High throughput time lapse imaging for analysis of phenotypic responses of >100,000 single clones for applications in drug screening and cell killing assays
The next generation of single cell analysis will involve the measurement of functional properties of living cells, including growth, death, protein secretions, and the interactions between multiple cell types. Similar to transcriptomic measurements, there is a need for measuring high dimensional functional properties of single cells so that the relevant subpopulations can be identified and retrieved for follow-on applications, including cell line development and immune marker discovery. However, existing platforms to date have either hand insufficient scale, low dimensionality and/or are too high cost to be practically implemented for routine biology experiments. In this talk, I will summarize our recent advances in using high dimensional time lapse imaging to measure the functional heterogeneity of living cells at the scale of 100,000 single clones per experiment. I will provide several examples of the applications of our platform in the ability to find rare drug-resistant cells, existing at frequencies of less than 1 in 10,000 in the parental population, as well as in measuring the potency of immune cells that are both effective at secreting cytokines and also in killing cancer cells in a target dependent manner. Ultimately, this platform can be used in the development of better drugs that more effectively suppress resistance, as well as better cell-based therapies that have greater clinical effectiveness.
Dr. Benjamin Yellen received his B.S. in Chemistry from Emory University, his Ph.D in Electrical & Computer Engineering from Drexel University, and served as a tenured faculty member in the Duke University Mechanical Engineering and Materials Science Department from 2005 to 2022, where his research interests were focused at the intersection of electricity and magnetism, colloids and soft matter, computer vision, and high throughput instrumentation. Ben published dozens of papers in prestigious journals including Nature, PNAS, Science Advances, and others. After founding Celldom in 2016, Ben joined Celldom full time first as the CTO in 2020, and later as the CEO in 2021. At Celldom, Ben has continued his scholarly interests in applying deep learning and AI models to cell biology, and in developing high impact cell biology tools that can measure function, phenotype, and molecular properties of single cells at scale.