The genome within all cells of a multicellular organism is identical, yet different cells within the heart, brain or small intestine for example, display varied functions and properties. Even the same tissue is composed of several distinct cell types that gives rise to a wide range of cellular heterogeneity. What makes these cells, with identical genomic blueprints, different? It has been shown that chemical modifications of DNA and certain other proteins associated with DNA, collectively termed as the epigenome, result in the same genome being read in different ways that results in cell-to-cell heterogeneity in the expression of genes. Thus, one of the central questions we will be addressing in our group is to understand how the genome or epigenome regulates gene expression, thereby influencing cellular functions. Regulation of the transcriptome, defined as the genome-wide distribution of mRNA molecules, can be viewed as the output of a complex network of chemical and physical processes, and understanding how these processes interact and integrate to govern cellular behaviors, or phenotypes, will be a major focus area of our research.

Over the last decade, understanding genome-wide correlations between the genome, epigenome and transcriptome typically involved starting from a large population of cells or complex tissues. However, as these measurements are made from a bulk population, they only provide an average description of the system. As individual cells can display dramatic cell-to-cell variability, to unambiguously understand how a particular gene expression program in a cell is regulated will require direct measurement of both the transcriptome together with the epigenome from the same cell. Therefore, in our group we will be developing novel integrated technologies that enable simultaneous genome-wide measurements of the epigenome and transcriptome from the same cell to gain insights into early mammalian development, maintenance and regeneration of adult tissues and mechanisms contributing to tumor progression. Finally, we will be employing tools from single-cell genomics to unravel the evolutionary relationship between cells, also known as developmental lineage trees, that are currently not well known in complex multicellular organisms. Reconstructing these lineage trees will offer significant new insights into cellular differentiation with important applications in regenerative medicine.