Single-cell RNA-sequencing of bronchoscopy specimens: development of a rapid, minimal-handling protocol
Bibliographic record
Abstract
Abstract Single-cell RNA-sequencing (scRNA-seq) is an important tool for understanding disease pathophysiology, including airways diseases. Currently, the majority of scRNA-seq studies in airways diseases have used invasive methods (airway biopsy, surgical resection) which carry inherent risks and thus present a major limitation to scRNA-seq investigation of airway pathology. Bronchial brushing, where the airway mucosa is sampled using a cytological brush, is a viable, less invasive method of obtaining airway cells for scRNA-seq. Here we are describing the development of a rapid and minimal-handling protocol for preparing single cell suspensions from bronchial brush specimens for scRNA-seq. Our optimized protocol maximises cell recovery and cell quality, and may facilitate large-scale profiling of the airway transcriptome at single cell resolution. Lay abstract Single-cell RNA-sequencing (scRNA-seq) measures the gene expression of individual cells, and may be useful for understanding disease processes. scRNA-seq may be used to investigate lung diseases, but using invasive methods such as biopsy or surgery limits our ability to conduct large research studies. Bronchial brushing, where a soft brush is used to collect cells from inside the lungs, is a safer method but we need a better way to isolate individual cells from the brush specimens. We developed a method that is faster and involves less handling of the specimens compared to other published methods. Our method may therefore be useful for conducting large scRNA-seq studies in lung diseases.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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".