High-resolution Immunoelectron Microscopy Techniques for Revealing Distinct Subcellular Type 1 Cannabinoid Receptor Domains in Brain
Bibliographic record
Abstract
Activation of type 1 cannabinoid (CB1) receptors by endogenous, exogenous (cannabis derivatives) or synthetic cannabinoids (i.e., CP 55.940, Win-2) has a wide variety of behavioral effects due to the presence of CB1 receptors in the brain. In situ hybridization and immunohistochemical techniques have been crucial for defining the CB1 receptor expression and localization at the cellular level. Nevertheless, more advanced methods are needed to reveal the precise topography of CB1 receptors in the brain, especially in unsuspected sites such as other cell types and organelles with low receptor expression (e.g., glutamatergic neurons, astrocytes, mitochondria). High-resolution immunoelectron microscopy provides a more precise detection method for the subcellular localization of CB1 receptors in the brain. Herein, we describe a single pre-embedding immunogold method for electron microscopy based on the use of specific CB1 receptor antibodies and silver-intensified 1.4 nm gold-labeled Fab' fragments, and a combined pre-embedding immunogold and immunoperoxidase method that employs biotinylated secondary antibodies and avidin-biotin-peroxidase complex for the simultaneous localization of CB1 receptors and protein markers of specific brain cells or synapses (e.g., GFAP, GLAST, IBA-1, PSD-95, gephyrin). In addition, a post-embedding immunogold method is also described and compared to the pre-embedding labeling procedure. These methods provide a relatively easy and useful approach for revealing the subcellular localization of low amounts of CB1 receptors in glutamatergic synapses, astrocytes, neuronal and astrocytic mitochondria in the brain.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".