这篇文章介绍了 GHIST，这是一个基于深度学习的框架，旨在从常规收集的组织学图像（如苏木精和伊红染色图像，H&E）中，以单细胞分辨率预测空间基因表达 (SGE)。文章指出，虽然空间分辨转录组学 (SRT) 技术提供了生物学洞察，但其高成本限制了广泛应用，因此 GHIST 通过整合多层生物学信息（包括细胞核形态、细胞类型和邻域组成）来克服现有方法的准确性限制。研究人员通过公共数据集和癌症基因组图谱 (TCGA) 数据验证了 GHIST 的性能，证明它在预测 SGE 方面优于现有的基于斑点的方法，并强调了该框架能够通过体外生成空间组学数据来丰富现有数据库，从而促进可扩展的多组学分析和生物标志物发现。References: Fu X, Cao Y, Bian B, et al. Spatial gene expression at single-cell resolution from histology using deep learning with ghist[J]. Nature Methods, 2025: 1-11.
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