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Quantitative PCR (qPCR) is one of the most common techniques for quantification of nucleic acid molecules in biological and environmental samples. Although the methodology is perceived to be relatively simple, there are a number of steps and reagents that require optimization and validation to ensure reproducible data that accurately reflect the biological question(s) being posed. This review article describes and illustrates the critical pitfalls and sources of error in qPCR experiments, along with a rigorous, stepwise process to minimize variability, time, and cost in generating reproducible, publication quality data every time. Finally, an approach to make an informed choice between qPCR and digital PCR technologies is described. qPCR is more complex than perceived by many scientists. The production of an amplification curve and an associated quantitative cycle value does not necessarily mean interpretable data. The MIQE guidelines and associated methodology articles published thereafter, underline the ongoing drive to help scientists produce reproducible data from qPCR, culminating in a simple, stepwise methodology to ensure high-quality, reproducible data from qPCR experiments. The concept of data normalization has led to the ongoing publication of articles solely focused on this subject for various sample types and experimental parameters. The analysis of qPCR data can be challenging, especially as experiments grow in sample number and complexity of biological groups. A defined approach to qPCR data analysis is necessary to clarify gene expression analysis.

Whole genome sequencing (WGS) is considered the best instrument to track both virus evolution and the spread of new, emerging variants. However, WGS still does not allow the analysis of as many samples as qPCR does. Epidemiological and clinical research needs to develop advanced qPCR methods to identify emerging variants of SARS-CoV-2 while collecting data on their spreading in a faster and cheaper way, which is critical for introducing public health measures. This study aimed at designing a one-step RT-qPCR assay for multiplex detection of the Omicron lineage and providing additional data on its subvariants in clinical samples. The RT-qPCR assay demonstrated high sensitivity and specificity on multiple SARS-CoV-2 variants and was cross-validated by WGS.

The high-throughput antibiotic resistance gene (ARG) qPCR array, initially published in 2012, is increasingly used to quantify resistance and mobile determinants in environmental matrices. Continued utility of the array; however, necessitates improvements such as removing or redesigning questionable primer sets, updating targeted genes and coverage of available sequences. Towards this goal, a new primer design tool (EcoFunPrimer) was used to aid in identification of conserved regions of diverse genes. The total number of assays used for diverse genes was reduced from 91 old primer sets to 52 new primer sets, with only a 10% loss in sequence coverage. While the old and new array both contain 384 primer sets, a reduction in old primer sets permitted 147 additional ARGs and mobile genetic elements to be targeted. Results of validating the updated array with a mock community of strains resulted in over 98% of tested instances incurring true positive/negative calls. Common queries related to sensitivity, quantification and conventional data analysis (e.g. Ct cutoff value, and estimated genomic copies without standard curves) were also explored. A combined list of new and previously used primer sets is provided with a recommended set based on redesign of primer sets and results of validation.

Hyperlipidemia is the root cause of numerous chronic conditions, leading to high mortality rates around the globe. Spirulina (Arthrospira platensis) microalgae serve as a promising reservoir of bioactive compounds with diverse pharmacological properties. The current study examined the nutritional profile of spirulina powder in relation to strict glycemic control, specifically focusing on its potential to lower lipid levels. In an in vivo investigation, normal healthy male Wistar albino rats (n = 60) were divided into two groups: a negative control group (NC) of ten rats and a high-fat diet group (n = 50) that were fed a cholesterol-rich diet until their cholesterol levels reached or exceeded 250 mg/dL. Subsequently, the hypercholesterolemic rats were then randomly allocated to several treatment groups: a positive control (PC); a standard treatment diet (STD) involving fenofibrate at a dose of 20 mg/kg body weight; and three experimental groups (T1, T2, and T3) that received spirulina powder supplementation at doses of 300, 600, and 900 mg per kg body weight, respectively, for the period of 12 weeks. Blood samples were analyzed for oxidative stress biomarkers, insulin levels, lipid profiles, liver function, and expression of gene levels in the diabetogenic pathway. The study utilized spectrophotometric colorimetric methods to identify oxidative stress biomarkers, serum kit methods to measure lipid profiles and liver enzymes, and the assessment of qPCR for mRNA quantity. According to the research findings, spirulina powder has certain noteworthy features. It had the greatest quantity of chlorogenic acid (4052.90 µg/g) among seven phenolics and two flavonoid compounds obtained by HPLC-UV analysis. Furthermore, the proximate analysis demonstrated that spirulina is high in protein (16.45 ± 0.8%) and has a significant energy yield of 269.51 K-calories per 100 g. A maximal spirulina dose of 900 mg/kg/wt significantly lowered oxidative stress, cholesterol, triglyceride, low-density lipoproteins (LDL), and insulin levels (p ≤ 0.05). In contrast, high-density lipoprotein (HDL) and total antioxidant capacity (TAC) levels increased significantly (p ≤ 0.05) compared to all other groups, except the NC group. The study provides remarkable proof about the pharmacological impact of spirulina powders. Significant reductions (p ≤ 0.05) in liver enzymes {alanine aminotransferase (ALT) and aspartate aminotransferase (AST)} were observed across all treatment groups, with the exception of the NC, compared to the positive control. The treatment groups had significantly greater gene expression levels of INS-1, PDX-1, IGF-1, and GLUT-2 than the positive control group (p ≤ 0.05). These findings highlight spirulina’s potential as a long-term regulator of hyperglycemia in rat models with induced hyperlipidemia, owing to its phenolic bioactive components that serve as antioxidants.

