AB040. Single-cell transcriptomics identifies cell-specific signatures of pathological angiogenesis
Bibliographic record
Abstract
Background: To treat vascular proliferative diseases, anti-VEGF therapies have shown systemic adverse effects attributable to the lack of selectivity between pathological and physiological angiogenesis. Thus, identifying the molecular mechanisms that are only specific to pathological cell types is crucial to develop better precision medicine. Methods: Here, we used different cell type enrichment approaches combined with single-cell RNA sequencing to define the transcriptomic changes within each retinal cell types in a mouse model of ischemic retinopathy. This retinal model develops pathological neovascularization (NV) in response to local hypoxia following oxygen-induced vessel obliteration (P7 to P12). The NV phenotype is characterized by the progressive appearance of vascular tufts resulting from misguided, abnormal proliferation of endothelial cells that we monitored at 3 consecutive time points—P12, P14 and P17 (peak of NV). Results: By following the dynamic response to hypoxia, our experimental design reveals how pathological angiogenesis is specifically associated with significant metabolic adaptations in different subtypes of endothelial cells (i.e., Tips vs Stalk cells). We also identify a pathological subtype of glial cells over-expressing VEGFA and pro-inflammatory IL-1 receptor subunits. This subtype of activated glial cells was targeted using selective IL1R antagonist treatment which reduced glial activation, inflammation, NV and promotes physiological angiogenesis, therefore improving tissue regeneration. Conclusions: Our results illustrate how analyzing cell type heterogeneity in tissues developing pathological angiogenesis allows establishing better targeting therapies to restore vascular integrity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".