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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Type
Research Project (R01)
Project #
4R01HD075605-05
Application #
9118777
Study Section
Special Emphasis Panel (ZRG1-CB-D (50)R)
Program Officer
Taymans, Susan
Project Start
2012-09-24
Project End
2017-06-30
Budget Start
2016-07-01
Budget End
2017-06-30
Support Year
5
Fiscal Year
2016
Total Cost
$864,660
Indirect Cost
$258,113
Name
California Institute of Technology
Department
Type
Schools of Arts and Sciences
DUNS #
009584210
City
Pasadena
State
CA
Country
United States
Zip Code
91125
Zhu, Qian; Shah, Sheel; Dries, Ruben et al. (2018) Identification of spatially associated subpopulations by combining scRNAseq and sequential fluorescence in situ hybridization data. Nat Biotechnol :
Park, Jin; Dies, Marta; Lin, Yihan et al. (2018) Molecular Time Sharing through Dynamic Pulsing in Single Cells. Cell Syst 6:216-229.e15
Trivedi, Vikas; Choi, Harry M T; Fraser, Scott E et al. (2018) Multidimensional quantitative analysis of mRNA expression within intact vertebrate embryos. Development 145:
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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