<em>Ex Utero</em> Electroporation and Organotypic Slice Cultures of Embryonic Mouse Brains for Live-Imaging of Migrating GABAergic Interneurons
Bibliographic record
Abstract
GABAergic interneurons (INs) are critical components of neuronal networks that drive cognition and behavior. INs destined to populate the cortex migrate tangentially from their place of origin in the ventral telencephalon (including from the medial and caudal ganglionic eminences (MGE, CGE)) to the dorsal cortical plate in response to a variety of intrinsic and extrinsic cues. Different methodologies have been developed over the years to genetically manipulate specific pathways and investigate how they regulate the dynamic cytoskeletal changes required for proper IN migration. In utero electroporation has been extensively used to study the effect of gene repression or overexpression in specific IN subtypes while assessing the impact on morphology and final position. However, while this approach is readily used to modify radially migrating pyramidal cells, it is more technically challenging when targeting INs. In utero electroporation generates a low yield given the decreased survival rates of pups when electroporation is conducted before e14.5, as is customary when studying MGE-derived INs. In an alternative approach, MGE explants provide easy access to the MGE and facilitate the imaging of genetically modified INs. However, in these explants, INs migrate into an artificial matrix, devoid of endogenous guidance cues and thalamic inputs. This prompted us to optimize a method where INs can migrate in a more naturalistic environment, while circumventing the technical challenges of in utero approaches. In this paper, we describe the combination of ex utero electroporation of embryonic mouse brains followed by organotypic slice cultures to readily track, image and reconstruct genetically modified INs migrating along their natural paths in response to endogenous cues. This approach allows for both the quantification of the dynamic aspects of IN migration with time-lapse confocal imaging, as well as the detailed analysis of various morphological parameters using neuronal reconstructions on fixed immunolabeled tissue.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.013 |
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".