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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Artículo científico |
| Language: | en |
| Published: |
Nature communications
2025
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| Subjects: | |
| Online Access: | https://pubmed.ncbi.nlm.nih.gov/41073418/ |
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Table of Contents:
- AI-powered high-throughput digital colony picker platform for sorting microbial strains by multi-modal phenotypes. Diao, Zhidian Peng, Qiqun Luo, Sijun Kan, Lingyan Ge, Anle Gao, Wei Li, Runxia Bao, Weiwei Wang, Xixian Ji, Yuetong Xu, Jian Yang, Shihui Ma, Bo Phenotype High-Throughput Screening Assays Lactic Acid Zymomonas Bacterial Proteins Lab-On-A-Chip Devices 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.