Interpreting Image-based Profiles using Similarity Clustering and Single-Cell Visualization

Image-based profiling quantitatively assesses the effects of perturbations on cells by capturing a breadth of changes via microscopy. Here we provide two complementary protocols to help explore and interpret data from image-based profiling experiments. In the first protocol, we examine the similarity among perturbed cell samples using data from compounds that cluster by their mechanism of action (MOAs). The protocol includes steps to examine feature-driving differences between samples and how to visualize correlations between features and treatments to create interpretable heatmaps using an open-source web tool, Morpheus. In the second protocol, we show how to interactively explore images together with the numerical data, while we provide scripts to create visualizations of representative single cells and image sites to understand how changes in features are reflected in the images. Together, these two tutorials help biologists and researchers interpret their image-based data to speed up research.Data are available at https://github.com/ciminilab/2022_Garcia-Fossa_submitted. Funding was provided by the National Institutes of Health (NIH COBA P41 GM135019 to BAC and AEC and MIRA R35 GM122547 to AEC). This project has been made possible partly by grant number 2020-225720 to BAC from the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation. São Paulo Research Foundation (FAPESP) was also provided funding #2022/01483-4, #2019/24033-1, and #2020/01218-3. SS and AEC serve as scientific advisors for companies that use image-based profiling and Cell Painting (AEC: Recursion, SS: Waypoint Bio, Dewpoint Therapeutics) and receive honoraria for occasional talks at pharmaceutical and biotechnology companies