Characterization of Monoclonal Antibodies to Measure Cell Surface Protein Levels of Human Interferon-Lambda Receptor 1
Bibliographic record
Abstract
Type III interferons (IFN-lambdas, IFN-λs) are important antiviral cytokines that can also modulate immune responses by acting through a heterodimeric receptor composed of the specific and limited expressed IFN-λR1 chain and the ubiquitous IL-10R2 chain, which is shared with IL-10 family cytokines. Conflicting data have been reported regarding which cells express the IFN-λR1 subunit and directly respond to IFN-λs. This is, in part, owing to transcript levels of the IFN-λR1 gene, IFNLR1 , not always correlating with cell surface protein levels. In this study, we tested a panel of novel monoclonal antibodies (mAbs) that specifically recognize human IFN-λR1. Initially, antigen specificity was confirmed by enzyme-linked immunosorbent assay (ELISA), from which a subset of antibodies was selected for additional flow cytometry and neutralization assays. We further characterized two antibodies based on their strong ELISA binding activity (HLR1 and HLR14) and found only HLR14 could reliably detect cell surface IFN-λR1 protein on a variety of cell lines by flow cytometry. HLR14 could also detect IFN-λR1 protein on certain primary human blood cells, including plasmacytoid dendritic cells and B cells from peripheral blood. Availability of the HLR14 mAb will enable the quantification of IFN-λR1 protein levels on cells and better characterization of the cell specificity of the IFN-λ response.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".