Preparation of Mouse Brain Tissue for Immunoelectron Microscopy
Bibliographic record
Abstract
Transmission electron microscopy (TEM) is extremely useful for visualizing microglial, oligodendrocytic, astrocytic, and neuronal subcellular compartments (dendrite, dendritic spine, axon, axon terminal, perikaryon), as well as their intracellular organelles and cytoskeleton, in the central nervous system at high spatial resolution. Combined with TEM, pre-embedding immunocytochemistry allows the discrimination of cellular elements with few distinctive features and identification criteria (e.g., microglial perikarya and processes, when using an antibody against the microglia-specific marker Iba1 (ionized calcium binding adaptor molecule 1; as presented here)), identifying the neurotransmitter contents of cellular elements (e.g., serotonergic) and their ultrastructural localization of soluble or membrane-bound proteins (e.g., 5 HT1A and EphA4 receptors). Here, we describe a protocol for transcardiac perfusion of mice with acrolein fixative, removal and sectioning of the brain, as well as immunoperoxidase-diaminobenzidine (DAB) staining, resin embedding, and ultrathin sectioning of the brain sections. Upon completion of these procedures, the immunostained material is ready for examination with TEM. When rigorously performed, this technique provides an excellent compromise between optimal ultrastructural preservation and immunocytochemical detection.
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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 | 0.003 | 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) | 0.017 | 0.010 |
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".