生物信息学与计算生物学

Spatial proteomics enables the mapping of protein distribution within tissues, which is crucial for understanding cellular functions in their native context. While spatial transcriptomics has seen rapid advancement, spatial proteomics faces challenges due to protein non-amplifiability and mass spectrometry sensitivity limitations. This protocol describes a sparse sampling strategy for spatial proteomics (S4P) that combines multi-angle tissue strip microdissection with deep learning–based image reconstruction. The method achieves whole-tissue slice coverage with significantly reduced sampling requirements, enabling mapping of over 9,000 proteins in mouse brain tissue at 525 μm resolution within 200 h of mass spectrometry time. Key advantages include reduced sample processing time, deep proteome coverage, and applicability to centimeter-sized tissue samples.

Organelle abundance is a key microscopic readout of organelle formation and, in many cases, function. Quantification of organelle abundance using confocal microscopy requires estimating their area based on the fluorescence intensity of compartment-specific markers. This analysis usually depends on a user-defined intensity threshold to distinguish organelle regions from the surrounding cytoplasm, which introduces potential bias and variability. To address this issue, we present a machine learning–assisted algorithm that allows for the quantification of organelle density using the open-source Fiji platform and WEKA segmentation. Our method enables the automated quantification of organelle number, area, and density by learning from training data. This standardizes threshold selection and minimizes user intervention. We demonstrate the utility of this approach for both membrane and non-membrane organelles, such as peroxisomes, lipid droplets, and stress granules, in human cells and whole fish samples.

RNA-binding protein (RBP)–RNA interactions are fundamental for gene regulation and cellular homeostasis. Ataxin-2 is an RBP that has been shown to play an instrumental role in pathophysiological processes by binding to mRNA. Methods such as RNA immunoprecipitation (RIP), cross-linking immunoprecipitation (CLIP), and their variants can be used to study the interactions between Ataxin-2 and its targets, although their high sample requirements and labor-intensive workflows can limit their widespread use. RNA editing-based approaches, such as targets of RBPs identified by editing (TRIBE), provide effective alternatives. TRIBE enables transcriptome-wide identification of RBP targets by inducing site-specific adenosine-to-inosine (A-to-I) editing, which is subsequently detected through high-throughput RNA sequencing in both in vivo and in vitro systems. Compared to in vivo models, cell lines offer a rapid and flexible experimental design. Drosophila S2 cells are a commonly used insect cell line to investigate RNA–protein dynamics and serve as a versatile platform for studying RBP function. Here, we describe a protocol used for identifying RNA targets of Ataxin-2, a versatile RBP involved in post-transcriptional and translational regulation, in S2 cells using TRIBE. This method allows rapid, efficient, and reliable identification of Ataxin-2-associated RNA targets and can be readily applied to other RBPs.

Evaluating single-domain antibody cooperativity is essential for developing potent, escape-resistant antiviral biologics. Here, we present a protocol that reproducibly quantifies functional synergy between neutralizing nanobody pairs in standardized viral infectivity assays. Controlled automated liquid handling prepares two-dimensional concentration matrices, minimizing pipetting variance and systematic error. Neutralization data are fitted using quantitative models that independently estimate potency, cooperativity, and efficacy to distinguish additive, synergistic, and antagonistic effects between nanobody pairs. Replicated measurements enable statistically interpretable parameter estimates, supporting robust evaluation of combinatorial nanobody therapeutics with commonly available equipment and open-source analysis tools. This framework is broadly applicable to assessing cooperative effects among other classes of binding or inhibitory molecules, facilitating systematic discovery of synergistic combinations.

The morphology of single-neuron axonal projections is critical for deciphering neural circuitry and information flow in the brain. Yet, manually reconstructing these complex, long-range projections from high-throughput whole-brain imaging data remains an exceptionally labor-intensive and time-consuming task. Here, we developed a points assignment-based method for axonal reconstruction, named PointTree. PointTree enables the precise identification of the individual axons from densely packed axonal population using a minimal information flow tree model to suppress the snowball effect of reconstruction errors. In this protocol, we have elaborated on how to configure the required environment for PointTree software, prepare suitable data for it, and run the software. This protocol can assist neuroscience researchers in more easily and rapidly obtaining the reconstruction results of neuronal axons.