Background Reliable biomarkers to differentiate gastrointestinal cancer (GIC) from chronic inflammatory enteropathy (CIE) in dogs are needed. Fecal and serum microRNAs (miRNAs) have been proposed as diagnostic and prognostic markers of GI disease in humans and dogs. Hypothesis/Objectives Dogs with GIC have fecal and serum miRNA profiles that differ from those of dogs with CIE. Aims: (a) identify miRNAs that differentiate GIC from CIE, (b) use high‐throughput reverse transcription quantitative real‐time PCR (RT‐qPCR) to establish fecal and serum miRNA panels to distinguish GIC from CIE in dogs. Animals Twenty‐four dogs with GIC, 10 dogs with CIE, and 10 healthy dogs, all client‐owned. Methods An international multicenter observational prospective case‐control study. Small RNA sequencing was used to identify fecal and serum miRNAs, and RT‐qPCR was used to establish fecal and serum miRNA panels with the potential to distinguish GIC from CIE. Results The best diagnostic performance for distinguishing GIC from CIE was fecal miR‐451 (AUC: 0.955, sensitivity: 86.4%, specificity: 100%), miR‐223 (AUC: 0.918, sensitivity: 90.9%, specificity: 80%), and miR‐27a (AUC: 0.868, sensitivity: 81.8%, specificity: 90%) and serum miR‐20b (AUC: 0.905, sensitivity: 90.5%, specificity: 90%), miR‐148a‐3p (AUC: 0.924, sensitivity: 85.7%, specificity: 90%), and miR‐652 (AUC: 0.943, sensitivity: 90.5%, specificity: 90%). Slightly improved diagnostic performance was achieved when combining fecal miR‐451 and miR‐223 (AUC: 0.973, sensitivity: 95.5%, specificity: 90%). Conclusions and Clinical Importance When used as part of a diagnostic RT‐qPCR panel, the abovementioned miRNAs have the potential to function as noninvasive biomarkers for the differentiation of GIC and CIE in dogs.

Background Environmental DNA (eDNA) analysis is increasingly being used to detect the presence and relative abundance of rare species, especially invasive or imperiled aquatic species. The rapid progress in the eDNA field has resulted in numerous studies impacting conservation and management actions. However, standardization of eDNA methods and reporting across the field is yet to be fully established, with one area being the calculation and interpretation of assay limit of detection (LOD) and limit of quantification (LOQ). Aims Here, we propose establishing consistent methods for determining and reporting of LOD and LOQ for single‐species quantitative PCR (qPCR) eDNA studies. Materials & Methods/ Results We utilize datasets from multiple cooperating laboratories to demonstrate both a discrete threshold approach and a curve‐fitting modeling approach for determining LODs and LOQs for eDNA qPCR assays. We also provide details of an R script developed and applied for the modeling method. Discussion/Conclusions Ultimately, standardization of how LOD and LOQ are determined, interpreted, and reported for eDNA assays will allow for more informed interpretation of assay results, more meaningful interlaboratory comparisons of experiments, and enhanced capacity for assessing the relative technical quality and performance of different eDNA qPCR assays. We propose establishing consistent methods for determining and reporting of LOD and LOQ for single‐species quantitative PCR (qPCR) eDNA studies. We demonstrate the use of both a discrete threshold approach and a curve‐fitting modeling approach for determining LODs and LOQs for these assays. Ultimately, standardization of how LOD and LOQ are determined, interpreted, and reported for eDNA assays will allow for more informed interpretation of assay results, more meaningful interlaboratory comparisons of experiments, and enhanced capacity for assessing the relative technical quality and performance of different eDNA qPCR assays.

