Spatiotemporal T cell dynamics in a 3D bioprinted immunotherapy model

3D bioprinting-focused research is often driven by the aspiration to manufacture functional tissues and organs for implantation, yet decades of additional research is likely needed to develop such technologies, broadly. By contrast, we currently have 3D bioprinting tools capable of generating precise, spatially defined distributions of different cell types that can be used in fundamental and applied research, without delay. Here, we demonstrate this capability by 3D printing adoptive immunotherapy models using KR158B cells (murine glioma), hematopoietic stem cells (HSCs), and tumor-reactive T cells. We leverage a recently developed material made from packed microgel particles that serves simultaneously as a 3D printing support material and a 3D culture medium. With this approach we create well-defined 3D cell distributions and investigate the interactions between the different cell populations with time-lapse confocal microscopy. We find that 3D printed tumor spheroids containing HSCs recruit T cells more rapidly than those lacking HSCs, where T cell motion appears to be guided by diffusing cytokines originating at the tumor spheroid surface. After the T cells interact with the glioma structures, we collect the different cell populations by running our 3D bioprinter in reverse and perform transcriptomic analysis. We find that the differences in gene expression, comparing tumor spheroids printed with and without HSCs, are consistent with those found in a murine model. These results establish a path forward for developing validated 3D printed models for pre-clinical testing. We developed an in vitro method for investigating the spatiotemporal interactions between multiple cell populations in a type of immunotherapy designed to treat glioblastoma. We carefully 3D printed structured populations of cancer cells, T cells, and hematopoietic stem cells into a packed continuum of microscopic hydrogel particles swollen in cell growth media. With time-lapse imaging, we discovered how the different cell populations migrate and interact, experimentally determining the diffusion coefficient of their signaling molecules. By running our 3D bioprinter in reverse, we collected the different cell populations after they interacted, finding changes in gene expression consistent with those previously found in mouse models. Our results represent the first steps toward developing validated models for pre-clinical testing using 3D bioprinting approaches.