Molecular Biology

Quantitative real-time PCR (qPCR) is widely used for the quantitative assessment of relative transcript abundance in biological and medical research. Rigorous interpretation of qPCR data requires appropriate correction and normalization workflows that account for both technical variability and experimental heterogeneity. Regarding the correction step, the most used qPCR analysis relies on the 2^-ΔΔCq method, which assumes identical and optimal amplification efficiencies across assays. Alternative strategies estimate amplification efficiencies using standard curves generated from serial dilutions, but these approaches require additional experimental work and may introduce serious dilution-related bias. Here, we describe a spreadsheet-based computational protocol for the correction of relative quantification of qPCR data that integrates amplification efficiencies derived directly from raw amplification curves using LinRegPCR. Cq values and per-reaction efficiency estimates are combined to calculate efficiency-corrected target quantities. Correction is then followed by normalization using the geometric mean of two reference genes. The workflow enables calculation of relative abundance fold-changes without the need for standard curves and produces output tables suitable for downstream statistical analysis. This protocol provides a transparent, dilution-free method for efficiency-corrected qPCR data analysis that can be implemented using commonly available software, facilitating reproducible and Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE)-compliant reporting of qPCR results.

Circular RNAs (circRNAs) are covalently closed RNA molecules known for their increased stability compared to linear RNAs. Synthetic circRNAs are being developed as RNA therapeutics, while natural circRNAs are being investigated for their biological roles in eukaryotes and their potential as disease biomarkers. Consequently, the accurate detection and validation of circRNAs is crucial for advancements in both fundamental RNA research and biotechnological applications. Common methods for circRNA validation involve RT-PCR using divergent primers, followed by sequencing across the circRNA junction. However, most described methods are high-throughput approaches that require time-consuming RNA processing steps, and they are unable to detect highly structured circRNAs. Additionally, methods for low-throughput sequencing of small circRNAs (<150 nt) require cloning prior to sequencing. A simplified protocol for the validation of circRNA sequences irrespective of structure, sequence complexity, and length has not yet been described. In this method, we describe an improved RT-PCR protocol for circRNA detection by using UltraMarathonRT^® (uMRT), a highly processive reverse transcriptase. Unlike other reverse transcriptases, uMRT can reverse-transcribe large, structured circRNAs of varying sizes, at ambient temperatures, enabling sequencing of the resulting concatemeric amplicons generated by RT-PCR and other methods. Using this method, we sequenced circRNAs containing highly structured internal ribosome entry sites commonly utilized in synthetic circRNAs, natural circRNAs containing repetitive elements, and small circRNAs, all without the need for cloning. With this new platform, we offer a protocol for the precise detection of nearly any circRNA species.

Regulated IRE1-dependent decay (RIDD) is a critical cellular mechanism mediated by the endoplasmic reticulum (ER) stress sensor IRE1α, which cleaves a variety of RNA targets to regulate ER homeostasis. Current in vitro assays to study IRE1α activity largely rely on synthetic or in vitro transcribed RNA substrates, which may not fully replicate the physiological complexities of native RNA molecules. Here, we present a comprehensive protocol to assess IRE1α-dependent RNA cleavage activity using total RNA isolated directly from mouse tissues. This protocol provides a step-by-step guide for tissue collection, RNA isolation, an ex vivo RIDD assay, cDNA synthesis, and subsequent RT-PCR analysis of target mRNA cleavage products. Key reagents include active IRE1α protein, the RIDD-specific inhibitor 4μ8C, and target-specific primers for RIDD-regulated genes such asBloc1s1 and Col6a1. Quantitative assessment is achieved using agarose gel electrophoresis and imaging software. This methodology enables the study of IRE1α's RNA cleavage activity under conditions that closely mimic in vivo environments, providing a more physiologically relevant approach to understanding the role of RIDD in cellular and tissue-specific contexts.

The accurate quantification of nucleic acid–based biomarkers, including long non-coding RNAs (lncRNAs), messenger RNAs (mRNAs), and microRNAs (miRNAs), is essential for disease diagnostics and risk assessment across the biological spectrum. Quantitative reverse transcription PCR (qRT-PCR) is the gold standard assay for the quantitative measurement of RNA expression levels, but its reliability depends on selecting stable reference targets for normalization. Yet, the lack of consensus on a universally accepted reference gene for a given sample type or species, despite being necessary for accurate quantification, presents a challenge to the broad application of such biomarkers. Various tools are currently being used to identify a stably expressed gene by using qRT-PCR data of a few potential normalizer genes. However, existing tools for normalizer gene selection are fraught with both statistical limitations and inadequate graphical user interfaces for data visualization. gQuant, the tool presented here, essentially overcomes these limitations. The tool is structured in two key components: the preprocessing component and the data analysis component. The preprocessing addresses missing values in the given dataset by the imputation strategies. After data preprocessing, normalizer genes are ranked using democratic strategies that integrate predictions from multiple statistical methods. The effectiveness of gQuant was validated through data available online as well as in-house data derived from urinary exosomal miRNA expression datasets. Comparative analysis against existing tools demonstrated that gQuant delivers more stable and consistent rankings of normalizer genes. With its promising performance, gQuant enhances the precision and reproducibility in the identification of normalizer genes across diverse research scenarios, addressing key limitations of RNA biomarker–based translational research.

