To overcome the aforementioned hurdles, we have developed primerXL, a state-of-the-art PCR primer design pipeline for targeted resequencing. It uses an optimized design criteria relaxation cascade and a thorough downstream in silico evaluation process to generate high quality singleplex PCR assays, reducing the need for amplicon normalization, and outperforming other target enrichment strategies and similar primer design tools when considering assay quality, coverage uniformity and target coverage. Results of four different sequencing projects with 2348 amplicons in total covering 470 kb are presented. PrimerXL can be accessed at www.primerxl.org . CONCLUSION: PrimerXL is an state-of-the-art, easy to use primer design webtool capable of generating high-quality targeted resequencing assays. The workflow is fully customizable to suit every researchers' needs, while an innovative relaxation cascade ensures maximal target coverage.

A unified censored normal regression model for qPCR differential gene expression analysis.

Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is considered as the gold standard for accurate, sensitive, and fast measurement of gene expression. Prior to downstream statistical analysis, RT-qPCR fluorescence amplification curves are summarized into one single value, the quantification cycle (Cq). When RT-qPCR does not reach the limit of detection, the Cq is labeled as "undetermined". Current state of the art qPCR data analysis pipelines acknowledge the importance of normalization for removing non-biological sample to sample variation in the Cq values. However, their strategies for handling undetermined Cq values are very ad hoc. We show that popular methods for handling undetermined values can have a severe impact on the downstream differential expression analysis. They introduce a considerable bias and suffer from a lower precision. We propose a novel method that unites preprocessing and differential expression analysis in a single statistical model that provides a rigorous way for handling undetermined Cq values. We compare our method with existing approaches in a simulation study and on published microRNA and mRNA gene expression datasets. We show that our method outperforms traditional RT-qPCR differential expression analysis pipelines in the presence of undetermined values, both in terms of accuracy and precision.

Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data.

RNA-sequencing has become the gold standard for whole-transcriptome gene expression quantification. Multiple algorithms have been developed to derive gene counts from sequencing reads. While a number of benchmarking studies have been conducted, the question remains how individual methods perform at accurately quantifying gene expression levels from RNA-sequencing reads. We performed an independent benchmarking study using RNA-sequencing data from the well established MAQCA and MAQCB reference samples. RNA-sequencing reads were processed using five workflows (Tophat-HTSeq, Tophat-Cufflinks, STAR-HTSeq, Kallisto and Salmon) and resulting gene expression measurements were compared to expression data generated by wet-lab validated qPCR assays for all protein coding genes. All methods showed high gene expression correlations with qPCR data. When comparing gene expression fold changes between MAQCA and MAQCB samples, about 85% of the genes showed consistent results between RNA-sequencing and qPCR data. Of note, each method revealed a small but specific gene set with inconsistent expression measurements. A significant proportion of these method-specific inconsistent genes were reproducibly identified in independent datasets. These genes were typically smaller, had fewer exons, and were lower expressed compared to genes with consistent expression measurements. We propose that careful validation is warranted when evaluating RNA-seq based expression profiles for this specific gene set.

Long non-coding RNA expression profiling in the NCI60 cancer cell line panel using high-throughput RT-qPCR.

Long non-coding RNAs (lncRNAs) form a new class of RNA molecules implicated in various aspects of protein coding gene expression regulation. To study lncRNAs in cancer, we generated expression profiles for 1707 human lncRNAs in the NCI60 cancer cell line panel using a high-throughput nanowell RT-qPCR platform. We describe how qPCR assays were designed and validated and provide processed and normalized expression data for further analysis. Data quality is demonstrated by matching the lncRNA expression profiles with phenotypic and genomic characteristics of the cancer cell lines. This data set can be integrated with publicly available omics and pharmacological data sets to uncover novel associations between lncRNA expression and mRNA expression, miRNA expression, DNA copy number, protein coding gene mutation status or drug response.

RDML-Ninja and RDMLdb for standardized exchange of qPCR data.

