An ultrasensitive and modular platform to detect Siglec ligands and control immune cell function
Bibliographic record
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 distinct patterns of Siglec ligands. Moreover, Siglec-liposomes are in vivo compatible, where we demonstrated that Siglec-7-liposomes bind to the brain vasculature in a mucin domain-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 an ST3Gal1-dependent and CD43-independent manner. 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 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 | 0.001 | 0.001 |
| 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".