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The last decades have seen great leaps made in the development of RNA sequencing technologies, yielding lower cost and greater throughput of experiments, to the point where the scale of the data produced on a daily basis is staggering. While computational hardware is also continuously improving, famously (or perhaps infamously) described by Gordon Moore (Moore, 1965), the rate at which data are produced eclipses advances on the hardware front. Over the last few years, many new methods have been proposed for bridging that ever-widening chasm, more than a few of which harness the latent graphical structure of genomic data to reduce the number of calculations required and pack the data tighter in memory. This body of work continues this development on three different, but related, fronts. Firstly, I present developments that greatly improve upon the efficiency of state-of-the-art methods for the quantification of RNA-seq reads, and describe a method that improves the accuracy of quantification without substantially increasing the computational over- head. Secondly, I introduce a procedure for the discovery of associations between novel gene isoforms and phenotypes, without prior knowledge of those isoforms. Lastly, I present the largest reconstruction of the transmission tree of a viral outbreak to date, modeled from viral genome sequences, contact tracing, and symptom data. I then use the reconstructed transmission tree to assess the efficacy of different vaccination strategies.

Background The COVID-19 pandemic caused by the severe acute SARS-CoV-2 virus has undeniably highlighted the importance of reliable nucleic acid quantification. Digital PCR (dPCR) is capable of the absolute quantification of nucleic acids. Method By using the droplet dPCR (QX200) and the digital real-time PCR (LOAA), the copy numbers were compared via multiple assays for three distinct targerts; EGFR DNA, SARS-CoV-2 and HIV-1 RNA. Results The droplet dPCR and digital real-time PCR showed similar copy numbers for both DNA and RNA quantification. When the limit of detection (LOD) and limit of quantitation (LOQ) of each method were estimated for DNA and RNA targets, the digital real-time PCR showed a higher sensitivity and precision especially with low copy number targets. Conclusion The breath of nucleic acid testing in diagnostic applications continues to expand. In this study we applied common diagnostic targets to a novel digital real-time PCR methodology. It performed comparably to the established dPCR method with distinctive advantages and disadvantages for implementing in laboratories. These rapidly developing dPCR systems can be applied to benefit the accurate and sensitive nucleic acid testing for various clinical areas.

The progress of the SARS-CoV-2 pandemic requires the design of large-scale, cost-effective testing programs. Pooling samples provides a solution if the tests are sensitive enough. In this regard, the use of the gold standard, RT-qPCR, raises some concerns. Recently, droplet digital PCR (ddPCR) was shown to be 10–100 times more sensitive than RT-qPCR, making it more suitable for pooling. Furthermore, ddPCR quantifies the RNA content directly, a feature that, as we show, can be used to identify nonviable samples in pools. Cost-effective strategies require the definition of efficient deconvolution and re-testing procedures. In this paper we analyze the practical implementation of an efficient hierarchical pooling strategy for which we have recently derived the optimal, determining the best ways to proceed when there are impediments for the use of the absolute optimum or when multiple pools are tested simultaneously and there are restrictions on the throughput time. We also show how the ddPCR RNA quantification and the nested nature of the strategy can be combined to perform self-consistency tests for a better identification of infected individuals and nonviable samples. The studies are useful to those considering pool testing for the identification of infected individuals.

Astroviruses are often associated with gastrointestinal diseases in mammals and birds. Murine astrovirus (MuAstV) is frequently detected in laboratory mice. Previous studies on MuAstV in mice did not report any symptoms or lesions. However, little information is available regarding its pathogenicity in immunodeficient mice. Therefore, in this study, we experimentally infected germ-free NOD.Cg-PrkdcscidIl2rgtm1Sug/ShiJic (NOG) mice, which are severely immunodeficient, with MuAstV. Germ-free mice were used for experimental infection to eliminate the effects of intestinal bacteria. Mice in each group were then necropsied and subjected to PCR for MuAstV detection, MuAstV RNA quantification in each organ, and histopathological examination at 4 and 28 days post inoculation (DPI). Tissue samples from the small intestine were examined by transmission electron microscopy. No symptoms or abnormalities were detected in any mice during necropsy. The MuAstV concentration was highest in the lower small intestine, where it increased approximately 8-fold from 4 to 28 DPI. Transmission electron microscopy revealed circular virus particles of approximately 25 nm in diameter in the cytoplasm of the villous epithelial cells of the lower small intestine. Histopathological examination did not reveal any abnormalities, such as atrophy, in the intestinal villi. Our results suggest that MuAstV proliferates in the villous epithelial cells of the lower small intestine and has weak pathogenicity.

