Minda M. McNally

The paper develops a machine learning method to distinguish donor human photoreceptor cells from recipient animal cells in xenotransplanted retinas using nuclear morphology features.

The paper develops an image-guided minimally invasive subretinal access approach for cell and gene therapy delivery, demonstrating feasibility in animal models but encountering technical challenges.

This study compares single-cell and single-nucleus RNA-seq in a rabbit PVR model, finding similar gene expression profiles but differences in cell type capture rates and fibrotic cell representation.

This study transplants human retinal organoids into mice and identifies two classes of graft-derived cells with distinct migration and maturation behaviors.

The paper develops a quantifiable imaging biomarker grading scheme using cSLO to track long-term changes in photoreceptor cell grafts in mice.