Design, infectability, and transcriptomic analysis of transregionally differentiated and scalable lung organoids derived from adult bronchial cells
Bibliographic record
Abstract
The lung is a primary target for many lethal respiratory viruses, leading to significant global mortality. Current organoid models fail to completely mimic the cellular diversity and intricate tubular and branching structures of the human lung. Lung organoids derived from adult primary cells have so far only included cells from the input cell region, proximal or distal. Existing models are expensive. They often require cells from invasive deep lung tissue biopsies. The present study aimed to address these limitations. The lung organoids obtained using an original protocol exhibited transregional differentiation and were derived from relatively more accessible primary cells from the trachea/bronchi. Immortal bronchial cell lines were also used to simplify organoid fabrication and improve its scalability. The lung organoids are formed starting from bronchial cells with fibroblasts feeder cells in an alginate hydrogel coated with base membrane zone proteins. Characterizations were performed using bulk RNA sequencing and tandem mass tags. The resulting organoids express markers of different lung regions and mimic to some extent the tubular and branching morphology of the lung. The proteomic profile of organoid from primary cells and from cell lines was found to evolve towards that of mature lung tissue. Upregulated genes were mostly related to the respiratory system, tube development, and various aspects of respiratory viral infections. Infection with SARS-CoV-2 and influenza H1N1 was successful and did not require organoid disassembly. The organoids matured within 21 days and did not require complex or expensive culture methods. Transregionally differentiated lung organoid may find applications for the study of emerging or re-emerging viral infections and fostering the development of novel in-vitro therapeutic strategies.
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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 | 0.001 |
| 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".