Novel methods that enable sensitive, accurate and rapid detection of RNA would not only benefit fundamental biological studies but also serve as diagnostic tools for various pathological conditions, including bacterial and viral infections and cancer. Although highly sensitive, existing methods for RNA detection involve long turn-around time and extensive capital equipment. Here, an ultrasensitive and amplification-free RNA quantification method is demonstrated by integrating CRISPR-Cas13a system with an ultrabright fluorescent nanolabel, plasmonic fluor. This plasmonically enhanced CRISPR-powered assay exhibits nearly 1000-fold lower limit-of-detection compared to conventional assay relying on enzymatic reporters. Using a xenograft tumor mouse model, it is demonstrated that this novel bioassay can be used for ultrasensitive and quantitative monitoring of cancer biomarker (lncRNA H19). The novel biodetection approach described here provides a rapid, ultrasensitive, and amplification-free strategy that can be broadly employed for detection of various RNA biomarkers, even in resource-limited settings.

Due to the coronavirus disease 2019 (COVID-19) pandemic, wastewater surveillance has become an important tool for monitoring the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within communities. In particular, reverse transcription-quantitative PCR (RT-qPCR) has been used to generate large datasets aimed at detecting and quantifying SARS-CoV-2 RNA in wastewater. Although RT-qPCR is rapid and sensitive, there is no standard method yet, there are no certified quantification standards, and experiments are conducted using different assays, reagents, instruments, and data analysis protocols. These variations can induce errors in quantitative data reports, thereby potentially misleading interpretations, and conclusions. We review the SARS-CoV-2 wastewater surveillance literature focusing on variability of RT-qPCR data as revealed by inconsistent standard curves and associated parameters. We find that variation in these parameters and deviations from best practices, as described in the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines suggest a lack of reproducibility and reliability in quantitative measurements of SARS-CoV-2 RNA in wastewater., Graphical Abstract Image, graphical abstract

The rapid emergence of wastewater based surveillance has led to a wide array of SARS-CoV-2 RNA quantification methodologies being employed. Here we compare methods to store samples, inactivate viruses, capture/concentrate viruses, and extract/measure viral RNA from primary influent into wastewater facilities. We found that heat inactivation of the viruses led to a 1-3 log10decrease compared to chemical inactivation. Freezing influent prior to concentration caused a 1-4 log10decrease compared to processing fresh samples, but viral capture by membrane adsorption prior to freezing was robust to freeze-thaw variability. Concentration vs. direct extraction, and PCR platform also affected outcome, but by a smaller amount. The choice of nucleocapsid gene target had nearly no effect. Pepper mild-mottle virus was much less sensitive to these methodological differences than was SARS-CoV-2, which challenges its use as a population-level control among studies using different methods. Better characterizing the variability associated with different methodologies, in particular the impact of methods on sensitivity, will aid decision makers in following the effects of vaccination campaigns, early detection of future outbreaks, and potentially monitoring the appearance of SARS-CoV-2 variants in the population.

The coronavirus disease 2019 (COVID-19) pandemic has led to wastewater surveillance becoming an important tool for monitoring the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within communities. As a result, molecular methods, in particular reverse transcription-quantitative PCR (RT-qPCR), have been employed to generate large data sets aimed at the detection and quantification of SARS-CoV-2 in wastewater. Although RT-qPCR is rapid and sensitive, there is no standard method that fits all use cases, there are no certified quantification standards and experiments are carried out using numerous different assays, reagents, instruments, and data analysis protocols. These variations can lead to the reporting of erroneous quantitative data resulting in potentially misleading interpretations and conclusions. We have reviewed the SARS-CoV-2 wastewater surveillance literature focusing on the variability of RT-qPCR data as revealed by inconsistent standard curves and associated parameters. We find that variation in these parameters and deviations from best practices as described in The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines suggest a lack of reproducibility and reliability in quantitative measurements of SARS-CoV-2 RNA in wastewater.

