A High-Throughput Distal Lung Air-Blood Barrier Model Enabled By Density-Driven Underside Epithelium Seeding

High-throughput tissue barrier models can yield critical insights on how barrier function responds to therapeutics, pathogens, and toxins. However, such models often emphasize multiplexing capability at the expense of physiologic relevance. Particularly, the distal lung's air-blood barrier is typically modeled with epithelial cell monoculture, neglecting the substantial contribution of endothelial cell feedback in the coordination of barrier function. An obstacle to establishing high-throughput coculture models relevant to the epithelium/endothelium interface is the requirement for underside cell seeding, which is difficult to miniaturize and automate. Therefore, this paper describes a scalable, low-cost seeding method that eliminates inversion by optimizing medium density to float cells so they attach under the membrane. This method generates a 96-well model of the distal lung epithelium-endothelium barrier with serum-free, glucocorticoid-free air-liquid differentiation. The polarized epithelial-endothelial coculture exhibits mature barrier function, appropriate intercellular junction staining, and epithelial-to-endothelial transmission of inflammatory stimuli such as polyinosine:polycytidylic acid (poly(I:C)). Further, exposure to influenza A virus PR8 and human beta-coronavirus OC43 initiates a dose-dependent inflammatory response that propagates from the epithelium to endothelium. While this model focuses on the air-blood barrier, the underside seeding method is generalizable to various coculture tissue models for scalable, physiologic screening.

Automated summary

The paper describes a new seeding method for a high-throughput tissue model of the distal lung epithelium-endothelium barrier, eliminating the need for inversion traditionally required in such models. The model exhibits mature barrier function and appropriate response to inflammatory stimuli, making it suitable for studying disease therapies and pathogen infections.