Early embryonic mutations reveal dynamics of somatic and germ cell lineages in mice
Bibliographic record
Abstract
De novo mutations accumulate with zygotic cell divisions. However, the occurrence of these mutations and the way they are inherited by somatic cells and germ cells remain unclear. Here, we present a novel method to reconstruct cell lineages. We identified mosaic mutations in mice using deep whole-genome sequencing and reconstructed embryonic cell lineages based on the variant allele frequencies of the mutations. The reconstructed trees were confirmed using nuclear transfer experiments and the genotyping of approximately 50 offspring of each tree. The most detailed tree had 32 terminal nodes and showed cell divisions from the fertilized egg to germ cell- and somatic cell-specific lineages, indicating at least five independent cell lineages that would be selected as founders of the primordial germ cells. The contributions of each lineage to germ cells and offspring varied widely. At the emergence of the germ cell-specific lineages, 10-15 embryonic mutations had accumulated, suggesting that the pregastrulation mutation rate is 1.0 mutation per mitosis. Subsequent mutation rates were 0.7 for germ cells and 13.2 for tail fibroblasts. Our results show a new framework to assess embryonic lineages; further, we suggest an evolutionary strategy for preserving heterogeneity owing to postzygotic mutations in offspring.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".