The quantitative viral outgrowth assay (qVOA) is the gold standard for measuring inducible, replication-competent HIV-1. Using MOLT4-R5 and SupT1-R5 cell lines instead of allogeneic blasts and HIV-1 RNA detection rather than p24 enzyme-immunoassay (EIA) has been proposed to improve the sensitivity of the qVOA. It is unclear, however, how these alternative approaches affect qVOA performance. We compared three qVOAs methods across 15 persons with HIV-1 on suppressive antiretroviral therapy and found that the MOLT4-R5 method yielded a significantly higher proportion of p24-positive wells (42%) than both the allogeneic blast (29%) and SupT1-R5 (32%) assays. Additionally, 5 of 7 qVOAs that were negative by p24 EIA showed viral outgrowth by HIV-1 RNA quantification (>10-fold increase within 7 days). These findings reveal the potential for underestimation of the latent, inducible reservoir by qVOA depending on the target cells used and the measure of viral outgrowth. Use of MOLT4-R5 cells with both p24 EIA and HIV-1 RNA to detect viral outgrowth was the most sensitive method.

Quantitative viral load assays have transformed our understanding of – and ability to manage − viral diseases. They hold similar potential to advance COVID-19 control and prevention, but SARS-CoV-2 viral load tests are not yet widely available. SARS-CoV-2 molecular diagnostic tests, which typically employ real-time reverse transcriptase-polymerase chain reaction (RT-PCR), yield semi-quantitative results only. Reverse transcriptase droplet digital PCR (RT-ddPCR), a technology that partitions each reaction into 20,000 nanolitre-sized droplets prior to amplification, offers an attractive platform for SARS-CoV-2 RNA quantification. We evaluated eight primer/probe sets originally developed for real-time RT-PCR-based SARS-CoV-2 diagnostic tests for use in RT-ddPCR, and identified three (Charité-Berlin E-Sarbeco and Pasteur Institute IP2 and IP4) as the most efficient, precise and sensitive for RT-ddPCR-based SARS-CoV-2 RNA quantification. Analytical efficiency of the E-Sarbeco primer/probe set, for example, was ~83%, while assay precision, as measured by the coefficient of variation, was ~2% at 1000 input copies/reaction. Lower limits of quantification and detection for this primer/probe set were 18.6 and 4.4 input SARS-CoV-2 RNA copies/reaction, respectively. SARS-CoV-2 RNA viral loads in a convenience panel of 48 COVID-19-positive diagnostic specimens spanned a 6.2log10 range, confirming substantial viral load variation in vivo. We further calibrated RT-ddPCR-derived SARS-CoV-2 E gene copy numbers against cycle threshold (Ct) values from a commercial real-time RT-PCR diagnostic platform. The resulting log-linear relationship can be used to mathematically derive SARS-CoV-2 RNA copy numbers from Ct values, allowing the wealth of available diagnostic test data to be harnessed to address foundational questions in SARS-CoV-2 biology.

Recent advances in high-throughput sequencing have shed new light on the diversity of small noncoding RNA (sncRNA) classes and their crucial roles in gene regulation and disease. One key step in sncRNA profiling consists in their quantification and assessment of their degradation extent. In this chapter, we will describe different gold standard methods used to achieve both purposes before using the sncRNAs in downstream applications.

