Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA.
Nature. 2020 Dec;588(7839):676-681. doi: 10.1038/s41586-020-2983-4. Epub 2020 Dec 2.
Mapping the complex biogeography of microbial communities in situ with high taxonomic and spatial resolution poses a major challenge because of the high density and rich diversity of species in environmental microbiomes and the limitations of optical imaging technology. Here we introduce high-phylogenetic-resolution microbiome mapping by fluorescence in situ hybridization (HiPR-FISH), a versatile technology that uses binary encoding, spectral imaging and decoding based on machine learning to create micrometre-scale maps of the locations and identities of hundreds of microbial species in complex communities. We show that 10-bit HiPR-FISH can distinguish between 1,023 isolates of Escherichia coli, each fluorescently labelled with a unique binary barcode. HiPR-FISH, in conjunction with custom algorithms for automated probe design and analysis of single-cell images, reveals the disruption of spatial networks in the mouse gut microbiome in response to treatment with antibiotics, and the longitudinal stability of spatial architectures in the human oral plaque microbiome. Combined with super-resolution imaging, HiPR-FISH shows the diverse strategies of ribosome organization that are exhibited by taxa in the human oral microbiome. HiPR-FISH provides a framework for analysing the spatial ecology of environmental microbial communities at single-cell resolution.
利用高分类分辨率和空间分辨率原位绘制微生物群落的复杂生物地理学图谱,这是一个重大挑战,因为环境微生物组中的物种密度高且多样性丰富,同时光学成像技术也存在局限性。在这里,我们介绍了通过荧光原位杂交(HiPR-FISH)进行高系统发育分辨率微生物组绘图的方法,这是一种多功能技术,它使用二进制编码、基于机器学习的光谱成像和解码,来创建复杂群落中数百种微生物物种位置和身份的微米级图谱。我们表明,10 位 HiPR-FISH 可以区分 1023 株大肠杆菌,每株大肠杆菌都用独特的二进制条码进行荧光标记。HiPR-FISH 结合了用于自动设计探针和分析单细胞图像的定制算法,可以揭示出抗生素处理后小鼠肠道微生物组中空间网络的破坏,以及人类口腔斑块微生物组中空间结构的纵向稳定性。与超分辨率成像相结合,HiPR-FISH 展示了人类口腔微生物组中分类群所表现出的核糖体组织的不同策略。HiPR-FISH 为分析环境微生物群落的空间生态学提供了一个在单细胞分辨率下进行分析的框架。