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细胞空间分析:使用多范围、多类型点过程模型学习细胞类型空间关系。

CytoSpatio: Learning cell type spatial relationships using multirange, multitype point process models.

作者信息

Chen Haoran, Zhang Yangyuan, Murphy Robert F

机构信息

Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

出版信息

PLoS Comput Biol. 2025 Aug 21;21(8):e1013409. doi: 10.1371/journal.pcbi.1013409. eCollection 2025 Aug.


DOI:10.1371/journal.pcbi.1013409
PMID:40839662
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12396756/
Abstract

Recent advances in multiplexed fluorescence imaging have provided new opportunities for deciphering the complex spatial relationships among various cell types across diverse tissues. We introduce CytoSpatio, open-source software that constructs generative, multirange, and multitype point process models that capture interactions among multiple cell types at various distances simultaneously. On analyzing five cell types across five tissues, our software showed consistent spatial relationships within the same tissue type, with certain cell types like proliferating T cells consistently clustering across tissue types. It also revealed that the attraction-repulsion relationships between cell types like B cells and CD4-positive T cells vary with tissue type. Models for a published dataset demonstrated consistency with prior findings. CytoSpatio can also generate synthetic tissue patterns from learned models, a capability not provided by previous descriptive, motif-based approaches. This potentially allows spatially realistic simulations of how cell relationships affect tissue biochemistry.

摘要

多路复用荧光成像技术的最新进展为解读不同组织中各种细胞类型之间复杂的空间关系提供了新机遇。我们推出了CytoSpatio,这是一款开源软件,它构建了生成式、多范围和多类型点过程模型,可同时捕捉多种细胞类型在不同距离下的相互作用。在分析五种组织中的五种细胞类型时,我们的软件显示出同一组织类型内存在一致的空间关系,某些细胞类型(如增殖性T细胞)在不同组织类型中始终聚集在一起。它还揭示了B细胞和CD4阳性T细胞等细胞类型之间的吸引-排斥关系会因组织类型而异。针对已发表数据集构建的模型与先前的研究结果一致。CytoSpatio还可以从学习到的模型中生成合成组织模式,这是以前基于描述性基序的方法所不具备的能力。这有可能实现对细胞关系如何影响组织生物化学的空间真实模拟。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/950f/12396756/de3f7b55e8f0/pcbi.1013409.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/950f/12396756/89fd8853d52e/pcbi.1013409.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/950f/12396756/11b3e7e7943c/pcbi.1013409.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/950f/12396756/4e8b59f7a9b5/pcbi.1013409.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/950f/12396756/7514a55c27ef/pcbi.1013409.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/950f/12396756/1cc83e77bcdb/pcbi.1013409.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/950f/12396756/6294c9798fc9/pcbi.1013409.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/950f/12396756/de3f7b55e8f0/pcbi.1013409.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/950f/12396756/89fd8853d52e/pcbi.1013409.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/950f/12396756/11b3e7e7943c/pcbi.1013409.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/950f/12396756/4e8b59f7a9b5/pcbi.1013409.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/950f/12396756/7514a55c27ef/pcbi.1013409.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/950f/12396756/1cc83e77bcdb/pcbi.1013409.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/950f/12396756/6294c9798fc9/pcbi.1013409.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/950f/12396756/de3f7b55e8f0/pcbi.1013409.g007.jpg

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本文引用的文献

[1]
A comprehensive single-cell map of T cell exhaustion-associated immune environments in human breast cancer.

Nat Commun. 2023-1-6

[2]
Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis.

Cell Rep Methods. 2022-11-21

[3]
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IEEE Trans Vis Comput Graph. 2023-1

[4]
Interactive single-cell data analysis using Cellar.

Nat Commun. 2022-4-14

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Front Genet. 2022-1-27

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Front Physiol. 2022-1-28

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Nat Commun. 2022-2-9

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