Friday, October 4 • 9:55am - 10:15am
AI in Screening- Automating Phenotypic Screening with AI

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Automating Phenotypic Screening with AI
Imaging-based phenotypic screening of cell-based disease models has become an indispensable tool for modern drug discovery. These assays are used in primary and secondary screening and can even be used to identify novel drug targets. Despite the adoption of automated microscopy-based screening, typically referred to as high-content screening (HCS), analyzing and interpreting the complex imaging data produced by these systems remains a challenging bottleneck. Although only requiring an hour or less to acquire images for each multi-well plate, analyzing the imaging data can take weeks and typically requires hands-on programming by data scientists and computer vision experts. Advances in machine learning, specifically deep learning, have enabled the development of software platforms that can automate this process and provide valuable insights to scientists within hours of completing experiments.

Here we describe a cloud-enabled end-to-end automated platform for storing, managing, analyzing, and visualizing HCS data using recent advancements in deep learning. The workflow for a typical screening experiment involves seamlessly uploading raw screening data from the HCS system to a cloud-based storage instance. From there, the data is automatically imported into a compute instance running an open-source image database and viewer developed primarily for microscopy data. The experimental metadata, including the assay plate layout, is imported simultaneously and is used to annotate all the wells in the screen.

Once the screening data is in the database, two deep learning-based workflows are launched to automatically analyze the screen. The first workflow clusters single cell phenotypes in the screen and allows researchers to explore and annotate the data using an interactive scatterplot. The workflow uses weakly supervised learning, a recently developed method in which a deep convolutional neural network (CNN) is trained to classify which unique experimental condition (i.e. well) each single cell belongs to. After this network is trained, activations from intermediate layers of the network are used as a powerful lower dimensional feature representation to cluster and visualize single cells in the screen. Researchers then use the interactive visualization tool to explore and annotate phenotypes of interest in the screen. After phenotypes and treatments of interest are identified, the second deep learning workflow is used to classify them using a segmentation-free approach. Here, a deep convolutional multiple instance learning model is trained to classify entire fields-of-view in the screen based on control treatments. This classifier is then used to score the rest of the treatments screened, typically identifying hits from a drug library.

 This end-to-end system has been deployed on internal projects at Phenomic AI focused on assay optimization, including selecting informative immunofluorescent probes and cytokine or drug concentrations. It’s also been used to identify functional phenotypic hits from small scale drug and antibody screens. Additionally, it’s been used to explore 3D tumor spheroids and complex cell-cell interactions with data provided by our industry partners.

avatar for Oren Kraus, BASc, MASc, PhD

Oren Kraus, BASc, MASc, PhD

Cofounder CTO, Phenomic AI
Oren Kraus co-founded Phenomic AI after completing his Ph.D. in Dr. Brendan Frey's lab at the University of Toronto. His research focused on applying deep learning to high-throughput microscopy screens used in drug discovery and cell biology research. Together with Jimmy Ba and collaborators... Read More →

avatar for Franck Madoux, PhD

Franck Madoux, PhD

Principal Scientist, Amgen
Franck Madoux heads Amgen’s Ultra-High Throughput Screening (UHTS) group where he and his team are involved in the early phases of drug discovery for small molecules via the execution of large scale biochemical, cell-based and high content assays in miniaturized formats and the... Read More →

Friday October 4, 2019 9:55am - 10:15am
Theater Ballroom (5th Fl) - Courtyard Boston Downtown 275 Tremont Street, Boston, MA 02116