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

Autor: Stickels, Robert R., Murray, Evan, Kumar, Pawan, Li, Jilong, Marshall, Jamie L., Di Bella, Daniela J., Arlotta, Paola, Macosko, Evan Z., Chen, Fei
Zdroj: Nature Biotechnology; Mar2021, Vol. 39 Issue 3, p313-319, 7p
Abstrakt: 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 μm. Here we report Slide-seqV2, which combines improvements in library generation, bead synthesis and array indexing to reach an RNA capture efficiency ~50% that of single-cell RNA-seq data (~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. An improved method for spatial transcriptomics with detection efficiency approaching that of droplet-based single-cell RNA-seq techniques. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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