小柯机器人

美国哥伦比亚大学Harris H. Wang小组利用自动化和机器学习实现高通量微生物培养。相关论文于2023年2月20日在线发表在《自然—生物技术》杂志上。

研究人员描述了一个开放的高通量机器人菌株分离平台，用于按需快速生成菌株。研究人员开发了一种机器学习方法，利用菌落形态和基因组数据，最大限度地提高分离出的微生物的多样性，并能够有针对性地挑选特定种属。将该平台应用于20人的粪便样本，研究人员得到了个性化的肠道微生物组生物库，共计26997个分离物，占所有丰富类群的80%。对视觉捕获的10万个菌落进行空间分析，揭示了瘤胃球菌科、拟杆菌科、红蝽菌科和双歧杆菌科之间的共同生长模式，这表明了重要的微生物相互作用。对来自这些生物库的1197个高质量基因组的比较分析显示了有趣的品系内部和人群间演化、选择和水平基因转移。

这种培养组学框架应该为许多新兴的微生物组研究提供新的研究成果，并使基于成像的表型的收集和定量分析与高分辨率基因组学数据系统化。

据介绍，纯细菌培养对于微生物组研究中的详细实验和机制研究仍然至关重要，而从复杂的微生物生态系统中分离单个细菌的传统方法是劳动密集型的，难以规模化，而且缺乏表型-基因型整合。

附：英文原文

Title: High-throughput microbial culturomics using automation and machine learning

Author: Huang, Yiming, Sheth, Ravi U., Zhao, Shijie, Cohen, Lucas A., Dabaghi, Kendall, Moody, Thomas, Sun, Yiwei, Ricaurte, Deirdre, Richardson, Miles, Velez-Cortes, Florencia, Blazejewski, Tomasz, Kaufman, Andrew, Ronda, Carlotta, Wang, Harris H.

Issue&Volume: 2023-02-20

Abstract: Pure bacterial cultures remain essential for detailed experimental and mechanistic studies in microbiome research, and traditional methods to isolate individual bacteria from complex microbial ecosystems are labor-intensive, difficult-to-scale and lack phenotype–genotype integration. Here we describe an open-source high-throughput robotic strain isolation platform for the rapid generation of isolates on demand. We develop a machine learning approach that leverages colony morphology and genomic data to maximize the diversity of microbes isolated and enable targeted picking of specific genera. Application of this platform on fecal samples from 20 humans yields personalized gut microbiome biobanks totaling 26,997 isolates that represented >80% of all abundant taxa. Spatial analysis on >100,000 visually captured colonies reveals cogrowth patterns between Ruminococcaceae, Bacteroidaceae, Coriobacteriaceae and Bifidobacteriaceae families that suggest important microbial interactions. Comparative analysis of 1,197 high-quality genomes from these biobanks shows interesting intra- and interpersonal strain evolution, selection and horizontal gene transfer. This culturomics framework should empower new research efforts to systematize the collection and quantitative analysis of imaging-based phenotypes with high-resolution genomics data for many emerging microbiome studies.

DOI: 10.1038/s41587-023-01674-2

Source: https://www.nature.com/articles/s41587-023-01674-2

Nature Biotechnology：《自然—生物技术》，创刊于1996年。隶属于施普林格·自然出版集团，最新IF：68.164
官方网址：https://www.nature.com/nbt/
投稿链接：https://mts-nbt.nature.com/cgi-bin/main.plex