Spatial dynamics of the insulin receptor in living neurons
Bibliographic record
Abstract
Insulin signaling through the insulin receptor has long been studied in classic target organs, such as adipose tissue and skeletal muscle, where one of its effects is to increase glucose uptake. Insulin and insulin receptor are present in many areas of the brain, but the functions of brain insulin signaling outside feeding circuits are not well defined. It has been proposed that hippocampal insulin signaling is important for memory, that brain insulin signaling is deficient in Alzheimer's disease, and that intranasal insulin treatment improves cognition, but the mechanisms remain unclear and do not seem to involve increased glucose uptake by neurons. The molecular behavior of the insulin receptor itself is not well known in living neurons; therefore, we investigated the spatial dynamics of the insulin receptor on somatodendritic membranes of live rat hippocampal neurons in culture. Using single-molecule tracking of quantum dot-tagged insulin receptors and single-particle tracking photoactivation localization microscopy, we show that the insulin receptor is distributed over both dendritic shafts and spines. Using colocalization with synaptic markers, we also show that in contrast to the glutamate receptor subunit glutamate receptor subunit A1, the dynamics of the insulin receptor are not affected by association with excitatory synapses; however, the insulin receptor is immobilized by components of inhibitory synapses. The mobility of the insulin receptor is reduced both by low concentrations of the pro-inflammatory cytokine tumor necrosis factor α and by cholesterol depletion, suggesting an association with sphingolipid-rich membrane domains. On the other hand, the insulin receptor dynamics in hippocampal neurons are not affected by increased excitatory signaling. Finally, using real-time single-event quantification, we find evidence of strong insulin receptor exocytosis on dendritic shafts. Our results suggest an association of the neuronal insulin receptor with specific elements of the dendritic shaft, rather than excitatory synapses.
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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 | 0.000 | 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) | 0.001 | 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 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".