生物信息学与计算生物学

Whole-mount 3D imaging of multilayered biological tissues enables quantitative analysis of cell states and organization in their spatial context. However, extracting unbiased and meaningful quantitative information from dense, multilayered samples remains challenging due to imaging artifacts, increased density, and limited signal-to-noise ratio. Open source bioimage analysis workflows tailored to this type of analysis are scarce, and analysis bottlenecks like image curation or cell segmentation are seldom available without coding expertise. Here, we present a step-by-step computational protocol for the analysis of dense 3D organoid datasets using the Tapenade (Thorough Analysis PipEliNe for Advanced DEep imaging) workflow. Starting from multichannel image stacks, the protocol guides users through software installation, registration and fusion of multi-view datasets, preprocessing, and nuclei segmentation. It further details the generation of quantitative outputs, including morphometric measurements, deformation fields, and spatial correlation analyses. The workflow can be executed through open-source Python scripts or user-friendly Napari interfaces, allowing interactive parameter tuning and 3D visualization at each stage. This pipeline provides an accessible and modular framework for nonspecialist users to perform reproducible, multiscale quantitative analysis of 3D organoid images, while retaining flexibility for advanced users to customize individual steps.

Immunohistochemistry (IHC) is a highly specific and widely used laboratory technique for assessing protein localization and expression in tissue samples. Interpretation of 3,3’ diamino benzidine (DAB)-based IHC is often based on observer-dependent manual scoring or traditional imaging software, which may show variability in DAB staining quantification. Furthermore, conventional image analysis tools often face limitations in precisely defining cell boundaries and quantifying membrane-specific signals. In this study, we present a standardized image analysis workflow using CellProfiler, an open-source software for image analysis for the quantification of membrane staining intensity in IHC images captured from slides prepared using formalin-fixed paraffin-embedded (FFPE) human cervical cancer tissue sections. The image analysis workflow was demonstrated using ASCT2 (SLC1A5), a membrane-localized amino acid transporter, as a representative biomarker for membrane-associated protein expression. This protocol involves image preprocessing, object identification, segmentation, and intensity measurement modules to distinguish cell membranes from cytoplasmic regions, enabling automated quantification of membrane intensity signals. The CellProfiler pipeline demonstrated improved accuracy in cell boundary identification and quantification of membrane-specific staining intensity. This is a rapid quantification process, since processing of each image only takes a few seconds; therefore, the analysis for 100 images can be performed within 10–15 min. This segmentation and quantification strategy is applicable to other membrane-based biomarkers after appropriate optimization of segmentation parameters. Following further minor modifications to the object identification modules, this pipeline can be used to detect and quantify cytoplasm- or nuclei-localized DAB-IHC markers across different tissue types. Overall, this protocol provides a standardized, user-friendly, and reproducible workflow for quantitative IHC image analysis that can be broadly applied to the study of protein biomarkers of different localizations, such as nuclei, cytoplasm, and cell membranes from different tissue types.

Antibody (Ab) and nanobody (Nb) humanization is essential for reducing immunogenicity in therapeutic applications. HuDiff is an adaptive autoregressive diffusion approach that generates humanized antibodies and nanobodies from scratch using only complementarity-determining region sequences as input, eliminating the need for preexisting human templates. The method follows a two-stage training pipeline: pretraining on human antibody sequences to learn framework region patterns, followed by fine-tuning on target-species sequences. HuDiff-Ab processes paired heavy and light chains for conventional antibodies, while HuDiff-Nb can incorporate a specialized inpainting mode to preserve critical nanobody framework residues. This protocol provides a complete step-by-step guide for implementing HuDiff, covering data preparation, model training, and sequence generation.

