Increasing phagocytosis of microglia through targeting CD33 with liposomes displaying glycan ligands
Bibliographic record
Abstract
Abstract CD33 is an immunomodulatory receptor expressed on microglia and genetically linked to Alzheimer’s disease (AD) susceptibility. While antibodies targeting CD33 have entered clinical trials to treat neurodegeneration, it is unknown whether the glycan-binding properties of CD33 can be exploited to modulate microglia. Here, we use liposomes that multivalently display glycan ligands of CD33 (CD33L liposomes) to engage CD33. We find that CD33L liposomes increase phagocytosis of cultured monocytic cells and microglia in a CD33-dependent manner. Enhanced phagocytosis strongly correlates with loss of CD33 from the cell surface and internalization of liposomes. Increased phagocytosis by treatment with CD33L liposomes is dependent on a key intracellular signaling motif on CD33 as well as the glycan-binding ability of CD33. These effects are specific to trans engagement of CD33 by CD33L liposomes, as cis engagement through insertion of lipid-linked CD33L into cells produces the opposite effect on phagocytosis. Moreover, intracerebroventricular injection of CD33L liposomes in mice enhances phagocytosis of microglia in a CD33-dependent manner. These results demonstrate that multivalent engagement of CD33 with glycan ligands can modulate microglial cell function.
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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".