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.

Speaker: Mackenzie Mathis, Assistant Professor, Swiss Federal Institute of Technology (EPFL)

Speaker Bio: Prof. Mackenzie Mathis is the Bertarelli Foundation Chair of Integrative Neuroscience and an Assistant Professor at the Swiss Federal Institute of Technology, Lausanne (EPFL). Following the award of her PhD at Harvard University in 2017 with Prof. Naoshige Uchida, she was awarded the prestigious Rowland Fellowship at Harvard to start her independent laboratory (2017-2020). Before starting her group, she worked with Prof. Matthias Bethge at the University of Tübingen in the summer of 2017 with the support of the Women & the Brain Project ALS Fellowship. She is an ELLIS Scholar, a former NSF Graduate Fellow, and her work has been featured in the news at Bloomberg BusinessWeek, Nature, and The Atlantic. She was awarded the FENS EJN Young Investigator Prize 2022. Her lab works on mechanisms underlying adaptive behavior in intelligent systems. Specifically, the laboratory combines machine learning, computer vision, and experimental work in rodents with the combined goal of understanding the neural basis of adaptive motor control.

Speaker: Hoifung Poon, Ph.D., General Manager, Microsoft.

Speaker Bio: Dr. Poon is a General Manager at Microsoft Health Futures where he leads biomedical AI research and incubation, with the overarching goal of structuring medical data to accelerate discovery and improve delivery for precision health. His team and collaborators are among the first to explore large language models (LLMs) in health applications, from foundational research to incubations at large health systems and life science companies, and ultimately to commercialization. Dr. Poon received his B.S. with Distinction in Computer Science from Sun Yat-Sen University in Guangzhou, China, and his PhD in Computer Science and Engineering from the University of Washington in Seattle, specializing in machine learning and natural language processing (NLP). He joined Microsoft Research in 2011.

Speaker: Drew Linsley, Assistant Professor, Brown University.

Speaker Bio: Dr. Linsley is an Assistant Professor (Research) in Computational Neuroscience and AI at Brown University. He previously did a postdoc with Thomas Serre, also at Brown. Before that, he received his PhD at Boston College with Sean MacEvoy, and BA at Hamilton College working with Jonathan Vaughan. He studies biological and artificial vision. He is Co-Founder and CEO at Operant Biopharma whose robotic microscope, powered by artificial intelligence, designs drugs by watching and controlling diseases over time.

Speaker: Greg Johnson, Ph.D, NewLimit.

Speaker Bio: Dr. Johnson co-founded NewLimit to develop new therapies based on reprogramming to reduce the burden of age-related disease. Before that, he was in Seattle at AWS working on new and diverse technologies to make the world a better place. Before that, he was also in Seattle at the Allen Institute for Cell Science where he built predicitve models to estimate the outcome of new experiments and describe how cells change their organization under different conditions. Before that, he was in grad school at Carnegie Mellon University as a student of Robert Murphy. His work focused on applying generative models to determine how cells respond to perturbations.