Parental Allele-Specific Protein Expression in Single Cells <i>In Vivo</i>
Bibliographic record
Abstract
Abstract Allelic expression from each parent-of-origin is important as a backup and to ensure that enough protein products of a gene are produced. Thus far, it is not known how each cell throughout a tissue differs in parental allele expression at the level of protein synthesis. Here, we measure the expression of the Ribosomal protein L13a (Rpl13a) from both parental alleles simultaneously in single cells in the living animal. We use genome-edited Drosophila that have a quantitative reporter of protein synthesis inserted into the endogenous Rpl13a locus. We find that individual cells can have large (>10-fold) differences in protein expression between the two parental alleles. Cells can produce protein from only one allele oftentimes, and time-lapse imaging of protein production from each parental allele in each cell showed that the imbalance in expression from one parental allele over the other can invert over time. One sentence summary Parental allele-specific protein expression varies widely across cells and over time. Highlights We used genome editing to insert a quantifiable protein translation reporter into the endogenous Ribosomal protein L13a gene and thus track the protein expression of both parental alleles simultaneously in every single cell in the awake animal. Cells can have a large difference in protein expression for one parental allele over the other, and this can invert over time, and can occur in clusters of cells within a tissue. We demonstrate the highly variable nature of heterozygous and homozygous definitions across single cells, and over time. Our study demonstrates a new paradigm that can be used to examine inherited epigenetic control of expression from a specific parent-of-origin allele across single clonal cells from a common progenitor in vivo .
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".