Advanced droplet microfluidic platform for high-throughput screening of industrial fungi.

Journal: Biosensors & bioelectronics
Published Date: May 16, 2025

Abstract

Industrial fungi are pivotal candidates for the production of a diverse array of bioproducts. To enhance their productivity, these strains are frequently subjected to genetic modifications. Following transformation, the selection of optimal production strains is critical; however, traditional screening methods often suffer from limitations in throughput and sensitivity. This article explores the transformative potential of Droplet Microfluidic Technology (DMFS) for high-throughput screening of industrial fungi. DMFS enables real-time monitoring and precise single-cell analysis by encapsulating individual fungal spores or cells within droplets, ranging from picoliters to nanoliters, functioning as isolated microreactors. This technology effectively addresses the challenges posed by conventional methods, such as agar plate assays and fluorescence-activated cell sorting. Key advancements discussed include microfluidic chip fabrication, droplet generation and regulation techniques, and multimodal signal detection methods-encompassing fluorescence, Raman spectroscopy, and mass spectrometry. Notably, strategies to mitigate droplet breakage in filamentous fungi, including physical constraints, bionic core-shell hydrogels, and genetic engineering approaches, are analyzed to prolong stable culture times. Future developments will likely emphasize interdisciplinary applications, including automation driven by artificial intelligence and label-free detection methods. We anticipate that this review will catalyze further research into high-quality industrial fungi, thereby promoting sustainable biomanufacturing through enhanced throughput, cost-effectiveness, and scalability.

Authors

  • Qiaoyi Yang
    State Key Laboratory of Microbial Technology, Nanjing Normal University, Nanjing, 210023, China; School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210046, China.
  • Siqi Lu
    School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210046, China.
  • Haoyu Wu
    School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China.
  • Danshan Zhao
    School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210046, China.
  • Wei Wei
    Dept. Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
  • Haoran Yin
    School of Mathematical Sciences, Ocean University of China, Qingdao, 266000, Shandong, China.
  • Xiang Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • Chao Ye
    School of Physical Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China. lingshj@shanghaitech.edu.cn.
  • Tianqiong Shi
    State Key Laboratory of Microbial Technology, Nanjing Normal University, Nanjing, 210023, China; School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210046, China. Electronic address: tqshi@njnu.edu.cn.
  • Zhe Wang
    Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China.
  • Yuetong Wang
    State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China.

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