Genome-wide CRISPR screens reveal a specific ligand for the glycan-binding immune checkpoint receptor Siglec-7
Bibliographic record
Abstract
, molecules that inhibit immune cell activity following binding to glycosylated cell-surface antigens, are emerging as attractive targets for cancer immunotherapy. Defining biologically relevant ligands that bind and activate such receptors, however, has historically been a significant challenge. Here, we present a CRISPRi genomic screening strategy that allowed unbiased identification of the key genes required for cell-surface presentation of glycan ligands on leukemia cells that bind the glyco-immune checkpoint receptors Siglec-7 and Siglec-9. This approach revealed a selective interaction between Siglec-7 and the mucin-type glycoprotein CD43. Further work identified a specific N-terminal glycopeptide region of CD43 containing clusters of disialylated O-glycan tetrasaccharides that form specific Siglec-7 binding motifs. Knockout or blockade of CD43 in leukemia cells relieves Siglec-7-mediated inhibition of immune killing activity. This work identifies a potential target for immune checkpoint blockade therapy and represents a generalizable approach to dissection of glycan-receptor interactions in living cells.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".