Tunable 3D Alveolosphere Model from Human Alveolar Cells: A Breakthrough Tool to Explore Emphysema Pathophysiology
Bibliographic record
Abstract
Abstract Rationale Three-dimensional (3D) culture models such as alveolosphere, provide a unique tool to study emphysema mechanisms. Reducing the heterogeneity of alveolospheres which are currently mostly grown in Matrigel remains challenging. Objectives To develop a tunable and reproducible 3D-alveolosphere model exclusively from human primary type II alveolar epithelial cells (AEC2) for modeling and understanding emphysema. Methods AEC2 cells (HTII-280+) were isolated from 52 smoker and non-smoker lung samples and cultured in preformed photopolymerized hydrogel microwells of adjustable shape and size. Topological and phenotypic characterization were performed at Day (D)1, 7 and 14. Lamellar bodies (LB) were quantified using artificial intelligence (AI)-based image analysis of transmission electron microscopy (TEM) serial block face images. Emphysema was modeled through chronic exposure to 1 and 5% cigarette smoke extract (CSE) during 5 consecutive days. Measurements and Main Results 3D-alveolospheres were maintained in culture for 14 days with central lumen formation observed from D7 to D14. Presence of tight junctions (TEM imaging and ZO-1 immunostaining) suggested epithelial barrier formation. AEC1 markers ( p2xr4, pdpn ) appeared progressively from D1 to D14 while AEC2 markers ( abca3, sftpa, sftpc ) persisted over time. TEM images indicated surfactant synthesis (LB, lipid bodies) and AI-driven LB quantification showed a decrease in the proportion of LB-containing cells over time. CSE exposure led to cell death, architectural disorganization, oxidative stress and inflammation. Conclusion This standardized and adjustable 3D-alveolosphere model from human primary AEC2, reproduced key native alveolar features. CSE exposure provides an opportunity to appropriately study the pathophysiological pathways involved in emphysema.
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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 | 0.001 | 0.001 |
| 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".