BACKGROUND: The universal qPCR data exchange file format RDML is today well accepted by the scientific community, part of the MIQE guidelines and implemented in many qPCR instruments. With the increased use of RDML new challenges emerge. The flexibility of the RDML format resulted in some implementations that did not meet the expectations of the consortium in the level of support or the use of elements. RESULTS: In the current RDML version 1.2 the description of the elements was sharpened. The open source editor RDML-Ninja was released (http://sourceforge.net/projects/qpcr-ninja/). RDML-Ninja allows to visualize, edit and validate RDML files and thus clarifies the use of RDML elements. Furthermore RDML-Ninja serves as reference implementation for RDML and enables migration between RDML versions independent of the instrument software. The database RDMLdb will serve as an online repository for RDML files and facilitate the exchange of RDML data (http://www.rdmldb.org). Authors can upload their RDML files and reference them in publications by the unique identifier provided by RDMLdb. The MIQE guidelines propose a rich set of information required to document each qPCR run. RDML provides the vehicle to store and maintain this information and current development aims at further integration of MIQE requirements into the RDML format. CONCLUSIONS: The editor RDML-Ninja and the database RDMLdb enable scientists to evaluate and exchange qPCR data in the instrument-independent RDML format. We are confident that this infrastructure will build the foundation for standardized qPCR data exchange among scientists, research groups, and during publication.

Flexible, scalable, and efficient targeted resequencing on a benchtop sequencer for variant detection in clinical practice.

The release of benchtop next-generation sequencing (NGS) instruments has paved the way to implement the technology in clinical setting. The need for flexible, qualitative, and cost-efficient workflows is high. We used singleplex-PCR for highly efficient target enrichment, allowing us to reach the quality standards set in Sanger sequencing-based diagnostics. For the library preparation, a modified NexteraXT protocol was used, followed by sequencing on a MiSeq instrument. With an innovative pooling strategy, high flexibility, scalability, and cost-efficiency were obtained, independent of the availability of commercial kits. The approach was validated for ∼250 genes associated with monogenic disorders. An overall sensitivity (>99%) similar to Sanger sequencing was observed in combination with a positive predictive value of >98%. The distribution of coverage was highly uniform, guaranteeing a minimal number of gaps to be filled with alternative methods. ISO15189-accreditation was obtained for the workflow. A major asset of the singleplex PCR-based enrichment is that new targets can be easily implemented. Diagnostic laboratories have validated assays available ensuring that the proposed workflow can easily be adopted. Although our platform was optimized for constitutional variant detection of monogenic disease genes, it is now also used as a model for somatic mutation detection in acquired diseases.

Removal of between-run variation in a multi-plate qPCR experiment.

Quantitative PCR (qPCR) is the method of choice in gene expression analysis. However, the number of groups or treatments, target genes and technical replicates quickly exceeds the capacity of a single run on a qPCR machine and the measurements have to be spread over more than 1 plate. Such multi-plate measurements often show similar proportional differences between experimental conditions, but different absolute values, even though the measurements were technically carried out with identical procedures. Removal of this between-plate variation will enhance the power of the statistical analysis on the resulting data. Inclusion and application of calibrator samples, with replicate measurements distributed over the plates, assumes a multiplicative difference between plates. However, random and technical errors in these calibrators will propagate to all samples on the plate. To avoid this effect, the systematic bias between plates can be removed with a correction factor based on all overlapping technical and biological replicates between plates. This approach removes the requirement for all calibrator samples to be measured successfully on every plate. This paper extends an already published factor correction method to the use in multi-plate qPCR experiments. The between-run correction factor is derived from the target quantities which are calculated from the quantification threshold, PCR efficiency and observed C q value. To enable further statistical analysis in existing qPCR software packages, an efficiency-corrected C q value is reported, based on the corrected target quantity and a PCR efficiency per target. The latter is calculated as the mean of the PCR efficiencies taking the number of reactions per amplicon per plate into account. Export to the RDML format completes an RDML-supported analysis pipeline of qPCR data ranging from raw fluorescence data, amplification curve analysis and application of reference genes to statistical analysis.

Selection of reliable reference genes for RT-qPCR analysis.

Reference genes have become the method of choice for normalization of qPCR data. It has been demonstrated in many studies that reference gene validation is essential to ensure accurate and reliable results. This chapter describes how a pilot study can be set up to identify the best set of reference genes to be used for normalization of qPCR data. The data from such a pilot study should be analyzed with dedicated algorithms such as geNorm to rank genes according to their stability--a measure for how well they are suited for normalization. geNorm also provides insights into the optimal number of reference genes and the overall quality of the selected set of reference genes. Importantly, these results are always in function of the sample type being studied. Guidelines are provided on the interpretation of the results from geNorm pilot studies as well as for the continued monitoring of reference gene quality in subsequent studies. For screening studies including a large, unbiased set of genes (e.g., complete miRNome) an alternative normalization method can be used: global mean normalization. This chapter also describes how the data from such studies can be used to identify reference genes for subsequent validation studies on smaller sets of selected genes.

