Identifying Genetic Regulators of Protein‐Glycan Interactions with Genome‐Wide CRISPR Screening
Bibliographic record
Abstract
Glycans are carbohydrate molecules appended to proteins and lipids on the surface of all living cells. Glycans play key roles in a wide array of biological processes, and structural changes in cell-surface glycosylation patterns have been connected to pathogenesis of several diseases. In particular, cancer cells frequently upregulate expression of glycans that bind to inhibitory receptors (lectins) on immune cells. These glycosylated antigens systematically inhibit immune activity and protect cancer cells from immune surveillance. Understanding how cancer cells generate these glycan ligands can thus lead to identification of novel druggable targets for therapeutic intervention. However, glycan ligand biosynthesis is subject to extremely complex genetic regulation, making it difficult to identify the key genes involved in production of immune-regulatory glycan antigens. In a recent publication, we described a CRISPR/Cas9 screening approach to identify genes that drive synthesis of ligands for glycan-binding immune receptors. Here, we outline a detailed, step-by-step protocol for completing this type of genome-wide screen. Our protocol produces a genome-wide atlas of all genes whose expression is required for cell-surface binding of a recombinant immune lectin. This dataset can be used both to identify novel ligands for immune lectins and annotate regulatory genes that drive changes in cancer-associated glycosylation. Our protocol serves as a general resource for researchers interested in the detailed study of cancer glyco-immunology. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Generation of a genome-wide CRISPR library using lentiviral transduction Support Protocol: Generation of dCas9KRAB-expressing K-562 cells Basic Protocol 2: Staining of genome-wide CRISPR libraries with Siglec-Fc reagents and fluorescence-activated cell sorting Basic Protocol 3: Library amplification and sequencing Basic Protocol 4: Data analysis and hit identification.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".