Characterizing gene regulation in developing systems using single cell expression data

• Applicant: Christoph Hafemeister, Ph.D.
• Subject Area: Bioinformatics and Theoretical Biology ; Developmental Neurobiology ; Cell Biology
• Term: from 2016 to 2017
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 328558384

Recently developed single-cell RNA-sequencing technologies enable an unbiased and comprehensive insight into cell-to-cell heterogeneity even for cell populations that otherwise seem homogeneous. These new approaches provides valuable information that allow 1) discovery of cell type specific expression through clustering, as well as 2) studying dynamics of gene expression during developmental processes by ordering them along a differentiation trajectory.Since these technologies are new developments, no analysis strategy has been developed that takes into account the unique intrinsic properties of the data (low number of detected molecules, many zero counts, overdispersion). Here I propose to develop, test and apply methods for the analysis of single-cell expression data, specifically focusing on methods that are suited to analyze data of thousands of cells where the signal per cell and gene is very low and methods designed for bulk data are not appropriate. I plan to design a normalization scheme that is suited to remove variance in the data that comes from technical factors, as well as from specific biological processes (cell cycle). Additionally, I propose to develop and evaluate further analysis steps like dimensionality reduction and cell-to-cell distance metrics in order to enable optimal clustering methods and methods that can discover complex developmental trajectories. Finally, I plan to employ these single-cell methods to examine similarities and differences during neuron development in progenitor zones of mouse embryos.

• DFG Programme: Research Fellowships
• International Connection: USA
• Host: Professor Dr. Rahul Satija