Riboswitches are structured non-coding RNA elements that regulate gene expression in response to small molecules; they serve as valuable systems in both public health and biophysical research by elucidating principles around RNA–ligand interactions, structure, and cellular function. Traditional approaches to studying riboswitches have relied on low-throughput techniques such as reporter assays or gel electrophoresis analysis of transcriptional products, which are limited in scalability. In this study, we present a high-throughput protocol to characterize the transcriptional activity of nearly 2,000 natural variants of the fluoride riboswitch in in vitro transcription. Starting with bioinformatics, we compiled a comprehensive dataset of riboswitch variants and then employed massive parallel oligonucleotide synthesis to generate an oligo pool of the riboswitch library. This pool was transcribed in vitro, converted into an Illumina-compatible next-generation sequencing (NGS) library, and analyzed to identify transcriptionally active riboswitch candidates. The workflow integrates natural riboswitch bioinformatic acquisition into a quantitative readout in a single streamlined pipeline, enabling large-scale exploration of transcriptional riboswitch function. This protocol offers a scalable method for mapping genotype-to-function relationships across transcriptional riboswitch families, accelerating the identification of functional variants for desired applications.

Accurate, sensitive quantification of B-lineage cells is critical for pharmacodynamic evaluation of B cell–targeted therapies in lupus nephritis (LN) clinical trials. While high-dimensional discovery platforms offer broad profiling, they often lack the sensitivity, quantitative rigor, and throughput needed for precise cell enumeration in renal trial needle biopsies. Traditional immunostaining is hampered by CD20-directed therapeutic masking or downregulation, inadequate sensitivity of CD19 in FFPE tissue, and confounding renal tubular CD138 expression. This protocol details an automated, fit-for-purpose, 5-plex sequential tyramide signal amplification (TSA)-based immunofluorescence assay (CD38, CD79a, CD19, Ki-67, CD138) developed on the Ventana Discovery Ultra platform for deployment on single tissue sections. The workflow anchors B-cell detection on CD79a to ensure sensitivity and utilizes CD38 as an obligate co-marker for CD138+ antibody-secreting cells (ASCs) to definitively exclude the CD138+ epithelial background. Following acquisition via fluorescence whole-slide imaging, a digital analysis pipeline utilizing InstanSeg-based automated segmentation rigorously classifies cell phenotypes to generate precise spatial densities (cells/mm²). This validated protocol maximizes data yield from scarce clinical biopsies while providing high-precision quantitative monitoring of longitudinal therapeutic depletion in the renal microenvironment.

Actin filaments undergo dynamic growth and disassembly at their ends, regulated by many actin-binding proteins. However, structural analysis of filament end dynamics has been challenging due to the low abundance of filament ends in cryo-electron microscopy (cryo-EM) micrographs, their intrinsic polymorphisms, and the diversity and flexibility of end-binding proteins. Here, we describe a standardized cryo-EM protocol for determining actin filament end structures. First, short actin filaments are generated either biochemically using capping or severing proteins or mechanically through shearing. Filaments are then vitrified under conditions optimized for each specific end-binding protein. We describe data collection parameters using a 300 kV Titan Krios G3i microscope, including optimized grid preparation and imaging settings. Finally, we present a data processing pipeline for filament end structure determination based on machine learning–based particle picking, masking, and sorting strategies. This protocol has enabled the determination of multiple high-resolution structures of free, capped, elongating, and depolymerizing actin filament ends, and we further discuss considerations for extending this approach to other end-binding proteins.

CRISPR-Cas9 barcoding technologies enable cells to record molecular events as permanent genetic changes that can be read out retrospectively. This protocol describes the implementation of a CRISPR-based recording system that gradually accumulates mutations over extended periods and is compatible with standard single-cell RNA sequencing (scRNA-seq) workflows. By temporally regulating CRISPR activity, the system generates mutational barcodes that can be captured together with individual cell transcriptomes. These barcodes are subsequently decoded using computational reconstruction approaches to infer temporal information, enabling the joint analysis of cellular states and time-resolved molecular histories. This approach provides a single-cell-compatible framework for studying dynamic biological processes in heterogeneous mouse embryonic stem cell (mESC)-derived systems, with potential extension to other biological systems.

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.