FEB 03, 2021 12:30 PM EST

Optimizing High-Content Imaging of 3D Models for Drug Discovery

Sponsored by: SBI2, InSphero, Yokogawa
  • Senior Software Application Specialist, Yokogawa Life Innovations

      Arvonn Tully is a Senior Software Application Specialist at Yokowaga Life Innovations. He has more than 15 years of experience working in 3D image analysis, with a special interest in sub cellular tracking, neuronal reconstruction, and finding solutions for complex analysis problems. Prior to joining Yokogawa, Arvonn worked for Arivis to solve problems with terabyte sized images and image analysis in VR collaboration. At Bitplane, he provided extensive training in 3d Image analysis for users and developing novel 3d co-localization solutions. His research background was focused on imaging and biophysics of large dense core vesicles in Drosophila at the University of Pittsburgh.

    • Senior Application Scientist, InSphero AG

        Judi Wardwell is a Senior Application Scientist at InSphero and President Elect of the SBI2 board of directors. She has more than 25 years of experience working in the pharma industry and has a keen interest in evolving imaging technologies and applications that help drive drug discovery. Prior to joining InSphero, Judi was a Principal Investigator in a functional genomics group at Bristol-Myers Squibb, where her team conducted phenotypic screens in physiologically-relevant cell models with a focus on the identification of new drug targets. Prior to BMS, she held scientific research positions at Wyeth and Hoffmann-La Roche. Judi is also the author of a book chapter and several journal articles on high content imaging-related topics such as HCS data management, analysis of multiparametric imaging data, and 3D imaging/analysis of multicellular spheroids.


      In vitro spheroid models are fast becoming the de facto standard for drug discovery applications, largely due to their human-like physiological and morphological characteristics, tissue-like cellular complexity, and long culture lifespan, which enables longitudinal studies that better reflect patient treatment plans in the clinic. High content imaging and analysis (HCA) of 3D spheroid models can provide valuable information to help researchers untangle disease pathophysiology and assess novel therapies more effectively. Making the move from simple monolayer 2D cell models to dense 3D spheroids in HCI applications, however, requires 3D-optimized protocols, instrumentation, and resources.

      In this webinar, we will discuss considerations for high content imaging and analysis of 3D spheroid disease models for drug discovery, share lessons we learned while in setting up and conducting proof-of-concept studies designed to test the full potential for high resolution image-based analysis of 3D spheroid models, and provide a working checklist for researchers and core services groups planning to exploit these technologies in their work.

      Learning Objectives:

      • High Content assat development- considerations when transitioning from 2D cell models to 3D multicellular models including examples of the rich, physiologically-relevant data that can be extracted from multicellular 3D models 
      • 3D image acquisition- high content instrument hardware/software and acquisition settings for efficient acquisition of high quality image stacks
      • 3D image analysis- visualization and image analysis toolsets for 3D imaging, including the application of Deep Learning methods

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