Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations
Bibliographic record
Abstract
Discovering mechanisms that pattern dendritic arbors requires methods to visualize, image, and analyze dendrites during development. The mouse retina is a powerful model system for the investigation of cell type-specific mechanisms of neuronal morphogenesis and connectivity. The organization and composition of retinal subtypes are well-defined, and genetic tools are available to access specific types during development. Many retinal cell types also constrain their dendrites and/or axons to narrow layers, which facilitates time-lapse imaging. Mouse retina explant cultures are well suited for live-cell imaging using confocal or multiphoton microscopy, but methods optimized for imaging dendrite dynamics with temporal and structural resolution are lacking. Presented here is a method to sparsely label and image the development of specific retinal populations marked by the Cre-Lox system. Commercially available adeno-associated viruses (AAVs) used here expressed membrane-targeted fluorescent proteins in a Cre-dependent manner. Intraocular delivery of AAVs in neonatal mice produces fluorescent labeling of targeted cell types by 4-5 days post-injection (dpi). The membrane fluorescent signals are detectable by confocal imaging and resolve fine branch structures and dynamics. High-quality videos spanning 2-4 h are acquired from imaging retinal flat-mounts perfused with oxygenated artificial cerebrospinal fluid (aCSF). Also provided is an image postprocessing pipeline for deconvolution and three-dimensional (3D) drift correction. This protocol can be used to capture several cellular behaviors in the intact retina and to identify novel factors controlling neurite morphogenesis. Many developmental strategies learned in the retina will be relevant for understanding the formation of neural circuits elsewhere in the central nervous system.
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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 | 0.000 | 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 | 0.000 |
| 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".