Imaging the Intracellular Trafficking of APP with Photoactivatable GFP
Bibliographic record
Abstract
Beta-amyloid (Aβ) is the major constituent of senile plaques found in the brains of Alzheimer’s disease patients. Aβ is derived from the sequential cleavage of Amyloid Precursor Protein (APP) by β and γ-secretases. Despite the importance of Aβ to AD pathology, the subcellular localization of these cleavages is not well established. Work in our laboratory and others implicate the endosomal/lysosomal system in APP processing after internalization from the cell surface. However, the intracellular trafficking of APP is relatively understudied. While cell-surface proteins are amendable to many labeling techniques, there are no simple methods for following the trafficking of membrane proteins from the Golgi. To this end, we created APP constructs that were tagged with photo-activatable GFP (paGFP) at the C-terminus. After synthesis, paGFP has low basal fluorescence, but it can be stimulated with 413 nm light to produce a strong, stable green fluorescence. By using the Golgi marker Galactosyl transferase coupled to Cyan Fluorescent Protein (GalT-CFP) as a target, we are able to accurately photoactivate APP in the trans-Golgi network. Photo-activated APP-paGFP can then be followed as it traffics to downstream compartments identified with fluorescently tagged compartment marker proteins for the early endosome (Rab5), the late endosome (Rab9) and the lysosome (LAMP1). Furthermore, using inhibitors to APP processing including chloroquine or the γ-secretase inhibitor L685, 458, we are able to perform pulse-chase experiments to examine the processing of APP in single cells. We find that a large fraction of APP moves rapidly to the lysosome without appearing at the cell surface, and is then cleared from the lysosome by secretase-like cleavages. This technique demonstrates the utility of paGFP for following the trafficking and processing of intracellular proteins from the Golgi to downstream compartments.
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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 | 0.001 | 0.001 |
| 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".