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

Samuel G. Rodriques,Robert R. Stickels,A. Goeva,Carly A Martin,Evan Murray,C. Vanderburg,Joshua D. Welch,Linlin M Chen,Fei Chen,Evan Z. Macosko

Published 2019 in Science

ABSTRACT

Gene expression at fine scale Mapping gene expression at the single-cell level within tissues remains a technical challenge. Rodriques et al. developed a method called Slide-seq, whereby RNA was spatially resolved from tissue sections by transfer onto a surface covered with DNA-barcoded beads. Applying Slide-seq to regions of a mouse brain revealed spatial gene expression patterns in the Purkinje layer of the cerebellum and axes of variation across Purkinje cell compartments. The authors used this method to dissect the temporal evolution of cell type–specific responses in a mouse model of traumatic brain injury. Science, this issue p. 1463 An RNA sequencing technique is developed that can measure genome-wide gene expression in complex tissues at 10-micrometer resolution. 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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