Deep learning can be applied to imaging assays across all R&D stages spanning primary screening campaigns, hit validation, target identification, and toxicity studies. You can also use Genedata Imagence to increase efficiency and get deeper insights into novel phenotypes and mechanism of action (MoA). A versatile solution, Genedata Imagence can be used for everything from simple single-marker HCS assays to multiplexed Cell Painting assays and iPSC or CRISPR screens.
High Content Screening:
Early Discovery and ADME-Tox.
By enabling screening within a cellular context, phenotypic high-content screens can reveal leads otherwise missed in target-based discovery. HCS is also used in cytotoxicity tests due to the higher physiological relevance of high-content assays. High content analysis typically involves multiple parameters from complex imaging datasets—a time-consuming and laborious process. Deep learning-based approaches greatly facilitate this process but depend upon specialized algorithmic expertise. Genedata Imagence removes this hurdle, allowing the biologist to employ deep learning with ease.
Using Genedata Imagence for HCS, whether for early discovery or toxicity studies, enables you to:
Accelerate assay development by automating image analysis setup including repetitive, time-consuming parameter tuning steps.
Rapidly classify phenotypes—whether in single-marker or complex co-culture and multiplexed assays—and immediately browse top results in Genedata Screener.
Consistently apply the same, unbiased analysis across multiple production runs.
Target ID and Validation: Mechanism of Action Studies.
A great strength of phenotypic assays is the ability to reveal new targets through the investigation of novel phenotypes and their underlying MoA. These molecular targets can then be further pursued in target-based screens. Genedata Imagence enables you to:
Capture novel phenotypes using intuitive visualizations alongside raw images to assess phenotypic similarity to other compounds with known MoA.
Rapidly analyze multiplexed target-independent assays such as Cell Painting.
Assess MoA and validate molecular targets through association of cellular phenotypes with genetic perturbations induced by RNAi, overexpression, or CRISPR technologies.
During hit validation or even early discovery, it’s important to test potentially diverse effects of a drug candidate in a cell line panel. It’s also increasingly common to employ patient-derived cellular models that more closely approximate patient biology. While important, these resource-intensive assays require precious samples and can be costly to develop. In cases like this, where data can be limited, Genedata Imagence can help to:
Transfer knowledge among different campaigns involving separate therapeutic modalities, and distinguish patient-specific effects.
Save valuable resources and reduce analysis and assay development times.