<em>In utero</em> and <em>ex vivo</em> Electroporation for Gene Expression in Mouse Retinal Ganglion Cells
Bibliographic record
Abstract
The retina and its sole output neuron, the retinal ganglion cell (RGC), comprise an excellent model in which to examine biological questions such as cell differentiation, axon guidance, retinotopic organization and synapse formation. One drawback is the inability to efficiently and reliably manipulate gene expression in RGCs in vivo, especially in the otherwise accessible murine visual pathways. Transgenic mice can be used to manipulate gene expression, but this approach is often expensive, time consuming, and can produce unwanted side effects. In chick, in ovo electroporation is used to manipulate gene expression in RGCs for examining retina and RGC development. Although similar electroporation techniques have been developed in neonatal mouse pups, adult rats, and embryonic murine retinae in vitro, none of these strategies allow full characterization of RGC development and axon projections in vivo. To this end, we have developed two applications of electroporation, one in utero and the other ex vivo, to specifically target embryonic murine RGCs. With in utero retinal electroporation, we can misexpress or downregulate specific genes in RGCs and follow their axon projections through the visual pathways in vivo, allowing examination of guidance decisions at intermediate targets, such as the optic chiasm, or at target regions, such as the lateral geniculate nucleus. Perturbing gene expression in a subset of RGCs in an otherwise wild-type background facilitates an understanding of gene function throughout the retinal pathway. Additionally, we have developed a companion technique for analyzing RGC axon growth in vitro. We electroporate embryonic heads ex vivo, collect and incubate the whole retina, then prepare explants from these retinae several days later. Retinal explants can be used in a variety of in vitro assays in order to examine the response of electroporated RGC axons to guidance cues or other factors. In sum, this set of techniques enhances our ability to misexpress or downregulate genes in RGCs and should greatly aid studies examining RGC development and axon projections.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".