4.8 Article

Multiplexed and reproducible high content screening of live and fixed cells using Dye Drop

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NATURE COMMUNICATIONS
卷 13, 期 1, 页码 -

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NATURE PORTFOLIO
DOI: 10.1038/s41467-022-34536-7

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  1. NIH [U54-CA225088, U54-HL127365, U24-DK116204]

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The authors report a dye drop method using sequential density displacement and microscopy for multi-step assays on living cells. They used this method to collect single-cell dose-response data for small molecules in breast cancer cells. The dye drop method is rapid, reproducible, customizable, and enables the collection of information-rich perturbagen-response datasets.
It is currently difficult to perform accurate single-cell assays in 384-well plates. Here the authors report Dye Drop which uses sequential density displacement and microscopy for multi-step assays on cells, and use this to collect single-cell dose-response data for small molecules in breast cancer cells. High-throughput measurement of cells perturbed using libraries of small molecules, gene knockouts, or different microenvironmental factors is a key step in functional genomics and pre-clinical drug discovery. However, it remains difficult to perform accurate single-cell assays in 384-well plates, limiting many studies to well-average measurements (e.g., CellTiter-Glo (R)). Here we describe a public domain Dye Drop method that uses sequential density displacement and microscopy to perform multi-step assays on living cells. We use Dye Drop cell viability and DNA replication assays followed by immunofluorescence imaging to collect single-cell dose-response data for 67 investigational and clinical-grade small molecules in 58 breast cancer cell lines. By separating the cytostatic and cytotoxic effects of drugs computationally, we uncover unexpected relationships between the two. Dye Drop is rapid, reproducible, customizable, and compatible with manual or automated laboratory equipment. Dye Drop improves the tradeoff between data content and cost, enabling the collection of information-rich perturbagen-response datasets.

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