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Dissecting genetic, transcriptomic and epigenetic heterogeneity with single cell assay
1  Department of Biomedical Engineering, NUS
2  Biomedical Institute for Global Health Research and Technology (BIGHEART)
3  Institute of Molecular and Cell Biology (IMCB)


A hallmark of multicellularism is the diversity of specialized cell types with specific gene expression patterns. Dynamic control of gene expression involves many layers of regulations including epigenetic factors such as DNA methylation. In view of the complexity of gene regulation, individual data type alone cannot fully depict the developmental stages or cellular subtypes of a sample. Integrating data of multiple “omic” dimensions, including DNA methylation, gene expression, and genotype information from the same biological sample can collectively provide a more comprehensive picture of genome regulation during normal development and pathogenesis.

Here, we present a method to simultaneously interrogate DNA methylation state, gene expression and gene mutation at particular loci in single cells in an automated, high throughput microfluidic platform. We applied this platform to profile cellular heterogeneity in single cells from human fibroblast cell population undergoing reprogramming to induced pluripotent stem cell, and show that we can simultaneously capture the gene expression and DNA methylation dynamics of the single cells at various stages of reprogramming. We then applied this platform to interrogate cell-to-cell variability in primary lung adenocarcinoma. We detected substantial cell type heterogeneity in the primary tumor based on gene expression of marker genes and a subpopulation of epithelial cells were found to be hypermethylated in a set of tumor associated loci. This result reflects the rich microenvironment of the tumor, and also a unique epigenetic and gene expression profile of the tumor subpopulation that distinguish them from the stromal cells. In conclusion, single cell analysis uncovers important molecular variations in cellular subpopulations that would guide disease diagnosis and shed light on fundamental biological processes.