A Novel Method for Isolating Individual Cellular Components from the Adult Human Distal Lung
Bibliographic record
Abstract
A variety of lung diseases, such as pulmonary emphysema and idiopathic pulmonary fibrosis, develop in the lung alveoli. Multiple cell types are localized in the alveoli, including epithelial, mesenchymal, and endothelial cells. These resident cells participate in the pathogenesis of lung disease in various ways. To elaborate clearly on the mechanisms of these pathologic processes, cell type-specific analyses of lung disease are required. However, no method exists for individually isolating the different types of cells found in the alveoli. We report on the development of a FACS-based method for the direct isolation of individual cell types from the adult human distal lung. We obtained human lung tissue from lung resections, and prepared single-cell suspension. After depleting CD45-positive cells, a combination of antibodies against epithelial cell adhesion molecule (EpCAM), T1α, and vascular endothelial (VE)-cadherin as used to delineate alveolar cell types. Alveolar Type II cells were highly purified in the EpCAM(hi)/T1α(-) subset, whereas the EpCAM(+)/T1α(-/low) subset contained a mixed epithelial population consisting of alveolar Type I and bronchiolar epithelial cells. The EpCAM(-)/T1α(-) subset included both microvascular endothelial and mesenchymal cells, and these were separated by immunoreactivity to VE-cadherin. Lymphatic endothelial cells existed in the EpCAM(-)/T1α(hi) subset. Isolated cells were viable, and further cell culture studies could be performed. These results suggest that this novel method enables the isolation of different cellular components from normal and diseased lungs, and is capable of elucidating phenotypes specific to certain alveolar cell types indicative of lung disease.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".