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A microtissue-based retinal fibrosis platform for drug efficacy testing

Hofstetter, T.;Roubeix, C.;Pawlowska, A.;Westenskow, P.;Fluri, D.

2026-06-16 Cell Biology
10.64898/2026.06.13.732056 bioRxiv
Show abstract

PURPOSEDevelopment of a microtissue-based phenotypic screening platform to assess the potency of antifibrotic drugs for patients with the neovascular form of age-related macular degeneration. METHODSA robust and scalable three-dimensional in vitro model based on primary retinal pigment epithelium (RPE) cells was developed, and a fibrotic disease phenotype was induced. The endpoints included scalable image-based segmentation and quantification of collagen I and fibronectin, bright-field analysis of phenotypic morphological changes, and the analysis of secreted procollagen I levels alongside whole-transcriptome gene expression profiling to demonstrate the potency of compounds in repressing the fibrotic phenotype. RESULTSThe developed model shows similarity to in vivo tissue structures. The three-dimensional constructs form a prominent, polarized monolayer at the periphery. Cellular markers, including Ezrin and ZO-1, confirm epithelial identity and a strongly polarized morphology with junctional structures. Transcriptomic analysis over the culture period demonstrates progressive microtissue maturation. Pathway modulators induced epithelial-to-mesenchymal transition (EMT) and fibrotic phenotypes. Transcriptomic analysis demonstrated strong marker upregulation. The fibrotic phenotype and its repression by specific small molecule inhibitors were confirmed by measuring secreted procollagen I, quantifying fibronectin and collagen I, and assessing morphological changes via bright-field imaging. CONCLUSIONSThe developed test system exhibits tissue-specific morphology and functionality, showing a high degree of retinal identity. Disease induction led to broad induction of EMT and fibrotic markers, rendering the test system amenable to testing compounds that inhibit or repress fibrotic phenotypes. Production, culture, and endpoint assessment on the Akura platform render the system fully automation-compatible and scalable to higher throughput.

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