SINGLE-CELL ANALYSIS OF SOMATIC MUTATIONS IN HUMAN LUNG REVEALS ASSOCIATION WITH TRANSCRIPTIONAL CHANGES IN AGING
Bibliographic record
Abstract
Abstract Age is a major risk factor for lung disease. Accumulation of somatic mutations has been implicated in both aging and cellular senescence. We analyzed somatic mutations called from single-cell RNAseq (scRNAseq) data. scRNAseq was performed on lung parenchyma samples from healthy donors (32 samples, 11-72 years, 21M/11F). Following standard pre-processing and cell type annotation, mutations were called using SComatic with the default parameters. Mutation burden was calculated for each cell type-sample combination by dividing the number of mutations by the number of callable sites. Our resulting scRNAseq dataset consisted of 199,400 cells, comprising 25 distinct cell types. Mutation burden was highest in alveolar type 1 (AT1) cells (93.6 mutations/MB), alveolar macrophages (79.1), and general capillary (gCap) cells (62.1). Mutation burden was positively correlated with age (r=0.28, p< 0.001). Globally, the top genes correlated with mutation burden included ubiquitin ligase genes (AMBRA1, ANAPC1, SEL1L, USP25, USP33) and DNA damage response genes (RAD50, PRKDC). Notably, mutation burden also correlated with expression of senescence marker CDKN2A (r=0.48, p< 0.05). In AT1 cells, mutation burden correlated with decreased expression of cell marker genes such as AGER (r=-0.64, p< 0.05) and HOPX (r=-0.83, p< 0.05). Similarly, gCap cells exhibited decreased expression of marker genes Il7R (r=-0.54) and VIPR1 (r=-0.64), while MAPK/ERK signaling genes were increased (p < 0.05). These genes were also significantly correlated with age in the same direction (p < 0.05). These results suggest that somatic mutation accumulation may contribute to age-associated transcriptional changes and loss of cell function, with cell types of the alveoli and endothelium experiencing the greatest effects.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".