Limiting Immunogenicity of Diabetic Retinopathy Patient-Derived Induced Pluripotent Stem Cells by Knocking out Human Leukocyte Antigen and Overexpressing Cluster of Differentiation 47
Bibliographic record
Abstract
Introduction: Diabetic retinopathy (DR), the most common complication of diabetes, is characterized by vision loss due to vascular endothelial cell damage of the retina. Notably, induced pluripotent stem cell (iPSC) therapy has shown promise in the regeneration of the retina after damage. A limitation of using these cells includes the risk of immune-rejection. The knockout of human leukocyte antigen (HLA) proteins prevents a host immune response to non-native cells; however, HLA depletion introduces natural killer (NK) cell-mediated responses. Overexpression of cluster of differentiation 47 (CD47) inhibits the activity of NK cells. This project aims to create a universal DR patient-derived iPSC platform whose immunogenicity is limited through genetic alterations. Methods: iPSCs will be reprogrammed from DR patient-derived fibroblasts and CRISPR-Cas9 will be used to knock out HLA and overexpress CD47. Alterations will be validated through Sanger sequencing, Western Blot and Immunofluorescence (IF) analysis. DR patient-derived iPSCs will be differentiated into endothelial cells to mimic the DR afflicted endothelial cells. These differentiated cells will then be co-cultured with NK cells and a cytotoxicity assay will be performed. Specifically, a chromium-release assay will be used by loading radioactive chromium into the genetically modified and unmodified endothelial cells and the chromium released by dying cells was monitored. Results: Compared to the unmodified cells, we expect less NK-mediated cell death for the genetically altered endothelial cells. Discussion: Effectively limiting the immunogenicity of the donor-derived iPSCs can establish a universal platform for future studies in DR therapy. Conclusion: The resulting donor-derived iPSCs can be used to test drug therapies for DR or new methods to repair blood vessel damage, among a multitude of new research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".