Slide-seq: A Scalable Technology for Measuring Genome-wide Expression at High Spatial Resolution

Overview

Journal Science

Specialty Science

Date 2019 Mar 30

PMID 30923225

Citations 925

Authors

Samuel G Rodriques

Robert R Stickels

Aleksandrina Goeva

Carly A Martin

Evan Murray

Charles R Vanderburg

Joshua Welch

Linlin M Chen

Fei Chen

Evan Z Macosko

Affiliations

Soon will be listed here.

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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