An ultrasensitive and modular platform to detect Siglec ligands and control immune cell function
Bibliographic record
Abstract
Abstract Siglecs are immunomodulatory receptors that regulate immune cell function. A fundamental challenge in studying Siglec-ligand interactions is the low affinity of Siglecs for their ligands. Inspired by how nature uses multivalency, we developed Siglec-liposomes as a highly multivalent and versatile platform for detecting Siglec glycan ligands in which recombinant Siglecs were conjugated to liposomes using the SpyCatcher-SpyTag system. Siglec-liposomes offer tunable multivalency and a modular assembly, enabling presentation of different Siglecs on the same liposome. Using Siglec-liposomes, we profiled Siglec ligands on human leukocytes, revealing new insights into Siglec ligands. Moreover, Siglec-liposomes are in vivo compatible, where we demonstrated that Siglec-7-liposomes bind to the brain vasculature in a mucin-dependent manner. Given the abundance of Siglec ligands on T cells, we investigated whether Siglec-liposomes modulate T cell function and find that Siglec-7-liposomes increase T cell proliferation in a ST3Gal1-dependent and CD43-independent manner. Taken together, Siglec-liposomes are a versatile and sensitive tool for detecting Siglec ligands and immunomodulation.
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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 | 0.001 | 0.000 |
| 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 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".