Lipid Nanoparticle Delivery of siRNA to Silence Neuronal Gene Expression in the Brain
Bibliographic record
Abstract
Manipulation of gene expression in the brain is fundamental for understanding the function of proteins involved in neuronal processes. In this article, we show a method for using small interfering RNA (siRNA) in lipid nanoparticles (LNPs) to efficiently silence neuronal gene expression in cell culture and in the brain in vivo through intracranial injection. We show that neurons accumulate these LNPs in an apolipoprotein E-dependent fashion, resulting in very efficient uptake in cell culture (100%) with little apparent toxicity. In vivo, intracortical or intracerebroventricular (ICV) siRNA-LNP injections resulted in knockdown of target genes either in discrete regions around the injection site or in more widespread areas following ICV injections with no apparent toxicity or immune reactions from the LNPs. Effective targeted knockdown was demonstrated by showing that intracortical delivery of siRNA against GRIN1 (encoding GluN1 subunit of the NMDA receptor (NMDAR)) selectively reduced synaptic NMDAR currents in vivo as compared with synaptic AMPA receptor currents. Therefore, LNP delivery of siRNA rapidly manipulates expression of proteins involved in neuronal processes in vivo, possibly enabling the development of gene therapies for neurological disorders.Molecular Therapy-Nucleic Acids (2013) 2, e136; doi:10.1038/mtna.2013.65; published online 3 December 2013.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".