Supervised and unsupervised learning to define the cardiovascular risk of patients according to an extracellular vesicle molecular signature

Burrello, Jacopo; Burrello, Alessio; Vacchi, Elena; Bianco, Giovanni; Caporali, Elena; Amongero, Martina; Airale, Lorenzo; Bolis, Sara; Vassalli, Giuseppe; Cereda, Carlo; Mulatero, Paolo; Bussolati, Benedetta; Camici, Giovanni G.; Melli, Giorgia; Monticone, Silvia; Barile, Lucio

doi:10.1016/j.trsl.2022.02.005

Cited by 10 publications

(12 citation statements)

References 37 publications

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“…[ 29 ] A dysfunction of heart-brain axis may lead to severe multi-organs failure and worsen the outcome of COVID-19 patients. [ [29] , [30] , [31] ] In this view, EVs have a potential for accurate stratification of patients according to the respective cardiovascular risk, [ 32 ] supporting medical staff in tailoring management strategies for patients.…”

Section: Discussionmentioning

confidence: 99%

Risk stratification of patients with SARS-CoV-2 by tissue factor expression in circulating extracellular vesicles

Burrello¹,

Caporali²,

Gauthier³

et al. 2022

Vascular Pharmacology

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Section: Discussionmentioning

confidence: 99%

Risk stratification of patients with SARS-CoV-2 by tissue factor expression in circulating extracellular vesicles

Burrello¹,

Caporali²,

Gauthier³

et al. 2022

Vascular Pharmacology

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“…In vitro platelet activation induced by serum separator tubes is expected to be similar in all groups, thus avoiding significant biases when comparing EV surface profiles. Indeed, both plasma and serum have been used in biomarkers discovery studies, and previous studies did not find any significant difference in EV profiling of serum and plasma from matched samples [ 11 , 29 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of a serum and urine extracellular vesicle signature predicting renal outcome after kidney transplant

Burrello

Monticone

Burrello

et al. 2022

Nephrology Dialysis Transplantation

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Background A long-standing effort is dedicated toward identification of biomarkers allowing the prediction of graft outcome after kidney transplant. Extracellular vesicles (EVs) circulating in body fluids represent an attractive candidate, as their cargo mirrors the originating cell and its pathophysiological status. The aim of the study was to investigate EV surface antigens as potential predictors of renal outcome after kidney transplant. Methods We characterized 37 surface-antigens by flow-cytometry, in serum- and urine- EVs from 58 patients which were evaluated before, and at 10-14 days, 3 months, and 1 year after transplant, for a total of 426 analyzed samples. The outcome was defined according to estimated glomerular filtration rate (eGFR) at 1 year. Results Endothelial cells and platelets markers (CD31, CD41b, CD42a and CD62P) in serum-EVs were higher at baseline in patients with persistent kidney dysfunction at 1 year, and progressively decreased after kidney transplant. Conversely, mesenchymal progenitor cell marker (CD1c, CD105, CD133, SSEEA-4) in urine-EVs progressively increased after transplant in patients displaying renal recovery at follow-up. These markers correlated with eGFR, creatinine and proteinuria, associated to patient outcome at univariate analysis and were able to predict patient outcome at ROC curves analysis. A specific EV molecular signature obtained by supervised learning correctly classified patients according to 1-year renal outcome. Conclusions An EV-based signature, reflecting the cardiovascular profile of the recipient, and the repairing/regenerative features of the graft, could be introduced as a non-invasive tool for a tailored management of follow-up of patients undergoing kidney transplant.

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“…Besides assessing the technical feasibility, it was crucial to determine whether MSP-based EVs epitope profiling would return the same diagnostic value obtained by gold standard platforms encompassing the use of antibodies as EV-binding probes, such as the one that was used in the previous study by Burrello et al 47 , 48 .…”

Section: Validation Of Ev Immunophenotyping Workflow By Msp In Clinic...mentioning

confidence: 99%

Addressing Heterogeneity in direct analysis of Extracellular Vesicles and analogues using Membrane-Sensing Peptides as Pan-Affinity Probes

Gori,

Frigerio,

Gagni

et al. 2023

Preprint

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Extracellular vesicles (EVs), crucial mediators of cell-to-cell communication, hold immense potential for diagnostic applications due to their ability to enrich protein biomarkers in body fluids. However, challenges in isolating EVs from complex biological specimens hinder their widespread use. In this frame, integrated isolation-and-analysis workflows are the go-to strategy, most of which see the prevalence of immunoaffinity methods. Yet, the high heterogeneity of EVs poses challenges, as proposed ubiquitous markers are less homogenously prevalent than believed, raising concerns about the reliability of downstream biomarker discovery programs. This issue extends to the burgeoning field of engineered EV-mimetics and bio-nanoparticles, where conventional immune-affinity methods may lack applicability. Addressing these challenges, we introduce the use Membrane Sensing Peptides (MSP) as “universal” affinity ligands for both EVs and EV-analogues. Employing a streamlined process integrating on-bead capture and vesicle phenotyping through Single Molecule Array (SiMoA) technology, we showcase the application of MSP ligands in the integrated analysis of circulating EVs in blood derivatives, eliminating the need for prior EV isolation. Demonstrating the possible clinical translation of MSP technology, we directly detect an EV-associated epitope signature in serum and plasma samples, demonstrating its potential for distinguishing patients with myocardial infarction versus stable angina. At last, notably, MSP exhibits a unique capability to enable the analysis of tetraspanin-lacking Red Blood Cell derived EVs (RBC-EVs). Overall, unlike traditional antibody-based methods, MSP probes work agnostically, overcoming limitations associated with surface protein abundance or scarcity. This highlights the potential of MSP in advancing EV analysis for clinical diagnostics and beyond. Of note, this represents also the first-ever peptide-based application in SiMoA technology.

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Supervised and unsupervised learning to define the cardiovascular risk of patients according to an extracellular vesicle molecular signature

Cited by 10 publications

References 37 publications

Risk stratification of patients with SARS-CoV-2 by tissue factor expression in circulating extracellular vesicles

Risk stratification of patients with SARS-CoV-2 by tissue factor expression in circulating extracellular vesicles

Identification of a serum and urine extracellular vesicle signature predicting renal outcome after kidney transplant

Addressing Heterogeneity in direct analysis of Extracellular Vesicles and analogues using Membrane-Sensing Peptides as Pan-Affinity Probes

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