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Three-dimensional intact-tissue sequencing of single-cell transcriptional states
Author(s) -
Xiao Wang,
William E. Allen,
Matthew A. Wright,
Emily L. Sylwestrak,
Nikolay Samusik,
Sam Vesuna,
Kathryn E. Evans,
Cindy Liu,
Charu Ramakrishnan,
Jia Liu,
Garry P. Nolan,
Felice-Alessio Bava,
Karl Deisseroth
Publication year - 2018
Publication title -
science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 12.556
H-Index - 1186
eISSN - 1095-9203
pISSN - 0036-8075
DOI - 10.1126/science.aat5691
Subject(s) - transcriptome , computational biology , biology , single cell sequencing , rna , in situ hybridization , dna sequencing , deep sequencing , illumina dye sequencing , in situ , gene , genetics , gene expression , genome , chemistry , exome sequencing , phenotype , organic chemistry
Retrieving high-content gene-expression information while retaining three-dimensional (3D) positional anatomy at cellular resolution has been difficult, limiting integrative understanding of structure and function in complex biological tissues. We developed and applied a technology for 3D intact-tissue RNA sequencing, termed STARmap (spatially-resolved transcript amplicon readout mapping), which integrates hydrogel-tissue chemistry, targeted signal amplification, and in situ sequencing. The capabilities of STARmap were tested by mapping 160 to 1020 genes simultaneously in sections of mouse brain at single-cell resolution with high efficiency, accuracy, and reproducibility. Moving to thick tissue blocks, we observed a molecularly defined gradient distribution of excitatory-neuron subtypes across cubic millimeter-scale volumes (>30,000 cells) and a short-range 3D self-clustering in many inhibitory-neuron subtypes that could be identified and described with 3D STARmap.

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