美国麻省理工学院和哈佛大学布罗德研究所Aviv Regev和美国加州斯坦福大学医学院Livnat Jerby-Arnon共同合作取得重要突破，他们提出用DIALOGUE方法从单细胞或空间转录组数据绘制组织中的多细胞程序。该研究成果2022年5月5日在线发表于《自然—生物技术》杂志上。

在这里，研究人员描述了DIALOGUE，这是一种从空间数据或在没有空间信息的情况下获得的单细胞数据系统地揭示多细胞程序 (MCP) 的方法——不同细胞类型中协调细胞程序的组合，在组织水平上形成高阶功能单元。在小鼠下丘脑、小脑、视觉皮层和新皮层的空间数据集上进行测试，DIALOGUE识别出与动物行为相关的MCP，并在对看不见的数据进行测试时恢复了空间特性，同时优于其他方法和指标。在来自人类肺癌的空间数据中，DIALOGUE确定了标记免疫激活和组织重塑的MCP。

应用于跨个人或地区的单细胞RNA测序数据，DIALOGUE发现了标记阿尔茨海默病、溃疡性结肠炎和对癌症免疫治疗的抵抗力的MCP。这些程序可预测独立队列中的疾病结果和易感性，包括来自全基因组关联研究的风险基因。DIALOGUE能够分析健康和疾病中的多细胞调节。

据了解，破译组织中细胞的功能相互作用仍然是一项重大的挑战。

附：英文原文

Title: DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data

Author: Jerby-Arnon, Livnat, Regev, Aviv

Issue&Volume: 2022-05-05

Abstract: Deciphering the functional interactions of cells in tissues remains a major challenge. Here we describe DIALOGUE, a method to systematically uncover multicellular programs (MCPs)—combinations of coordinated cellular programs in different cell types that form higher-order functional units at the tissue level—from either spatial data or single-cell data obtained without spatial information. Tested on spatial datasets from the mouse hypothalamus, cerebellum, visual cortex and neocortex, DIALOGUE identified MCPs associated with animal behavior and recovered spatial properties when tested on unseen data while outperforming other methods and metrics. In spatial data from human lung cancer, DIALOGUE identified MCPs marking immune activation and tissue remodeling. Applied to single-cell RNA sequencing data across individuals or regions, DIALOGUE uncovered MCPs marking Alzheimer’s disease, ulcerative colitis and resistance to cancer immunotherapy. These programs were predictive of disease outcome and predisposition in independent cohorts and included risk genes from genome-wide association studies. DIALOGUE enables the analysis of multicellular regulation in health and disease.

DOI: 10.1038/s41587-022-01288-0

Source: https://www.nature.com/articles/s41587-022-01288-0