The ability to accurately determine the molecular states of individual cells is critical for key applications in stem cell biology, development, and cancer, but remains difficult with existing techniques. We recently demonstrated a new technique to image multiple mRNAs in single cells in a highly multiplexed fashion with super-resolution microscopy and combinatorial labeling. In proof-of-principle experiments, we have shown that 32 genes can be multiplexed simultaneously in individual cells and quantitated with single molecule resolution. However, the multiplex capacity of this system can be drastically increased by using more of the commercially available fluorophores and more complex coding schemes. Here, we have set 1000 genes as a practical target realizable with current technology. In addition, we will develop the techniques further by combining it with light sheet microscopy to access deep tissue samples, and thereby provide spatial information of gene expression in cells and tissues. We further propose to translate the technology to two key public health relevant model systems: embryonic stem cells and breast cancer tumors, with a multi-disciplinary team composed of biologists, clinicians, engineers and chemists. As optical microscopy is a widely available and relatively inexpensive laboratory and clinical tool, success of this project will pav a path toward wide adoption of the technology in transforming single cell analysis in basic research and clinical diagnosis.
This project proposes to translate a newly developed technique using advanced optical microscopy to profile the gene expression states of individual mouse embryonic stem cells (mESCs) and breast cancer tumors. mESCs serve as a model system for understanding differentiation and self-renewal circuits central to regenerative medicine and stem cell therapeutics. Early diagnosis and molecular identification of breast cancer is crucial for patient survival. By illuminating the genetic states of tumor cells with supe-resolution, we aim to understand the basic biology of tumor cells, as well as provide tools to guide individual treatment options for improved clinical outcomes.
|Coskun, Ahmet F; Cai, Long (2016) Dense transcript profiling in single cells by image correlation decoding. Nat Methods 13:657-60|
|Hormoz, Sahand; Singer, Zakary S; Linton, James M et al. (2016) Inferring Cell-State Transition Dynamics from Lineage Trees and Endpoint Single-Cell Measurements. Cell Syst 3:419-433.e8|
|Shah, Sheel; Lubeck, Eric; Schwarzkopf, Maayan et al. (2016) Single-molecule RNA detection at depth by hybridization chain reaction and tissue hydrogel embedding and clearing. Development 143:2862-7|
|Shah, Sheel; Lubeck, Eric; Zhou, Wen et al. (2016) In Situ Transcription Profiling of Single Cells Reveals Spatial Organization of Cells in the Mouse Hippocampus. Neuron 92:342-357|
|Bintu, Lacramioara; Yong, John; Antebi, Yaron E et al. (2016) Dynamics of epigenetic regulation at the single-cell level. Science 351:720-4|
|Kim, Daniel H; Marinov, Georgi K; Pepke, Shirley et al. (2015) Single-cell transcriptome analysis reveals dynamic changes in lncRNA expression during reprogramming. Cell Stem Cell 16:88-101|
|Singer, Zakary S; Yong, John; Tischler, Julia et al. (2014) Dynamic heterogeneity and DNA methylation in embryonic stem cells. Mol Cell 55:319-31|
|Lubeck, Eric; Coskun, Ahmet F; Zhiyentayev, Timur et al. (2014) Single-cell in situ RNA profiling by sequential hybridization. Nat Methods 11:360-1|
|Yang, Bin; Treweek, Jennifer B; Kulkarni, Rajan P et al. (2014) Single-cell phenotyping within transparent intact tissue through whole-body clearing. Cell 158:945-58|
|Minor, Paul J; He, Ting-Fang; Sohn, Chang Ho et al. (2013) FGF signaling regulates Wnt ligand expression to control vulval cell lineage polarity in C. elegans. Development 140:3882-91|
Showing the most recent 10 out of 11 publications