High sensitivity of qPCR assay can be compromised by the presence of PCR inhibitors in samples analyzed. The aim of this study was to analyze the RT-qPCR assay efficiency considering the RNA quality/quantity and the presence of PCR inhibitors in patients with chemotherapy and/or antibiotic therapy. We analyzed 60 samples using RT-qPCR from individuals suspected of leukemia and 44 samples were quantified by fluorimetry and spectrophotometry. The efficiency of the RT-qPCR assay was evaluated comparing the threshold cycle (Ct) from tested samples and the standard curve. The 260/280 and 260/230 ratios, the presence of PCR inhibitors and the amount of sample (ng) used in the RT-qPCR reaction can be associated with 56.8% (R²=0.56, p

microRNAs (miRNAs) are small non-coding RNAs (~22 nts) that are considered central post-transcriptional regulators of gene expression and key components in many pathological conditions. Next-Generation Sequencing (NGS) technologies have led to inexpensive, massive data production, revolutionizing every research aspect in the fields of biology and medicine. Particularly, small RNA-Seq (sRNA-Seq) enables small non-coding RNA quantification on a high-throughput scale, providing a closer look into the expression profiles of these crucial regulators within the cell. Here, we present DIANA-microRNA-Analysis-Pipeline (DIANA-mAP), a fully automated computational pipeline that allows the user to perform miRNA NGS data analysis from raw sRNA-Seq libraries to quantification and Differential Expression Analysis in an easy, scalable, efficient, and intuitive way. Emphasis has been given to data pre-processing, an early, critical step in the analysis for the robustness of the final results and conclusions. Through modularity, parallelizability and customization, DIANA-mAP produces high quality expression results, reports and graphs for downstream data mining and statistical analysis. In an extended evaluation, the tool outperforms similar tools providing pre-processing without any adapter knowledge. Closing, DIANA-mAP is a freely available tool. It is available dockerized with no dependency installations or standalone, accompanied by an installation manual through Github.

BACKGROUND The genetic divergence of HIV-1 group O is high relative to pandemic group M, which could impact detection and quantification of plasma RNA. Recent commercial kits for RNA quantification seem to show good performances in HIV-1/O, but discrepancies are still observed. Here, we compare the performances of 3 commercial assays for the RNA quantification of HIV-1/O. METHODS We studied the RNA quantification of 117 clinical samples using Abbott RealTime HIV-1, Cepheid Xpert HIV-1 Viral Load, or Roche Cobas TaqMan HIV-1 v2. First, we conducted a qualitative description, and second, we focused on a quantitative analysis of the results above 40 cp/mL. The degree of agreement between methods and the strength of the correlation of viral load determination were estimated using Bland-Altman plot and Passing-Bablok regression with the Spearman coefficient, respectively. RESULTS Our 2-by-2 analysis showed that the Abbott and Cepheid assays were very close in terms of correlation and dispersion of points, whereas Roche presented higher values in the highest range of quantification (>5 log10). The Cepheid assay combined better correlation with the consensus value and a lower dispersion of values, leading to an overall better performance of quantification. The quantification was still impacted by intragroup genetic diversity with, here, 1 strain (YBF26). CONCLUSIONS Using a new approach to compare the performances of RNA quantification between more than 2 techniques, we demonstrated that Cepheid could be the most suitable assay for HIV-1/O quantification, although the results from all assays remained strain dependent.

The global spread of SARS-CoV-2 has continued to be a serious concern after WHO declared the virus to be the causative agent of the coronavirus disease 2019 (COVID-19) a global pandemic. Monitoring of wastewater is a useful tool for assessing community prevalence given that fecal shedding of SARS-CoV-2 occurs in high concentrations by infected individuals, regardless of whether they are asymptomatic or symptomatic. Using tools that are part of wastewater-based epidemiology (WBE) approach, combined with molecular analyses, wastewater monitoring becomes a key piece of information used to assess trends and quantify the scale and dynamics of COVID-19 infection in a specific community, municipality, or area of service. This study investigates a six-month long SARS-CoV-2 RNA quantification in influent wastewater from four municipal wastewater treatment plants (WWTP) serving the Charlotte region of North Carolina (NC) using both RT-qPCR and RT-ddPCR platforms. Influent wastewater was analyzed for the nucleocapsid (N) genes N1 and N2. Both RT-qPCR and RT-ddPCR performed well for detection and quantification of SARS-CoV-2 using the N1 target, while for the N2 target RT-ddPCR was more sensitive. SARS-CoV-2 concentration ranged from 103 to 105 copies/L for all four plants. Both RT-qPCR and RT-ddPCR showed a significant positive correlation between SARS-CoV-2 concentrations and the 7-day rolling average of clinically reported COVID-19 cases when lagging 5 to 12 days (ρ = 0.52–0.92, p, Graphical abstract Unlabelled Image

