title: Comparison of methods for image-based profiling of cellular morphological responses to small-molecule treatment.
abstract: Quantitative microscopy has proven a versatile and powerful phenotypic screening technique. Recently, image-based profiling has shown promise as a means for broadly characterizing molecules' effects on cells in several drug-discovery applications, including target-agnostic screening and predicting a compound's mechanism of action (MOA). Several profiling methods have been proposed, but little is known about their comparative performance, impeding the wider adoption and further development of image-based profiling. We compared these methods by applying them to a widely applicable assay of cultured cells and measuring the ability of each method to predict the MOA of a compendium of drugs. A very simple method that is based on population means performed as well as methods designed to take advantage of the measurements of individual cells. This is surprising because many treatments induced a heterogeneous phenotypic response across the cell population in each sample. Another simple method, which performs factor analysis on the cellular measurements before averaging them, provided substantial improvement and was able to predict MOA correctly for 94% of the treatments in our ground-truth set. To facilitate the ready application and future development of image-based phenotypic profiling methods, we provide our complete ground-truth and test data sets, as well as open-source implementations of the various methods in a common software framework.
authors.affiliation: 1Broad Institute of MIT and Harvard, Cambridge, MA, USA.
authors.forename: Vebjorn
authors.initials: V
authors.lastname: Ljosa
authors.forename: Peter D
authors.initials: PD
authors.lastname: Caie
authors.forename: Rob
authors.initials: R
authors.lastname: Ter Horst
authors.forename: Katherine L
authors.initials: KL
authors.lastname: Sokolnicki
authors.forename: Emma L
authors.initials: EL
authors.lastname: Jenkins
authors.forename: Sandeep
authors.initials: S
authors.lastname: Daya
authors.forename: Mark E
authors.initials: ME
authors.lastname: Roberts
authors.forename: Thouis R
authors.initials: TR
authors.lastname: Jones
authors.forename: Shantanu
authors.initials: S
authors.lastname: Singh
authors.forename: Auguste
authors.initials: A
authors.lastname: Genovesio
authors.forename: Paul A
authors.initials: PA
authors.lastname: Clemons
authors.forename: Neil O
authors.initials: NO
authors.lastname: Carragher
authors.forename: Anne E
authors.initials: AE
authors.lastname: Carpenter
mesh_terms: D048430:Cell Shape / Q000187:drug effects*; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D005163:Factor Analysis, Statistical; D006801:Humans; D061986:MCF-7 Cells; D008856:Microscopy, Fluorescence; D010641:Phenotype; D054852:Small Molecule Libraries; D060388:Support Vector Machine
chemical_list: D054852:Small Molecule Libraries
keywords: drug profiling; high-content screening; image-based screening; phenotypic screening
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