Molecular Biology

Thousands of RNAs are localized to specific subcellular locations, and these localization patterns are often required for optimal cell function. However, the sequences within RNAs that direct their transport are unknown for almost all localized transcripts. Similarly, the RNA content of most subcellular locations remains unknown. To facilitate the study of subcellular transcriptomes, we developed the RNA proximity labeling method OINC-seq. OINC-seq utilizes photoactivatable, spatially restricted RNA oxidation to specifically label RNA in proximity to a subcellularly localized bait protein. After labeling, these oxidative RNA marks are then read out via high-throughput sequencing due to their ability to induce predictable misincorporation events by reverse transcriptase. These induced mutations are then quantitatively assessed for each gene using our software package PIGPEN. The observed mutation rate for a given RNA species is therefore related to its proximity to the localized bait protein. This protocol describes procedures for assaying RNA localization via OINC-seq experiments as well as computational procedures for analyzing the resulting data using PIGPEN.

Transcriptional pausing dynamically regulates spatiotemporal gene expression during cellular differentiation, development, and environmental adaptation. Precise measurement of pausing duration, a critical parameter in transcriptional control, has been challenging due to limitations in resolution and confounding factors. We introduce Fast TV-PRO-seq, an optimized protocol built on time-variant precision run-on sequencing (TV-PRO-seq), which enables genome-wide, single-base resolution mapping of RNA polymerase II pausing times. Unlike standard PRO-seq, Fast TV-PRO-seq employs sarkosyl-free biotin-NTP run-on with time gradients and integrates on-bead enzymatic reactions to streamline workflows. Key improvements include (1) reducing experimental time from 4 to 2 days, (2) reducing cell input requirements, and (3) improved process efficiency and simplified command-line operations through the use of bash scripts.

The subcellular localization of RNA plays a critical role in various biological processes, including development and stress response. Proximity labeling eases the detection of localized transcripts and protein enrichment compared to previous techniques that rely on biochemical isolation of subcellular structures. The rapid reaction and small labeling radius of APEX2 make it an attractive alternative to other proximity labeling approaches, such as BioID. However, we found that standard protocols for APEX proximity labeling fail in human induced pluripotent stem cells. Moreover, standard protocols yield heterogeneous labeling of biomolecules across single cells in MCF10A breast epithelial cells. Our results indicate that low biotin permeability in these cell lines is the main cause for failed or inefficient labeling. This protocol outlines improved labeling by combining the rapid hydrogen peroxide-driven APEX2 reaction with the addition of a mild detergent during biotin incubation. This adaptation leads to efficient proximity labeling in hiPSCs and more homogeneous biotinylation across single cells in MCF10As. The adapted protocol extends the use of APEX2 proximity labeling to cell lines with poor biotin permeability.

The complexity of the human transcriptome poses significant challenges for complete annotation. Traditional RNA-seq, often limited by sensitivity and short read lengths, is frequently inadequate for identifying low-abundant transcripts and resolving complex populations of transcript isoforms. Direct long-read sequencing, while offering full-length information, suffers from throughput limitations, hindering the capture of low-abundance transcripts. To address these challenges, we introduce a targeted RNA enrichment strategy, rapid amplification of cDNA ends coupled with Nanopore sequencing (RACE-Nano-Seq). This method unravels the deep complexity of transcripts containing anchor sequences—specific regions of interest that might be exons of annotated genes, in silico predicted exons, or other sequences. RACE-Nano-Seq is based on inverse PCR with primers targeting these anchor regions to enrich the corresponding transcripts in both 5' and 3' directions. This method can be scaled for high-throughput transcriptome profiling by using multiplexing strategies. Through targeted RNA enrichment and full-length sequencing, RACE-Nano-Seq enables accurate and comprehensive profiling of low-abundance transcripts, often revealing complex transcript profiles at the targeted loci, both annotated and unannotated.

Osteoarthritis (OA) is the primary cause of joint impairment, particularly in the knee. The prevalence of OA has significantly increased, with knee OA being a major contributor whose pathogenesis remains unknown. Articular cartilage and the synovium play critical roles in OA, but extracting high-quality RNA from these tissues is challenging because of the high extracellular matrix content and low cellularity. This study aimed to identify the most suitable RNA isolation method for obtaining high-quality RNA from microquantities of guinea pig cartilage and synovial tissues, a relevant model for idiopathic OA. We compared the traditional TRIzol^® method with modifications to spin column–based methods (TRIspin-TRIzol^®/RNeasy^TM, RNeasy^TM kit, RNAqueous^TM kit, and Quick-RNA^TM Miniprep Plus kit), and an optimized RNA isolation protocol was developed to increase RNA yield and purity. The procedure involved meticulous sample collection, specialized tissue processing, and measures to minimize RNA degradation. RNA quality was assessed via spectrophotometry and RT–qPCR. The results demonstrated that among the tested methods, the Quick-RNA^TM Miniprep Plus kit with proteinase K treatment yielded the highest RNA purity, with A_260:280 ratios ranging from 1.9 to 2.0 and A_260:230 ratios between 1.6 and 2.0, indicating minimal to no salt contamination and RNA concentrations up to 240 ng/μL from ⁓20 mg of tissue. The preparation, storage, homogenization process, and choice of RNA isolation method are all critical factors in obtaining high-purity RNA from guinea pig cartilage and synovial tissues. Our developed protocol significantly enhances RNA quality and purity from micro-quantities of tissue, making it particularly effective for RTqPCR in resource-limited settings. Further refinements can potentially increase RNA yield and purity, but this protocol facilitates accurate gene expression analyses, contributing to a better understanding of OA pathogenesis and the development of therapeutic strategies.

