Controlling donor and newborn neuron migration and maturation in the eye through microenvironment engineering.

Ongoing cell therapy trials have demonstrated the need for precision control of donor cell behavior within the recipient tissue. We present a methodology to guide stem cell-derived and endogenously regenerated neurons by engineering the microenvironment. Being an "approachable part of the brain," the eye provides a unique opportunity to study neuron fate and function within the central nervous system. Here, we focused on retinal ganglion cells (RGCs)-the neurons in the retina are irreversibly lost in glaucoma and other optic neuropathies but can potentially be replaced through transplantation or reprogramming. One of the significant barriers to successful RGC integration into the existing mature retinal circuitry is cell migration toward their natural position in the retina. Our in silico analysis of the single-cell transcriptome of the developing human retina identified six receptor-ligand candidates, which were tested in functional in vitro assays for their ability to guide human stem cell-derived RGCs. We used our lead molecule, SDF1, to engineer an artificial gradient in the retina, which led to a 2.7-fold increase in donor RGC migration into the ganglion cell layer (GCL) and a 3.3-fold increase in the displacement of newborn RGCs out of the inner nuclear layer. Only donor RGCs that migrated into the GCL were found to express mature RGC markers, indicating the importance of proper structure integration. Together, these results describe an "in silico-in vitro-in vivo" framework for identifying, selecting, and applying soluble ligands to control donor cell function after transplantation.

Investigating Determinants and Evaluating Deep Learning Training Approaches for Visual Acuity in Foveal Hypoplasia.

Purpose: To describe the relationships between foveal structure and visual function in a cohort of individuals with foveal hypoplasia (FH) and to estimate FH grade and visual acuity using a deep learning classifier. Design: Retrospective cohort study and experimental study. Participants: A total of 201 patients with FH were evaluated at the National Eye Institute from 2004 to 2018. Methods: Structural components of foveal OCT scans and corresponding clinical data were analyzed to assess their contributions to visual acuity. To automate FH scoring and visual acuity correlations, we evaluated the following 3 inputs for training a neural network predictor: (1) OCT scans, (2) OCT scans and metadata, and (3) real OCT scans and fake OCT scans created from a generative adversarial network. Main Outcome Measures: The relationships between visual acuity outcomes and determinants, such as foveal morphology, nystagmus, and refractive error. Results: The mean subject age was 24.4 years (range, 1-73 years; standard deviation = 18.25 years) at the time of OCT imaging. The mean best-corrected visual acuity (n = 398 eyes) was equivalent to a logarithm of the minimal angle of resolution (LogMAR) value of 0.75 (Snellen 20/115). Spherical equivalent refractive error (SER) ranged from -20.25 diopters (D) to +13.63 D with a median of +0.50 D. The presence of nystagmus and a high-LogMAR value showed a statistically significant relationship (P < 0.0001). The participants whose SER values were farther from plano demonstrated higher LogMAR values (n = 382 eyes). The proportion of patients with nystagmus increased with a higher FH grade. Variability in SER with grade 4 (range, -20.25 D to +13.00 D) compared with grade 1 (range, -8.88 D to +8.50 D) was statistically significant (P < 0.0001). Our neural network predictors reliably estimated the FH grading and visual acuity (correlation to true value > 0.85 and > 0.70, respectively) for a test cohort of 37 individuals (98 OCT scans). Training the predictor on real OCT scans with metadata and fake OCT scans improved the accuracy over the model trained on real OCT scans alone. Conclusions: Nystagmus and foveal anatomy impact visual outcomes in patients with FH, and computational algorithms reliably estimate FH grading and visual acuity.