压缩感知在高效成像转录组学中的应用。
Compressed sensing for highly efficient imaging transcriptomics.
机构信息
Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA.
出版信息
Nat Biotechnol. 2021 Aug;39(8):936-942. doi: 10.1038/s41587-021-00883-x. Epub 2021 Apr 15.
Recent methods for spatial imaging of tissue samples can identify up to ~100 individual proteins or RNAs at single-cell resolution. However, the number of proteins or genes that can be studied in these approaches is limited by long imaging times. Here we introduce Composite In Situ Imaging (CISI), a method that leverages structure in gene expression across both cells and tissues to limit the number of imaging cycles needed to obtain spatially resolved gene expression maps. CISI defines gene modules that can be detected using composite measurements from imaging probes for subsets of genes. The data are then decompressed to recover expression values for individual genes. CISI further reduces imaging time by not relying on spot-level resolution, enabling lower magnification acquisition, and is overall about 500-fold more efficient than current methods. Applying CISI to 12 mouse brain sections, we accurately recovered the spatial abundance of 37 individual genes from 11 composite measurements covering 180 mm and 476,276 cells.
最近的组织样本空间成像方法可以在单细胞分辨率下鉴定多达约 100 种单个蛋白质或 RNA。然而，这些方法可以研究的蛋白质或基因数量受到长成像时间的限制。在这里，我们介绍了复合原位成像（CISI），这是一种利用细胞和组织中基因表达的结构来限制获得空间分辨基因表达图谱所需的成像循环次数的方法。CISI 定义了可以使用成像探针的组合测量来检测的基因模块，这些探针针对基因的子集。然后对数据进行解压缩以恢复各个基因的表达值。CISI 通过不依赖于点级分辨率进一步减少了成像时间，从而能够实现更低的放大倍数采集，并且整体效率比当前方法高约 500 倍。我们将 CISI 应用于 12 个小鼠脑切片，仅用 11 次复合测量就从覆盖 180mm 和 476276 个细胞的 180mm 区域中准确地恢复了 37 个单个基因的空间丰度。
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