Advanced 3D spheroid-based skin models and deep-learning based image analysis enable in-depth investigation of keratinocyte differentiation and barrier function

Abstract

To date, a panel of different biological models have been used in skin research, ranging from in vivo testing to 2D and 3D cultures. Among these, organotypic skin models represent the current gold standard for preclinical dermatology and toxicology studies. However, they are variable in quality and require long maturation times and a lot of work and cells, the latter often of primary origin. Here, we propose dermal-epidermal spheroids as an alternative model that balances physiological relevance and throughput. Next to corresponding full thickness skin models, different fibroblast/keratinocyte coculture spheroids were generated. These used the commonly employed HaCaT cells as well as two recently immortalized keratinocyte cell lines, NHK-SV/TERT and NHK-E6/E7. To investigate their differentiation with detailed spatio-temporal resolution, a deep-learning segmentation-based pipeline, capable of revealing nuclear morphology and positioning as well as marker expression with single-cell precision, was developed and applied. Moreover, the formation of a functional barrier was assessed by live-imaging of Lucifer yellow diffusion. These experiments identified the NHK-E6/E7 cell line as the most and HaCaT cells as the least suitable alternative to primary keratinocytes in both spheroids and full thickness models. Furthermore, NHK-based coculture spheroids displayed functional maturation, including stratification, cornification, and barrier formation, closely recapitulating these features of corresponding full thickness models. Given the scalability and compatibility with automation, these micro-skin fibroblast/NHK-based 3D coculture spheroids might represent a promising new platform for pharmaceutical, cosmetic, and toxicological testing.