Platelets interact with CD169+ macrophages and cDC1 and enhance liposome-induced CD8+ T cell responses
Bibliographic record
Abstract
Historically platelets are mostly known for their crucial contribution to hemostasis, but there is growing understanding of their role in inflammation and immunity. The immunomodulatory role of platelets entails interaction with pathogens, but also with immune cells including macrophages and dendritic cells (DCs), to activate adaptive immune responses. In our previous work, we have demonstrated that splenic CD169 + macrophages scavenge liposomes and collaborate with conventional type 1 DCs (cDC1) to induce expansion of CD8 + T cells. Here, we show that platelets associate with liposomes and bind to DNGR-1/Clec9a and CD169/Siglec-1 receptors in vitro . In addition, platelets interacted with splenic CD169 + macrophages and cDC1 and further increased liposome internalization by cDC1. Most importantly, platelet depletion prior to liposomal immunization resulted in significantly diminished antigen-specific CD8 + T cell responses, but not germinal center B cell responses. Previously, complement C3 was shown to be essential for platelet-mediated CD8 + T cell activation during bacterial infection. However, after liposomal vaccination CD8 + T cell priming was not dependent on complement C3. While DCs from platelet-deficient mice exhibited unaltered maturation status, they did express lower levels of CCR7. In addition, in the absence of platelets, CCL5 plasma levels were significantly reduced. Overall, our findings demonstrate that platelets engage in a cross-talk with CD169 + macrophages and cDC1 and emphasize the importance of platelets in induction of CD8 + T cell responses in the context of liposomal vaccination.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".