Real-time nanoscale organization of amyloid precursor protein
Bibliographic record
Abstract
Despite an intuitive understanding of the role of APP in health and disease, there exist few attempts to dissect its molecular localization at excitatory synapses. Though the biochemistry involved in the enzymatic processing of APP is well understood, there is a void in understanding the nonuniformity of the product formation in vivo. Here, we employed multiple paradigms of single molecules and ensemble based nanoscopic imaging to reveal that APP molecules are organized into regulatory nanodomains that are differentially compartmentalized in the functional zones of an excitatory synapse. Furthermore, with the aid of high density single particle tracking, we show that the lateral diffusion of APP in live cells dictates an equilibrium between these nanodomains and their nano-environment, which is affected in a detrimental variant of APP. Additionally, we incorporate this spatio-temporal detail 'in silico' to generate a realistic nanoscale topography of APP in dendrites and synapses. This approach uncovers a nanoscale heterogeneity in the molecular organization of APP, depicting a locus for differential APP processing. This holistic paradigm, to decipher the real-time heterogeneity of the substrate molecules on the nanoscale, could enable us to better evaluate the molecular constraints overcoming the ensemble approaches used traditionally to understand the kinetics of product formation.
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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".