分子生物学

Determining the recruitment relationships of nuclear proteins is essential for understanding the mechanisms underlying nuclear complex assembly and gene regulation. A widely used method for studying recruitment is chromatin immunoprecipitation (ChIP), but it requires fixation, chromatin shearing, and specific antibodies and cannot easily resolve recruitment directionality. Other systems like lacO/LacI are restricted to a limited number of specialized cell lines containing this lacO array’s integration. To overcome these limitations, we developed a novel microscopy-based assay, CRISPR-PITA (protein interaction and telomere recruitment assay), to assess whether a nuclear protein can recruit other nuclear factors in living cells. The protein of interest is targeted to repetitive genomic loci (e.g., telomeres) using catalytically inactive Cas9 (dCas9) fused to a SunTag array, resulting in visible nuclear foci. Recruitment of endogenous proteins is evaluated by immunofluorescence. For proof-of-concept, we tested the Kaposi’s sarcoma herpesvirus (KSHV) latency-associated nuclear antigen (LANA). CRISPR-PITA revealed that LANA recruits known interactors, such as ORC2 and SIN3A, but not MeCP2. Conversely, MeCP2 recruits LANA, indicating a unidirectional recruitment relationship. Similarly, MeCP2 could recruit HDAC1, while HDAC1 could not recruit MeCP2, further supporting directional nuclear interactions. Here, we present an easy, straightforward protocol applicable to any transfectable cell line, enabling researchers to dissect recruitment dynamics at high spatial resolution. CRISPR-PITA provides a powerful, flexible, and accessible platform to interrogate recruitment directionality between nuclear proteins in their native cellular context.

In the last two decades, the field of molecular entomology has seen a shift toward next-generation sequencing techniques as a means of uncovering genetic and developmental processes. However, the standardization of methods is not well-established, and studies for insect–fungus consortia lack established protocols for advanced molecular techniques and downstream analysis compared to approaches applied in model systems involving insect–bacteria interactions. To investigate insect–microbe interactions, RNA sequencing and analysis is often used to identify genes involved in the symbiosis. But such protocols do not often consider insect–fungus systems, which vary significantly in community member abundance and/or fail to describe the details of the process from collection to data processing. This paper will introduce a comprehensive approach for RNA sequencing using two non-model insect–fungus consortia, which lack established, published protocols seen in model systems: the ambrosia beetle mutualism and cicada Massospora parasitism. The protocol includes a detailed TRIzol RNA extraction and quantification, RNA sequencing, and data processing using Nextflow pipeline software. Validation of a range of symbiotic interactions from mutualistic to parasitic is considered to justify this procedure to be utilized in a range of insect–fungus interactions with varied abundances and host interactions.

CRISPR/Cas9-based genome editing is a powerful approach for functional genomics and bioenergy research in woody plants. However, conventional single guide RNA (gRNA) strategies predominantly generate small insertions or deletions that may not fully disrupt gene function and often require extensive sequencing for mutation identification. Here, we present an optimized protocol for the efficient generation of large-fragment deletion mutants in Populus tremula × P. alba clone INRA 717-1B4 using a dual-gRNA CRISPR/Cas9 system. Co-expression of two gRNAs flanking the target region induces double-strand breaks at both sites, enabling the deletion of the intervening genomic fragment, typically larger than 50 bp. This protocol describes step-by-step procedures for gRNA design, vector construction, Agrobacterium-mediated transformation, plant regeneration, and molecular validation. Using the PtFBX230 gene as a representative target, large deletions are readily identified by conventional PCR and agarose gel electrophoresis, enabling rapid and cost-effective genotyping. This protocol can be readily adopted to other loci in poplar and related woody species and provides a robust framework for generating null alleles to support functional genomics and bioenergy-related trait engineering in woody plants.

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.

RNA detection techniques have expanded into a diverse methodological landscape spanning hybridization, amplification, imaging, and sequencing. In this review, we provide a method‑centric synthesis of the major technologies that define this landscape, emphasizing how each method’s core principle, practical strengths, and sources of variability shape its reproducibility. Beginning with foundational approaches, we trace the development of isothermal amplification, quantitative and digital PCR, microarrays, single‑molecule imaging, multiplexed spatial methods, and amplification‑free digital quantification. We then examine the transformative impact of bulk, single‑cell, long‑read, direct‑RNA, and spatial transcriptomics, as well as CRISPR‑based detection and metabolic labeling for RNA dynamics. Across these technologies, we focus on reproducibility as a defining dimension of evaluation: mature methods benefit from established standards, whereas newer approaches remain pre‑standardization and require careful, experiment‑specific controls. Rigorous method selection must be guided by the biological question, required resolution, sample constraints, and the maturity of each method’s reproducibility framework. We conclude that RNA detection methods form interconnected methodological paths of problem‑solving rather than simple replacements.

