Selection of Optimal Cell Lines for High-Content Phenotypic Screening

High-content microscopy
offers a scalable approach to screen against
multiple targets in a single pass. Prior work has focused on methods
to select “optimal” cellular readouts in microscopy
screens. However, methods to select optimal cell line models have
garnered much less attention. Here, we provide a roadmap for how to
select the cell line or lines that are best suited to identify bioactive
compounds and their mechanism of action (MOA). We test our approach
on compounds targeting cancer-relevant pathways, ranking cell lines
in two tasks: detecting compound activity (“phenoactivity”)
and grouping compounds with similar MOA by similar phenotype (“phenosimilarity”).
Evaluating six cell lines across 3214 well-annotated compounds, we
show that optimal cell line selection depends on both the task of
interest (e.g., detecting phenoactivity vs inferring phenosimilarity)
and distribution of MOAs within the compound library. Given a task
of interest and a set of compounds, we provide a systematic framework
for choosing optimal cell line(s). Our framework can be used to reduce
the number of cell lines required to identify hits within a compound
library and help accelerate the pace of early drug discovery.