Differential microvascular endothelial cell responses in the retina in diabetes compared to the heart and kidneys, a spatial transcriptomic analysis
Bibliographic record
Abstract
Endothelial cells and high glucose-induced endothelial dysfunction are the common origin of chronic diabetic complications such as retinopathy, nephropathy, and cardiomyopathy. Yet their common origins, the vascular manifestations of such complications are different. We examined the basal heterogeneity between microvascular endothelial cells(MECs) from the retina, kidneys, and heart, as well as their differential responses to hyperglycemia in diabetes. To this extent, we used a spatial transcriptomic approach to investigate gene expression differences across retinal, renal, and cardiac MECs in diabetic and non-diabetic mouse models. We validated MEC heterogeneity in vitro using human retinal and cardiac MECs. The spatial transcriptomic approach was also used to explore potential similarities in retinal MECs and neuronal cells in response to hyperglycemia. We found that MECs from different target organs of major diabetic complications were transcriptomically distinct at the basal state and respond differently to hyperglycemia. These findings were recapitulated in cell culture, with selected analytes. We found minimal similarities between retinal MECs and neuronal cells. Our findings show considerable heterogeneity across retinal, renal, and cardiac MECs, both at the basal state and in their responses to hyperglycemia in diabetes. These findings show that organ specific MEC heterogeneity can influence differential development of pathological changes across various target organs of chronic diabetic complications, and suggest that MEC heterogeneity may influence treatment target(s) and drug development.
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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) | 0.001 | 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".