In vitro manipulation of cell fate, either through nuclear reprogramming or differentiation, remains to be an imprecise and inefficient process. Improving the accuracy and efficiency of in vitro cell fate conversion is of great interest to regenerative medicine. In order to gain a better control of cell fate conversion, it is important t identify the key genes or molecular pathways that drive individual cells to the desirable lineage, and to contrast them with the activities of the genes or pathways in the cells at the undesirable states. This will generate a short list of candidate genes/pathways that can be intervened for a more precise cell fate conversion. For this purpose, it is crucial to align the full transcriptome information of single cells along the time axis, and to connect the molecular signatures to the cellular phenotypes at the later stages. In this project, we will develop strategies based on cell lineage tracing and single-cell full transcriptome sequencing, and will apply it to the investigatin of the transcriptional regulation underlying cell dedifferentiation using novel models of human cells dedifferentiation with clear implications for the treatment of cardiovascular diseases.
The first aim i nvolves establishing a technical platform for single cell lineage tracing based on stochastic fluorescent labeling, single- cell isolation and transcriptome analysis. In the second aim we will investigate two cell-fate conversion procedures based on the concept of partial de-differentiation and re-differentiation.
To improve the regeneration of two major cell populations (cardiomyocytes and vascular cells) damaged in cardiovascular diseases, we will use single-cell lineage tracing and transcriptome sequencing approach to investigate the cell fate conversion process, and to understand the molecular pathways that control the cell fate decision.
Parekh, Udit; Wu, Yan; Zhao, Dongxin et al. (2018) Mapping Cellular Reprogramming via Pooled Overexpression Screens with Paired Fitness and Single-Cell RNA-Sequencing Readout. Cell Syst 7:548-555.e8 |
Wu, Yan; Tamayo, Pablo; Zhang, Kun (2018) Visualizing and Interpreting Single-Cell Gene Expression Datasets with Similarity Weighted Nonnegative Embedding. Cell Syst 7:656-666.e4 |
Liao, Hsin-Kai; Hatanaka, Fumiyuki; Araoka, Toshikazu et al. (2017) In Vivo Target Gene Activation via CRISPR/Cas9-Mediated Trans-epigenetic Modulation. Cell 171:1495-1507.e15 |
Yamasaki, Amanda E; Panopoulos, Athanasia D; Belmonte, Juan Carlos Izpisua (2017) Understanding the genetics behind complex human disease with large-scale iPSC collections. Genome Biol 18:135 |
Suzuki, Keiichiro; Tsunekawa, Yuji; Hernandez-Benitez, Reyna et al. (2016) In vivo genome editing via CRISPR/Cas9 mediated homology-independent targeted integration. Nature 540:144-149 |