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Rapid screening of engineered microbial therapies in a 3D multicellular model

Significance An emerging area of synthetic biology is the engineering of bacteria to diagnose and treat various diseases in the body. However, the lack of physiologically relevant in vitro testing environments to rapidly screen bacterial therapies limits their development for clinical use. Here, we develop a platform that enables parallel and long-term monitoring of engineered bacteria in multicellular spheroids. Using this system, we rapidly screened tumor-targeting bacteria engineered to deliver a library of anticancer molecules via synthetic gene circuits. We demonstrate high similarity between in vitro and in vivo results and show broad applicability of the system with various bacterial species and cell types. This technology may serve to accelerate future clinical applications for synthetic biology. Synthetic biology is transforming therapeutic paradigms by engineering living cells and microbes to intelligently sense and respond to diseases including inflammation, infections, metabolic disorders, and cancer. However, the ability to rapidly engineer new therapies far outpaces the throughput of animal-based testing regimes, creating a major bottleneck for clinical translation. In vitro approaches to address this challenge have been limited in scalability and broad applicability. Here, we present a bacteria-in-spheroid coculture (BSCC) platform that simultaneously tests host species, therapeutic payloads, and synthetic gene circuits of engineered bacteria within multicellular spheroids over a timescale of weeks. Long-term monitoring of bacterial dynamics and disease progression enables quantitative comparison of critical therapeutic parameters such as efficacy and biocontainment. Specifically, we screen Salmonella typhimurium strains expressing and delivering a library of antitumor therapeutic molecules via several synthetic gene circuits. We identify candidates exhibiting significant tumor reduction and demonstrate high similarity in their efficacies, using a syngeneic mouse model. Last, we show that our platform can be expanded to dynamically profile diverse microbial species including Listeria monocytogenes, Proteus mirabilis, and Escherichia coli in various host cell types. This high-throughput framework may serve to accelerate synthetic biology for clinical applications and for understanding the host–microbe interactions in disease sites.