Abstract
Conventional gene expression studies analyze multiple cells simultaneously or single cells, for which the exact in vivo or in situ position is unknown. Although cellular heterogeneity can be discerned when analyzing single cells, any spatially defined attributes that underpin the heterogeneous nature of the cells cannot be identified. Here, we describe how to use geographical position sequencing (Geo-seq), a method that combines laser capture microdissection (LCM) and single-cell RNA-seq technology. The combination of these two methods enables the elucidation of cellular heterogeneity and spatial variance simultaneously. The Geo-seq protocol allows the profiling of transcriptome information from only a small number cells and retains their native spatial information. This protocol has wide potential applications to address biological and pathological questions of cellular properties such as prospective cell fates, biological function and the gene regulatory network. Geo-seq has been applied to investigate the spatial transcriptome of mouse early embryo, mouse brain, and pathological liver and sperm tissues. The entire protocol from tissue collection and microdissection to sequencing requires ∼5 d, Data analysis takes another 1 or 2 weeks, depending on the amount of data and the speed of the processor.
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Change history
13 April 2017
In the version of this article initially published, Shengbao Suo was not indicated as contributing equally to the work with Jun Chen; Jing-Dong J. Han was not indicated as a corresponding author; the expanded form of "Geo-seq" was not given in the abstract; in the heading prior to Step 77, it was not stated that the time indicated excludes the time required for the design of 2D corn plots and for zip-code mapping; "Corn plot visualization and zip-code mapping can be performed through the iTranscriptome portal at http://www.itranscriptome.org." was omitted from the end of Step 77; and some grant numbers were omitted from the Acknowledgments section. These omissions have been corrected in the HTML and PDF versions of the article.
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Acknowledgements
We are grateful to Q. Zhou (Institute of Zoology, Chinese Academy of Sciences) for helpful suggestions, and to Y. Qian of the Jing laboratory for technical support. The sequencing was performed on an Illumina HiSeq2500 system by BerryGenomics (Beijing, China). This work was supported in part by the 'Strategic Priority Research Program' of the Chinese Academy of Sciences (XDA01010201 to N.J., XDB19020301 and XDA01010303 to J.-D.J.H.), the National Key Basic Research and Development Program of China (2014CB964804 and 2015CB964500 to N.J., and 2015CB964803 and 2016YFE0108700 to J.-D.J.H.) and the National Natural Science Foundation of China (31430058, 31571513, 31630043, 91519314 to N.J., and 91329302, 31210103916, 91519330 to J.-D.J.H.).
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N.J. supervised the project. N.J. and G.P. designed the experiment. J.C. and G.P. performed the laser microdissection and RNA-seq. J.C. and S.S. wrote the manuscript. G.P., P.P.L.T., J.-D.J.H. and N.J. edited the manuscript.
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Chen, J., Suo, S., Tam, P. et al. Spatial transcriptomic analysis of cryosectioned tissue samples with Geo-seq. Nat Protoc 12, 566–580 (2017). https://doi.org/10.1038/nprot.2017.003
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DOI: https://doi.org/10.1038/nprot.2017.003
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