Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution.
IF: 63.714
Cited by: 1,179


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.


Spatial Gene Expression

MeSH terms

Brain Injuries, Traumatic
Cell Size
Disease Models, Animal
Frozen Sections
Gene Expression Regulation
Genome-Wide Association Study
High-Throughput Nucleotide Sequencing
Purkinje Cells
RNA, Messenger
Sequence Analysis, RNA
Single-Cell Analysis
Transcription, Genetic


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

Recommend literature

Similar data