Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes
By Mark-Anthony Bray, Shantanu Singh, Han Han, C. Davis, Blake Borgeson, C. Hartland, M. Kost-Alimova, S. Gústafsdóttir, C. Gibson, Anne E Carpenter. Nature Protocols, 2016
960 citations recorded by the source corpus.
In morphological profiling, quantitative data are extracted from microscopy images of cells to identify biologically relevant similarities and differences among samples based on these profiles. This protocol describes the design and execution of experiments using Cell Painting, which is a morphological profiling assay that multiplexes six fluorescent dyes, imaged in five channels, to reveal eight broadly relevant cellular components or organelles. Cells are plated in multiwell plates, perturbed with the treatments to be tested, stained, fixed, and imaged on a high-throughput microscope. Next, an automated image analysis software identifies individual cells and measures ∼1,500 morphological features (various measures of size, shape, texture, intensity, and so on) to produce a rich profile that is suitable for the detection of subtle phenotypes. Profiles of cell populations treated with different experimental perturbations can be compared to suit many goals, such as identifying the phenotypic impact of chemical or genetic perturbations, grouping compounds and/or genes into functional pathways, and identifying signatures of disease. Cell culture and image acquisition takes 2 weeks; feature extraction and data analysis take an additional 1–2 weeks.
Subfields: Biomedical Engineering, Biophysics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/36748de338909976f72ffbadaf097470ec040da0
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Citations in the corpus
- The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency. (2019, methodology): “The second approach uses high-content imaging of cultured cells labeled with multiple fluorescent probes to measure the effects of chemical treatment on subcellular organelles and structural features (eg, mitochondria, endoplasmic reticulum, nuclear morphology) (Bray et al., 2016).”
- Applications in image-based profiling of perturbations. (2016, methodology): “This was the first large-scale imaging experiment to carry out imagebased profiling using the Cell Painting assay [75].”
- CellProfiler 4: improvements in speed, utility and usability (2021, methodology): “Running more complex analysis workflows such as 3D segmentation and the commonly used Cell Painting assay [11] was time-consuming in CellProfiler 3.”
- Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images (2018, methodology): “The effect of treatments was imaged using the Cell Painting assay (23) which labels cell structures using six stains, including Hoechst for nuclei.”