The PrimeFlow^TM assay is a flow cytometry–based method for the co-detection of RNAs and proteins in cells. When combined with cell characterization by immunophenotyping, PrimeFlow^TM can be used to simultaneously detect RNA and proteins in a cell type–specific manner in complex heterogeneous samples, offering an advantage over bulk tissue analysis methods. Here, we describe the implementation of the PrimeFlow^TM assay protocol for the detection of transgene mRNA and protein expression in spleen samples from mice treated in vivo with luciferase mRNA-lipid nanoparticles (LNPs). This protocol involves spleen tissue dissociation for cell isolation, followed by cell fixation and permeabilization to allow immunolabeling of intracellular luciferase protein. The immunophenotyping strategy is based on immunolabeling with mouse CD marker antibodies for the identification of T cells, B cells, monocytes, granulocytes/macrophages, NK cells, and non-hematopoietic cells. The RNAs of luciferase and a housekeeping gene, β-actin, are detected with sequence-specific probe sets by employing sequential oligonucleotide annealing steps and fluorescent labeling using a branched DNA (bDNA) technology. Samples are analyzed by flow cytometry. Based on our analysis, we conclude it is feasible to apply the PrimeFlow^TM approach for evaluating successful drug targeting to the cell types of interest and any potential differences in the kinetics of RNA delivery and protein expression in various tissue cells, supporting the discovery and development of RNA therapeutics.

RNA alternative splicing (AS) is an essential process that expands transcriptomic and proteomic diversity in eukaryotic cells and contributes to cellular heterogeneity across physiological and pathological conditions in humans. With the advent of single-cell RNA sequencing (scRNA-seq), it has become possible to study AS at cellular resolution, although robust and standardized analytical workflows remain to be developed. Here, we present a stepwise protocol for analyzing AS in single cells from pediatric high-grade gliomas (pHGGs) harboring the histone H3.3 lysine 27-to-methionine (H3.3K27M) mutation using SMART-Seq2 scRNA-seq data. Starting from raw sequencing reads, the workflow includes read alignment, gene-level quantification, splice junction and intron quantification, and single-nucleotide variant-based mutation detection. Gene expression–based clustering and cell-type annotation are performed by using the Seurat R package. AS analysis in tumor cells is then conducted using the MARVEL R package in combination with customized scripts to calculate percent spliced-in (PSI) values, identify variable AS events, perform dimensionality reduction, cluster cells, conduct differential AS analysis, and visualize splicing patterns. This protocol provides a reproducible and comprehensive framework for dissecting AS dynamics at single-cell resolution. It is readily adaptable to other SMART-Seq2 datasets and facilitates systematic investigation of splicing heterogeneity in diverse biological contexts.

Pathogen gene expression from host samples is often challenging to study due to low signal and high host RNA background. PCR probes have been recently used to hybridize and extract bacterial sequences from next-generation sequencing (NGS) libraries generated from in vitro and animal models of infection; however, these strategies require purchasing commercially synthesized probes that often do not capture the entire transcriptome. Transcript-capture sequencing is a novel capture approach for extracting RNA of a target bacterial species from samples in which there is substantial contamination by the host or other microbes. Biotinylated 150-base-pair DNA probes are generated in-house from bacterial DNA spanning the entire bacterial genome. Probes are hybridized to the cDNA of NGS sequencing libraries prepared from host samples to capture and enrich for bacterial-specific RNA reads before sequencing. This method results in a >200-fold increase in bacterial RNA reads from infected host samples (including in vitro, animal, and human samples) and generates complete bacterial transcriptomes with high gene coverage (>80%). Use of this protocol on infected host samples reveals a snapshot of bacterial activity during disease that may improve understanding of the physiological state of pathogens within their hosts.

Transfer RNAs (tRNAs) are important regulators of translation and cellular function. Several high-throughput sequencing methods have been developed to quantitatively analyze tRNA isoacceptors in cells. However, the strong secondary structures and extensive post-transcriptional modification of most tRNA molecules present significant challenges for many reverse transcriptases, negatively impacting sequencing library preparation and causing quantification biases. Currently, the field utilizes processive next-generation reverse transcriptases (ngRTs), such as Induro (New England Biolabs) and UltraMarathonRT (RNAConnect), to address these issues. Despite being used in multiple protocols, these commercial products face little competition and remain costly. However, non-commercial alternatives, such as the original MarathonRT (MRT), are available from gene repositories. MRT is a next-generation reverse transcriptase derived from the Eubacterium rectale group II intron maturase, which can read through RNA secondary structures and chemical modifications. Here, we present a simplified expression and purification protocol for producing highly active MRT that is stable over 1 year. This cost-effective protocol yields a heterogeneous protein preparation with no discernible competing enzymatic activities; it mitigates previously reported precipitation issues, saving one day of laboratory work and eliminating two chromatography-based purification steps. Moreover, the use of the resulting protein preparation has been verified in the mim-tRNAseq pipeline, where it was shown to perform equally to the commercial alternatives Induro and UltraMarathonRT. In addition, we have developed a simple and cost-effective assay for measuring the enzymatic activity of MRT, allowing for batch comparison.

Fluorescence in situ hybridization (FISH) can be employed to study the expression and subcellular localization of nucleic acids by using labeled antisense strands that hybridize with the target RNA or DNA molecules. Likewise, immunofluorescence antibody staining (IF) takes advantage of the specific interaction between a fluorophore-labeled antibody and its corresponding antigen. This protocol reports the combination of RNA-FISH and IF antibody staining for simultaneous detection of both RNA transcripts and proteins of interest in routine formalin-fixed paraffin-embedded (FFPE) bone marrow biopsy samples. Herein, we provide a detailed description of the methodology that we have developed and optimized to study the spatial expression of two transcripts—TGFB1 and PDGFA1—in human hematopoietic (CD45⁺) and non-hematopoietic (CD271⁺) cells in the bone marrow of patients with acute lymphoblastic leukemia (ALL).