Analysis of mRNA Nuclear Export Kinetics in Mammalian Cells by Microinjection
Bibliographic record
Abstract
In eukaryotes, messenger RNA (mRNA) is transcribed in the nucleus and must be exported into the cytoplasm to access the translation machinery. Although the nuclear export of mRNA has been studied extensively in Xenopus oocytes and genetically tractable organisms such as yeast and the Drosophila derived S2 cell line, few studies had been conducted in mammalian cells. Furthermore the kinetics of mRNA export in mammalian somatic cells could only be inferred indirectly. In order to measure the nuclear export kinetics of mRNA in mammalian tissue culture cells, we have developed an assay that employs the power of microinjection coupled with fluorescent in situ hybridization (FISH). These assays have been used to demonstrate that in mammalian cells, the majority of mRNAs are exported in a splicing dependent manner, or in manner that requires specific RNA sequences such as the signal sequence coding region (SSCR). In this assay, cells are microinjected with either in vitro synthesized mRNA or plasmid DNA containing the gene of interest. The microinjected cells are incubated for various time points then fixed and the sub-cellular localization of RNA is assessed using FISH. In contrast to transfection, where transcription occurs several hours after the addition of nucleic acids, microinjection of DNA or mRNA allows for rapid expression and allows for the generation of precise kinetic data.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".