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RESEARCH SUMMARY

We study gene regulation and cellular behavior by developing statistical and experimental methods. Our primary goal is to develop new technologies to map molecular networks, including RNA-RNA interactome [Nat Comm, 2016], RNA-chromatin interactome, and protein-protein interactome. Our secondary quest is to model the variations of these networks in three axes, namely developmental time, personal difference, and evolutionary change. Our major tools include epigenomic and single-cell assays, single-molecule imaging, statistical modeling, and large scale computation.

We discovered transposon-mediated re-wiring of transcription networks that govern pre-implantation embryonic development [Genome Res, 2010, cover; Research Highlight in Nature, 2010]. We contributed to initiating "comparative epigenomics", a research field that studies genomic functions by cross-species epigenomic comparison [Cell, 2012]. We contributed to the derivation of the rules of dynamic gene regulation and temporal epigenomic changes [Genome Res, 2013, cover]. We pioneered in modeling the impact of epigenome-genome interaction to transcription factor binding, and to personal variation [PLoS Comp Biol, 2013].  


 

Other active projects

Single cell analysis of cell fate

An important question to cell biology is how cells break the symmetry during mitotic divisions. During mammalian preimplantation embryonic development, the embryo has to decide how to set apart the first two cell populations. It remains an open question when and how the first cell fate decision is made. Cell-fate associated inter-blastomere differences of transcript and protein concentrations were reported from as early as the 8-16 cell stage. However, it is not clear whether these are the earliest differences. Using deep single-cell RNA-seq of matched sister blastomeres, we found highly reproducible differences among the single cells within early stage (2- and 4-cell) mouse embryos [Genome Res, 2014, cover].

Single-cell data, especially time-course single cell transcriptomic data demand new statistical methods. We developed a time-variant clustering method for this need [PNAS, 2014]. Time-variant clustering is a Hidden Branching Process. At each time point, this model degenerates into a finite mixture model.

Single-molecule RNA imaging

We are developing single-molecule RNA FISH techniques with high sensitivity and simple operation precedure. We leveraged quantum dots for single-molecule imaging and quantification [see Figure 3, Nat Comm, 2016]. 

Internet search engine on genomic data

Unlike text-based search engines, our search engine is based on pattern matching of functional genomic regions. This enables searches inside a new data type, namely the genome-wide intensity files including WIG and bigWig files [Nucleic Acd Res, 2016].

 

PREVIOUS RESEARCH PROJECTS

Evolution of mammalian gene regulatory networks

Genetic and epigenetic re-wiring of transcription networks

Thermodynamic modeling of interactions among transcription factors, DNA, and epigenome

Temporal epigenomic changes and dynamics of gene expresssion

 


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