Mapping the origin and fate of myeloid cells in distinct compartments of the eye by single‐cell profiling
Bibliographic record
Abstract
Similar to the brain, the eye is considered an immune‐privileged organ where tissue‐resident macrophages provide the major immune cell constituents. However, little is known about spatially restricted macrophage subsets within different eye compartments with regard to their origin, function, and fate during health and disease. Here, we combined single‐cell analysis, fate mapping, parabiosis, and computational modeling to comprehensively examine myeloid subsets in distinct parts of the eye during homeostasis. This approach allowed us to identify myeloid subsets displaying diverse transcriptional states. During choroidal neovascularization, a typical hallmark of neovascular age‐related macular degeneration (AMD), we recognized disease‐specific macrophage subpopulations with distinct molecular signatures. Our results highlight the heterogeneity of myeloid subsets and their dynamics in the eye that provide new insights into the innate immune system in this organ which may offer new therapeutic targets for ophthalmological diseases. The study reveals a substantial heterogeneity of macrophages in distinct compartments of the eye. Microglia represent the predominant cell type in a model of choroidal neovascularization and disease‐associated subpopulations quickly emerge complemented by infiltrating periphery‐derived cells. Single‐cell RNA‐sequencing reveals heterogeneity of macrophages in the cornea, ciliary body and retina under homeostatic conditions.
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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) | 0.000 | 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".