Authors

V Wong¹; DE Egan¹; W Omta¹; 
¹ Core Life Analytics, Netherlands

Discussion

There is a growing interest in adopting image-based phenotypic profiling for drug discovery. Such high content approaches yield rich phenotypic data that can reveal critical holistic insights into mechanisms of candidate drug action and toxicity. Much of the growth has been driven by the use of Cell Painting, a standardized high content profiling method originally developed at the Broad Institute. The method has been adopted so widely that; the JUMP (Joint Undertaking in Morphological Profiling) Cell Painting (CP) consortium has been established to generate a large public reference Cell Painting dataset with the aim to create a new data-driven approach to drug discovery. This high content imaging dataset has been generated using 140,000 different conditions with compounds, CRISPRs and ORFs. The goal is to present the ‘ground truth’ for investigating phenotypic relationships between chemical and genetic perturbations that target the same genes in cells. The JUMP-CP dataset has the potential to be an outstanding resource for drug discovery but the complexity of the data will make it challenging for users outside of the initial consortium. We have attempted to address this challenge using the preliminary JUMP-CP pilot dataset that is publicly available. We will present a robust and iterative data analytics workflow to evaluate phenotypic data in the JUMP-CP pilot dataset. We will demonstrate the basics of detecting and eliminating redundant features, adding annotations to enrich data visualizations, access to quality control metrics, and creating new classifiers. Moreover, we can make phenotypic comparisons between two cell lines, and track phenotypic change over various time points and conditions. We identified compounds that give robust and distinct phenotypes across two cell lines and time points. This poster will share insights on how biologists can take full advantage of the complete data set when it becomes publicly available at the end of 2022.