Dataset Information

Identifying Small Molecules which Inhibit Autophagy: a Phenotypic Screen Using Image-Based High-Content Cell Analysis.

ABSTRACT: Autophagy plays an important role in cancer and it has been suggested that it functions not only as a tumor suppressor pathway to prevent tumor initiation, but also as a pro-survival pathway that helps tumor cells endure metabolic stress and resist death triggered by chemotherapeutic agents, including acquired resistance. We aimed to identify small-molecule autophagy inhibitors using a HTS/HCA approach through a phenotypic, cell image-based assay, in order to screen multiple biological targets simultaneously and to screen compounds in a physiologically relevant environment. LC3 is a component of the autophagosome, which undergoes a cytoplasmic redistribution from diffuse to punctate dots during autophagy. We employed HeLa cells stably expressing EGFP-LC3 in a primary phenotypic screen. As a first step, a "Validation Library" of about 8,000 pre-selected compounds, about 25% of which had known biological activity and the others representing a range of chemical structures, was run in duplicate both to assess screening suitability and likely hit rate, and to give a valuable preview of possible active structures or biological targets. The primary screen of about 0.25 million compounds yielded around 10,500 positive compounds. These were tested in a suite of further cellular assays designed to eliminate unwanted positives, together with the application of chemi- and bioinformatics to pick out compounds with known biological activity. These processes enabled the selection of compounds that were the most promisingly active and specific. The screening "tree" identified, amongst others with as yet unidentified targets, chemical series active against autophagy-relevant biological targets ULK or Vsp34, validating the phenotypic screening methods selected. Finally, about 400 compounds were fully qualified after following this triage. The development of the assays, compound screening process and the compound triage is described.

SUBMITTER: Peppard JV

PROVIDER: S-EPMC3941084 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Identifying Small Molecules which Inhibit Autophagy: a Phenotypic Screen Using Image-Based High-Content Cell Analysis.

Peppard J V JV Rugg C C Smicker M M Dureuil C C Ronan B B Flamand O O Durand L L Pasquier B B

Current chemical genomics and translational medicine 20140207 Suppl 1

Autophagy plays an important role in cancer and it has been suggested that it functions not only as a tumor suppressor pathway to prevent tumor initiation, but also as a pro-survival pathway that helps tumor cells endure metabolic stress and resist death triggered by chemotherapeutic agents, including acquired resistance. We aimed to identify small-molecule autophagy inhibitors using a HTS/HCA approach through a phenotypic, cell image-based assay, in order to screen multiple biological targets s ...[more]

PMID: 24596680

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Project description:Phenotypic screening through high-content automated microscopy is a powerful tool for evaluating the mechanism of action of candidate therapeutics. Despite more than a decade of development, however, high content assays have yielded mixed results, identifying robust phenotypes in only a small subset of compound classes. This has led to a combinatorial explosion of assay techniques, analyzing cellular phenotypes across dozens of assays with hundreds of measurements. Here, using a minimalist three-stain assay and only 23 basic cellular measurements, we developed an analytical approach that leverages informative dimensions extracted by linear discriminant analysis to evaluate similarity between the phenotypic trajectories of different compounds in response to a range of doses. This method enabled us to visualize biologically-interpretable phenotypic tracks populated by compounds of similar mechanism of action, cluster compounds according to phenotypic similarity, and classify novel compounds by comparing them to phenotypically active exemplars. Hierarchical clustering applied to 154 compounds from over a dozen different mechanistic classes demonstrated tight agreement with published compound mechanism classification. Using 11 phenotypically active mechanism classes, classification was performed on all 154 compounds: 78% were correctly identified as belonging to one of the 11 exemplar classes or to a different unspecified class, with accuracy increasing to 89% when less phenotypically active compounds were excluded. Importantly, several apparent clustering and classification failures, including rigosertib and 5-fluoro-2'-deoxycytidine, instead revealed more complex mechanisms or off-target effects verified by more recent publications. These results show that a simple, easily replicated, minimalist high-content assay can reveal subtle variations in the cellular phenotype induced by compounds and can correctly predict mechanism of action, as long as the appropriate analytical tools are used.

Dataset Information

Identifying Small Molecules which Inhibit Autophagy: a Phenotypic Screen Using Image-Based High-Content Cell Analysis.

Publications

Identifying Small Molecules which Inhibit Autophagy: a Phenotypic Screen Using Image-Based High-Content Cell Analysis.

Similar Datasets

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