On Feb. 10, 2026, National Institutes of Health (NIH) researchers announced they have developed a digital replica of crucial eye cells, providing a new tool for studying how the cells organize themselves when they are healthy and affected by diseases. The platform opens a new door for therapeutic discovery for blinding diseases such as age-related macular degeneration (AMD), a leading cause of vision loss in people over 50.

The researchers created a highly detailed, 3D data-driven digital twin of a retinal pigment epithelial (RPE) cells, which perform vital recycling and supportive roles to light-sensing photoreceptors in the retina. In diseases such as AMD, RPE cells die, which eventually leads to the death of photoreceptor cells, causing loss of vision.

For RPE cells to do their multiple jobs properly, they require a top-to-bottom polarity: The cell’s “top” (apical) side faces photoreceptors, where they recycle worn out photoreceptor parts daily. The cell’s “bottom”(basal) side faces the blood supply where it brings in nutrients and oxygen and ships out waste.

Researchers constructed the digital twin based on RPE cells made at NEI from induced pluripotent stem cells (iPS) cells developed by Allen Institute for Cell Science, Seattle. 3D imaging data for 1.3 million RPE cells, taken from nearly 4,000 fields of view cells, was collected using an automated confocal microscope.

Using the imaging data, the researchers trained an artificial intelligence (AI) algorithm they called polarity organization with learning-based analysis for RPE image segmentation, or POLARIS, to recognize the nucleus and other cell structures, and the cell’s shape and volume. 3D segmentation data (labels assigned to image voxels) were generated over different stages of cell development.

The researchers paid particular attention to polarity, quantifying the size and shape of the cell, its organelles and cytoskeletal structures including 3D spatial localization at various stages of development. They found that healthy, developing RPE cells follow a predictable path towards a polarized state.

The resulting AI-driven atlas of polarized and non-polarized RPE cells provides researchers with a reference for studying how diseases affect RPE at the cellular and subcellular levels, which could be transformative for therapeutic discovery.