美国博德研究所Sanja Vickovic等研究人员合作发现，空间宿主微生物组测序揭示小鼠肠道的微环境。相关论文于2023年11月20日在线发表在《自然—生物技术》杂志上。

研究人员报道了空间宿主-微生物组测序（SHM-seq），这是一种基于全测序的方法，通过修改空间条形码玻璃表面，直接从组织中捕获组织组织学、多聚腺苷酸RNA和细菌16S序列，从而同时捕获宿主转录本和16S细菌核糖体RNA的超变异区。研究人员将小鼠肠道作为模型系统，使用深度学习方法绘制了数据图谱，并检测了由细胞组成和微生物地理学定义的空间微环境。研究人员发现，肠道细胞亚群在区域共生细菌特有的不同微环境中表达特定的基因程序，并影响宿主与细菌之间的相互作用。SHM-seq能加强对健康和疾病中宿主与微生物相互作用的研究。

研究人员表示，肠道、肺部或皮肤等黏膜和屏障组织由复杂的细胞和微生物网络组成，形成一个严密的微环境，从而防止病原体定植并支持宿主-微生物组共生。以分子和细胞的高分辨率描述这些网络对于了解体内平衡和疾病至关重要。

附：英文原文

Title: Spatial host–microbiome sequencing reveals niches in the mouse gut

Author: Ltstedt, Britta, Straar, Martin, Xavier, Ramnik, Regev, Aviv, Vickovic, Sanja

Issue&Volume: 2023-11-20

Abstract: Mucosal and barrier tissues, such as the gut, lung or skin, are composed of a complex network of cells and microbes forming a tight niche that prevents pathogen colonization and supports host–microbiome symbiosis. Characterizing these networks at high molecular and cellular resolution is crucial for understanding homeostasis and disease. Here we present spatial host–microbiome sequencing (SHM-seq), an all-sequencing-based approach that captures tissue histology, polyadenylated RNAs and bacterial 16S sequences directly from a tissue by modifying spatially barcoded glass surfaces to enable simultaneous capture of host transcripts and hypervariable regions of the 16S bacterial ribosomal RNA. We applied our approach to the mouse gut as a model system, used a deep learning approach for data mapping and detected spatial niches defined by cellular composition and microbial geography. We show that subpopulations of gut cells express specific gene programs in different microenvironments characteristic of regional commensal bacteria and impact host–bacteria interactions. SHM-seq should enhance the study of native host–microbe interactions in health and disease.

DOI: 10.1038/s41587-023-01988-1

Source: https://www.nature.com/articles/s41587-023-01988-1