Image-Based Profiling in Live Cells Using Live Cell Painting
Thaís Moraes-Lacerda1, Mariana Rodrigues-Da-Silva1, Shantanu Singh2
1Departamento de Bioquímica e Biologia Tecidual, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil.
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High-content analysis (HCA) is a powerful image-based approach for phenotypic profiling and drug discovery, enabling the extraction of multiparametric data from individual cells. Traditional HCA protocols often rely on fixed-cell imaging, with assays like cell painting widely adopted as standard. While these methods provide rich morphological information, the integration of live-cell imaging expands analytical capabilities by enabling the study of dynamic biological processes and real-time cellular responses. This protocol presents a simple, cost-effective, and scalable method for live-cell HCA using acridine orange (AO), a metachromatic fluorescent dye that highlights cellular organization by staining nucleic acids and acidic compartments. The assay provides visualization of distinct subcellular structures, including nuclei and cytoplasmic organelles, using a two-channel fluorescence readout. Compatible with high-throughput microscopy and computational analysis, the method supports diverse applications such as phenotypic screening, cytotoxicity assessment, and morphological profiling. By preserving cell viability and enabling dynamic, real-time measurements, this live-cell imaging approach complements existing fixed-cell assays and offers a versatile platform for uncovering complex cellular phenotypes. Key features • Builds upon Garcia-Fossa et al. [1], providing an accessible workflow for image-based profiling in live cells. • Enables phenotypic profiling and dose-response analysis of diverse perturbants, including small molecules, oligonucleotides, and nanoparticles. • Provides a live-cell framework to detect subtle, sublethal phenotypic changes, overcoming fixation assay limitations in toxicology and drug discovery. • Includes a streamlined analysis pipeline supporting efficient and reproducible interpretation of image-based data.
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