Genetically encoded biosensors enabled high-throughput screening of microbial cell factories
• Genetically encoded biosensors couple product synthesis to detectable HTS outputs. • Synthetic biology and AI-driven strategies accelerate precise biosensor engineering. • Optimization protocols are detailed for integrating biosensors with FACS and FADS. • Novel solutions address critical HTS challenges such as false positives and crosstalk. Genetically encoded biosensors provide powerful tools for coupling desired phenotypes to detectable outputs and have been extensively developed to detect a wide range of natural and unnatural products. When integrated with diverse high-throughput screening (HTS) approaches, these biosensors enable efficient product-driven screening across various throughputs, thereby expediting the engineering and optimization of microbial cell factories to produce various target compounds. For effective HTS of microbial cell factories, biosensors need to possess certain crucial characteristics. The performance features of biosensors significantly influence their application potential in HTS and can be precisely engineered through synthetic biology strategies. Furthermore, to ensure biosensor-driven HTS, additional engineering and optimizations of the biosensors are often required to increase the success rate and reduce false positives in the screening process. This review discusses the essential features of genetically encoded biosensors designed for HTS and then summarizes the latest advances in biosensor engineering for HTS purposes via synthetic biology strategies. Following this, the challenges and optimization of biosensors to adapt to different HTS processes are also discussed and exemplified. Finally, the key concerns and research prospects of developing biosensors for HTS applications are highlighted. Overall, this review provides comprehensive guidance on the engineering of genetically encoded biosensors and their applications in HTS for developing microbial cell factories to produce diverse target compounds.
Authors
- Longqian Zhao
- Xueyan Liu
- Yue Zhang
- Meng Wang
- Jin Wang
Institutions
- Tianjin University of Science and Technology (CN)
- Chinese Academy of Sciences (CN)
- Tianjin Institute of Industrial Biotechnology (CN)
Publication Details
- Journal
- Engineering Microbiology
- Published
- 2025-12-25
- DOI
- https://doi.org/10.1016/j.engmic.2025.100258
- Primary Topic
- Microbial Metabolic Engineering and Bioproduction
- Type
- article
- Field-Weighted Citation Impact
- 0.00