The function of multi-cellular systems emerges from the complex interactions between cells that have distinct behaviors and functions. Because cellular behaviors are in large part determined by their transcriptomes, the ability to quantify all transcripts in every single cell in a biological system would transform our understanding of a wide range of systems as well our ability to diagnose and treat diseases. Although single-cell transcriptomics methods based on high-throughput sequencing provide a powerful approach towards this goal, these methods requires dissociation of cells from their native tissue and extraction of RNA from the cells. As a result, it is difficult for these sequencing-based approaches to retain an important class of information that is crucial to a wide variety of biological processes: the spatial context of RNAs, i.e. where these RNAs are located within a cell and where the cells are located within the tissue. On the other hand, the spatial positions of RNAs within the cell can have a potent effect on their post-transcriptional fate and have been implicated in a diverse set of cellular behaviors from cell motility to cell polarization. Furthermore, the spatial organization of different types of cells within a tissue is of paramount importance to the tissue function: such spatial context modulates cell behavior, directs cell differentiation, and shapes the emergent behavior of the tissue as a whole. Therefore, a spatially-resolved approach to single-cell transcriptomics is in great demand and promises to transform many areas of biology. Here we propose to develop an imaging-based method that is capable of determining the precise copy numbers and spatial locations of most, if not all, RNA species (i.e. the whole transcriptome) within individual cells preserved in their native context. This approach functions by massively multiplexing single-molecule fluorescence in situ hybridization. In this approach, we will encode each RNA species in the cell with a barcode that is defined by a set of specially designed DNA probes that can specifically bind to and uniquely encode the target RNA. This barcode will then be read by a series of hybridization and imaging rounds, allowing us to determine the identity the RNA. Using an error-robust encoding scheme, we estimate that we should be able to image the entire mammalian transcriptome, i.e. several tens of thousands of RNA species, with high accuracy in just a few tens of imaging rounds or even fewer with multicolor imaging! This technique also promises a very high throughput of measuring hundreds of thousands of cells per single-day experiment. In this project, we will not only develop this technology and the above-mentioned capabilities, but also demonstrate the transformative impact of this technology in biology by mapping the spatial organization of the transcriptome inside individual cultured neurons and determining the number of transcriptionally distinct cell types and their spatial organization in a functionally important region in the mouse brain.

Public Health Relevance

In this project, we propose to develop an imaging-based single-cell transcriptomics approach that can provide not only the precise expression levels of RNAs but also the spatial locations of RNAs in cells and the spatial locations of transcriptionally distinct cells in tissues. This approach promises to provide important new insights into the molecular origin of distinct cell functions and behaviors. It can also be applied directly to examine diseased tissues, providing a prospect of disease prognosis and screening therapeutics effects.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Special Emphasis Panel (ZRG1-BCMB-A (51)R)
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Yao, Yong
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Harvard University
Schools of Arts and Sciences
United States
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Moffitt, Jeffrey R; Bambah-Mukku, Dhananjay; Eichhorn, Stephen W et al. (2018) Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362:
Wang, Guiping; Moffitt, Jeffrey R; Zhuang, Xiaowei (2018) Multiplexed imaging of high-density libraries of RNAs with MERFISH and expansion microscopy. Sci Rep 8:4847
Emanuel, George; Moffitt, Jeffrey R; Zhuang, Xiaowei (2017) High-throughput, image-based screening of pooled genetic-variant libraries. Nat Methods 14:1159-1162
Moffitt, Jeffrey R; Hao, Junjie; Wang, Guiping et al. (2016) High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. Proc Natl Acad Sci U S A 113:11046-51
Moffitt, Jeffrey R; Hao, Junjie; Bambah-Mukku, Dhananjay et al. (2016) High-performance multiplexed fluorescence in situ hybridization in culture and tissue with matrix imprinting and clearing. Proc Natl Acad Sci U S A 113:14456-14461