Endosomal trafficking regulates receptor-mediated transcytosis of antibodies across the blood brain barrier
Why this work is in the frame
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Bibliographic record
Abstract
Current methods for examining antibody trafficking are either non-quantitative such as immunocytochemistry or require antibody labeling with tracers. We have developed a multiplexed quantitative method for antibody 'tracking' in endosomal compartments of brain endothelial cells. Rat brain endothelial cells were co-incubated with blood-brain barrier (BBB)-crossing FC5, monovalent FC5Fc or bivalent FC5Fc fusion antibodies and control antibodies. Endosomes were separated using sucrose-density gradient ultracentrifugation and analyzed using multiplexed mass spectrometry to simultaneously quantify endosomal markers, receptor-mediated transcytosis (RMT) receptors and the co-incubated antibodies in each fraction. The quantitation showed that markers of early endosomes were enriched in high-density fractions (HDF), whereas markers of late endosomes and lysosomes were enriched in low-density fractions (LDF). RMT receptors, including transferrin receptor, showed a profile similar to that of early endosome markers. The in vitro BBB transcytosis rates of antibodies were directly proportional to their partition into early endosome fractions of brain endothelial cells. Addition of the Fc domain resulted in facilitated antibody 'redistribution' from LDF into HDF and additionally into multivesicular bodies (MVB). Sorting of various FC5 antibody formats away from late endosomes and lysosomes and into early endosomes and a subset of MVB results in increased antibody transcytosis at the abluminal side of the BBB.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it