Cell Painting has been used to identify small-molecule mechanisms of action, study the impact of overexpressing cancer mutations, and discover new bioactive mechanisms, among many other applications ( Wawer et al., 2014 Rohban et al., 2017 Caicedo et al., 2018 Simm et al., 2018 Christoforow et al., 2019 Pahl and Sievers, 2019 Hughes et al., 2020).
One unbiased assay, called Cell Painting, stains for various cellular compartments and organelles using nonspecific and inexpensive reagents ( Gustafsdottir et al., 2013). Image-based profiling assays are increasingly being used to quantitatively study the morphological impact of chemical and genetic perturbations in various cell contexts ( Caicedo et al., 2016 Scheeder et al., 2018). These traditional approaches limit the ability to scale to large perturbation libraries such as candidate compounds in academic and pharmaceutical screening centers.
CELLPROFILER CELL INTENSITY MANUAL
ATP assays) or multiple in combination via FACS-based or image-based analyses, which involves a manual gating approach, complicated staining procedures, and significant reagent cost. Cell health is normally assessed by eye or measured by specifically targeted reagents, which are either focused on a single cell health parameter (e.g. For example, certain perturbations impact cell health by stalling cells in specific cell cycle stages, increasing or decreasing proliferation rate, or inducing cell death via specific pathways ( Markowetz, 2010 Szalai et al., 2019). Perturbing cells with specific genetic and chemical reagents in different environmental contexts impacts cells in various ways ( Kitano, 2002). Our approach can be used to add cell health annotations to Cell Painting datasets. We provide a web app to browse predictions. For Cell Painting images from a set of 1500+ compound perturbations across multiple doses, we validated predictions by orthogonal assay readouts. We hypothesized that these models can be applied to accurately predict cell health assay outcomes for any future or existing Cell Painting dataset. We found that simple machine learning algorithms can predict many cell health readouts directly from Cell Painting images, at less than half the cost. In matched CRISPR perturbations of three cancer cell lines, we collected both Cell Painting and cell health data. We then tested an approach to predict multiple cell health phenotypes using Cell Painting, an inexpensive and scalable image-based morphology assay.
We developed two customized microscopy assays, one using four targeted reagents and the other three targeted reagents, to collectively measure 70 specific cell health phenotypes including proliferation, apoptosis, reactive oxygen species, DNA damage, and cell cycle stage. These readouts reveal toxicity and antitumorigenic effects relevant to drug discovery and personalized medicine. Genetic and chemical perturbations impact diverse cellular phenotypes, including multiple indicators of cell health.
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