Abstract 16783: Discovery and Characterization of Transient Endothelial Stem-Like Cells Responsible for Rapid Lung Microvascular Regeneration Post Endothelial Ablation
Bibliographic record
Abstract
Background: Single-cell RNA sequencing (scRNA-seq) has revealed two main types of alveolar capillary endothelial cells (ECs): aCap ECs (or aerocytes) express typical aCap markers, including apelin, whereas general (gCap) ECs express apelin receptor, Aplnr /APJ. Aerocytes constitute the air-blood barrier and are incapable of proliferation, unlike smaller gCap ECs that can proliferate. Hypothesis: After lung microvascular injury, gCap ECs give rise to stem-like ECs that can regenerate depleted lung EC populations, including highly specialized aerocytes. Approach: scRNA-seq was used to study depletion and regeneration of lung EC populations after intratracheal instillation of diphtheria toxin (DT) in transgenic mice harboring an EC-targeted human DT receptor. Results: DT resulted in ~80% loss of lung microvascular EC populations including aerocytes, associated with severe acute lung injury (ALI), and there was full spontaneous recovery by day 7. Interestingly, a novel EC population appeared at day 3 post injury in a transitional gCap EC cluster characterized by paradoxical expression of ‘aCap’ marker, apelin, together with endothelial progenitor/stem cell markers, Cd34 and endothelial protein C receptor ( Procr /EPCR). These ‘transient endothelial stem-like cells’ (TESCs) transitioned to highly proliferative progenitor-like FoxM1 + cells ultimately repopulating all depleted EC populations by day 7. Using flow cytometry of lung cells harvested at 3 days post DT instillation, TESCs were identified by unique co-expression of CD34 and EPCR (Figure), thereby allowing isolation and culture in vitro for further characterization of their proliferation and differentiation potential. Conclusions: Lung microvascular repair is orchestrated by the emergence of stem-like, gCap ECs (TESCs), transiently expressing aCap/aerocyte marker, apelin, together with Procr /EPCR, which have the capacity to regenerate lung microvasculature rapidly and efficiently.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".