Machine learning-assisted assessment of extracellular vesicles can monitor cellular rejection after heart transplant
Abstract Background Heart transplant rejection, particularly acute cellular rejection (ACR), remains a critical post-operative concern, despite declining incidence rates. Current diagnostic standards rely on invasive endomyocardial biopsy, which presents limitations in sensitivity and reproducibilit...
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Nature Portfolio
2025-07-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-025-00999-0 |
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| author | Jacopo Burrello Stefano Panella Ilaria Barison Chiara Castellani Alessio Burrello Lorenzo Airale Jessica Goi Veronica Dusi Roberto Frigerio Gino Gerosa Chiara Tessari Nicola Pradegan Giuseppe Toscano Giovanni Pedrazzini Mattia Corianò Francesco Tona Sara Bolis Alessandro Gori Marina Cretich Marny Fedrigo Annalisa Angelini Lucio Barile |
| author_facet | Jacopo Burrello Stefano Panella Ilaria Barison Chiara Castellani Alessio Burrello Lorenzo Airale Jessica Goi Veronica Dusi Roberto Frigerio Gino Gerosa Chiara Tessari Nicola Pradegan Giuseppe Toscano Giovanni Pedrazzini Mattia Corianò Francesco Tona Sara Bolis Alessandro Gori Marina Cretich Marny Fedrigo Annalisa Angelini Lucio Barile |
| author_sort | Jacopo Burrello |
| collection | DOAJ |
| description | Abstract Background Heart transplant rejection, particularly acute cellular rejection (ACR), remains a critical post-operative concern, despite declining incidence rates. Current diagnostic standards rely on invasive endomyocardial biopsy, which presents limitations in sensitivity and reproducibility. There is an unmet need for noninvasive, accurate biomarkers that can detect and monitor rejection. This study aims to evaluate whether extracellular vesicle (EV) surface antigens, analyzed through flow cytometry and interpreted with artificial intelligence (AI), can serve as reliable biomarkers for ACR detection and monitoring in heart transplant recipients. Methods We conducted a prospective longitudinal cohort study involving 24 heart transplant recipients over a median follow-up of 303 days. A total of 285 blood samples were analyzed for EV surface antigens exploiting two flow cytometry-based protocols. An adaptive AI model (random forest regressor) was developed to interpret EV antigen profiles, dynamically calibrating thresholds per patient. Results Here we show that 14 EV surface antigens progressively increase with ACR severity. These changes are evident even before histological diagnosis. The AI model achieves an accuracy of 93.3% at leave-one-out testing (AUC 0.968), and 78.9% at validation in an independent cohort (AUC 0.832), with high specificity and negative predictive value. EV profiling outperforms conventional biochemical markers and provides anticipatory insight into rejection dynamics. Conclusions EV profiling, enhanced by patient-specific AI modeling, offers a powerful noninvasive method for early detection and monitoring of ACR. This approach holds the potential to reduce reliance on biopsies and tailor immunosuppressive strategies more precisely. |
| format | Article |
| id | doaj-art-36a4392f5bce4f3b82ae8609b05fdf0d |
| institution | Kabale University |
| issn | 2730-664X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-36a4392f5bce4f3b82ae8609b05fdf0d2025-08-20T03:43:26ZengNature PortfolioCommunications Medicine2730-664X2025-07-015111110.1038/s43856-025-00999-0Machine learning-assisted assessment of extracellular vesicles can monitor cellular rejection after heart transplantJacopo Burrello0Stefano Panella1Ilaria Barison2Chiara Castellani3Alessio Burrello4Lorenzo Airale5Jessica Goi6Veronica Dusi7Roberto Frigerio8Gino Gerosa9Chiara Tessari10Nicola Pradegan11Giuseppe Toscano12Giovanni Pedrazzini13Mattia Corianò14Francesco Tona15Sara Bolis16Alessandro Gori17Marina Cretich18Marny Fedrigo19Annalisa Angelini20Lucio Barile21Cardiovascular Theranostics, Istituto Cardiocentro Ticino, Laboratories for Translational Research, Ente Ospedaliero CantonaleCardiovascular Theranostics, Istituto Cardiocentro Ticino, Laboratories for Translational Research, Ente Ospedaliero CantonaleCardiovascular Theranostics, Istituto Cardiocentro Ticino, Laboratories for Translational Research, Ente Ospedaliero CantonaleCardiovascular Pathology and Pathological Anatomy, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of PadovaDepartment of Electrical, Electronic and Information Engineering (DEI), University of BolognaDivision of Internal Medicine and Hypertension Unit, Department of Medical Sciences, University of TorinoDivision of Internal Medicine and Hypertension Unit, Department