The long-term goals of our lab are to develop computational methods to overcome the challenges for analyzing, interpreting, and integrating various genomic-scale data types. and to partner with experimental biologists to uncover the fundamental principles in gene regulatory mechanisms and to use some knowledge to develop effective treatment for various human diseases. Our current research is focused on the following areas.
(1) Single-cell RNAseq analysis. How many distinct cell types are there in a human and, for comparison, in a model organism? How do individual cells interact with each other and work coordinately to maintain the function and structure of a tissue or organ? The rapid development of single-cell RNAseq technology has provided a great opportunity to address this fundamental question in depth. We develop computational methods to systematically characterize the heterogeneity, interaction, and dynamics of cellular states by analyzing single-cell RNAseq data (Marco et al. 2014; Jiang et al. 2016; Tsoucas et al. 2019) and apply these methods to characterize the cell state changes in diseases such as cancer (Luoma et al. 2020). Recently, we have extended our research to single-cell multi-omic analyses.
(2) Spatial transcriptomics. While powerful, single-cell RNAseq analysis provides little information about the spatial structure within a tissue/organ. Because cells do not live in isolation but constantly interact with their neighboring cells and extra-cellular environment, it is crucial to map out the spatial distribution of cell types in their native environment. This can be done by the recently developed spatial transcriptomic technologies. We develop computational methods to comprehensively analyze spatial data and user-friendly, generally applicable software packages that enable experimental biologists to analyze their own data without the need to write their own programs (Zhu et al. 2018; Dries et al. 2020). See the Giotto project for details.
(3) Epigenetics. In a multi-cellular organism, all cells share essentially the same genome, yet each cell type has its distinct structure and biological functions. This is possible because of epigenetic regulation. Biologists have developed a number of experimental techniques to gain genome-wide view of various epigenetic markers inside different cell types, such as ChIPseq, ATAseq, CUT&RUN, Hi-C. We develop computational methods to analyze such data as well as integrative approaches to build gene regulatory networks (Pinello et al. 2014; Pinello et al. 2016; Marco et al. 2017; Huang et al. 2018; Zhu et al. 2019). We also work closely with experimental biologists to study the role of epigenetic regulation in sickle cell disease (Canver et al. 2015) and cancer (Chipumuro et al. 2014; Debruyne et al. 2019).