QLS Seminar Series - Jun Ding

Decoding cellular dynamics from single-cell data for more effective cell fate manipulation

Jun Ding, McGill University
Tuesday January 11, 12-1pm
Zoom Link: https://mcgill.zoom.us/j/85428056343

Abstract: In recent years, the emerging single-cell technologies provide unprecedented opportunities for studying many challenging biomedical problems, especially in the cell differentiation and cancer biology areas, in which there exists tremendous cell heterogeneity. However, the single-cell datasets generated in those studies are usually high-dimensional, large-scale, noisy, and heterogeneous, making it challenging for the biomedical scientists to directly utilize the single-cell data in support of their health science research. In this talk, I will discuss how to make novel biological discoveries and medical innovations in cell differentiation studies that will eventually benefit public health via analyzing, modeling, and visualizing large-scale single-cell genomics dataset with machine learning methods. First, I will talk about some critical computational challenges in analyzing the single-cell genomics data, followed by the discussion of graphical models that I developed to address those challenges (i.e., identifying sub-populations, trajectory inference, efficient data/model presentation, gene regulatory network reconstruction). Next, I will discuss how we innovated the protocol to differentiate lung epithelial cells from the induced pluripotent stem (iPS) cells, with the help of the developed single-cell computational methods and a computationally guided way to design the single-cell experiments. In this study, we have doubled the lung epithelial differentiation efficiency compared to the state-of-the-art protocol by repressing the Wnt pathway (within a specific time window), all predicted from the developed computational model.

Bio: Jun Ding (https://www.meakinsmcgill.com/ding/) is an assistant professor in the Department of Medicine, McGill University Health Centre. Before that, he was trained as a postdoc at the Computational Biology Department, School of Computer Science, Carnegie Mellon University, under the supervision of Dr. Ziv Bar-Joseph. In 2016, he received his PhD. in Computer Science from the University of Central Florida. His research focuses on developing computational methods to drive biological discoveries and medical innovations by analyzing and modeling large-scale biomedical data, especially single-cell genomics data. Jun had published ~30 papers in leading computational biology journals such as genome research and cell stem cell. Currently, Jun is particularly interested in developing computational models and visualizations for cell differentiation and tumor microenvironment studies that enormously benefit from the emerging single-cell omics technologies.