Doxycycline-inducible Expression of Proteins at Near-endogenous Levels in Mammalian Cells Using the Sleeping Beauty Transposon System
Bibliographic record
Abstract
The function of a protein within a cell critically depends on its interaction with other proteins as well as its subcellular localization. The expression of mutants of a particular protein that have selective perturbation of specific protein interaction motifs is a very useful strategy for resolving a protein's mechanism of action in a cellular process. In addition, expression of fluorescent protein fusions is a key strategy for determining the subcellular localization of a protein. These strategies require tight regulation to avoid potential alterations in protein interactions or localizations that can result from protein overexpression. Previous work led to the development of a Sleeping Beauty transposon system that allows doxycycline-inducible expression of protein mutants or fusions; titration of doxycycline allows expression of protein fusions or mutants at near endogenous levels. When used in combination with siRNA gene silencing, this strategy allows for knockdown-rescue experiments to assess the function of specific protein mutants. In this protocol, we describe the use of this Sleeping Beauty strategy for expression of eGFP fusion or mutant proteins in ARPE-19 and MDA-MB-231 cells. This includes design of expression plasmids, transfection, and selection to obtain stable engineered cells, as well as doxycycline treatment for controlled induction of protein expression, either alone or in combination with siRNA silencing for knockdown-rescue experiments. This strategy is advantageous as it allows rapid generation of stable cells for controlled protein expression, suitable for functional studies that require knockdown-rescue as well as various forms of live cell fluorescence imaging. Key features • Highly versatile doxycycline-inducible expression system that can be used in various mammalian cell lines. • Stable integration of transgene allows for sustained and stable expression. • Titration of doxycycline levels allows expression of transgene at near endogenous levels.
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 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".