Target enrichment using parallel nanoliter quantitative PCR amplification.

BACKGROUND: Next generation targeted resequencing is replacing Sanger sequencing at high pace in routine genetic diagnosis. The need for well validated, high quality enrichment platforms to complement the bench-top next generation sequencing devices is high. RESULTS: We used the WaferGen Smartchip platform to perform highly parallelized PCR based target enrichment for a set of known cancer genes in a well characterized set of cancer cell lines from the NCI60 panel. Optimization of PCR assay design and cycling conditions resulted in a high enrichment efficiency. We provide proof of a high mutation rediscovery rate and have included technical replicates to enable SNP calling validation demonstrating the high reproducibility of our enrichment platform. CONCLUSIONS: Here we present our custom developed quantitative PCR based target enrichment platform. Using highly parallel nanoliter singleplex PCR reactions makes this a flexible and efficient platform. The high mutation validation rate shows this platform's promise as a targeted resequencing method for multi-gene routine sequencing diagnostics.

The need for transparency and good practices in the qPCR literature.

Two surveys of over 1,700 publications whose authors use quantitative real-time PCR (qPCR) reveal a lack of transparent and comprehensive reporting of essential technical information. Reporting standards are significantly improved in publications that cite the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines, although such publications are still vastly outnumbered by those that do not.

Single-nucleotide polymorphisms and other mismatches reduce performance of quantitative PCR assays.

BACKGROUND: Genome-sequencing studies have led to an immense increase in the number of known single-nucleotide polymorphisms (SNPs). Designing primers that anneal to regions devoid of SNPs has therefore become challenging. We studied the impact of one or more mismatches in primer-annealing sites on different quantitative PCR (qPCR)-related parameters, such as quantitative cycle (Cq), amplification efficiency, and reproducibility. METHODS: We used synthetic templates and primers to assess the effect of mismatches at primer-annealing sites on qPCR assay performance. Reactions were performed with 5 commercially available master mixes. We studied the effects of the number, type, and position of priming mismatches on Cq value, PCR efficiency, reproducibility, and yield. RESULTS: The impact of mismatches was most pronounced for the number of mismatched nucleotides and for their distance from the 3' end of the primer. In addition, having ≥4 mismatches in a single primer or having 3 mismatches in one primer and 2 in the other was required to block a reaction completely. Finally, the degree of the mismatch effect was concentration independent for single mismatches, whereas concentration independence failed at higher template concentrations as the number of mismatches increased. CONCLUSIONS: Single mismatches located >5 bp from the 3' end have a moderate effect on qPCR amplification and can be tolerated. This finding, together with the concentration independence for single mismatches and the complete blocking of the PCR reaction for ≥4 mismatches, can help to chart mismatch behavior in qPCR reactions and increase the rate of successful primer design for sequences with a high SNP density or for homologous regions of sequence.

The digital MIQE guidelines: Minimum Information for Publication of Quantitative Digital PCR Experiments.

There is growing interest in digital PCR (dPCR) because technological progress makes it a practical and increasingly affordable technology. dPCR allows the precise quantification of nucleic acids, facilitating the measurement of small percentage differences and quantification of rare variants. dPCR may also be more reproducible and less susceptible to inhibition than quantitative real-time PCR (qPCR). Consequently, dPCR has the potential to have a substantial impact on research as well as diagnostic applications. However, as with qPCR, the ability to perform robust meaningful experiments requires careful design and adequate controls. To assist independent evaluation of experimental data, comprehensive disclosure of all relevant experimental details is required. To facilitate this process we present the Minimum Information for Publication of Quantitative Digital PCR Experiments guidelines. This report addresses known requirements for dPCR that have already been identified during this early stage of its development and commercial implementation. Adoption of these guidelines by the scientific community will help to standardize experimental protocols, maximize efficient utilization of resources, and enhance the impact of this promising new technology.