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

  • Science. 2019 Mar 29;363(6434):1463-1467. doi: 10.1126/science.aaw1219.
Samuel G Rodriques  1  2  3 ,  Robert R Stickels  3  4  5 ,  Aleksandrina Goeva  3 ,  Carly A Martin  3 ,  Evan Murray  3 ,  Charles R Vanderburg  3 ,  Joshua Welch  3 ,  Linlin M Chen  3 ,  Fei Chen  6 ,  Evan Z Macosko  6  7
Affiliations
  • 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. [email protected] [email protected].
  • 7. Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA.
PMID: 30923225 DOI: 10.1126/science.aaw1219
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.