Multiscale Cell–Cell Interactive Spatial Transcriptomics Analysis

doi:10.1002/advs.202508358

2025-09-15

Sean Cottrell

Guo‐Wei Wei *

DOI：10.1002/advs.202508358

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摘要

En 中文

空间转录组学数据分析将基因表达谱与其对应的空间位置相结合，以识别空间域、推断细胞类型动态，并在组织中检测基因表达模式。然而，当前的空間轉錄組學分析忽视了在生物学中至关重要的多尺度细胞-细胞相互作用。为填补这一空白，提出了多尺度细胞-细胞交互空间转录组学（MCIST）分析。MCIST结合了基因表达空间中多尺度拓扑表示的细胞-细胞相互作用的集成优势与尖端空间深度学习技术的优势。MCIST通过在37个基准空间转录组学数据集的大规模集合上与14种尖端方法的比较进行了验证。结果表明，MCIST在空间域检测方面表现出卓越的性能。它在23/37个数据集上获得了最佳聚类分数，并在33/37个数据集上位列前三，而次优方法在这些指标上的得分仅为6/37和17/37。就定量指标下的整体性能而言，MCIST在空间域检测方面较先前最先进方法提升了超过11%。此外，MCIST在轨迹推断、差异表达基因检测和信号通路富集分析方面提供了多尺度见解。这一MCIST为空间转录组学的多尺度视角提供了宝贵的见解。

Keyword:

deep learning

multiscale cell–cell interactions

persistent Laplacian

spatial transcriptomics

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期刊

Advanced Science

IF:

14.1

论文数:

1.8W

被引数:

11.5W

机构

michigan state university

学者数:

3.6W

论文数: 3.2W

被引数: 44

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