Advanced In vivo Use of CRISPR/Cas9 and Anti-sense DNA Inhibition for Gene Manipulation in the Brain
Bibliographic record
Abstract
Gene editing tools are essential for uncovering how genes mediate normal brain-behavior relationships and contribute to neurodegenerative and neuropsychiatric disorders. Recent progress in gene editing technology now allows neuroscientists unprecedented access to edit the genome efficiently. Although many important tools have been developed, here we focus on approaches that allow for rapid gene editing in the adult nervous system, particularly CRISPR/Cas9 and anti-sense nucleotide-based techniques. CRISPR/Cas9 is a flexible gene editing tool, allowing the genome to be manipulated in diverse ways. For instance, CRISPR/Cas9 has been successfully used to knockout genes, knock-in mutations, overexpress or inhibit gene activity, and provide scaffolding for recruiting specific epigenetic regulators to individual genes and gene regions. Moreover, the CRISPR/Cas9 system may be modified to target multiple genes at one time, affording simultaneous inhibition and overexpression of distinct genetic targets. Although many of the more advanced applications of CRISPR/Cas9 have not been applied to the nervous system, the toolbox is widely accessible, such that it is poised to help advance neuroscience. Anti-sense nucleotide-based technologies can be used to rapidly knockdown genes in the brain. The main advantage of anti-sense based tools is their simplicity, allowing for rapid gene delivery with minimal technical expertise. Here, we describe the main applications and functions of each of these systems with an emphasis on their many potential applications in neuroscience laboratories.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".