Regioselective Biolistic Targeting in Organotypic Brain Slices Using a Modified Gene Gun
Bibliographic record
Abstract
Transfection of DNA has been invaluable for biological sciences and with recent advances to organotypic brain slice preparations, the effect of various heterologous genes could thus be investigated easily while maintaining many aspects of in vivo biology. There has been increasing interest to transfect terminally differentiated neurons for which conventional transfection methods have been fraught with difficulties such as low yields and significant losses in viability. Biolistic transfection can circumvent many of these difficulties yet only recently has this technique been modified so that it is amenable for use in mammalian tissues. New modifications to the accelerator chamber have enhanced the gene gun's firing accuracy and increased its depths of penetration while also allowing the use of lower gas pressure (50 psi) without loss of transfection efficiency as well as permitting a focused regioselective spread of the particles to within 3 mm. In addition, this technique is straight forward and faster to perform than tedious microinjections. Both transient and stable expression are possible with nanoparticle bombardment where episomal expression can be detected within 24 hr and the cell survival was shown to be better than, or at least equal to, conventional methods. This technique has however one crucial advantage: it permits the transfection to be localized within a single restrained radius thus enabling the user to anatomically isolate the heterologous gene's effects. Here we present an in-depth protocol to prepare viable adult organotypic slices and submit them to regioselective transfection using an improved gene gun.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".