SO Research Seminar - "The application of single-cell methods to advance retinal neuron differentiation and transplantation"

Petr Baranov runs a cell transplantation lab at the Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School. He graduated from Russian State Medical School as an MD, PhD and before joining the faculty at HMS, he completed  a postdoctoral fellowship with Dr. Michael Young, where he brought the human retinal progenitor transplantation program from enabling in vitro studies to a successful FDA IND submission. His lab’s work on RGC production from stem cells, and methods to improve neuron survival and maturation in transplantation setting is supported by the National Eye Institute, Department of Defense, BrightFocus and Gilbert Family Foundation. His group has  extensive experience with small and large animal models of retinal disease, stem cell differentiation, imaging and automation, transplantation, and cell therapy development. To address the significant steps of the bench to bedside translation, they rely on productive collaborations with academia (advanced imaging, bioinformatics, and deep learning, genetic engineering) and industry (scaled up cell manufacture, cell isolation, and characterization).

Emil Kriukov is a lead bioinformatician in Petr Baranov lab. With MD background from Russian State Medical School, he obtained experience in working with developing neural crest system in Medical University of Vienna, and induced development of bone and cartilage tissues in Karolinska Institute. Currently, his research is focused on building the unifying atlas of the retina of the eye in single-cell resolution, that covers such conditions as in vivo developing, adult, disease and disease models, and in vitro products. His approach in large-scale single-cell studies is focused on implementing classical analysis tools together with the support of machine learning and novel mathematics methods, particularly in analyzing cell fate trajectories and designing the methods for biological function-oriented analysis.