美国宾夕法尼亚大学Kai Tan等研究人员合作实现从细胞表型中无监督和有监督地发现组织细胞邻域。这一研究成果于2024年1月8日在线发表在国际学术期刊《自然—方法学》上。

研究人员介绍了基于细胞表型及其空间分布识别组织细胞邻域（TCN）的CytoCommunity算法。CytoCommunity使用图神经网络模型直接学习从细胞表型空间到TCN空间的映射，而无需对细胞嵌入进行中间聚类。通过利用图库，CytoCommunity可以在样本标签的监督下从头开始识别条件特异性和预测性TCN。利用几种类型的空间多组学数据，研究人员证明CytoCommunity可以识别不同大小的TCN，比现有方法有很大改进。

通过分析风险分层的结直肠癌和乳腺癌数据，CytoCommunity发现了新的粒细胞富集和癌症相关成纤维细胞富集的高危肿瘤特异性TCN，以及TCN内部和之间肿瘤细胞与免疫细胞或基质细胞之间改变的相互作用。CytoCommunity可以对空间多组学图谱进行无监督和有监督分析，并能发现跨空间尺度的特异性细胞-细胞通讯模式。

据了解，人们对组织中不同细胞如何组织起来支持组织功能还知之甚少。

附：英文原文

Title: Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes

Author: Hu, Yuxuan, Rong, Jiazhen, Xu, Yafei, Xie, Runzhi, Peng, Jacqueline, Gao, Lin, Tan, Kai

Issue&Volume: 2024-01-08

Abstract: It is poorly understood how different cells in a tissue organize themselves to support tissue functions. We describe the CytoCommunity algorithm for the identification of tissue cellular neighborhoods (TCNs) based on cell phenotypes and their spatial distributions. CytoCommunity learns a mapping directly from the cell phenotype space to the TCN space using a graph neural network model without intermediate clustering of cell embeddings. By leveraging graph pooling, CytoCommunity enables de novo identification of condition-specific and predictive TCNs under the supervision of sample labels. Using several types of spatial omics data, we demonstrate that CytoCommunity can identify TCNs of variable sizes with substantial improvement over existing methods. By analyzing risk-stratified colorectal and breast cancer data, CytoCommunity revealed new granulocyte-enriched and cancer-associated fibroblast-enriched TCNs specific to high-risk tumors and altered interactions between neoplastic and immune or stromal cells within and between TCNs. CytoCommunity can perform unsupervised and supervised analyses of spatial omics maps and enable the discovery of condition-specific cell–cell communication patterns across spatial scales.

DOI: 10.1038/s41592-023-02124-2

Source: https://www.nature.com/articles/s41592-023-02124-2