这篇文章介绍了 iSCALE，这是一种新颖的机器学习框架，旨在通过整合来自小捕获区域的空间转录组学 (ST) 数据和来自整个大组织的苏木精和伊红 (H&E) 染色高分辨率图像，来 预测大尺寸组织中的超分辨率基因表达。 iSCALE 克服了现有 ST 平台在成本、分辨率和组织捕获面积方面的局限性。基准测试显示，与传统方法相比，iSCALE 在胃癌和正常胃组织样本中能够 更准确地进行组织分割和基因表达预测。该方法还应用于多发性硬化症 (MS) 人脑样本，成功地 识别了与病灶相关的细胞特征、推断了细胞类型组成，并展示了在缺乏基因表达数据的测试样本上进行 样本外预测的能力。References: Schroeder A, Loth M L, Luo C, et al. Scaling up spatial transcriptomics for large-sized tissues: uncovering cellular-level tissue architecture beyond conventional platforms with iSCALE[J]. Nature methods, 2025: 1-12.
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