Reprogrammed apoptotic platelets drive rapid hemostasis through phosphatidylserine and prostaglandin E2 signaling in preclinical models
Data files
Apr 16, 2026 version files 4.51 MB
Abstract
Uncontrolled hemorrhage in trauma, surgical, organ-related, and endoscopic settings, particularly in patients receiving antiplatelet therapy, remains difficult to manage clinically. Here, we introduce hPPL, a reprogrammed procoagulant platelet derivative generated by calcium ionophore A23187-induced apoptosis, enriched in surface phosphatidylserine (PS) and capable of driving rapid hemostasis. Retaining a protein profile akin to resting platelets, hPPL robustly promotes platelet activation and aggregation, demonstrating superior hemostatic efficacy compared with clinical thrombin and hemostatic materials (MPH and FIBRILLAR™) in murine and porcine bleeding models, even under antiplatelet treatment. Mechanistically, hPPL uniquely upregulates prostaglandin E synthase (PTGES), thereby increasing prostaglandin E2 (PGE2) production and EP3 receptor-mediated platelet activation, which synergize with PS to amplify clot formation. Our findings uncover a previously unrecognized apoptosis-driven PTGES/PGE2/EP3 signaling axis that reinforces PS-mediated coagulation, establishing hPPL as a transformative, natural topical hemostatic agent with broad translational potential for organ-related bleeding and distinct advantages in managing complex endoscopic hemorrhages under both physiological and coagulopathic conditions.
Dataset DOI: 10.5061/dryad.3n5tb2rzq
Description of the data and file structure
Proteomic analysis was performed to compare the protein profiles of lPPL, mPPL, hPPL, and resting platelets (PLTs), followed by membrane proteomic analysis between hPPL and PLTs.
Files and variables
File: Comparative_membrane_proteomic_analysis_of_hPPLs_and_PLTs.csv
Variables
- Description:
- Gene Names: Gene names
- pvalue(hPPL/PLT): P value of the comparison between hPPL and PLT. TMT data were preprocessed as follows: raw quantitative values of 0 were treated as missing values (NA), followed by log2 transformation and quantile normalization. Missing values were imputed using the minimum value. Differentially expressed proteins were identified by calculating the t-test p-value and fold-change ratio between the two groups, with ratio > 1.2 or ratio < 0.833 and P < 0.05 as the cutoff criteria.
- BH adjusted P(hPPL/PLT): Benjamini–Hochberg adjusted P value of the comparison between hPPL and PLT.
- ratio(hPPL/PLT):Fold-change ratio between hPPL and PLT
File: Comparative_protein_profile_analysis_of_PPLs_and_PLTs.csv
Variables
- name: Protein ID
- Description:
- Gene Names: Gene names
- Gene Ontology (biological process):
- Gene Ontology (cellular component):
- Function [CC]:
- Pathway:
- pvaluelPPL/PLT:P value of the comparison between lPPL and PLT. TMT data were preprocessed as follows: raw quantitative values of 0 were treated as missing values (NA), followed by log2 transformation and quantile normalization. Missing values were imputed using the minimum value. Differentially expressed proteins were identified by calculating the t-test p-value and fold-change ratio between the two groups, with ratio > 1.2 or ratio < 0.833 and P < 0.05 as the cutoff criteria.
- BH adjusted PlPPL/PLT:Benjamini–Hochberg adjusted P value of the comparison between lPPL and PLT.
- ratiolPPL/PLT:Fold-change ratio between lPPL and PLT
- pvalue(mPPL/PLT):P value of the comparison between mPPL and PLT
- BH adjusted P(mPPL/PLT):Benjamini–Hochberg adjusted P value of the comparison between mPPL and PLT.
- ratio(mPPL/PLT):Fold-change ratio between mPPL and PLT
- pvalue(hPPL/PLT):P value of the comparison between hPPL and PLT
- BH adjusted P(hPPL/PLT):Benjamini–Hochberg adjusted P value of the comparison between hPPL and PLT.
- ratio(hPPL/PLT):Fold-change ratio between hPPL and PLT
- In our dataset, empty cells indicate that the corresponding information was not recorded or could not be obtained.