Generation of an enhancer-driven gene expression viral tool specific to dentate granule cell-types through direct hippocampal injection
Bibliographic record
Abstract
Accurate investigations of neural circuitry require specific genetic access to individual circuit elements, i.e., the myriad neuronal cell-types in the brain. However, native promoters cannot achieve this because while most genes are expressed in the brain, few are expressed in a single neuronal cell-type. We recently used enhancers, the subcomponents of the transcriptional apparatus which tell promoters when and where to express, combined with heterologous minimal promoters to increase specificity of transgene expression, an approach we call Enhancer-Driven Gene Expression (EDGE). As we discuss, EDGE is a marked improvement in specificity over native promoters, but still requires careful anatomical analysis to avoid off-target effects. In this study we present a more complete set of genomic markers from the mouse brain and characterize a novel EDGE viral vector capable of specifically driving expression in distinct subtypes of hippocampal neurons, even though it can express in other cell-types elsewhere. The advent of cell-type specific viral tools in wild-type animals provides a powerful strategy for neural circuit investigation and holds promise for studies using animal models for which transgenic tools are not available.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
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".