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 Analysis
Single Cell Whole Genome Amplification (Linear MALBAC)
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. As the demonstration of the principle, we are able to detect the genomic variations between individual cancer cells. We are 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.
Quantitative Single cell RNA-seq (MATQ-seq)
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 report 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. By systematically determining technical noise, we show that MATQ-seq captures genuine biological variation between whole transcriptomes 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). These 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 with 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 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.
Clinical Application of Single Cell Methods
We actively pursue clinical applications of single cell techniques, including prenatal genetic testing as well as early cancer diagnosis.
Tian L, Goldstein A, Wang H, Kim JK, Lo HC, Welte T, Sheng KW, Dobrolecki L, Zhang XM, Putluri N, Phung T, Mani SA, Stossi F, Sreekumar A, Mancini MA, Zong CH, Decker WK, Lewis MT, and Zhang X. Th1 helper cells promote tumor vessel normalization and mitigate hypoxia, Nature 2017
Zhang X, Wang H, Tian L, Goldstein A, Lo HC, Liu J, Sheng KS, Stossi F, Zong CH, Mancini M, Gugala Z, Welte T, Li ZH, Wong S, Bone-in-culture array as a platform to model early-stage bone metastases and discover anti-metastasis therapies, Nature Communication 2017
Sheng KW, Cao WJ, Niu YC, Deng Q, Zong CH, Effective detection of variation in single-cell transcriptomes using MATQ-seq. Nature Methods. 2017, Doi:10.1038/NMETH.4145
Zong C, Lu S, Chapman AR, Xie XS (2012). Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science 338(6114): 1622-6. PubMed PMID: 23258894
Lu S, Zong C, Fan W, Yang M, Li J, Chapman AR, Zhu P, Hu X, Xu L, Yan L, Bai F, Qiao J, Tang F, Li R, Xie XS (2012). Probing meiotic recombination and aneuploidy of single sperm cells by whole-genome sequencing. Science 338(6114): 1627-30. PubMed PMID: 23258895
So LH, Ghosh A, Zong C, Sepúlveda LA, Segev R, Golding I (2011). General properties of transcriptional time series in Escherichia coli. Nat. Genet. 43(6): 554-60. PubMed PMID: 21532574
Zong C, So LH, Sepúlveda LA, Skinner SO, Golding I (2010). Lysogen stability is determined by the frequency of activity bursts from the fate-determining gene. Mol. Syst. Biol. 2010 6: 440. PubMed PMID: 21119634
Zeng L, Skinner SO, Zong C, Sippy J, Feiss M, Golding I (2010). Decision making at a subcellular level determines the outcome of bacteriophage infection. Cell 141(4): 682-91. PubMed PMID: 20478257