Abstract
Microfluidic cell culture systems are becoming increasingly useful for studying biology questions, particularly those involving small cell populations that are cultured within microscale geometries mimicking the complex cellular microenvironment. Depending on the geometry and spatial organization of these cell populations, however, paracrine signaling between cell types can depend critically on spatial concentration profiles of soluble factors generated by diffusive transport. In scenarios where single cell data are acquired to study cell population heterogeneities in functional response, uncertainty associated with concentration profiles can lead to interpretation bias. To address this issue and provide important evidence on how diffusion develops within typical microfluidic cell culture systems, a combination of experimental and computational approaches were applied to measure and predict concentration patterns within microfluidic geometries, and characterize the functional response of culture cells based on single-cell resolution transcription factor activation. Using a model coculture system consisting of multiple myeloma cells (MMCs) and neighboring bone marrow stromal cells (BMSCs), we measured concentrations of three cytokines (IL-6, VEGF, and TNF-α) in conditioned media collected from separate culture compartments using a multiplex ELISA system. A 3D numerical model was developed to predict biomolecular diffusion and resulting concentration profiles within the tested microsystems and compared with experimental diffusion of 20 kDa FITC-Dextran. Finally, diffusion was further characterized by controlling exogenous IL-6 diffusion and the coculture spatial configuration of BMSCs to stimulate STAT3 nuclear translocation in MMCs. Results showed agreement between numerical and experimental results, provided evidence of a shallow concentration gradient across the center well of the microsystem that did not lead to a bias in results, and demonstrated that microfluidic systems can be tailored with specific geometries to avoid spatial bias when desired.