Understanding the variation of induced β-cell differentiation.

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Project Abstract Pancreatic β-cells is essential for the regulation of blood glucose. One major hope for diabetes therapy is to generate a large number of functional, transplantable beta-cells from patient-derived pluripotent cells. In the past decade, a few in vitro protocols have been developed to differentiate human pluripotent stem cells (hPSCs) into functional β-like cells, which also serve as fantastic tools for the study of human pancreatic development to reveal the etiology of relevant diseases. However, the major limitations to use β-cell differentiation system in research and therapeutics is that the protocol is still not robust. (i) The differentiation generates heterogenous cell populations; (ii) Differentiation efficiency is variable between different hPSC lines, and also between batches. (iii) The resulting β-like cells are still not quite equivalent to primary β-cells from human islets at molecular and physiological levels. To address this problem, we propose to use the latest single cell and low-input genomic technology to generate a reliable map of lineage determination in this system. Importantly, we will for the first time map the individual variation between the differentiation of 24 hPSC lines. To ensure robust comparison, we have devised a pooling- demultiplexing single cell genomic approach that allows simultaneous mapping of many hPSC lines in one scRNA-seq or scATAC-seq experiment. This strategy minimizes the batch variation and significantly reduces the experimental cost. In Aim 1, we will use this approach and scRNA-seq to map the dynamics and variation of single cell transcriptome while differentiating 24 hPSC lines towards pancreatic β-cells. In Aim 2, we will map the dynamics and variation of open chromatin using scATAC-seq, and we will also use a low-input Hi-C technology to reveal the dynamic 3D genome during β-cell differentiation. In aim 3, we will perform high-throughput CRISPR screen and locus-specific genome editing to discover and validate key differentiation regulators at both gene and enhancer levels. This project is built upon a rich set of published and preliminary data, which already led to improved differentiation protocol and better understanding of disease genetics. Completion of this project will deliver a comprehensive data resource of transcriptome, epigenome, and 3D genome during the β-cell differentiation, which will shed light on the disease etiology, and reveal novel therapeutic opportunities.