Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution
Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution
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DOI：
10.1126/science.aaw1219
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发表时间：
2019-03-29
期刊：
影响因子：
56.9
通讯作者：
Macosko, Evan Z.
中科院分区：
文献类型：
--
作者：
Rodriques, Samuel G.;Stickels, Robert R.;Macosko, Evan Z.
细胞在组织中的空间位置强烈影响功能，但缺乏具有细胞分辨率的高通量、全基因组的基因表达读数。我们开发了Slide-Seq，一种将RNA从组织切片转移到覆盖着已知位置的DNA条形码珠子的表面的方法，允许通过测序来推断RNA的位置。使用Slide-Seq，我们定位了由单细胞RNA测序数据集识别的小脑和海马区的细胞类型，表征了小鼠小脑浦肯野层的空间基因表达模式，并定义了创伤性脑损伤小鼠模型中细胞类型特异性反应的时间演变。这些研究突出了Slide-Seq如何提供了一种可扩展的方法，用于以与单个细胞大小相当的分辨率获得空间分辨的基因表达数据。
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.