Statistical Methods for Single-Cell RNA-Seq in Studies of Mammalian Development

• Activity Number: 318 - Advances on the Analysis of Single-Cell Sequencing Data
• Type: Invited
• Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
• Sponsor: WNAR
• Author(s): Christina Kendziorski* and Zijian Wang and Ron Stewart and Chris Barry and Li-Fang Chu
• Companies: University of Wisconsin - Madison and University of Wisconsin - Madison and Morgridge Institute for Research and Morgridge Institute for Research and Morgridge Institute for Research
• Keywords: statistical genomics; RNA-seq; single-cell RNA-seq

Abstract

Human embryonic stem cells (hESCs) are central in regenerative medicine. However, the extended time required for producing specific cell types and the lack of physiological maturity are critical challenges to their study in vitro and to their use in clinic. Consequently, understanding what controls species-specific rates of differentiation is essential for determining if and how these rates can be altered. Toward this end, single-cell RNA-sequencing was used to profile genome wide gene expression in human and mouse embryonic stem cells every four minutes for ten hours, as well as every hour for four days. An extended Bayesian time-warping approach was developed to quantify similarities and differences in species-specific developmental timing. Using the algorithm, we identified collections of genes underlying important differentiation stages as well as two novel developmental landmarks common to mouse and human.