New On-Demand Webinar: Advancing Phenotypic Drug Hunting - Enabling Data-Driven Discovery for Biologists
To address the persistently high costs and failure rates associated with drug development, scientists are increasingly turning to data-driven drug discovery approaches. High content morphological profiling is an example of such an approach, where microscopic images are used to generate detailed cellular profiles. This information can then reveal important insights into a compound’s mechanism of action or toxicity, for example.
In an effort to scale up this image-based, data-driven drug discovery strategy, the JUMP-CP consortium have optimized the Cell Painting assay(1), and used that to generate an unprecedented reference dataset. This dataset, containing phenotypic data on ~140,000 different genetic and small molecule perturbations, was publicly released last year.
However, to leverage this resource–and morphological profiling in general–biologists require significant IT infrastructure and data science support. During this webinar, we will discuss how our newly launched StratoVerse platform gives biologists easy access to these resources. Using JUMP-CP data(2) as an example, we will demonstrate how biologists can store images in the cloud, use cloud-computing to quickly run CellProfiler™(3) pipelines, and mine the numeric data for biological insight.
Join this webinar, and learn:
- How to unravel biology in large and complex datasets;
- How the cloud can dramatically speed up this process, opening up new possibilities;
- How to easily perform these analyses in your lab, without coding.
References:
1: Cimini BA, et al. Optimizing the Cell Painting assay for image-based profiling. Nat Protoc. 2023 Jul;18(7):1981-2013. doi: 10.1038/s41596-023-00840-9. Epub 2023 Jun 21. PMID: 37344608.
2: We used the JUMP Cell Painting datasets (Chandrasekaran et al., 2023: doi:10.1101/2023.03.23.534023), available from the Cell Painting Gallery on the Registry of Open Data on AWS (https://registry.opendata.aws/cellpainting-gallery/).
3: Stirling DR, et al. CellProfiler 4: improvements in speed, utility and usability. BMC Bioinform. 2021 Sep 10;22(1):433. doi: 10.1186/s12859-021-04344-9.