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 data reproducibility is a growing concern in many research areas. This is especially relevant when examining quantification methods of RNA profiling. The HEROIC consortium members propose to the scientific and medical communities as well as editors to adopt a unified, simple format for submission of RT‐qPCR raw data to journals at the time of manuscript submission.

Digital Polymerase Chain Reaction (dPCR) is a novel method for the absolute quantification of target nucleic acids. Quantification by dPCR hinges on the fact that the random distribution of molecules in many partitions follows a Poisson distribution. Each partition acts as an individual PCR microreactor and partitions containing amplified target sequences are detected by fluorescence. The proportion of PCR-positive partitions suffices to determine the concentration of the target sequence without a need for calibration. Advances in microfluidics enabled the current revolution of digital quantification by providing efficient partitioning methods. In this review, we compare the fundamental concepts behind the quantification of nucleic acids by dPCR and quantitative real-time PCR (qPCR). We detail the underlying statistics of dPCR and explain how it defines its precision and performance metrics. We review the different microfluidic digital PCR formats, present their underlying physical principles, and analyze the technological evolution of dPCR platforms. We present the novel multiplexing strategies enabled by dPCR and examine how isothermal amplification could be an alternative to PCR in digital assays. Finally, we determine whether the theoretical advantages of dPCR over qPCR hold true by perusing studies that directly compare assays implemented with both methods.

Identifying early gene expression responses to hypoxia (i.e., low dissolved oxygen) as a tool to assess the degree of exposure to this stressor is crucial for salmonids, because they are increasingly exposed to hypoxic stress due to anthropogenic habitat change, e.g., global warming, excessive nutrient loading, and persistent algal blooms. Our goal was to discover and validate gill gene expression biomarkers specific to the hypoxia response in salmonids across multi-stressor conditions. Gill tissue was collected from 24 freshwater juvenile Chinook salmon (Oncorhynchus tshawytscha), held in normoxia [dissolved oxygen (DO) > 8 mg L-1] and hypoxia (DO = 4‒5 mg L-1) in 10 and 18° temperatures for up to six days. RNA-sequencing (RNA-seq) was then used to discover 240 differentially expressed genes between hypoxic and normoxic conditions, but not affected by temperature. The most significantly differentially expressed genes had functional roles in the cell cycle and suppression of cell proliferation associated with hypoxic conditions. The most significant genes (n = 30) were selected for real-time qPCR assay development. These assays demonstrated a strong correlation (r = 0.88; P < 0.001) between the expression values from RNA-seq and the fold changes from qPCR. Further, qPCR of the 30 candidate hypoxia biomarkers was applied to an additional 322 Chinook salmon exposed to hypoxic and normoxic conditions to reveal the top biomarkers to define hypoxic stress. Multivariate analyses revealed that smolt stage, water salinity, and morbidity status were relevant factors to consider with the expression of these genes in relation to hypoxic stress. These hypoxia candidate genes will be put into application screening Chinook salmon to determine the identity of stressors impacting the fish.

Quantitative PCR (qPCR) has become a widely used technique for bacterial quantification. The affordability, ease of experimental design, reproducibility, and robustness of qPCR experiments contribute to its success. The establishment of guidelines for minimum information for publication of qPCR experiments, now more than 10 years ago, aimed to mitigate the publication of contradictory data. Unfortunately, there are still a significant number of recent research articles that do not consider the main pitfalls of qPCR for quantification of biological samples, which undoubtedly leads to biased experimental conclusions. qPCR experiments have two main issues that need to be properly tackled: those related to the extraction and purification of genomic DNA and those related to the thermal amplification process. This mini-review provides an updated literature survey that critically analyzes the following key aspects of bacterial quantification by qPCR: (i) the normalization of qPCR results by using exogenous controls, (ii) the construction of adequate calibration curves, and (iii) the determination of qPCR reaction efficiency. It is primarily focused on original papers published last year, where qPCR was applied to quantify bacterial species in different types of biological samples, including multi-species biofilms, human fluids, and water and soil samples. Key points • qPCR is a widely used technique used for absolute bacterial quantification. • Recently published papers lack proper qPCR methodologies. • Not including proper qPCR controls significantly affect experimental conclusions.