Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
Nat Biotechnol. 2024 Sep;42(9):1394-1403. doi: 10.1038/s41587-023-01988-1. Epub 2023 Nov 20.
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.
黏膜和屏障组织,如肠道、肺部或皮肤,由细胞和微生物组成的复杂网络构成,形成一个紧密的小生境,防止病原体定植并支持宿主-微生物共生。以高分子和细胞分辨率表征这些网络对于理解体内平衡和疾病至关重要。在这里,我们提出了空间宿主-微生物测序(SHM-seq),这是一种基于全测序的方法,通过修饰空间条形码玻璃表面,直接从组织中捕获组织学、多聚腺苷酸化 RNA 和细菌 16S 序列,从而能够同时捕获宿主转录物和 16S 细菌核糖体 RNA 的高变区。我们将我们的方法应用于小鼠肠道作为模型系统,使用深度学习方法进行数据映射,并检测由细胞组成和微生物地理学定义的空间小生境。我们表明,肠道细胞的亚群在不同的微环境中表达特定的基因程序,这些微环境特征是区域共生细菌的特征,并影响宿主-细菌相互作用。SHM-seq 应该增强对健康和疾病中天然宿主-微生物相互作用的研究。