A rapid screening platform to coculture bacteria within tumor spheroids

Bacteria spheroid coculture allows long-term growth of bacteria in the hypoxic, necrotic core of tumor spheroids. This enables the study of bacteria-tumor interactions and rapid development of engineered microbial therapies. The prevalence of tumor-colonizing bacteria along with advances in synthetic biology are leading to a new generation of living microbial cancer therapies. Because many bacterial systems can be engineered to recombinantly produce therapeutics within tumors, simple and high-throughput experimental platforms are needed to screen the large collections of bacteria candidates and characterize their interactions with cancer cells. Here, we describe a protocol to selectively grow bacteria within the core of tumor spheroids, allowing for their continuous and parallel profiling in physiologically relevant conditions. Specifically, tumor spheroids are incubated with bacteria in a 96-well low-adhesion plate followed by a series of washing steps and an antibiotic selection protocol to confine bacterial growth within the hypoxic and necrotic core of tumor spheroids. This bacteria spheroid coculture (BSCC) system is stable for over 2 weeks, does not require specialized equipment and is compatible with time-lapse microscopy, commercial staining assays and histology that uniquely enable analysis of growth kinetics, viability and spatial distribution of both cellular populations, respectively. We show that the procedure is applicable to multiple tumor cell types and bacterial species by varying protocol parameters and is validated by using animal models. The BSCC platform will allow the study of bacteria-tumor interactions in a continuous manner and facilitate the rapid development of engineered microbial therapies.

Automated summary

Bacteria spheroid coculture system allows long-term growth of bacteria in the core of tumor spheroids, facilitating the study of bacteria-tumor interactions and the development of microbial therapy. This system is stable, simple, high-throughput, and compatible with visualization methods.