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.

Laser-assisted microdissection (LAM) coupled with next-generation sequencing technologies offers a powerful approach to dissecting the complex cellular heterogeneity within lung adenocarcinoma (LUAD) tumors. This protocol outlines the method for isolating specific high-risk LUAD tissues containing micropapillary/solid (MIP/SOL) patterns, which is linked to poor prognosis. We detail the process of LAM, which involves tissue fixation, microtome sectioning, and the precise dissection and collection of cells of interest under microscopic guidance. The isolated cells are then subjected to RNA extraction, library preparation, and sequencing to profile transfer RNA–derived fragments (tRFs) and tRNA-derived stress-induced RNAs (tiRNAs), which are emerging as key regulators in cancer. This protocol enables researchers to obtain high-quality transcriptomic data from specific LUAD cell populations, aiming to uncover tRF-Val-CAC-024 and tiRNA-Gly-CCC as potential biomarkers for early diagnosis and therapeutic targets for LUAD treatment.

Antimicrobial peptides are effective agents against various pathogens, often targeting essential processes like protein translation to exert their antimicrobial effects. Traditional methods such as puromycin labeling have been extensively used to measure protein synthesis in mammalian and yeast systems; however, protocols tailored for plant pathogenic filamentous fungi, particularly those investigating translation inhibition by antifungal peptides, are lacking. This protocol adapts puromycin labeling to quantify translation inhibition in Botrytis cinerea germlings treated with antifungal peptides. Optimizing the method specifically for fungal germlings provides a precise tool to investigate peptide effects on fungal protein synthesis, advancing our understanding of translation dynamics during pathogen–host interactions in filamentous fungi.

Traditional approaches for the detection and differentiation of Bacillus cereus group species often face challenges due to the complexity of genetic discrimination between species. In this protocol, we propose a simple and straightforward assay based on the detected unamplified bacterial 16S rRNA by DNA nanomachine (DNM). The assay incorporates a universal fluorescent reporter and four DNA binding fragments, three of which are responsible for “opening up” the folded rRNA while the fourth strand is responsible for detecting single nucleotide variation (SNV) with high selectivity. The binding of the DNM to 16S rRNA results in the formation of the 10-23 DNAzyme catalytic core that cleaves the fluorescent reporter and produces a signal, which is amplified over time due to catalytic turnover. The developed biplex assay enables the detection of B. thuringiensis 16S rRNA and B. mycoides at fluorescein and Cy5 channels, respectively. The protocol offers two detection options: one utilizing extracted total RNA and the other involving crude cell lysate. The latter enables a fast and straightforward detection after 1.5 h with a hands-on time of ~15 min. The new protocol may simplify the analysis of biological RNA samples and might be useful for environmental monitoring as a simple and inexpensive alternative to amplification-based nucleic acid analysis.

Transfer RNAs (tRNAs), the essential adapter molecules in protein translation, undergo various post-transcriptional modifications. These modifications play critical roles in regulating tRNA folding, stability, and codon–anticodon interactions, depending on the modified position. Methods for detecting modified nucleosides in tRNAs include isotopic labeling combined with chromatography, antibody-based techniques, mass spectrometry, and high-throughput sequencing. Among these, high-performance liquid chromatography (HPLC) has been a cornerstone technique for analyzing modified nucleosides for decades. In this protocol, we provide a detailed, streamlined approach to purify and digest tRNAs from yeast cells and analyze the resulting nucleosides using HPLC. By assessing UV absorbance spectra and retention times, modified nucleosides can be reliably quantified with high accuracy. This method offers a simple, fast, and accessible alternative for studying tRNA modifications, especially when advanced technologies are unavailable.

Cyclic diadenosine monophosphate (c-di-AMP) is a recently discovered second messenger that modulates several signal transduction pathways in bacterial and host cells. Besides the bacterial system, c-di-AMP signaling is also connected with the host cytoplasmic surveillance pathways (CSP) that induce type-I IFN responses through STING-mediated pathways. Additionally, c-di-AMP demonstrates potent adjuvant properties, particularly when administered alongside the Bacillus Calmette–Guérin (BCG) vaccine through mucosal routes. Because of its pivotal role in bacterial signaling and host immune response, this molecule has garnered significant interest from the pharmaceutical industry. This protocol outlines the quantification of c-di-AMP by an HPLC-based assay to enumerate the activity of c-di-AMP synthase from Mycobacterium smegmatis. The following protocol is designed to be generic, enabling the study of c-di-AMP synthase activity from other bacterial species. However, modifications may be required depending on the specific activity of c-di-AMP synthase from different bacterial sources.

Lysosome-related organelles (LROs) are a class of heterogeneous subcellular organelles conserved in eukaryotes, performing various functions. An important function of LROs is to mediate phosphorus and metal homeostasis. Chlamydomonas reinhardtii serves as a model organism for investigating metal ion metabolism. Considering that LROs contain polyphosphate and various metal elements, the purification strategy is based on their higher density by fractionating cell lysate through OptiPrep density gradient ultracentrifugation. Here, we optimized a method for purifying LROs from C. reinhardtii cells that have reached stationary phase (sta-LROs) or are overloaded with iron (Fe-LROs). Our protocol provides technical support for further investigations on the biogenesis and function of LROs in C. reinhardtii.