The research of our laboratory lies in the interface between systems biology and cancer biology. We pursue the development of novel single cell methods for characterizing genomic, epigenetic and transcriptional variations at single cell resolution. We use genetically engineered mice models to investigate tumorigenesis, specifically pancreatic cancer.
Single Cell Whole Genome Amplification for Studying Genome Instability and DNA damage and their roles in various complex human diseases and disorders
With the rapid development of next-generation sequencing technology, high-throughput sequencing has become a powerful tool for biological research. In our lab, we are interested in examining the genome at single-cell resolution, in contrast to the genome averaged from an ensemble of cells.
With the development of Multiple-annealing and Multiple-displacement based Linear Splitting Amplification (MM-LSA), we are able to accurately determine the levels of de novo mutations existing in single cells, but more importantly, we achieved the detection of the spontaneous DNA damage as "de novo" variants in single cells. We denote these damage-associated “de novo” variants as damSNVs and the genome-wide distribution of damSNVs as damagenome.
With single-cell whole-genome analysis, we are also interested in detecting early events that drive tumorigenesis as well as the early stage of tumor heterogeneity that will influence later tumor development. The lab will focus on pancreatic cancer in particular.
Development of Quantitative Single-cell RNA-seq methods for Studying Tumorigenesis and Aging processes
The quantification of transcriptional variation in single cells, particularly within the same cell population, is currently limited by the low sensitivity and high technical noise of single-cell RNA-seq assays. We have developed multiple annealing and dC-tailing-based quantitative single-cell RNA-seq (MATQ-seq), a highly sensitive and quantitative method for single-cell sequencing of total RNA. The detection sensitivity of MATQ-seq is ~90% for single transcripts. By systematically determining technical noise, we show that MATQ-seq captures genuine biological variation between whole transcriptomes of single cells. We recently developed a MATQ-drop platform to increase the throughput of single cells.
Overall, the goal of developing novel single-cell methods is to provide the characterization of biological processes with much finer temporal and spatial resolution. This will allow us to unveil the high order information ( variations and heterogeneity), which are totally ignored by ensemble profiling (zero and first-order). This information is highly desired for modeling complex cellular decision-making processes at multiple layers of pathways and regulatory networks.
Tumorigenesis in Pancreatic Cancer
Tumorigenesis involves dedifferentiation/transdifferentiation, which allows the cancer-initiating cell to gain more plasticity and proliferation by hijacking existing developmental or tissue self-renewal mechanisms. We are interested in investigating the mutations of genes involved in epigenetic regulations. The perturbation to epigenetic regulation by mutations can initiate the instability and lead to dedifferentiation/transdifferentiation at the early stage of tumorigenesis. The onset of this instability and the potential heterogeneity following that demands single-cell analysis for better characterization of functional variances among the tumor-initiating cells. In particular, we have developed transcriptome profiling of individual pancreatic intraepithelial neoplasia to capture the early events of this transformation.
Clinical Application of Single Cell Methods
We actively pursue clinical applications of single-cell techniques, including prenatal genetic testing as well as an early cancer diagnosis.