An improved platform for functional assessment of large protein libraries in mammalian cells
Bibliographic record
Abstract
Multiplex genetic assays can simultaneously test thousands of genetic variants for a property of interest. However, limitations of existing multiplex assay methods in cultured mammalian cells hinder the breadth, speed and scale of these experiments. Here, we describe a series of improvements that greatly enhance the capabilities of a Bxb1 recombinase-based landing pad system for conducting different types of multiplex genetic assays in various mammalian cell lines. We incorporate the landing pad into a lentiviral vector, easing the process of generating new landing pad cell lines. We also develop several new landing pad versions, including one where the Bxb1 recombinase is expressed from the landing pad itself, improving recombination efficiency more than 2-fold and permitting rapid prototyping of transgenic constructs. Other versions incorporate positive and negative selection markers that enable drug-based enrichment of recombinant cells, enabling the use of larger libraries and reducing costs. A version with dual convergent promoters allows enrichment of recombinant cells independent of transgene expression, permitting the assessment of libraries of transgenes that perturb cell growth and survival. Lastly, we demonstrate these improvements by assessing the effects of a combinatorial library of oncogenes and tumor suppressors on cell growth. Collectively, these advancements make multiplex genetic assays in diverse cultured cell lines easier, cheaper and more effective, facilitating future studies probing how proteins impact cell function, using transgenic variant libraries tested individually or in combination.
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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 | 0.001 | 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".