Our study aimed to investigate the kinetics of SARS-CoV-2 RNA in bile and in different body fluids of two SARS-CoV-2 positive patients with acute cholecystitis by innovative droplet digital PCR (ddPCR) assays. For each patient, nasopharyngeal- and rectal swabs, bile, urine, and plasma samples were collected at different time points for SARS-CoV-2 RNA quantification by two ddPCR assays. For both patients, ddPCR revealed persistent and prolonged detection of viral RNA in the nasopharyngeal swab despite triple-negative or single-positive results by qRT-PCR. In Patient 1, SARS-CoV-2 RNA dropped more rapidly in bile and rectal-swab and declined slowly in nasopharyngeal swab and plasma, becoming undetectable in all compartments 97 days after symptoms started. Conversely, in patient 2, SARS-CoV-2 RNA was detected, even if at low copies, in all body samples (with the exception of urine) up to 75 days after the onset of symptoms. This study highlights that SARS-CoV-2 RNA can persist for a prolonged time in respiratory samples and in several biological samples despite negativity to qRT-PCR, supporting SARS-CoV-2’s ability to provoke persistent and disseminated infection and therefore to contribute to extra-pulmonary clinical manifestations.

Saliva is an easy-to access body fluid with high diagnostic potential. The utilization of saliva for oral cancer diagnosis can be an attractive possibility. Besides the oral cancer, it is important to better understand the precancerous lesions such as oral lichen planus (OLP) and leukoplakia (OLK). In order to examine the changes of salivary proteins in controls, patients with oral cancer, and patients with precancerous conditions, proximity extension assay was utilized. Some proteins and functions were characteristic to the examined groups and can serve as a starting point for further biomarker studies. The different nature of OLK and OLP was demonstrated, showing the malignant transformation and the inflammation as the prominent biological processes in the OLK and OLP, respectively. The salivary level of IL6 was verified using quantitative ELISA and the mRNA level was also studied. Elevated IL6 levels could be detected in precancerous groups compared to controls.

Overview This SOP describes pre-analytical procedures to be followed for the isolation and identification of SARS-CoV-2 RNA in primary settled solids samples from wastewater treatment plants. This protocal should be paired with our digital PCR protocal. The protocol follows the approximate workflow: Separate and aliquot sample (~4 hrs) Spike with BCoV (~1 hr) Extract RNA (~ 12 hours total, may be split into 2-3 steps/days) Inhibitor removal (~1 hr) Biosafety Concerns Concentration and extraction procedures that utilize raw samples must adhere to strict Biosafety Level 2+ procedures. These procedures should be performed in a dedicated room. Downstream products may be handled using standard laboratory safety guidelines. This protocol was developed by Stephanie Loeb, Katy Graham, Marlene Wolfe, Krista Wigginton, and Alexandria Boehm at Stanford University and University of Michigan.

Bluetongue (BT) is a major Office International des Epizooties (OIE)-listed disease of wild and domestic ruminants caused by several serotypes of Bluetongue virus (BTV), a virus with a segmented dsRNA genome belonging to the family Reoviridae, genus Orbivirus. BTV is transmitted through the bites of Culicoides midges. The aim of this study was to develop a new method for quantification of BTV Seg-10 by droplet digital RT-PCR (RTdd-PCR), using nucleic acids purified from complex matrices such as blood, tissues, and midges, that notoriously contain strong PCR inhibitors. First, RTdd-PCR was optimized by using RNAs purified from serially 10-fold dilutions of a BTV-1 isolate (105.43TCID50/ml up to 10−0.57 TCID50/ml) and from the same dilutions spiked into fresh ovine EDTA-blood and spleen homogenate. The method showed a good degree of linearity (R2 ≥ 0.995). The limit of detection (LoD) and the limit of quantification (LoQ) established were 10−0.67TCID50/ml (0.72 copies/μl) and 100.03TCID50/ml (3.05 copies/μl) of BTV-1, respectively. Second, the newly developed test was compared, using the same set of biological samples, to the quantitative RT-PCR (RT-qPCR) detecting Seg-10 assay widely used for the molecular diagnosis of BTV from field samples. Results showed a difference mean of 0.30 log between the two assays with these samples (p < 0.05). Anyway, the analysis of correlation demonstrated that both assays provided similar measurements with a very close agreement between the systems.

