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Current Issue in 2026
Volume: 16, Issue: 4
Bioinformatics and Computational Biology
sc3D: A Comprehensive Tool for 3D Spatial Transcriptomic Analysis
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.
Deaminase-Assisted Sequencing for the Identification of 5-glyceryl-methylcytosine
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.
How to Train Custom Cell Segmentation Models Using Cell-APP
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.
Cancer Biology
In Ovo CAM-Based Xenograft Model for Investigating Tumor Developmental Biology in Breast Cancer
Breast cancer remains one of the most prevalent and deadly malignancies affecting women worldwide. Its progression and metastatic behavior are driven by complex mechanisms. To develop more effective therapeutic strategies, it is crucial to understand tumor growth, angiogenesis, and microenvironmental interactions. Although traditional in vivo models such as murine xenografts have long been used to study tumor biology, these approaches are often time-consuming, costly, and ethically constrained. In contrast, the chick embryo chorioallantoic membrane (CAM) assay offers a rapid, cost-effective, and ethically flexible alternative for evaluating tumor development and angiogenesis. This protocol describes an in ovo CAM-based xenograft model in which human breast cancer cells are implanted onto the vascularized CAM of chick embryos. This method enables real-time evaluation of tumor growth. Furthermore, the model allows for manipulation of experimental conditions, including pharmacological treatments or genetic modifications, to study specific molecular mechanisms involved in breast cancer progression. The major advantages of this protocol lie in its simplicity, reduced cost, and capacity for high-throughput screening, making it a valuable tool for translational cancer research.
Cell Biology
Identification of the Subcompartment-Specific Mitochondrial Proteome by APEX2 Proximity Labeling in Saccharomyces cerevisiae
The cellular compartments of eukaryotic cells are defined by their specific protein compositions. Different strategies are used for the identification of the subcellular proteomes, such as fractionation by differential centrifugation of cellular extracts. The localization of mitochondrial proteins is particularly challenging, as mitochondria consist of two membranes of different protein composition and two aqueous subcompartments, the intermembrane space (IMS) and the matrix. Previous studies identified subcompartment-specific proteomes by using combinations of hypotonic swelling and protease digestion followed by mass spectrometry. Here, we present an alternative, more unbiased method to identify the proteomes of mitochondrial subcompartments by use of an improved ascorbate peroxidase (APEX2) that is targeted to the IMS and the matrix. This method allows the subcompartment-specific labeling of proteins in mitochondria isolated from cells of the baker’s yeast Saccharomyces cerevisiae, followed by their purification on streptavidin beads. With this method, the proteins located in the different mitochondrial subcompartments of yeast cells can be efficiently and comprehensively identified.
Optimized Mechanical Isolation of Mitochondria From Saccharomyces cerevisiae Preserving Atg32 for Quantitative Analysis
Mitophagy is a highly conserved process among eukaryotic cells, playing a primordial role in mitochondrial quality control and overall cellular homeostasis. In Saccharomyces cerevisiae, Atg32 is the only identified mitophagy receptor localized to the mitochondrial outer membrane, making this yeast a particularly powerful model for molecular studies of mitophagy that require the isolation of intact mitochondria. However, traditional methods for isolating mitochondria from yeast often rely on enzymatic cell wall digestion and homogenization, which can compromise the stability of mitochondrial surface proteins such as Atg32. In this protocol, we describe an optimized mechanical approach for yeast cell disruption using glass beads in a cold, protease-inhibited buffer to preserve mitochondrial integrity and facilitate the detection of Atg32. Subsequent differential centrifugation and washing steps yield mitochondrial fractions suitable for downstream biochemical analyses. This workflow eliminates enzymatic digestion steps, reduces sample variability, and allows parallel processing of multiple strains or experimental conditions. Overall, this method offers a rapid, low-cost, and reproducible alternative for crude mitochondrial isolation, ensuring excellent preservation of Atg32 and broad compatibility with quantitative and comparative studies.
Developmental Biology
Introducing Exogenous DNA Vectors Directly into Trypoxylus dichotomus Larvae Via In Vivo Electroporation
In the Japanese rhinoceros beetle Trypoxylus dichotomus, gene function studies have relied mainly on systemic larval RNA interference (RNAi), as gain-of-function techniques remain underdeveloped and germline transgenesis is impractical given the species’ approximately one-year generation time. In addition, because larval RNAi is systemic, it has been difficult to analyze the function of lethal genes. Here, we present a simple and efficient protocol for the direct introduction of exogenous DNA into T. dichotomus larvae via in vivo electroporation. This protocol includes optimized procedures for adult breeding and egg collection, as well as a rigorously parameterized electroporation technique that delivers a piggyBac transposon vector into region-specific larval tissues. Within one day after electroporation, treated larvae exhibit mosaic expression of a reporter gene, enabling rapid tissue-specific functional analysis without the need to establish stable germline transgenic lines. Moreover, the key promoter used in this system (T. dichotomus actinA3 promoter) is effective across diverse insect species, indicating that the method can be readily adapted to other non-model insects. Overall, this electroporation-based approach provides a valuable gain-of-function tool for T. dichotomus and potentially many other insect species.
