A Rapid Human Lung Tissue Dissociation Protocol Maximizing Cell Yield and Minimizing Cellular Stress
Bibliographic record
Abstract
The human lung is a complex organ that comprises diverse populations of epithelial, mesenchymal, vascular, and immune cells, which gains even greater complexity during disease states. To effectively study the lung at a single-cell level, a dissociation protocol that achieves the highest yield of viable cells of interest with minimal dissociation-associated protein or transcription changes is key. Here, we detail a rapid collagenase-based dissociation protocol (Col-Short) that provides a high-yield single-cell suspension that is suitable for a variety of downstream applications. Diseased human lung explants were obtained and dissociated through the Col-Short protocol and compared with four other dissociation protocols. Resulting single-cell suspensions were then assessed with flow cytometry, differential staining, and quantitative real-time PCR to identify major hematopoietic and nonhematopoietic cell populations, as well as their activation states. We observed that the Col-Short protocol provides the greatest number of cells per gram of lung tissue, with no reduction in viability when compared with previously described dissociation protocols. Col-Short had no observable surface protein marker cleavage as well as lower expression of protein activation markers and stress-related transcripts compared with four other protocols. The Col-Short dissociation protocol can be used as a rapid strategy to generate single cells for respiratory cell biology research.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".