Cell Painting: a decade of discovery and innovation in cellular imaging

Abstract

Modern quantitative image analysis techniques have enabled high-throughput, high-content imaging experiments. Image-based profiling leverages the rich information in images to identify similarities or differences among biological samples, rather than measuring a few features, as in high-content screening. Here, we review a decade of advancements and applications of Cell Painting, a microscopy-based cell-labeling assay aiming to capture a cell's state, introduced in 2013 to optimize and standardize image-based profiling. Cell Painting's ability to capture cellular responses to various perturbations has expanded owing to improvements in the protocol, adaptations for different perturbations, and enhanced methodologies for feature extraction, quality control, and batch-effect correction. Cell Painting is a versatile tool that has been used in various applications, alone or with other -omics data, to decipher the mechanism of action of a compound, its toxicity profile, and other biological effects. Future advances will likely involve computational and experimental techniques, new publicly available datasets, and integration with other high-content data types.

Figure 1.

Morphological profiling using the Cell Painting assay. (a) Schematic representation of Cell Painting assay; cells are incubated and perturbed and a set of six stains is applied. (b) Images are then obtained in five channels by automated microscopy followed by nucleus and cell body segmentation. (c) Appropriate software or deep learning-based methods are applied to measure or calculate morphological features from the images. (d) After feature pre-processing, downstream analysis is performed. This includes a variety of methods, including supervised and unsupervised machine learning, to better elucidate the biological effects of a compound, such as its mechanism of action or safety profile. Adaptations of the Cell Painting assay include (e) BODIPY to mark lipid droplets in lipid-accumulating cells and (f) a coronavirus antibody against human coronavirus 229E (CoV-229E) viral protein.

Figure 2.

(a) The Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) flow chart diagramming the selection of the 90 studies included in this systematic review. Records from manual search included select articles published after the June 2023 cut-off date. (b) The growth in publications reviewed in this systematic review between 2013 and 2023, and (c) the journals where the publications were published.

Figure 3.

(a) Analysis frequency of various Cell Painting datasets used in reviewed studies. Of the 90 studies reviewed in this work (some studying more than one dataset), smaller scale datasets or in-house datasets were analyzed in at least 44 studies, Broad Institute datasets in 35 studies, and the JUMP-CP dataset was used in at least six studies, despite its recent release. “New/Others” refer to studies using datasets that were smaller in scale and/or in-house datasets that were not released publicly. (b) Academic Institutions, Government Agencies, Pharmaceutical Companies, Non-Profits who led studies evaluated in this work and/or are members of the JUMP-CP and OASIS consortium. Further details see Supplementary Table 2 and Supplementary Table 4.

Figure 4.

Summary of Convolutional Neural Network analyses of Cell Painting images, one type of deep learning network that can be used to extract image features. The input image consists of a matrix with pixel values, which can be a single cropped cell or a larger field of view. The convolution filters (smaller weight matrices) slide over the input image, detecting patterns such as edges, textures, and shapes, resulting in a feature map. An activation function (e.g., ReLU) is then applied elementwise, which introduces non-linearity into the model. Pooling then reduces the spatial dimensions of the feature maps (Step 1). The final step involves extracting high-level features from the image which can then be used for model training (Step 2).