Drug Discovery
Orthogonal Temperature-Related Intensity Change and Time-Resolved Förster Resonance Energy Transfer High-Throughput Screening Platform for the Discovery of SLIT2 Binders
SLIT2 is a secreted glycoprotein implicated in axon guidance, immune modulation, and tumor biology, whose extracellular and glycosylated nature can complicate conventional biophysical screening workflows. Here, we provide a complete, step-by-step protocol for an orthogonal high-throughput discovery pipeline that integrates temperature-related intensity change (TRIC) as a solution-based primary binding screen with time-resolved Förster resonance energy transfer (TR-FRET, homogeneous time-resolved fluorescence format) as a functional assay for inhibition of the SLIT2–ROBO1 interaction. The workflow is designed to be fast and convenient, uses low reaction volumes and low nanomolar protein concentrations to minimize material use, and includes built-in quality control steps to support reproducible hit triage. In TRIC (NanoTemper Dianthus), binding is detected as temperature-dependent fluorescence intensity changes of a labeled target protein under an infrared (IR)-mediated thermal gradient, enabling immobilization-free detection of small-molecule interactions and instrument-assisted filtering of autofluorescent, quenching, or aggregating compounds. Candidate binders are advanced to multi-point TRIC/microscale thermophoresis (MST) measurements on Monolith X to determine binding affinity (Kd). In TR-FRET, disruption of SLIT2–ROBO1 association is quantified by changes in the ratiometric 665/620 nm emission readout, measured with a time delay to suppress short-lived background fluorescence, enabling concentration-response analysis and reporting of relative IC50 values (including partial inhibition behavior where applicable). Although presented using the SLIT2–ROBO1 extracellular interaction as a representative model system, this orthogonal screening strategy is designed to be adaptable to other extracellular protein-protein interactions where minimizing immobilization artifacts and fluorescence interference is critical.
Immunology
In Vitro Model of Cytokine-Induced Inflammatory 3T3-L1 Adipocytes Mimicking Obesity
Obesity is a risk factor for many diseases. The 3T3-L1 cell line is often used to obtain mature adipocytes, but these lack the inflammatory phenotype observed in obesity. Using a cocktail of cytokines that mimics the secretome of macrophages found in the inflammatory adipose tissue, we developed a protocol for obtaining mature inflammatory adipocytes. This model was validated at gene (RT-qPCR) and protein levels (multiplex adipokine array) as we found a decrease of adipogenic markers (C/EBPα, PPARУ, adiponectin, and CD36) and an increase of pro-inflammatory cytokines (IL-6, IL-1β, CXCL1, CXCL10, TNF-α, ICAM-1, and lipocalin-2). We provide a relevant in vitro model for studying the impact of low-grade chronic inflammation caused by obesity and its downstream effects on metabolic disorders and tumor microenvironments.
Mechanobiology
Time-Lapse Into Immunofluorescence Imaging Using a Gridded Dish
Time-lapse into immunofluorescence (TL into IF) imaging combines the wealth of information acquired during live-cell imaging with ease of access for static immunofluorescence markers. In the field of mechanobiology, connecting live and static imaging to visualize cell biology dynamics is often troublesome. For instance, nuclear blebs are deformations of the nucleus that often rupture spontaneously, leading to changes in the molecular composition of the nucleus and the nuclear bleb. Current techniques to connect cellular dynamics and their downstream effects via live-cell imaging, followed by immunofluorescence, often require third-party analysis programs or stage position measurements to accurately track cells. This protocol simplifies the connection between live and static imaging by utilizing a gridded imaging dish. In our protocol, cells are plated on a dish with an engraved coordinate plane. Individual cells are then matched from when the time-lapse ends to the immunofluorescence images simply by their known coordinate location. Overall, TL into IF offers a straightforward method for connecting dynamic live-cell with static immunofluorescence imaging, in an easy and accessible tool for cell biologists.
Microbiology
Quick and Cheap: Optimized Purification and Concentration of Bacteriophages Produced in Rich Culture Media
This protocol describes an easy, quick, cheap, and effective method for the purification and concentration of bacteriophages (phages) produced in rich culture media, meeting the quality criteria required for structural analyses. It is based on a tube dialysis system that replaces the classical but expensive and tedious density gradient ultracentrifugation step. We developed this protocol for the Oenococcus oeni bacteriophage OE33PA from its amplification to imaging by negative stain electron microscopy (NS-EM). The host bacterium, O. oeni, is a lactic acid bacterium that lives in harsh oenological ecosystems and grows only in rich and complex media such as Man–Rogosa–Sharpe (MRS) or fruit juice-based media in laboratory conditions. This raises experimental challenges in pure and concentrated phage preparations for further uses such as structure-function studies.
Molecular Biology
Step-by-Step Protocol for In Situ Profiling of RNA Subcellular Localization Using TATA-seq
Membrane-less organelles play essential roles in both physiological and pathological processes by compartmentalizing biomolecules through phase separation to form dynamic hubs. These hubs enable rapid responses to cellular stress and help maintain cellular homeostasis. However, a straightforward and efficient method for detecting and illustrating the distribution and diversity of RNA species within membrane-less organelles is still highly sought after. In this study, we present a detailed protocol for in situ profiling of RNA subcellular localization using Target Transcript Amplification and Sequencing (TATA-seq). Specifically, TATA-seq employs a primary antibody against a marker protein of the target organelle to recruit a secondary antibody conjugated with streptavidin, which binds an oligonucleotide containing a T7 promoter. This design enables targeted, in situ reverse transcription of RNAs with minimal background noise, a key advantage further refined during data analysis by subtracting signals obtained from a parallel IgG control experiment. The subsequent T7 RNA polymerase-mediated linear amplification ensures high-fidelity RNA amplification from low-input material, which directly contributes to optimized sequencing metrics, including a duplication rate of no more than 25% and a mapping ratio of approximately 90%. Furthermore, the modular design of TATA-seq provides broad compatibility with diverse organelles. While initially developed for membrane-less organelles, the protocol can be readily adapted to profile RNA in other subcellular compartments, such as nuclear speckles and paraspeckles, under both normal and pathogenic conditions, offering a versatile tool for spatial transcriptomics.
