Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+-Reference-Cited by-同舟云学术

Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+

Published:2019-03-25 Issue:7751 Volume:568 Page:235-239
ISSN:0028-0836
Container-title:Nature
language:en
Short-container-title:Nature

Author:

Eng Chee-Huat Linus,Lawson Michael,Zhu Qian,Dries Ruben,Koulena Noushin,Takei Yodai,Yun Jina,Cronin Christopher,Karp Christoph,Yuan Guo-Cheng,Cai Long

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

http://www.nature.com/articles/s41586-019-1049-y.pdf

Reference41 articles.

1. Lubeck, E., Coskun, A. F., Zhiyentayev, T., Ahmad, M. & Cai, L. Single-cell in situ RNA profiling by sequential hybridization. Nat. Methods 11, 360–361 (2014).

2. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

3. Shah, S., Lubeck, E., Zhou, W. & Cai, L. In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 92, 342–357 (2016).

4. Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014).

5. Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).

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