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

Author

Listed:
  • Zhidian Diao

    (Chinese Academy of Sciences
    Qingdao New Energy Shandong Laboratory
    Shandong Energy Institute
    University of Chinese Academy of Sciences)

  • Qiqun Peng

    (Hubei University)

  • Sijun Luo

    (Qingdao Single-Cell Biotech., Co., Ltd.)

  • Lingyan Kan

    (Chinese Academy of Sciences
    Qingdao New Energy Shandong Laboratory
    Shandong Energy Institute)

  • Anle Ge

    (Chinese Academy of Sciences
    Qingdao New Energy Shandong Laboratory
    Shandong Energy Institute)

  • Wei Gao

    (Chinese Academy of Sciences
    Qingdao New Energy Shandong Laboratory
    Shandong Energy Institute)

  • Runxia Li

    (Hubei University)

  • Weiwei Bao

    (Hubei University)

  • Xixian Wang

    (Chinese Academy of Sciences
    Qingdao New Energy Shandong Laboratory
    Shandong Energy Institute
    University of Chinese Academy of Sciences)

  • Yuetong Ji

    (Qingdao Single-Cell Biotech., Co., Ltd.)

  • Jian Xu

    (Chinese Academy of Sciences
    Qingdao New Energy Shandong Laboratory
    Shandong Energy Institute
    University of Chinese Academy of Sciences)

  • Shihui Yang

    (Hubei University)

  • Bo Ma

    (Chinese Academy of Sciences
    Qingdao New Energy Shandong Laboratory
    Shandong Energy Institute
    University of Chinese Academy of Sciences)

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.

Suggested Citation

  • Zhidian Diao & Qiqun Peng & Sijun Luo & Lingyan Kan & Anle Ge & Wei Gao & Runxia Li & Weiwei Bao & Xixian Wang & Yuetong Ji & Jian Xu & Shihui Yang & Bo Ma, 2025. "AI-powered high-throughput digital colony picker platform for sorting microbial strains by multi-modal phenotypes," Nature Communications, Nature, vol. 16(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-63929-7
    DOI: 10.1038/s41467-025-63929-7
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    References listed on IDEAS

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    1. U. T. Bornscheuer & G. W. Huisman & R. J. Kazlauskas & S. Lutz & J. C. Moore & K. Robins, 2012. "Engineering the third wave of biocatalysis," Nature, Nature, vol. 485(7397), pages 185-194, May.
    2. Xiongying Yan & Weiwei Bao & Yalun Wu & Chenyue Zhang & Zhitao Mao & Qianqian Yuan & Zhousheng Hu & Penghui He & Qiqun Peng & Mimi Hu & Binan Geng & Hongwu Ma & Shouwen Chen & Qiang Fei & Qiaoning He , 2024. "Paradigm of engineering recalcitrant non-model microorganism with dominant metabolic pathway as a biorefinery chassis," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
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