生物信息学与计算生物学

Chemotherapy-resistant persister cells are a major driver of cancer recurrence, yet their epigenetic basis remains poorly characterized. This protocol describes a computational pipeline for identifying DNA-binding factors (DBFs) that are enriched in accessible chromatin that collectively define a persister cell signature (PCS). Starting from single-nucleus ATAC-seq (snATAC-seq) data processed through the 10x Genomics CellRanger ARC pipeline, this protocol covers (1) the creation of a Seurat/Signac object with ATAC peaks, (2) the optional integration of DNA-binding data from the ReMap2022 database as a per-cell chromatin module assay, (3) differential accessibility analysis across clinically defined comparison groups, and (4) identifying and defining the top enriched DBFs as the PCS. This approach is applicable to any snATAC-seq dataset in which cells can be grouped by clinical response, treatment status, or resistance phenotype.

Accurate quantification of plant disease severity is essential for evaluating host–pathogen interactions and assessing the effectiveness of disease management strategies. Traditional visual scoring methods and manual estimation of infected tissue are widely used but are often subjective and prone to observer bias. Digital image analysis offers an objective alternative by enabling automated identification and quantification of symptomatic plant tissues based on color and spatial characteristics.

Here, we present a MATLAB-based image processing protocol for differentiating diseased and healthy plant tissue from digital leaf images. The workflow involves acquisition of standardized leaf images, conversion of RGB images into hue-saturation-value (HSV) color space, segmentation of diseased tissue using defined HSV thresholds, refinement of the segmented mask through morphological operations, and extraction of the whole leaf area. The protocol then calculates the diseased area and total leaf area in pixels and computes the percentage of infected tissue. The method uses MATLAB together with the Image Processing Toolbox and can be implemented using simple scripts. This protocol enables rapid and reproducible quantification of disease severity in plant leaves exhibiting visually distinct symptoms such as necrotic lesions or blight patches. By minimizing observer bias and providing quantitative measurements of infected area, the protocol offers a practical and reproducible approach for plant disease phenotyping and evaluation of disease management strategies across diverse plant-pathogen systems where diseased tissues can be clearly distinguished from healthy tissues under reasonably controlled imaging conditions.

Estimation of model accuracy (EMA) is a critical step in protein structure prediction, enabling the ranking and selection of models in the absence of experimental structures. EMA methods aim to function independently of modeling approaches, ensuring broad applicability across diverse prediction workflows. Recent state-of-the-art EMA methods often improve estimation accuracy by incorporating consensus information from model pools, multiple sequence alignments (MSAs), structural templates, or protein language model representations. However, these strategies typically incur substantial computational cost or rely on information derived from the modeling process itself, which may introduce bias and compromise the independence of the assessment. This protocol describes the use of MViewEMA for global accuracy estimation of protein complex models from a single input structure. MViewEMA extracts residue–residue interaction features from complementary micro-, meso-, and macro-environmental perspectives and integrates multi-scale structural representations through a multi-view representation learning framework to predict global confidence scores. The protocol provides detailed procedures for input structure preparation, feature extraction, model inference, and global confidence score output, together with a tutorial for using the MViewEMA web server. The protocol provides a workflow based solely on structural information from the input model, achieving a balance between computational efficiency and estimation accuracy. It enables large-scale evaluation and selection of predicted models for protein structure prediction and downstream structural analysis applications.

Super-resolution fluorescence microscopy enables the visualization of protein structures at nanometer resolution, providing insights into receptor organization on the plasma membrane that are essential for the development and optimization of immunotherapies. In this context, monoclonal antibodies are employed, which typically bind only a subset of available membrane receptors, due to steric hindrance or otherwise limited epitope accessibility, to quantify the accessible targets. These accessible targets, rather than the total receptor density, are critical for determining therapeutic efficacy. Here, we present a simplified, robust protocol to quantify antibody-accessible endogenous receptors using monoclonal antibodies directly labeled with fluorescent dyes in combination with total internal reflection fluorescence (TIRF) direct stochastic optical reconstruction microscopy (dSTORM). The method employs optimized labeling and fixation conditions to preserve the native receptor distribution, enabling precise quantification of accessible receptors and their stoichiometry at single-molecule resolution. Omitting secondary antibodies and minimizing fixation-induced artifacts prevents artificial clustering and maintains the physiological binding pattern of therapeutic antibodies. The standardized workflow delivers therapy-relevant information about receptor accessibility and organization underlying therapeutic antibody binding, thereby advancing the mechanistic understanding of immunotherapy resistance and personalized treatment strategies across diverse membrane protein targets.

