A Python-Based Workflow for Image-Based Single-Cell Phenotypic Profiling From Fluorescence Microscopy Images
基于 Python 的荧光显微图像单细胞表型谱分析流程
While advances in omics technologies have greatly improved our understanding of cellular heterogeneity, there is an increasing need for complementary approaches that capture the spatial organization and structural dynamics of cells. Image-based single-cell phenotypic profiling provides quantitative information on cell morphology and organelle organization, offering valuable insights into cellular function and regulation. Although numerous image analysis tools are available, establishing a complete analysis workflow, from image preprocessing and segmentation to feature extraction and multivariate analysis, often requires substantial computational expertise and software integration. Here, we describe a Python-based workflow for image-based single-cell phenotypic profiling from immunofluorescence microscopy images and provide a detailed protocol for its implementation. Using synchronized HeLa cells with drug-induced mitotic spindle defects as an example, the workflow covers image loading, cell segmentation, quantitative feature extraction, profile integration, dimensionality reduction, clustering, and data visualization. The protocol is accompanied by example datasets, annotated Jupyter Notebooks, and instructions for execution in either a local Python environment or Google Colab, facilitating straightforward implementation and customization. By integrating the entire analysis pipeline within a single coding environment, this workflow enables reproducible and accessible single-cell morphological profiling without requiring specialized imaging equipment or extensive programming expertise. The workflow therefore provides a practical platform for studying cell morphology, organelle organization, and cellular dynamics across a broad range of biological applications.
Quantification of Asymmetric Division in Cancer Cells via a Flow Cytometry and Cell Sorting–Based Protocol
基于流式细胞术与细胞分选的癌细胞不对称分裂定量分析方法
Asymmetric partitioning of cellular components during division contributes to generating phenotypic heterogeneity and plasticity in genetically identical cells, like bacteria or cancer cells. Traditionally, this partitioning noise has been investigated using live-cell time-lapse confocal microscopy; however, while imaging offers high spatial resolution, it suffers from low statistical throughput and labor-intensive cell tracking. To overcome these limitations, we present a protocol that combines high-throughput flow cytometry and sorting with a theoretical model to precisely quantify partitioning noise during cell division. This method details the experimental setup for measuring cellular components in dividing populations, the data acquisition pipeline, and the implementation of the mathematical framework to extract inheritance parameters. Applicable to both suspension and adherent cells, we have validated our approach against live-cell imaging of adherent colon cancer cells. Hence, this flow cytometry–based approach offers a highly scalable, accessible, and statistically robust alternative to traditional imaging. It is widely applicable across diverse cell lines and biological systems, significantly advancing our capacity to study partitioning-driven phenotypic heterogeneity.
A Batch-Processing Pipeline for RNA-seq Gene Abundance Estimation
RNA-seq 基因表达丰度估算的批量处理流程
RNA sequencing (RNA-seq) datasets provide valuable opportunities to investigate gene and transcript abundance across species, tissues, developmental stages, and experimental conditions. However, processing multiple datasets consistently remains challenging because sequencing runs may differ in library layout, read length, sequencing depth, metadata quality, and analytical settings. Existing RNA-seq workflows often depend on dedicated workflow managers, substantial computational infrastructure, or advanced bioinformatics expertise, which may limit their accessibility for routine analyses. We present a modular Bash- and Python-based batch-processing pipeline for estimating gene and isoform abundance and generating expression matrices from multiple RNA-seq runs. Using an SRA RunTable, a reference genome, and its corresponding gene annotation, the workflow automates reference preparation, sequencing-data retrieval, quality assessment, read preprocessing, alignment, abundance estimation, and post-processing. It produces gene-level TPM and FPKM matrices, individual gene- and isoform-level RSEM outputs, quality-control summaries, and integrated MultiQC reports. Configurable computational resources, sample-level status tracking, automatic download retries, selective reprocessing of failed samples, and controlled removal of intermediate files allow interrupted analyses to resume without repeating completed runs while reducing storage requirements. By combining batch processing, transparent configuration, and restartable execution in a lightweight workflow, this protocol provides an accessible approach for standardized RNA-seq abundance estimation.
Computational Approach for the Reliable Inference of Task States Encoded by Neurons Using Calcium Imaging
基于钙成像数据推断神经元所编码任务状态的可靠计算方法
Calcium imaging has become a widely used technique for measuring the activity of identified neurons during behavior. A key goal in such investigations is to determine whether, and to what extent, neurons preferentially encode one task-relevant state over another—a property referred to as neuronal selectivity. Despite the widespread use of calcium imaging for this purpose, no consensus exists on the parameter choices required to assess neuronal selectivity reliably: choices regarding neural data representation, behavioral position tracking, temporal binning, data balancing, and statistical shuffling all vary substantially across published studies. We recently developed a systematic computational approach to address this gap. Here, we describe the step-by-step protocol for implementing this computational pipeline. Starting from preprocessed calcium imaging data, the protocol guides users through all stages of the analysis: loading and aligning neural and behavioral data, deriving four metrics of neural activity, computing a selectivity index (SI), generating null distributions using four shuffling strategies, and assigning selectivity labels to individual neurons. The protocol introduces a principled, data-driven framework for evaluating the quality of any combination of parameter choices using quantitative metrics of accuracy and robustness and for identifying the optimal combination. The framework is validated using bootstrap resampling and hold-out cross-validation, and its generality is demonstrated by application to a different, regression-based approach for assessing selectivity of individual neurons. Finally, we describe the natural extension of this framework to inferences about task states encoded by populations of neurons. Thus, our protocol provides a reusable, extensible approach to identify optimal parameter settings for reliably characterizing neural encoding of two discrete task states.
