AI-powered high-throughput digital colony picker platform for sorting microbial strains by multi-modal phenotypes

Zhidian Diao, Qiqun Peng, Sijun Luo, Lingyan Kan, Anle Ge, Wei Gao, Runxia Li, Weiwei Bao, Xixian Wang, Yuetong Ji, Jian Xu (), Shihui Yang () and Bo Ma ()
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Zhidian Diao: Chinese Academy of Sciences
Qiqun Peng: Hubei University
Sijun Luo: Qingdao Single-Cell Biotech., Co., Ltd.
Lingyan Kan: Chinese Academy of Sciences
Anle Ge: Chinese Academy of Sciences
Wei Gao: Chinese Academy of Sciences
Runxia Li: Hubei University
Weiwei Bao: Hubei University
Xixian Wang: Chinese Academy of Sciences
Yuetong Ji: Qingdao Single-Cell Biotech., Co., Ltd.
Jian Xu: Chinese Academy of Sciences
Shihui Yang: Hubei University
Bo Ma: Chinese Academy of Sciences

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract Phenotype-based screening remains a major bottleneck in the development of microbial cell factories. Here, we present a Digital Colony Picker (DCP), an AI-powered platform for automated, high-throughput screening and export of microbial clones based on growth and metabolic phenotypes at single-cell resolution, without agar or physical contact. Using a microfluidic chip comprising 16,000 addressable picoliter-scale microchambers, individual cells are compartmentalized, dynamically monitored by AI-driven image analysis, and selectively exported via laser-induced bubble technique. Applied to Zymomonas mobilis, DCP enabled en masse screening and identified a mutant with 19.7% increased lactate production and 77.0% enhanced growth under 30 g/L lactate stress. This phenotype was linked to overexpression of ZMOp39x027, a canonical outer membrane autotransporter that promotes lactate transport and cell proliferation under stress. DCP provides a multi-modal phenotyping solution with spatiotemporal precision and scalable throughput, offering a generalizable strategy for accelerated strain engineering and functional gene discovery.

Date: 2025
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DOI: 10.1038/s41467-025-63929-7

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