Analysis of Endocytic Trafficking by Single‐Cell Fluorescence Ratio Imaging
Bibliographic record
Abstract
The post-endocytic sorting of internalized membrane proteins plays a critical role in numerous physiological processes, including receptor desensitization, degradation of non-native plasma membrane proteins, and cell surface retrieval of receptors from early endosomes upon ligand dissociation. Here, we describe a fluorescence ratiometric image analysis (FRIA) method used to determine the post-endocytic fate and transport kinetics of transmembrane proteins based on the pH measurement of internalized cargo-containing compartments in living cells. The method relies on the notion that the pH of a cargo-containing transport vesicle (vesicular pH, pH(v)) could be taken as an indicator of its identity, considering that endocytic organelles (e.g., sorting endosome, recycling endosome, late endosome/MVB, and lysosome) have characteristic pH(v). The pH-sensitive FITC-conjugated secondary antibody is attached to the cargo via a primary antibody, recognizing the cargo extracellular domain. The pH(v) is determined by single-cell FRIA. Internalized cargo colocalization with organellar markers, as well as pH(v) measurement of recycling endosome, lysosome, and the TGN are discussed to validate the technique and facilitate data interpretation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".