Serial Cryosectioning for the Spatial Transcriptomics of Plant Tissues
用于植物组织空间转录组分析的连续冷冻切片法
Spatial transcriptomics enables genome-wide gene expression profiling while preserving tissue architecture, making it a powerful approach for studying plant developmental transitions. However, preparing small and structurally complex plant tissues for spatial transcriptomics remains technically challenging because samples must be rapidly preserved, precisely oriented, serially sectioned, and accurately positioned within the limited capture area of the Visium slides. Here, we describe an optimized workflow for cryo-embedding, serial cryosectioning, section placement, and data analysis of small plant samples for 10x Genomics Visium spatial transcriptomics. Using maize seedling shoot apices as the target, this protocol includes preparation of custom molds for optimal cutting temperature embedding, rapid fresh sample embedding, serial cryosectioning, section-position marking for Visium HD workflows, and morphological quality assessment of replicate tissue slides before transcript capture. The associated data analysis workflow includes Space Ranger processing, Seurat-based normalization and Harmony integration, anatomical domain annotation, pseudobulk and developmental trend analyses, RNA velocity, pseudotime analysis, transcription factor network analysis, single-cell reference mapping, and 3D transcriptome reconstruction. This computational workflow was developed and tested using maize Visium V1 data, but not Visium HD data. This protocol was used to generate serial spatial transcriptomes of maize shoot apices and developing leaf primordia, enabling reconstruction of gene expression transitions from the shoot apical meristem to sequential leaf developmental stages. The approach is also applicable to other small plant tissues, including Arabidopsis first true leaves and Marchantia thalli.
PIC-RNA-seq for Region-Specific Transcriptomic Analysis of Chicken Limb Bud Progenitors
基于 PIC-RNA-seq 的鸡胚肢芽祖细胞区域特异性转录组分析
Region-specific RNA sequencing is a powerful approach for investigating tissue differentiation and dynamic changes in gene expression during embryonic development. The chicken embryo has long served as an important model system in developmental biology. However, the limited availability of tissue-specific reporter lines makes region-specific RNA-seq approaches particularly valuable in this organism. Here, we applied photo-isolation chemistry-based RNA sequencing (PIC-RNA-seq) to the somatic lateral plate mesoderm (sLPM) before the emergence of limb bud progenitor cells (LPCs) and to early LPCs in chicken embryos. These analyses revealed the upregulation of multiple genes, including Hox genes, in LPCs, suggesting the initiation of their regional patterning program. This workflow enables visualization of dynamic changes in the gene expression profile of LPCs and should also be applicable to other tissues in avian embryos.
Quantitative Colocalization Analysis in Fluorescence Microscopy
荧光显微图像的共定位定量分析
Spatial organization of macromolecules is fundamental to cellular function, with colocalization providing key insights into molecular interactions and biological processes. However, quantification remains challenging due to diverse localization patterns and irregular sample geometries. Here, we present a protocol for analyzing colocalization between two fluorescent probes using coAnalyzer, a MATLAB-based software package. coAnalyzer features a user-friendly graphical interface and supports region of interest (ROI) selection, image merging, line scanning, signal isolation, scatterplot generation, and quantitative analysis. coAnalyzer enables colocalization analysis of any two fluorophores, regardless of the proteins or dyes involved. Its broad applicability across a wide range of organisms and sample types demonstrates the robustness, flexibility, and versatility of the platform.
Tapenade: Spatial Quantification of Mechanical and Genetic Fields in Dense 3D Organoids From Cell to Tissue Scale
Tapenade：从细胞到组织尺度定量分析高密度三维类器官中的力学场与遗传场空间分布
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.
Digital Quantification of Membrane DAB Immunohistochemical Staining in FFPE Cervical Cancer Tissues Using an Open-Source CellProfiler Pipeline
基于开源 CellProfiler 流程的 FFPE 宫颈癌组织膜性 DAB 免疫组化染色数字化定量
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.
Humanizing Antibodies and Nanobodies From Scratch With HuDiff
利用 HuDiff 从头开展抗体与纳米抗体的人源化设计
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.
Massively Parallel In Vitro Functional Analysis of Evolution-Derived Transcriptional Riboswitch Sequences
进化筛选获得的转录型核糖开关序列的大规模并行体外功能分析
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.
An Automated, Ventana Discovery Platform-based Imaging Workflow for Simultaneous Quantification of B Cells, Plasma Cells, and Plasmablasts in FFPE Human Tissues
基于 Ventana Discovery 平台的自动化成像流程：同步定量 FFPE 人组织中的 B 细胞、浆细胞和浆母细胞
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.
Cryo-EM Pipeline for Actin Filament End Structures
肌动蛋白丝末端结构的冷冻电镜分析流程
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.
scDynaBar: A Step-By-Step Experimental and Computational Guide for Time-Resolved CRISPR Barcoding at Single-Cell Resolution
scDynaBar：单细胞时间分辨 CRISPR 条形码技术的实验与计算分析指南
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.
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