N⁶-methyladenosine (m6A) is an abundant internal mRNA modification with roles in regulating cellular and organismal physiology, including development, differentiation, and disease. The deposition of m6A is highly regulated, with various m6A levels across different environmental conditions, cellular states, and cell types. Available methods for measuring bulk m6A levels are often time-consuming, have low throughput, and/or require specialized instrumentation or data analyses. Here, we present a detailed protocol for measuring bulk m6A levels in purified poly(A) RNA samples with m6A-ELISA using a standard-based approach. Critical steps of the protocol are highlighted and optimized, including poly(A) RNA quality controls and antibody specificity testing. The protocol is fast, scalable, adaptable, and cost-effective. It does not require specialized instrumentation, training, or skills in data analysis. We have successfully tested this protocol on mRNAs isolated from budding yeast and mouse cell lines.

A covalently closed loop structure provides circular RNA (circRNA) with more stability than conventional RNAs in linear form, making circRNA an emerging tool in RNA therapeutics. The qualification and quantification of circRNA after production is critical for its design and effectiveness assessments, particularly when the following applications could be affected by byproduct RNAs. Despite PCR-based methods effectively detecting low-abundance circRNA, they are unsuitable for assessing uncircularized RNA in a mass production fraction to maintain quality control. Here, we present a straightforward protocol for evaluating uncircularized byproduct RNAs from circRNA production. This method enrolls the template-independent RNA polymerase activity to add adenine tails (polyA) to the 3' ends of a linear RNA, making it easy to distinguish trace byproducts or uncircularized RNA from a pool of mass circRNA products. With conventional linear RNA and RNase R-treated circRNA as the positive and negative controls, the purity of a circRNA preparation could be readily resolved. Regardless of circRNA production strategies, this protocol provides a reliable and practical way to ensure the consistent quality of homemade circRNAs or to recheck circRNA quality from commercial manufacturing.

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.

Traditional tissue dissociation methods for bulk- and single-cell sequencing use various protease and/or collagenase combinations at temperatures ranging from 28 to 37 °C, which cause transcriptional cell stress that may alter data interpretation. Such artifacts can be reduced by dissociating cells in cold-active proteases, but few studies have shown that this improves cell-type specific transcription, particularly in tissues hypersensitive to mechanical integrity and extracellular matrix (ECM) interactions. To address this, we have dissociated zebrafish tendons and ligaments in subtilisin A at 4 °C and compared the results with 37 °C collagenase dissociation using bulk RNA sequencing. We find that high-temperature collagenase dissociation causes general cell stress in tendon fibroblasts (tenocytes) as reported in previous studies with other cell types, but also that high temperature specifically downregulates hallmark genes involved in tenocyte specification and ECM production in vivo. Our results suggest that cold-protease dissociation reduces transcriptional artifacts and increases the robustness of RNA-sequencing datasets such that they better reflect native in vivo tissue microenvironments.

Our goal is to understand how hematopoietic stem cell precursors emerge from vessels and to visualize their settling in developmental and more definitive niches that will persist in the adult. For this, we use as a biological model the zebrafish, which offers invaluable advantages owing to its transparency and small size, allowing high-resolution imaging and investigations of the entire animal. In vertebrate species, precursors of hematopoietic stem cells emerge from arterial vessels, mainly from the ventral side of the dorsal aorta. From there, they can either reside in the underlying vascular niche and/or pass through the vein to enter the blood circulation and conquer the caudal hematopoietic tissue, a functional equivalent to the fetal liver in mammals. Here, we provide experimental details of a protocol we have recently optimized to identify, based on mRNA in situ hybridization, precursors of hematopoietic stem cells while still embedded in the aortic wall (at the embryonic stage) as well as when they reside in specific niches a few days after emergence (at the early larval stage). Our experimental approach uses RNAscope technology, which allows combining high-sensitivity mRNA detection with high-resolution fluorescence confocal imaging to achieve spatial transcriptomics. Importantly, the small size of the probes allows better penetration inside tissues, which is a significant improvement in comparison to long mRNA probes; this is an invaluable advantage for reaching deeply embedded niches such as the ones of the pronephros region in the larva and, in addition, provides an increased signal-to-noise ratio.