A Python-Based Workflow for Image-Based Single-Cell Phenotypic Profiling From Fluorescence Microscopy Images

Background

Cells continuously adjust their morphology, intracellular organization, and organelle architecture in response to both intrinsic and extrinsic signals. These morphological characteristics provide informative readouts of cellular state, function, and regulation, complementing recent advances in omics technologies [1–3]. Accordingly, quantitative analysis of cellular and subcellular morphology has become increasingly important for investigating biological processes such as cell cycle progression, cell polarization, migration, and differentiation [4–8]. During mitosis, for example, cells undergo characteristic structural changes, including cell rounding, chromosome condensation, spindle assembly, and Golgi reorganization [9]. Although many of these changes are readily recognizable, others are subtle and heterogeneous, making them difficult to evaluate consistently by manual observation alone. Artificial intelligence (AI)-assisted image analysis, including CNN-based approaches for extracting phenotypic features from microscopy images, has demonstrated considerable potential for detecting such subtle morphological changes at the single-cell level [10]. These advances have created a growing need for robust and accessible workflows that convert microscopy images into quantitative, reproducible phenotypic data.

Recent advances in fluorescence microscopy and computational image analysis have greatly expanded the ability to extract quantitative information from cell images. However, image-based phenotyping typically requires multiple computational steps, including image preprocessing, cell segmentation, feature extraction, dimensionality reduction, clustering, and visualization, which are often performed using different software packages or custom scripts. As a result, establishing reproducible and easily adaptable analysis workflows remains a practical challenge for many laboratories.

To facilitate routine image-based phenotyping, we developed a Python-based workflow that integrates image loading, preprocessing, segmentation, feature extraction, multivariate analysis, and visualization within a single analysis pipeline [11]. Numerous image analysis platforms are available, including ImageJ/Fiji [12,13], napari [14], CellProfiler [15], Cellpose [16,17], and ilastik [18]. Rather than replacing these established tools, our workflow is designed to provide (i) a seamless pipeline from raw images to quantitative results within a single coding environment, (ii) concise and transparent code that is easy to understand and modify, and (iii) flexibility for customization to accommodate different imaging datasets and biological questions. The workflow combines Cellpose-based cell segmentation with quantitative feature extraction and multivariate analysis. Using fluorescence images of drug-treated HeLa cells as an example [11], this protocol provides a step-by-step tutorial covering the complete workflow, from image loading to downstream biological interpretation.