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.
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|Park, Jin; Dies, Marta; Lin, Yihan et al. (2018) Molecular Time Sharing through Dynamic Pulsing in Single Cells. Cell Syst 6:216-229.e15|
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|Shah, Sheel; Takei, Yodai; Zhou, Wen et al. (2018) Dynamics and Spatial Genomics of the Nascent Transcriptome by Intron seqFISH. Cell 174:363-376.e16|
|Eng, Chee-Huat Linus; Shah, Sheel; Thomassie, Julian et al. (2017) Profiling the transcriptome with RNA SPOTs. Nat Methods 14:1153-1155|
|Lignell, Antti; Kerosuo, Laura; Streichan, Sebastian J et al. (2017) Identification of a neural crest stem cell niche by Spatial Genomic Analysis. Nat Commun 8:1830|
|Cutrale, Francesco; Trivedi, Vikas; Trinh, Le A et al. (2017) Hyperspectral phasor analysis enables multiplexed 5D in vivo imaging. Nat Methods 14:149-152|
|Ojosnegros, Samuel; Cutrale, Francesco; Rodríguez, Daniel et al. (2017) Eph-ephrin signaling modulated by polymerization and condensation of receptors. Proc Natl Acad Sci U S A 114:13188-13193|
|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; 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|
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