A high-fidelity human cornea chip by harnessing the impact of curvature and interfacial membranes on corneal tissue formation

The cornea is the clear, dome-shaped front layer of the eye that focuses light and protects against infection. Millions of Americans suffer from corneal diseases each year, and this number could double or quadruple by 2050. Developing new drugs to treat these conditions is slow and expensive, in part because researchers rely heavily on animal testing. This raises ethical concerns and often fails to predict how drugs will work in humans. This project will create a "cornea-on-a-chip," - a miniature, coin-sized device that mimics the structure and function of a real human cornea. The chip will contain the three main living cell layers of the cornea separated by thin biological membranes. It will reproduce the natural curvature of the eye, which is important for healthy cells. By combining microfluidic channels, biodegradable nanofibers, and human corneal cells, the device will allow scientists to study eye diseases and test new drugs without using animals. The project will train the next generation of scientists and engineers. Undergraduate and graduate students will gain experience in microfabrication, tissue engineering, and cell biology. Students will participate in summer programs and interdisciplinary design teams to tackle real-world engineering challenges. The team will contribute to Engineering Day and Elementary Engineering Week, and a summer camp with hands-on laboratory experiences. This project will develop a high-fidelity human cornea-on-a-chip as a physiologically accurate in vitro platform for ocular drug evaluation. Existing corneal models—primarily transwell systems and flat microfluidic devices—fail to reproduce the native curvature, authentic basement membranes, and heterotypic cell-cell interactions of the human cornea. This leads to poor predictivity in ophthalmic drug permeability and toxicity studies. To address these limitations, the project will integrate three corneal cell layers (epithelium, stroma, and endothelium), bioengineered interfacial membranes that mimic the epithelial basement membrane (EBM) and Descemet's membrane (DM), and a hydraulically actuated curvature module that reproduces the physiological geometry of the cornea. A curvature-array chip will be used to define how physiological curvature regulates phenotype, extracellular matrix (ECM) organization, focal adhesion dynamics, and mechanotransduction across primary corneal epithelial, stromal, and endothelial cells, with bulk and spatial transcriptomics resolving curvature-responsive gene programs. Self-aligning nanojet electrospinning will be used to fabricate biodegradable, highly porous poly(lactic-co-glycolic acid) (PLGA) membranes that permit direct heterotypic cell-cell contact and template the self-assembly of native-like EBM and DM. Characterization will be performed using immunostaining, transmission electron microscopy, label-free proteomics, transepithelial electrical resistance (TEER), and tracer permeability. These elements will be integrated into a curvature-actuated, multi-compartment cornea chip and benchmarked against quantitative success metrics. Drug-evaluation will be demonstrated through the measurement of permeability, retention, and cytotoxicity for representative hydrophilic and lipophilic ocular compounds. This platform will advance biotechnology research and the national health by accelerating ophthalmic drug development, reducing reliance on animal testing, and providing design principles for complex model tissue systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2546898 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Jungkyu Kim | Institution: University of Utah, SALT LAKE CITY, UT | Award Amount: $500,106 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2546898 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2546898.html