Talk Title: Information in images for drug discovery: image-based profiling

Start Time: 9:40 AM PDT
Speaker: Inti Zlobec, Professor, University of Bern.

Abstract: Cell images contain a vast amount of quantifiable information about the status of the cell: for example, whether it is diseased, whether it is responding to a drug treatment, or whether a pathway has been disrupted by a genetic mutation. We extract hundreds of features of cells from images. Just like transcriptional profiling, the similarities and differences in the patterns of extracted features reveal connections among diseases, drugs, and genes. Improving this pipeline is an active area of research, from feature extraction to batch correction to quality control to assessing similarities. We are harvesting similarities in image-based profiles to identify, at a single-cell level, how diseases, drugs, and genes affect cells, which can uncover small molecules’ mechanism of action, discover gene functions, predict assay outcomes, discover disease-associated phenotypes, identify the functional impact of disease-associated alleles, and find novel therapeutic candidates. As part of the JUMP-Cell Painting Consortium (Joint Undertaking for Morphological Profiling-Cell Painting) we are aiming to establish experimental and computational best practices for image-based profiling (https://jump-cellpainting.broadinstitute.org/results) and produce the world’s largest public Cell Painting gene/compound image resource, with 140,000 perturbations in five replicates, to be released November 2022. With these data and new technologies like Pooled Cell Painting and variants of the assay like LipocyteProfiler and CardioProfiler, we hope to bring drug discovery-accelerating applications to practice.

Speaker Bio: Inti Zlobec holds the position of Professor (Extraordinarius) of Digital Pathology at the Institute of Pathology, University of Bern, Switzerland. She graduated with a PhD degree in Experimental Pathology, from McGill University, Montreal, Canada in 2007 before completing a post-doctoral fellowship at the Institute of Pathology, University Hospital Basel, where she conducted tissue-based research in the field of colorectal cancer using biostatistical models. After habilitating in 2010, she received a position at the Institute of Pathology, University of Bern, where she established and led the Translational Research Unit (TRU) and later the Tissue Bank Bern (TBB). Inti Zlobec became Associate Professor in 2014. Now, she leads an inter-disciplinary research group of students and researchers using artificial intelligence and machine learning as tools to study pathology images along with other data types to discover and validate novel prognostic and predictive biomarkers for colorectal cancer patients. Inti Zlobec is a member of the Executive Team of the Center for Artificial Intelligence in Medicine (CAIM) of the University of Bern, Co-Founder and President of the Swiss Consortium for Digital Pathology (SDiPath) and Chair of the European Society of Pathology (ESP) Working Group IT.