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Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2

delete2020-12-07
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OA
AI
R
Robert R. Stickels
E
Evan Murray
P
Pawan Kumar
J
Jilong Li
J
Jamie L. Marshall
D
Daniela J. Di Bella
P
Paola Arlotta
E
Evan Z. Macosko *
F
Fei Chen *
DOI:10.1038/s41587-020-0739-1delete
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摘要

摘要

En 中文
Measurement of the location of molecules in tissues is essential for understanding tissue formation and function. Previously, we developed Slide-seq, a technology that enables transcriptome-wide detection of RNAs with a spatial resolution of 10 mu m. Here we report Slide-seqV2, which combines improvements in library generation, bead synthesis and array indexing to reach an RNA capture efficiency similar to 50% that of single-cell RNA-seq data (similar to 10-fold greater than Slide-seq), approaching the detection efficiency of droplet-based single-cell RNA-seq techniques. First, we leverage the detection efficiency of Slide-seqV2 to identify dendritically localized mRNAs in neurons of the mouse hippocampus. Second, we integrate the spatial information of Slide-seqV2 data with single-cell trajectory analysis tools to characterize the spatiotemporal development of the mouse neocortex, identifying underlying genetic programs that were poorly sampled with Slide-seq. The combination of near-cellular resolution and high transcript detection efficiency makes Slide-seqV2 useful across many experimental contexts.
Keyword:
RETINOIC ACID SYNTHESIS
GENOME-WIDE EXPRESSION
PROTEIN-SYNTHESIS
CELLS
GENE
SEQ
DIVERSITY
TISSUE
EYE
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期刊

Nature Biotechnology 封面图
Nature Biotechnology
IF:
41.7
论文数:
1.2W
被引数:
10.1W

机构

H
Harvard University
学者数:
26.5W
论文数: 22.0W
被引数: 28.7W
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