MicroRNAs (miRNAs) are small, non-coding RNAs that play pivotal roles in gene regulation; they are increasingly recognized as vital biomarkers for various diseases, notably cancer. Conventional methods for miRNA detection, such as quantitative PCR and microarray analysis, often entail intricate sample preparation and lack the requisite sensitivity to detect low-abundance miRNAs like miRNA-21. This protocol presents an innovative approach that combines branched hybridization chain reaction (bHCR) with DNAzyme technology for the precise detection of miRNA-21. The bHCR amplifies the target signal through a branched structure, while the DNAzyme boosts detection sensitivity through catalytic cleavage, enabling swift and specific identification of miRNA-21. This dual amplification strategy offers a highly sensitive, specific, and rapid alternative to traditional techniques, making it particularly well-suited for early-stage disease diagnosis.

Circular RNA (circRNA) is an intriguing class of non-coding RNA that exists as a continuous closed loop. With the improvements in high throughput sequencing, biochemical analysis, and bioinformatic algorithms, studies on circRNA expression became abundant in recent years. However, functional studies of circRNA are still limited. Subcellular localization of circRNA may provide some clues in elucidating its biological functions by performing subcellular fractionation assay. Notably, circRNAs that are predominantly found in the cytoplasm are more likely to be involved in post-transcriptional gene regulation, e.g., acting as micoRNA sponge, whereas nuclear-retained circRNAs are predicted to play a role in transcriptional regulation. Subcellular fractionation could help researchers to narrow down and prioritize downstream experiments. The majority of the currently available protocols describe the steps for subcellular fractionation followed by western blot analysis for protein molecules. Here, we present a protocol for the subcellular fractionation of cells to detect circRNA via RT-qPCR with divergent primers. Moreover, detailed steps for the generation of specific circRNAs-enriched cDNA included in this protocol will enhance the amplification and detection of low-abundance circRNAs. This will be useful for researchers studying low-abundance circRNAs.

Key features

• This protocol builds upon the method developed by Gagnon et al. (2014) and extends its application to circRNA study.

• Protocol for amplification of low levels of circRNA expression.

• Analysis takes into consideration the ratio of cytoplasmic RNA concentration to nuclear RNA concentration.

Graphical overview

When performing expression analysis either for coding RNA (e.g., mRNA) or non-coding RNA (e.g., miRNA), reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is a widely used method. To normalize these data, one or more stable endogenous references must be identified. RefFinder is an online web-based tool using four almost universally used algorithms for assessing candidate endogenous references—delta-Ct, BestKeeper, geNorm, and Normfinder. However, the online interface is presently cumbersome and time consuming. We developed an R package, RefSeeker, which performs easy and straightforward RefFinder analysis by enabling raw data import and calculation of stability from each of the algorithms and provides data output tools to create graphs and tables. This protocol uses RefSeeker R package for fast and simple RefFinder stability analysis.

Key features

• Perform stability analysis using five algorithms: Normfinder, geNorm, delta-Ct, BestKeeper, and RefFinder.

• Identification of endogenous references for normalization of RT-qPCR data.

• Create publication-ready graphs and tables output.

• Step-by-step guide dialog window for novice R users.

Graphical overview

Simple workflow diagram. Two main workflow paths are presented. A) Using the RefSeeker wizard allows non-R programmers to easily load data and choose between selected output formats. B) Command line interface provides more options to control input and output formats and to automate analysis.

The nematode Haemonchus placei is a pathogenic parasite, the most seriously affecting ruminant’s health and being responsible for enormous economic losses all over the world. The present protocol describes different in vitro techniques to select potential candidate antigens with immune-protective activity from excretory and secretory products (ESP) from H. placei transitory infective larvae (xL₃). ESP from xL₃ were obtained from the in vitro infective larvae (L₃) maintained in Hank’s medium at 37 °C with 5% CO₂ for 48 h. Then, the presence of ESP proteins was confirmed by SDS-PAGE to be used in an in vitro proliferation assay with bovine peripheral blood mononuclear cells (PBMCs). The ESP were exposed to the PBMCs during two different periods (24 and 48 h). Genes associated with immune response against the nematode were analyzed using relative gene expression and bioinformatic tools. These are simple, economic, and helpful tools to identify potential immune-protective molecules under in vitro conditions for confirming the efficacy of future in vivo assays.

Graphical overview

E-cigarette (E-cig) inhalation affects health status by modulating inflammation profiles in several organs, including the brain, lung, heart, and colon. The effect of flavored fourth-generation pod-based E-cigs (JUUL) on murine gut inflammation is modulated by both flavor and exposure period. Exposure of mice to JUUL mango and JUUL mint for one month upregulated inflammatory cytokines, particularly TNF-α, IL-6, and Cxcl-1 (IL-8). JUUL Mango effects were more prominent than those incurred by JUUL Mint after one month of exposure. However, JUUL Mango reduced the expression of colonic inflammatory cytokines after three months of exposure. In this protocol, we detail the process of RNA isolation from the mouse colon and the use of extracted RNA in profiling the inflammatory milieu. Efficient RNA extraction from the murine colon is the most important step in the evaluation of inflammatory transcripts in the colon.