A Thin-skull Window Technique for Chronic Two-photon <em>In vivo</em> Imaging of Murine Microglia in Models of Neuroinflammation
Bibliographic record
Abstract
Traditionally in neuroscience, in vivo two photon imaging of the murine central nervous system has either involved the use of open-skull or thinned-skull preparations. While the open-skull technique is very versatile, it is not optimal for studying microglia because it is invasive and can cause microglial activation. Even though the thinned-skull approach is minimally invasive, the repeated re-thinning of skull required for chronic imaging increases the risks of tissue injury and microglial activation and allows for a limited number of imaging sessions. Here we present a chronic thin-skull window method for monitoring murine microglia in vivo over an extended period of time using two-photon microscopy. We demonstrate how to prepare a stable, accessible, thinned-skull cortical window (TSCW) with an apposed glass coverslip that remains translucent over the course of three weeks of intermittent observation. This TSCW preparation is far more immunologically inert with respect to microglial activation than open craniotomy or repeated skull thinning and allows an arbitrary number of imaging sessions during a time period of weeks. We prepare TSCW in CX₃CR₁ GFP/+ mice to visualize microglia with enhanced green fluorescent protein to ≤150 μm beneath the pial surface. We also show that this preparation can be used in conjunction with stereotactic brain injections of the HIV-1 neurotoxic protein Tat, adjacent to the TSCW, which is capable of inducing durable microgliosis. Therefore, this method is extremely useful for examining changes in microglial morphology and motility over time in the living brain in models of HIV Associated Neurocognitive Disorder (HAND) and other neurodegenerative diseases with a neuroinflammatory component.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".