The following protocol is intended for the quantification of RNA using Quant-iT™ RiboGreen™ RNA Assay Kit (ThermoFisher). This protocol is a simplified and condensed version of the full protocol from the manufacturer. The procedure described here is for 96 reactions. If samples are run in duplicates, then this should allow quantifying 40 samples.

Background: Human saliva has been identified as a novel, practical, and noninvasive source of biomarkers and genetic materials. However, it is equally challenging due to the availability of an abundance of impurities in the form of microbes and other proteinaceous compounds. The objective of this study was to develop a robust, reproducible, and economic method of extracting high-yield and high-quality RNA from whole human saliva. Methods: The modified TRIzol protocol was developed to extract RNA from saliva (n = 14), followed by complementary DNA synthesis and reverse transcription quantitative polymerase chain reaction analyses for the genes encoding IL1B, ALPL, RUNX2, and ACTB. To compare our protocol with the spin column–based method, we used Qiagen Salivary Protect Micro-RNA spin columns (n = 6). To evaluate and compare the yields and quality of extracted RNAs from both methods, we used (1) Experion Bioanalyzer, (2) QuantiFluor RNA dye, and (3) NanoDrop 2000 Spectrometer. Results: With the modified TRIzol lysis protocol, a high yield of total RNA, on average 12.34 μg, from saliva was extracted compared with on average 0.2 μg with a spin column–based method. The average RQI (RNA quality index) with the TRIzol method was 7.86, which is also comparable with that of the spin column–based method (RQI = 7.58). QuantiFluor dye used for RNA quantification showed a 16-fold higher yield of RNA concentration using our TRIzol protocol. Conclusions: Our modified TRIzol protocol is a reproducible method to extract RNA from whole human saliva which can be used for gene expression analysis. This method allows also ensures the quality of RNA required for specific applications such as RNA sequencing.

Accurate RNA quantification at the single-cell level is critical for understanding the dynamics of gene expression and regulation across space and time. Single molecule FISH (smFISH), such as RNAscope, provides spatial and quantitative measurements of individual transcripts, therefore, can be used to explore differential gene expression among a heterogeneous cell population if combined with cell identify information. However, such analysis is not straightforward, and existing image analysis pipelines cannot integrate both RNA transcripts and cellular staining information to automatically output cell type-specific gene expression. We developed an efficient and customizable analysis method, Single-Molecule Automatic RNA Transcription Quantification (SMART-Q), to enable the analysis of gene transcripts in a cell type-specific manner. SMART-Q efficiently infers cell identity information from multiplexed immuno-staining and quantifies cell type-specific transcripts using a 3D Gaussian fitting algorithm. Furthermore, we have optimized SMART-Q for user experiences, such as flexible parameters specification, batch data outputs, and visualization of analysis results. SMART-Q meets the demands for efficient quantification of single-molecule RNA and can be widely used for cell type-specific RNA transcript analysis.

Accurate RNA quantification at the single-cell level is critical for understanding the dynamics of gene expression and regulation across space and time. Single molecule FISH (smFISH), such as RNAscope, provides spatial and quantitative measurements of individual transcripts, therefore, can be used to explore differential gene expression among a heterogeneous cell population if combined with cell identify information. However, such analysis is not straightforward, and existing image analysis pipelines cannot integrate both RNA transcripts and cellular staining information to automatically output cell type-specific gene expression. We developed an efficient and customizable analysis method, Single-Molecule Automatic RNA Transcription Quantification (SMART-Q), to enable the analysis of gene transcripts in a cell type-specific manner. SMART-Q efficiently infers cell identity information from multiplexed immuno-staining and quantifies cell type-specific transcripts using a 3D Gaussian fitting algorithm. Furthermore, we have optimized SMART-Q for user experiences, such as flexible parameters specification, batch data outputs, and visualization of analysis results. SMART-Q meets the demands for efficient quantification of single-molecule RNA and can be widely used for cell type-specific RNA transcript analysis.

