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Science. 2019 Mar 29;363(6434):1463-1467. doi: 10.1126/science.aaw1219. Epub 2019 Mar 28.

Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution.

Author information

1
Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
2
MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
3
Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
4
Graduate School of Arts and Sciences, Harvard University, Cambridge, MA 02138, USA.
5
Division of Medical Science, Harvard Medical School, Boston, MA 02115, USA.
6
Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA. chenf@broadinstitute.org emacosko@broadinstitute.org.
7
Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA.

Abstract

Spatial positions of cells in tissues strongly influence function, yet a high-throughput, genome-wide readout of gene expression with cellular resolution is lacking. We developed Slide-seq, a method for transferring RNA from tissue sections onto a surface covered in DNA-barcoded beads with known positions, allowing the locations of the RNA to be inferred by sequencing. Using Slide-seq, we localized cell types identified by single-cell RNA sequencing datasets within the cerebellum and hippocampus, characterized spatial gene expression patterns in the Purkinje layer of mouse cerebellum, and defined the temporal evolution of cell type-specific responses in a mouse model of traumatic brain injury. These studies highlight how Slide-seq provides a scalable method for obtaining spatially resolved gene expression data at resolutions comparable to the sizes of individual cells.

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PMID:
30923225
DOI:
10.1126/science.aaw1219
[Indexed for MEDLINE]

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