RNA sequencing (RNA-seq) has revolutionized transcriptomics, ribosome footprinting, and polysome profiling, providing a wealth of data. Many RNA-based omics typically remove ribosomal RNA (rRNA) or select for messenger RNA (mRNA) prior to sequencing, thereby enriching reads that map to the translationally active part of the transcriptome. Prokaryotic mRNA lacks the 3′ polyadenylated tail, which excludes the use of poly(A)-based selection methods. While commercial rRNA depletion products exist for prokaryotes, their proprietary nature and potential inefficiency with non-model organisms are factors that may limit broad-scale application. To mitigate this issue, we designed DepStep, a consolidated workflow for one-step rRNA depletion using species-specific biotinylated antisense probes for selective hybridization and removal of the target rRNA molecules. As a proof-of-concept, RNA-seq libraries of the psychrophilic gram-negative bacterium Shewanella glacialimarina TZS-4_T were prepared using both DepStep and a commercial rRNA depletion kit for gram-negative bacteria, to which DepStep was benchmarked. DepStep compares favorably to the commercial depletion kit; it removes >98.6% of the rRNA content in the sample, resulting in sequencing libraries where the coding DNA sequence (CDS) reads account for >80% of the total read count. Importantly, DepStep’s cost-per-sample is three times lower than the commercial kit, establishing DepStep as a simple yet cost-effective alternative to commercial solutions.

Studying actin-filament assembly into distinct subcellular structures can provide insights into both physiological cellular processes and the mechanisms of disease. However, there are a limited number of tools that can quantify the organization and abundance of different actin structures from confocal microscopy images of cells expressing Lifeact or fixed and stained with phalloidin. Filamentous actin segmentation tool (FAST) is a deep learning model trained with a unique approach of antibody-assisted annotation, resulting in accurate and efficient quantification of distinct classes of actin structures. Here, we detail the protocol for using antibody-assisted annotation to generate datasets that could be applied to train machine learning models. Additionally, we provide step-by-step instructions for applying FAST on phalloidin-stained or live-cell confocal imaging data using our pretrained model. FAST is open source and freely available, with user-friendly notebooks that enable quantification of different classes of actin structure, without the need for structure-specific antibodies. As such, FAST can be a practical tool for researchers investigating the role of cytoskeletal organization in a range of processes.

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.

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.

The canonical role of transfer RNAs (tRNAs) in protein synthesis has been extensively characterized; however, recent studies have uncovered novel functions for tRNA as a mediator of long-distance signaling in plants. Several studies have identified dicistronic tRNA-mRNA transcripts that contain a tRNA gene and an adjacent protein-coding gene (PCG) that are transcribed as a single unit. These transcripts are associated with RNA systemic mobility through the plant’s vascular tissues, potentially acting as non-cell-autonomous signaling messengers in coordinating development and stress responses. Here, we report a computational pipeline to detect dicistronic tRNA-mRNA transcripts from short-read next-generation RNA-sequencing datasets; to our knowledge, this is the only established pipeline for the systematic identification of such candidates in plants. The dicistronic RNA transcript version 2 (v2) described here improves on the earlier version DiRT v1 by expanding the repertoire of dicistronic transcripts detected to include tRNA-like structures (TLS) as well as functional tRNAs, which were already supported in the pipeline. The updated protocol also includes detection of dicistronic tRNA or TLS sequences within genomic features such as untranslated regions (UTRs). The accurate detection of both tRNAs and UTR-embedded tRNA-like sequences (TLS) is critical, as these RNA structures have been reported to function as mediators of long-distance RNA mobility. Furthermore, as NGS datasets are prone to sequencing artifacts and potential DNA contamination, we improved the pipeline’s statistical robustness by including read coverage of flanking intronic regions as a baseline control. To account for potential DNA contamination during RNA-seq library preparation, detected tRNA-mRNA transcripts are deemed as putatively dicistronic only if the coverage of their intergenic region is significantly higher (Student’s t-test, FDR < 0.05) than flanking intronic regions. Furthermore, the updated pipeline allows this statistical test to be applied to intronless and single-intron genes. Using this updated protocol, we identified novel tRNA and TLS dicistronic transcripts in both grapevine (Vitis spp. Ruggeri 140) and Arabidopsis thaliana datasets and validated in vitro using RT-PCR. We provide a fast and reliable method to detect dicistronic transcripts that can be applied to any short-read RNA-sequencing dataset, fast-tracking the functional characterization of these newly emerging transcripts.