Abstract
Tuesday, 18 September
Introductory Session Topics: Assay types and development, Image and data analysis.
Advanced Session Topics: New technologies, Flow-cytometry and imaging mass-spec, Phenotypic screening, Statistical analysis, Systems biology, CRISPR Technology, Machine Learning, Statistical Analysis of Screening Data.
Lunch Courses: 3D Cell Models and Machine Learning
How are Emerging Technologies in Disease Models, High content imaging and Data analytics changing our approach to Drug Discovery?
Wednesday, 19 September
Co-Chair Neil Carragher, Edinburgh Cancer Research UK Centre, UK
Co-Chair Patrick Faloon, Biogen, Cambridge, MA
Session Abstract: An increased focus on translational research in academia and the desire to reduce the attrition rate in industrial drug discovery programs has lead biologists to increasingly adopt more physiologically relevant cellular models. These include organoids, co-cultures, organ-on-a-chip technologies, iPSC-derived cultures and CRISPR-modified cell lines. These systems, while offering great opportunities, also bring attendant challenges that need to be addressed with novel methods and technologies. Validation is also required to determine whether these more complex, and more expensive platforms, are worth the additional investment required. In this session speakers will describe a variety of advanced cellular systems and address the critical issues related to their adoption.
Invited speaker 1 - Adela Ben-Yakar, FentoLAB, The University of Texas, Austin. “A large scale microfluidic platform in 96- and 384-well formats for high content screening of C. elegans.”
Invited speaker 2 - Christopher Obara, Janelia Farms/HHMI, Ashburn, VA. “High speed, multiplexed super-resolution imaging to uncouple nano-domain function in living cells.”
Speakers selected from abstracts
High attrition rates in drug development can be traced back to a lack of physiological relevance when performing target identification. Animal studies are time consuming and expensive. For this reason, researchers have been turning to three dimensional organoid and tumor spheroid systems. In this system cells benefit from cell to cell and cell to ECM contacts. Moreover, the cells exist in a more biochemically relevant state with gradients through the 3D system. Noninvasive approaches such as fluorescence microscopy are highly advantageous as they allow for the study of these 3-dimensional systems. We describe the application of a suite of fluorescent biosensors in combination with automated fluorescence microscopy for the high throughput, quantitative analysis of 3D cell models. Data will be shown quantifying the induction of several cell health and cell cycle parameters in 3D spheroid models.
Co-Chair Steve Haney, President SBI2 2018
Co-Chair Kaylene Simpson, Victorian Centre for Functional Genomics, Peter MacCallum Cancer Centre, Melbourne, Australia
Session Abstract: Imaging studies are moving towards better and more relevant clinical models, including primary clinical samples. Challenges include imaging acquisition of complex culture systems such as primary tumor organoids, co-culture systems, microfluidics and informatics support to integrate phenotypic responses with sample genetics. This session will highlight research that incorporates quantitative image analysis with clinically relevant biological systems and integrative “omics” data analysis.
Invited speaker 1 - Rachelle Prantil-Baun, Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA. “Human organ on chips in translational biology.”
Invited speaker 2 – John Graef, Principal Scientist, Fulcrum Therapeutics, Boston, MA. “Evaluating the safety and efficacy of DUX4 reduction in FSHD skeletal muscle myotubes using high content imaging.”
Speakers selected from abstracts
The general meeting is a business meeting required for non-profits. This is an excellent opportunity to learn more about the society and find out how you can participate.
Thursday, 20 September
Co-Chair Madhu Lal-Nag, Trans NIH RNAi Facility, NIH/NCATS, Washington DC, USA
Co-Chair Jeffrey Morgan, Center for Biomedical Engineering, Brown University, RI, USA
Session abstract: Several recent revolutions are increasing the amount and quality of information that can be extracted from large-scale imaging experiments. From 3D models of organs and tumors to complex cell and material systems, biologists are making ever more complex assay systems that push the limits of what can be accurately quantified. Advances in the world of computer science are pushing the field forward as well, from deep learning to high-dimensional data analysis techniques to methods that leverage the single-cell data inherent in images of cell populations.
Invited speaker 1 - Thierry Dorval, Servier, Université Pierre et Marie Curie, Paris, France. “Practical high content analysis for drug discovery.”
Invited speaker 2 - Geoffrey Bartholomeusz, Department of Experimental Therapeutics, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX. “Predictive value of 3D models for identifying tumor-specific therapies.”
Speakers selected from abstracts
Co-Chair David Andrews, Sunnybrook Research Institute; Dept. of Biochemistry, University of Toronto, Canada
Co-Chair Shannon Mumenthaler, Lawrence J. Ellison Institute for Transformative Medicine of University of Southern California; Center for Applied Molecular Medicine; Stephenson Family Personalized Medicine Center, California, USA
Session Abstract: Phenotypic screening is typically based on intensity measurements of the localization of fluorophores within cells. With the development of fully automated confocal microscopy it became possible to make fluorescence intensity measurements for the large numbers of cells required to begin probing biology with automated image analysis techniques. Current software solutions permit the use of hundreds of calculated features per cell while deep learning techniques use pixel based image features to generate multidimensional descriptions of cell images. Both sets of features can be automatically classified to probe cellular responses to genetic and chemical perturbations. Current emphasis is on the development of methods to explore large datasets to identify novel unanticipated cellular responses or to classify images when there is uncertainty in the ground truth. This session will explore the current and future promise of machine learning and multi-parametric analytics to report on complex physiological responses.
Invited speaker 1 - Arvind Rao, University of Michigan Medical School, Ann Arbor, MI. “Enabling drug and target discovery in high content imaging using machine learning based workflows.”
Invited speaker 2 - Chris Bakal, The Institute of Cancer Research, London, UK. “Translating the language of cell shape.”
Speakers selected from abstracts