Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology.
Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology.
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通过将空间转录组学与组织学相结合来推断超分辨率组织结构。
DOI：
10.1038/s41587-023-02019-9
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发表时间：
2024
影响因子：
46.9
通讯作者：
Li,Mingyao
中科院分区：
文献类型：
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作者：
Zhang,Daiwei;Schroeder,Amelia;Yan,Hanying;Yang,Haochen;Hu,Jian;Lee,MichelleYY;Cho,KyungS;Susztak,Katalin;Xu,GeorgeX;Feldman,MichaelD;Lee,EdwardB;Furth,EmmaE;Wang,Linghua;Li,Mingyao
空间转录组学(ST)已经显示出在组织内产生复杂的细胞分子图谱的巨大潜力。在这里，我们提出了一种基于分层图像特征提取的方法iStar，它集成了ST数据和高分辨率组织学图像，以超分辨率预测空间基因表达。我们的方法将ST中的基因表达分辨率提高到接近单细胞水平，并在只有组织学图像可用的组织切片中实现基因表达预测。
Spatial transcriptomics (ST) has demonstrated enormous potential for generating intricate molecular maps of cells within tissues. Here we present iStar, a method based on hierarchical image feature extraction that integrates ST data and high-resolution histology images to predict spatial gene expression with super-resolution. Our method enhances gene expression resolution to near-single-cell levels in ST and enables gene expression prediction in tissue sections where only histology images are available.