Immobilized IgG-containing immune complexes require platelets to recruit neutrophils during inflammation
Bibliographic record
Abstract
During vascular injury, platelets are essential for halting bleeding and recruiting neutrophils to prevent microbial invasion. However, in antibody-mediated autoimmune diseases occurring without vascular damage, neutrophils infiltrate tissues and contribute to pathology. Here, we investigated whether the dependence of neutrophils on platelets is conserved in the context of antibody-driven inflammation. Using human cells from individuals with rheumatoid arthritis and a microfluidic system mimicking physiological shear over IgG-containing immune complexes, we demonstrate that despite expressing Fc receptors, neutrophils required platelets to stably adhere to immune complexes under flow. Platelet Fcγ receptor 2a (FcγRIIA) binding was critical for resisting shear stress, while neutrophils used FcγRIIA and FcγRIIIB for immune complex recognition. Platelet P-selectin binding to neutrophil P-selectin glycoprotein ligand 1 (PSGL-1) was essential for recruitment, whereas macrophage-1 antigen (Mac-1) was dispensable. In a mouse model of autoantibody-mediated arthritis, intravital imaging confirmed that neutrophil recruitment relied on PSGL-1. Importantly, expression of FcγRIIA aggravated arthritis, and blockade of PSGL-1, but not Mac-1, in these mice abrogated both the platelet and neutrophil interactions and disease. These findings identify key molecular interactions in platelet-neutrophil cooperation and reveal that platelets are essential enablers of FcR-mediated neutrophil adhesion in antibody-driven inflammation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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".