Real-time Fluorescence Tracking of Dynamic Transgene Variegation in Stem Cells
Bibliographic record
Abstract
Transgene variegation is caused by epigenetic switching between expressing and silent states. γ-retrovirus vectors can be variegated in stem cells, but the dynamics of epigenetic remodeling during transgene variegation are unknown. Here, we measured variegated enhanced green fluorescent protein γ-retrovirus expression over 4 days in individual embryonic stem cells while tracking cells in order to create expression lineage trees: 56 colony founder cells and their progeny were tracked over seven generations. Nineteen lineages produced synchronized inheritable trajectories of transgene silencing or reactivation, indicative of epigenetic remodeling with long-term stable inheritance. Short-term fluctuations in fluorescence intensity were also observed, which contributed low-amplitude variation to transgene expression level. These two processes have different frequencies and inheritability, but together contribute to variegated transgene expression. Inhibition of DNA methylation with 5-azacytidine eliminated long-term transgene silencing over 4 days, but short-term fluctuations continued. Our approach applies real-time imaging technology to track the long-term dynamics of transgene expression to investigate the timing and expression patterns leading to variegation. Transgene variegation is caused by epigenetic switching between expressing and silent states. γ-retrovirus vectors can be variegated in stem cells, but the dynamics of epigenetic remodeling during transgene variegation are unknown. Here, we measured variegated enhanced green fluorescent protein γ-retrovirus expression over 4 days in individual embryonic stem cells while tracking cells in order to create expression lineage trees: 56 colony founder cells and their progeny were tracked over seven generations. Nineteen lineages produced synchronized inheritable trajectories of transgene silencing or reactivation, indicative of epigenetic remodeling with long-term stable inheritance. Short-term fluctuations in fluorescence intensity were also observed, which contributed low-amplitude variation to transgene expression level. These two processes have different frequencies and inheritability, but together contribute to variegated transgene expression. Inhibition of DNA methylation with 5-azacytidine eliminated long-term transgene silencing over 4 days, but short-term fluctuations continued. Our approach applies real-time imaging technology to track the long-term dynamics of transgene expression to investigate the timing and expression patterns leading to variegation.
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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 | 0.000 | 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 | 0.000 |
| 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".