DNA epigenetic modifications play crucial roles in regulating gene expression and cellular function across diverse organisms. Among them, 5-glyceryl-methylcytosine (5gmC), a unique DNA modification first discovered in Chlamydomonas reinhardtii, represents a novel link between redox metabolism and epigenetic regulation. Accurate genome-wide detection of 5gmC is essential for investigating its biological functions, yet no streamlined method has been available. Here, we present deaminase-assisted sequencing (DEA-seq), a simple and robust approach for base-resolution mapping of 5gmC. DEA-seq employs a single DNA deaminase that efficiently converts unmodified cytosines (C) and 5-methylcytosine (5mC) into uracils or thymines, while leaving 5gmC intact. This selective resistance generates a clear sequence signature that enables precise identification of 5gmC sites across the genome. The method operates under mild reaction conditions and is compatible with low-input DNA, minimizing sample loss and improving detection sensitivity. Overall, DEA-seq provides an accessible, efficient, and highly accurate protocol for profiling 5gmC, offering clear advantages in workflow simplicity, DNA integrity, and analytical performance.

Serial spatial omics technologies capture genome-wide gene expression patterns in thin tissue sections but lose spatial continuity along the third dimension. Reconstructing these two-dimensional measurements into coherent three-dimensional volumes is necessary to relate molecular domains, gradients, and tissue architecture within whole organs or embryos. sc3D is an open-source Python framework that registers consecutive spatial transcriptomic sections, interpolates bead coordinates in three dimensions, and stores the result in an AnnData object compatible with Scanpy. The workflow performs slice alignment, 3D reconstruction, optional downsampling, and interactive visualization in a napari-sc3D-viewer, enabling virtual in situ hybridization and spatial differential gene expression analysis. We tested sc3D on Slide-seq and Stereo-seq datasets, including E8.5 and E16.5 mouse embryos, recovering continuous tissue morphologies, cardiac anatomical markers, and the expected anterior–posterior gradients of Hox gene expression. These results show that sc3D allows reproducible reconstruction and analysis of volumetric spatial omics data across different samples and experimental platforms.

The deep learning revolution has accelerated discovery in cell biology by allowing researchers to outsource their microscopy analyses to a new class of tools called cell segmentation models. The performance of these models, however, is often constrained by the limited availability of annotated data for them to train on. This limitation is a consequence of the time cost associated with annotating training data by hand. To address this bottleneck, we developed Cell-APP (cellular annotation and perception pipeline), a tool that automates the annotation of high-quality training data for transmitted-light (TL) cell segmentation. Cell-APP uses two inputs—paired TL and fluorescence images—and operates in two main steps. First, it extracts each cell’s location from the fluorescence images. Then, it provides these locations to the promptable deep learning model μSAM, which generates cell masks in the TL images. Users may also employ Cell-APP to classify each annotated cell; in this case, Cell-APP extracts user-specified, single-cell features from the fluorescence images, which can then be used for unsupervised classification. These annotations and optional classifications comprise training data for cell segmentation model development. Here, we provide a step-by-step protocol for using Cell-APP to annotate training data and train custom cell segmentation models. This protocol has been used to train deep learning models that simultaneously segment and assign cell-cycle labels to HeLa, U2OS, HT1080, and RPE-1 cells.

Pinpointing causal genes for complex traits from genome-wide association studies (GWAS) remains a central challenge in crop genetics, particularly in species with extensive linkage disequilibrium (LD) such as rice. Here, we present CisTrans-ECAS, a computational protocol that overcomes this limitation by integrating population genomics and transcriptomics. The method’s core principle is the decomposition of gene expression into two distinct components: a cis-expression component (cis-EC), regulated by local genetic variants, and a trans-expression component (trans-EC), influenced by distal genetic factors. By testing the association of both components with a phenotype, CisTrans-ECAS establishes a dual-evidence framework that substantially improves the reliability of causal inference. This protocol details the complete workflow, demonstrating its power not only to identify causal genes at loci with weak GWAS signals but also to systematically reconstruct gene regulatory networks. It provides a robust and powerful tool for advancing crop functional genomics and molecular breeding.

Expansion microscopy (ExM) is an innovative and cost-effective super-resolution imaging technique that enables nanoscale visualization of biological structures using conventional fluorescence microscopes. By physically enlarging biological specimens, ExM circumvents the diffraction limit and has become an indispensable tool in cell biology. Ongoing methodological advances have further enhanced its spatial resolution, labeling versatility, and compatibility with diverse sample types. However, ExM imaging is often hindered by sample drift during image acquisition, caused by subtle movements of the expanded hydrogel. This drift can distort three-dimensional reconstruction, compromising both visualization accuracy and quantitative analysis. To overcome this limitation, we developed 3D-Aligner, an advanced and user-friendly image analysis software that computationally corrects sample drift in fluorescence microscopy datasets, including but not limited to those acquired using ExM. The algorithm accurately determines drift trajectories across image stacks by detecting and matching stable background features, enabling nanometer-scale alignment to restore structural fidelity. We demonstrate that 3D-Aligner robustly corrects drift across ExM datasets with varying expansion factors and fluorescent labels. This protocol provides a comprehensive, step-by-step workflow for implementing drift correction in ExM datasets, ensuring reliable three-dimensional imaging and quantitative assessment.