A novel method for the detection of IFN-lambda 3 binding to cells to quantify IFN-lambda receptor expression on epithelial and immune cell subsets
Bibliographic record
Abstract
Abstract Type III interferons (IFN-lambdas) are important antiviral cytokines that also modulate immune responses by acting through a unique IFN-λR1/IL-10R2 heterodimeric receptor. Conflicting data has been reported for which human cells express the IFN-λR1 subunit and directly respond to IFN-λ. Since the commercially available anti-IFN-λR1 flow cytometry antibodies we tried were suboptimal, we developed a novel method to measure IFN-λ3 binding to IFN-λR1/IL-10R2 on the surface of cells via flow cytometry. We found that Huh7.5 hepatoma cells bound IFN-λ3 to the greatest extent with the lowest Kd(app) (83.2nM), and had the corresponding highest induction of IFN stimulated genes (ISGs). Raji and Jurkat cell lines, representing B and T cells, respectively, moderately bound IFN-λ3 and had lower ISG responses. U937 cells, representing monocytes, did not bind IFN-λ3 and therefore, did not have detectable ISG induction. We confirmed that IFN-λ3 was bound to the surface of cells through imaging flow cytometry. Importantly, lentivirus shRNA knockdown of IFNLR1 in Huh7.5 cells decreased our binding signal proportionally and reduced ISG induction by up to 93%. IFN-λ3 responsiveness increased over time with maximal ISG responses seen at 24 hrs for all but one gene. We next applied our assay to human peripheral blood mononuclear cells and saw that only specific immune cell subsets bound IFN-λ3 (eg. plasmacytoid dendritic cells and B cells). These data confirm our new IFN-λ3 binding assay can be used to quantify where the IFN-λ receptor is expressed and reflects IFN-λ3 responsiveness. Knowing which cells express the IFN-λ receptor will be crucial for determining how IFN-λ3 modulates the adaptive immune 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".