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

Research paper by Samuel G SG Rodriques, Robert R RR Stickels, Aleksandrina A Goeva, Carly A CA Martin, Evan E Murray, Charles R CR Vanderburg, Joshua J Welch, Linlin M LM Chen, Fei F Chen, Evan Z EZ Macosko

Indexed on: 31 May '19Published on: 30 Mar '19Published in: Science



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. Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.