由人工智能驱动的高吞吐量数字殖民地选择器平台,用于按多模态表型对微生物菌株进行分类
Zhidian Diao1,2,3,4, Qiqun Peng5, Sijun Luo6
1Key Laboratory of Photoelectric Conversion and Utilization of Solar Energy, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong, China.
Nature communications
|October 10, 2025
概括
我们开发了一个由人工智能驱动的数字殖民地选择器 (DCP),用于高通量微生物查. DCP发现了一种Zymomonas mobilis突变,显著增强了乳酸生产和耐压能力,加速了菌株工程.
科学领域:
- 合成生物学 合成生物学
- 微生物工程 微生物工程
- 生物技术是生物技术.
背景情况:
- 基于表型的查是开发微生物细胞工厂的关键瓶.
- 需要自动化,高通量方法来克服这些局限性.
研究的目的:
- 介绍一个新的AI驱动平台,数字殖民地选择器 (DCP),用于自动化,高通量查和微生物克隆的出口.
- 为了证明DCP在识别有利于工业应用的微生物突变物的能力.
主要方法:
- 使用微流体芯片与16000皮科升尺度的微室进行单细胞分离.
- 采用人工智能驱动的图像分析来动态监测微生物生长和代谢表型.
- 实施了激光诱导的泡技术,用于选择性地出口所需的微生物克隆,而无需物理接触.
主要成果:
- 使用DCP选Zymomonas mobilis,识别了一种突变体,乳酸盐产量增加了19.7%.
- 鉴定的突变体在高乳酸压力 (30 g/L) 下表现出77.0%的增长.
- 将改进的表型与ZMOp39x027的过度表达联系在一起,ZMOp39x027是一种增强乳酸运输和应激耐受性的外膜自传输体.
结论:
- DCP提供了一种具有高时空精度和可扩展吞吐量的多模式表型化解决方案.
- 该平台为加速微生物菌株工程和功能基因发现提供了可通用的策略.
- 通过克服选瓶,DCP显著推进了微生物细胞工厂的发展.
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