Application of Machine Learning to Discriminate Photoreceptor Cell Species in Xenotransplanted Chimeric Retinas.

Photoreceptor transplantation is being studied to restore visual function in retinal diseases causing blindness, including age-related macular degeneration, hereditary eye diseases, and traumatic retinopathy. Preclinical studies often involve delivering exogenous human photoreceptor cells into animal models' retinas. A key readout in such experiments is distinguishing donor cell integration from artificial labeling secondary to material transfer of cytosolic or nuclear labels. Recognizing donor (human) versus animal photoreceptor nuclei is key, but purely immunohistology discrimination is challenging due to antigenic species overlap or intercellular antigen transfer. To address this, we sought to develop and validate a computational technique to discriminate between photoreceptor cells of different animal species based on machine learning of nuclear morphology. We aim to evaluate the feasibility of computer-assisted nuclear detection combined with random forest classification to automate species differentiation in DAPI-stained photoreceptors after xenotransplantation into mouse and pig retinas. Our models were trained on single-species samples and validated with mixed-species samples. We then transplanted human embryonic stem cell-derived retinal organoid cells into rodent and pig retinal degeneration models. The random forest model accurately determined cell identity post-xenotransplantation, validated by histological assessment using an antihuman nuclear antibody. Our results support the potential efficacy of employing machine learning image analysis and classification techniques that may promote experimental rigor, minimize observer bias, and enable high throughput semiautomated workflows for transplantation outcomes analysis. The methodological framework reported here may enable a more nuanced and precise analysis of the behavior of transplanted photoreceptors for the purposes of human retinal regeneration.