of Medical Sciences, University of TorinoDivision of Cardiology, Department of Medical Sciences, University of TorinoNational Research Council of Italy, Institute of Chemical Science and Technologies (SCITEC-CNR)Division of Cardiac Surgery, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of PadovaDivision of Cardiac Surgery, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of PadovaDivision of Cardiac Surgery, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of PadovaDivision of Cardiac Surgery, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of PadovaDivision of Cardiology, Istituto Cardiocentro Ticino, Ente Ospedaliero Cantonale Lugano SwitzerlandCardiology Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of PadovaCardiology Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of PadovaCardiovascular Theranostics, Istituto Cardiocentro Ticino, Laboratories for Translational Research, Ente Ospedaliero CantonaleNational Research Council of Italy, Institute of Chemical Science and Technologies (SCITEC-CNR)National Research Council of Italy, Institute of Chemical Science and Technologies (SCITEC-CNR)Cardiovascular Pathology and Pathological Anatomy, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of PadovaCardiovascular Pathology and Pathological Anatomy, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of PadovaCardiovascular Theranostics, Istituto Cardiocentro Ticino, Laboratories for Translational Research, Ente Ospedaliero CantonaleAbstract Background Heart transplant rejection, particularly acute cellular rejection (ACR), remains a critical post-operative concern, despite declining incidence rates. Current diagnostic standards rely on invasive endomyocardial biopsy, which presents limitations in sensitivity and reproducibility. There is an unmet need for noninvasive, accurate biomarkers that can detect and monitor rejection. This study aims to evaluate whether extracellular vesicle (EV) surface antigens, analyzed through flow cytometry and interpreted with artificial intelligence (AI), can serve as reliable biomarkers for ACR detection and monitoring in heart transplant recipients. Methods We conducted a prospective longitudinal cohort study involving 24 heart transplant recipients over a median follow-up of 303 days. A total of 285 blood samples were analyzed for EV surface antigens exploiting two flow cytometry-based protocols. An adaptive AI model (random forest regressor) was developed to interpret EV antigen profiles, dynamically calibrating thresholds per patient. Results Here we show that 14 EV surface antigens progressively increase with ACR severity. These changes are evident even before histological diagnosis. The AI model achieves an accuracy of 93.3% at leave-one-out testing (AUC 0.968), and 78.9% at validation in an independent cohort (AUC 0.832), with high specificity and negative predictive value. EV profiling outperforms conventional biochemical markers and provides anticipatory insight into rejection dynamics. Conclusions EV profiling, enhanced by patient-specific AI modeling, offers a powerful noninvasive method for early detection and monitoring of ACR. This approach holds the potential to reduce reliance on biopsies and tailor immunosuppressive strategies more precisely.https://doi.org/10.1038/s43856-025-00999-0 |
| spellingShingle | Jacopo Burrello Stefano Panella Ilaria Barison Chiara Castellani Alessio Burrello Lorenzo Airale Jessica Goi Veronica Dusi Roberto Frigerio Gino Gerosa Chiara Tessari Nicola Pradegan Giuseppe Toscano Giovanni Pedrazzini Mattia Corianò Francesco Tona Sara Bolis Alessandro Gori Marina Cretich Marny Fedrigo Annalisa Angelini Lucio Barile Machine learning-assisted assessment of extracellular vesicles can monitor cellular rejection after heart transplant Communications Medicine |
| title | Machine learning-assisted assessment of extracellular vesicles can monitor cellular rejection after heart transplant |
| title_full | Machine learning-assisted assessment of extracellular vesicles can monitor cellular rejection after heart transplant |
| title_fullStr | Machine learning-assisted assessment of extracellular vesicles can monitor cellular rejection after heart transplant |
| title_full_unstemmed | Machine learning-assisted assessment of extracellular vesicles can monitor cellular rejection after heart transplant |
| title_short | Machine learning-assisted assessment of extracellular vesicles can monitor cellular rejection after heart transplant |
| title_sort | machine learning assisted assessment of extracellular vesicles can monitor cellular rejection after heart transplant |
| url | https://doi.org/10.1038/s43856-025-00999-0 |
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