美国德克萨斯大学Tao Wang等研究人员合作从空间分辨的转录组学数据中绘制细胞相互作用图谱。2024年9月3日，《自然—方法学》杂志在线发表了这项成果。

研究人员介绍了一种多实例学习框架Spacia，用于检测由空间分辨转录组学（SRT）生成的数据中的细胞间通讯（CCC），独特地利用其空间模态。

研究人员强调了Spacia在克服推断CCC的常用分析工具的基本局限性方面的强大能力，这些局限性包括丧失单细胞分辨率、仅限于配体-受体关系和已有的相互作用数据库、高假阳性率，最重要的是缺乏对多发送者到一个接收者范式的考虑。

研究人员评估了Spacia在三种商业化单细胞分辨率SRT技术（MERSCOPE/Vizgen、CosMx/NanoString和Xenium/10x）中的适用性。总体而言，Spacia代表了在推进细胞通讯定量理论方面的重要进展。

据介绍，CCC是生命形成和功能的关键。然而，只有最近通过SRT技术，特别是那些实现单细胞分辨率的技术，才有可能准确、高通量地绘制出一种细胞中所有基因的表达如何影响另一种细胞中所有基因的表达。然而，正确分析如此复杂的数据仍面临巨大挑战。

附：英文原文

Title: Mapping cellular interactions from spatially resolved transcriptomics data

Author: Zhu, James, Wang, Yunguan, Chang, Woo Yong, Malewska, Alicia, Napolitano, Fabiana, Gahan, Jeffrey C., Unni, Nisha, Zhao, Min, Yuan, Rongqing, Wu, Fangjiang, Yue, Lauren, Guo, Lei, Zhao, Zhuo, Chen, Danny Z., Hannan, Raquibul, Zhang, Siyuan, Xiao, Guanghua, Mu, Ping, Hanker, Ariella B., Strand, Douglas, Arteaga, Carlos L., Desai, Neil, Wang, Xinlei, Xie, Yang, Wang, Tao

Issue&Volume: 2024-09-03

Abstract: Cell–cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently through the introduction of spatially resolved transcriptomics (SRT) technologies, especially those that achieve single-cell resolution. Nevertheless, substantial challenges remain to analyze such highly complex data properly. Here, we introduce a multiple-instance learning framework, Spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight Spacia’s power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand–receptor relationships and prior interaction databases, high false positive rates and, most importantly, the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of Spacia for three commercialized single-cell resolution SRT technologies: MERSCOPE/Vizgen, CosMx/NanoString and Xenium/10x. Overall, Spacia represents a notable step in advancing quantitative theories of cellular communications. Spacia is a multiple-instance learning model for cell–cell communication (CCC) interference in single-cell resolution spatially resolved transcriptomics data. Spacia can map complex CCCs by modeling cell proximity and CCC-driven gene perturbation.

DOI: 10.1038/s41592-024-02408-1

Source: https://www.nature.com/articles/s41592-024-02408-1