原位
转录组
生物
计算生物学
高分辨率
仿形(计算机编程)
图像分辨率
基因表达
RNA序列
作者
Sanja Vicković,Gökçen Eraslan,Fredrik Salmén,Johanna Klughammer,Linnea Stenbeck,Denis Schapiro,Tarmo Äijö,Richard Bonneau,Ludvig Bergenstråhle,José Fernández Navarro,Joshua Gould,Gabriel Kenneth Griffin,Åke Borg,Mostafa Ronaghi,Jonas Frisén,Joakim Lundeberg,Aviv Regev,Patrik L. Ståhl
出处
期刊:Nature Methods
[Nature Portfolio]
日期:2019-09-09
卷期号:16 (10): 987-990
被引量:1149
标识
DOI:10.1038/s41592-019-0548-y
摘要
Abstract Tissue function relies on the precise spatial organization of cells characterized by distinct molecular profiles. Single-cell RNA-Seq captures molecular profiles but not spatial organization. Conversely, spatial profiling assays either lack global transcriptome information or are not at the single-cell level. Here, we develop High-Density Spatial Transcriptomics (HDST), a method for RNA-seq at high spatial resolution. Spatially barcoded reverse transcription oligonucleotides are coupled to beads that are then randomly deposited in individual wells on a slide. The barcoded beads are decoded and coupled to a specific spatial address. We then capture and spatially in situ label RNA from the same histological tissue sections placed on the bead array slide. HDST recovers hundreds of thousands of transcript-coupled barcodes per experiment at 2 μm resolution. We demonstrate HDST in the mouse brain, use it to resolve spatial expression patterns and cell types, and show how to combine it with histological stains to relate expression patterns to tissue architecture and anatomy. HDST opens the way to 2D spatial analysis of tissues at high resolution.
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