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Decoding cell state transitions driven by dynamic cell–cell communication in spatial transcriptomics

delete2026-01-05
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PRE
AI
L
Lulu Yan
D
Dongyan Zhang
孙小强 封面图
孙小强 (Xiaoqiang Sun) *
DOI：10.1038/s43588-025-00934-2delete
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摘要

摘要

En 中文
在多细胞系统中，细胞命运决定源于细胞内信号与细胞间通讯的整合。空间转录组学（ST）为阐明这些调控过程提供了机会，但推断由细胞间通讯（CCC）调控的细胞状态转换（CST）的空间-时间动态仍具挑战。本文介绍CCCvelo，通过联合优化动态CCC信号网络和潜在CST时钟来重建CCC驱动的CST动态。CCCvelo构建了一个统一的跨尺度非线性动力学模型，整合细胞间配体-受体信号梯度与细胞内转录因子激活级联，以捕获编码CST的基因表达动态。此外，我们设计了PINN-CELL，一种基于物理信息神经网络（PINN）的协同演化学习算法，可同时优化模型参数和伪时间排序。将CCCvelo应用于高分辨率ST数据集（包括小鼠皮质、胚胎躯干发育和人类前列腺癌数据集），证明了其能够成功恢复已知的形态发生轨迹，同时揭示调控CST进程的动态CCC信号重布线。本文介绍了CCCvelo，一种计算工具，利用空间组学数据识别细胞间通讯如何指导细胞命运决定，揭示调控发育和疾病的空间-时间信号动态。
Keyword:
cell state transitions
cell–cell communication
spatial transcriptomics
pseudotemporal ordering
multiscale kinetic modeling

期刊

Nature Computational Science 封面图
Nature Computational Science
IF:
18.3
论文数:
3.1K
被引数:
4.0K

机构

S
Sun Yat-sen University
学者数:
3.7K
论文数: 1.1K
被引数: 1.6W
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