Dynamics of Macrophage Trogocytosis of Rituximab-Coated B Cells
Bibliographic record
Abstract
Macrophages can remove antigen from the surface of antibody-coated cells by a process termed trogocytosis. Using live cell microscopy and flow cytometry, we investigated the dynamics of trogocytosis by RAW264.7 macrophages of Ramos B cells opsonized with the anti-CD20 monoclonal antibody rituximab. Spontaneous and reversible formation of uropods was observed on Ramos cells, and these showed a strong enrichment in rituximab binding. RAW-Ramos conjugate interfaces were highly enriched in rituximab, and transfer of rituximab to the RAW cells in submicron-sized puncta occurred shortly after cell contact. Membrane from the target cells was concomitantly transferred along with rituximab to a variable extent. We established a flow cytometry-based approach to follow the kinetics of transfer and internalization of rituximab. Disruption of actin polymerization nearly eliminated transfer, while blocking phosphatidylinositol 3-kinase activity only resulted in a delay in its acquisition. Inhibition of Src family kinase activity both slowed acquisition and reduced the extent of trogocytosis. The effects of inhibiting these kinases are likely due to their role in efficient formation of cell-cell conjugates. Selective pre-treatment of Ramos cells with phenylarsine oxide blocked uropod formation, reduced enrichment of rituximab at cell-cell interfaces, and reduced the efficiency of trogocytic transfer of rituximab. Our findings highlight that dynamic changes in target cell shape and surface distribution of antigen may significantly influence the progression and extent of trogocytosis. Understanding the mechanistic determinants of macrophage trogocytosis will be important for optimal design of antibody therapies.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.006 | 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 teacher head, 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".