Sensitive and accurate hepatitis C virus (HCV) RNA quantification is essential for the management of chronic hepatitis C therapy. Currently, in Shanghai, China, many laboratories offer to quantify the HCV RNA level using various reverse transcription-polymerase chain reaction (rRT-PCR) assays, but the degree of proficiency may vary between them. The objective of this study was to realistically evaluate the performance of clinical laboratories for HCV RNA quantification by a new way of proficiency test program (unannounced inspection). Inactivated chimeric influenza viral particles (CIVP) encapsulating specific RNA sequences of HCV were prepared as positive samples. The sample panel, which consisted of two negative and eight positive samples with different concentrations of CIVP (25 IU/ml to 2.5×106 IU/ml), was distributed to 40 clinical laboratories for HCV RNA quantification. The results reported by the participating laboratories were compared and scored. It was found that 75 % (30/40) of the laboratories obtained an acceptable or better performance score, while the other 10 laboratories had room for improvement. Reported results for individual samples ranged from 125 IU/ml (minimum) to 6.0×104 IU/ml (maximum). The greatest variation was observed for samples with a relatively low concentration. Interlaboratory variability among replicate samples was significantly greater than intralaboratory variability (p

Physiological RNA dynamics cause problems in transcriptome analysis. Physiological RNA accumulation affects the analysis of RNA quantification, and physiological RNA degradation affects the analysis of the RNA sequence length, feature site and quantification. In the present article, we review the effects of physiological degradation and accumulation of RNA on analysing RNA sequencing data. Physiological RNA accumulation and degradation probably led to such phenomena as incorrect estimations of transcription quantification, differential expressions, co-expressions, RNA decay rates, alternative splicing, boundaries of transcription, novel genes, new single-nucleotide polymorphisms, small RNAs and gene fusion. Thus, the transcriptomic data obtained up to date warrant further scrutiny. New and improved techniques and bioinformatics software are needed to produce accurate data in transcriptome research.

Graphical abstract, Accurate and reliable analysis of gene expression depends on the extraction of pure and high-quality RNA. However, while the conventional phenol-chloroform RNA extraction is preferable over silica-based columns, particularly when cost is a concern or higher RNA yield is desired, it can result in significant RNA contamination. Contaminants including excess phenol, chloroform, or salts, can have significant impacts on downstream applications, including RNA quantification and reverse transcription, that can skew data collection and interpretation. To overcome the issue of RNA contamination in the conventional phenol-chloroform based RNA extraction method, we have optimized the protocol by adding one chloroform extraction step, and several RNA washing steps. Importantly, RNA quality and purity and accuracy in the quantification of RNA concentration were significantly improved with the modified protocol, resulting in reliable data collection and interpretation in downstream gene expression analysis. • Our protocol is customized by the addition of a second chloroform extraction step. Chloroform is carefully pipetted so as to not disturb the interphase layer. Any contaminants accidentally removed from interphase will be present in subsequent steps and can result in RNA contaminated with protein or phenol. The additional chloroform step increases RNA purity. • Additionally, the addition of 2 additional ethanol washes, initially intended to remove any residual salts from the isopropanol RNA precipitation step, also removed residual phenol contamination, enhancing RNA purity. • In summary, these modifications serve to enhance not only the purity of the RNA but, also increase the accuracy and reliability of RNA quantification.

We have developed a highly sensitive microarray-based method that determines the absolute amounts of mRNA in a total RNA sample in a multiplex manner without reverse transcription. This direct mRNA measurement promotes high-throughput testing and reduces bias in transcriptome analyses. Furthermore, quantification of the absolute amount of mRNA allows transcriptome analysis without common controls or additional, complicated normalization. The method, called Photo-DEAN, was validated using chemically synthesized RNAs of known quantities and mouse liver total RNA samples. We found that the absolute amounts of mRNA were successfully measured without the cDNA synthesis step, with a sensitivity of 15 zmol achieved in 7 h.