A lentiviral system for efficient knockdown of proteins in neuronal cultures
Bibliographic record
Abstract
We have devised a protocol for highly efficient and specific knockdown of proteins in neuronal cultures. Small hairpin RNAs (shRNAs) are embedded into a microRNA (miRNA) context by oligo annealing to create shRNAmiRs, which are expressed from within the 3'-UTR of a reporter protein. This reporter protein/synthetic miRNA cassette is transferred to a targeting vector and lentivirus is produced in HEK-293-T cells following co-transfection of the targeting vector with three additional vectors encoding essential lentiviral proteins. Mature virus is harvested by collecting culture medium from transfected HEK-293-T cells, the virus is purified by centrifugation, and virus titers are determined prior to addition to neuronal cultures. Near 100% transduction efficiency of cultured hippocampal neurons is routinely observed and allows for the population-wide inhibition of target protein expression and the simultaneous knockdown of multiple proteins with little or no toxicity. The lentivirus generated can be used for protein knockdown in multiple neuronal culture models and at a variety of developmental stages. The steps from shRNAmiR design to ready-to-use virus stocks can be completed in as little as two weeks.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".