Looking at the developing lung in single-cell resolution
Bibliographic record
Abstract
Lung development is a complicated and delicate process, facilitated by spatially and temporarily coordinated cross talk of up to 40 cell types. Developmental origin and heterogeneity of lung cell lineages in context of lung development have been a focus of research efforts for decades. Bulk RNA and protein measurements, RNA and protein labeling, and lineage tracing techniques have been traditionally employed. However, the complex and heterogeneous nature of lung tissue presents a particular challenge when identifying subtle changes in gene expression in individual cell types. Rapidly developing single-cell RNA sequencing (scRNA-seq) techniques allow for unbiased and robust assessment of complex cellular dynamics during biological processes in unprecedented ways. Discovered a decade ago, scRNA-seq has been applied in respiratory research to understand lung cellular composition and to identify novel cell types. Still, very few studies to date have addressed the single-cell transcriptome in healthy or aberrantly developing lung. In this review, we discuss principal discoveries with scRNA-seq in the field of prenatal and postnatal lung development. In addition, we examine challenges and expectations, and propose future steps associated with the use of scRNA-seq to study developmental 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".