Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling
| Autor: | C. Suk-Yee Hon, Melissa A. Marton, Nicole E. Bodycombe, Sigrun M. Gustafsdottir, Vebjorn Ljosa, Paul A. Clemons, Vlado Dančík, Stuart L. Schreiber, Ellen Winchester, J. Anthony Wilson, Katherine L Sokolnicki, Joshua A. Bittker, Rajiv Narayan, Todd R. Golub, Wendy Winckler, Kejie Li, Jeremy R. Duvall, George B. Grant, Mathias Wawer, Aravind Subramanian, Mark-Anthony Bray, Melissa M. Kemp, Anne E. Carpenter, Bradley K. Taylor, Alykhan F. Shamji |
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| Rok vydání: | 2014 |
| Předmět: |
Multidisciplinary
Drug discovery Gene Expression Profiling Phenotypic screening Drug Evaluation Preclinical Small Molecule Libraries Computational biology Chemical similarity Biology computer.software_genre Cell morphology Multiplexing Gene expression profiling Gene Expression Regulation Cell Line Tumor Physical Sciences Humans Profiling (information science) Data mining human activities computer |
| Zdroj: | Proceedings of the National Academy of Sciences. 111:10911-10916 |
| ISSN: | 1091-6490 0027-8424 |
| DOI: | 10.1073/pnas.1410933111 |
| Popis: | High-throughput screening has become a mainstay of small-molecule probe and early drug discovery. The question of how to build and evolve efficient screening collections systematically for cell-based and biochemical screening is still unresolved. It is often assumed that chemical structure diversity leads to diverse biological performance of a library. Here, we confirm earlier results showing that this inference is not always valid and suggest instead using biological measurement diversity derived from multiplexed profiling in the construction of libraries with diverse assay performance patterns for cell-based screens. Rather than using results from tens or hundreds of completed assays, which is resource intensive and not easily extensible, we use high-dimensional image-based cell morphology and gene expression profiles. We piloted this approach using over 30,000 compounds. We show that small-molecule profiling can be used to select compound sets with high rates of activity and diverse biological performance. |
| Databáze: | OpenAIRE |
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