A novel multiple marker microarray analyzer and methodology to predict major obstetric syndromes using surface markers of circulating extracellular vesicles from maternal plasma.
Autor: | Jørgensen MM; Department of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark.; Department of Clinical Medicine, Aalborg University, Aalborg, Denmark., Bæk R; Department of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark., Sloth JK; Department of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark., Sammour R; Department of Obstetrics and Gynecology, Maternal and Fetal Medicine Unit, Bnai-Zion University Medical Center, Haifa, Israel., Sharabi-Nov A; Department of Statistics, Tel Hai Academic College, Tel Hai and Ziv Medical Center, Safed, Israel., Vatish M; Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK., Meiri H; TeleMarpe Ltd, Tel Aviv, Israel., Sammar M; Prof. Ephraim Katzir Department of Biotechnology Engineering, Braude College of Engineering, St, Karmiel, Israel. |
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Jazyk: | angličtina |
Zdroj: | Acta obstetricia et gynecologica Scandinavica [Acta Obstet Gynecol Scand] 2025 Jan; Vol. 104 (1), pp. 151-163. Date of Electronic Publication: 2024 Nov 28. |
DOI: | 10.1111/aogs.15020 |
Abstrakt: | Introduction: Placental-derived extracellular vesicles (EVs) are nano-organelles that facilitate intercellular communication between the feto-placental unit and the mother. We evaluated a novel Multiple Microarray analyzer for identifying surface markers on plasma EVs that predict preterm delivery and preeclampsia compared to term delivery controls. Material and Methods: In this prospective exploratory cohort study pregnant women between 24 and 40 gestational weeks with preterm delivery (n = 16), preeclampsia (n = 19), and matched term delivery controls (n = 15) were recruited from Bnai Zion Medical Center, Haifa, Israel. Plasma samples were tested using a multiple microarray analyzer. Glass slides with 17 antibodies against EV surface receptors - were incubated with raw plasma samples, detected by biotinylated secondary antibodies specific to EVs or placental EVs (PEVs), and labeled with cyanine 5-streptavidin. PBS and whole human IgG served as controls. The fluorescent signal ratio to negative controls was log 2 transformed and analyzed for sensitivity and specificity using the area under the receiver operating characteristics curves (AUROC). Best pair ratios of general EVs/PEVs were used for univariate analysis, and top pairs were combined for multivariate analysis. Results were validated by comparison with EVs purified using standard procedures. Results: Heatmaps differentiated surface profiles of preeclampsia, preterm delivery, and term delivery receptors on total EVs and PEVs. Similar results were obtained with enriched EVs and EVs from raw plasma. Univariate analyses identified markers predicting preterm delivery and preeclampsia over term delivery controls with AUC >0.6 and sensitivity >50% at 80% specificity. Combining the best markers in a multivariate model, preeclampsia prediction over term delivery had an AUC of 0.89 (95% CI: 0.72-1.0) with 90% sensitivity and 90% specificity, marked by inflammation (TNF RII), relaxation (placenta protein 13 (PP13)), and immune-modulation (LFA1) receptors. Preterm delivery prediction over term delivery had an AUC of 0.97 (0.94-1.0), 84% sensitivity, and 90% specificity, marked by cell adhesion (ICAM), immune suppression, and general EV markers (CD81, CD82, and Alix). Preeclampsia prediction over preterm delivery had an AUC of 0.91 (0.79-0.99) with 80% sensitivity and 90% specificity with markers for complement activation (C1q) and autoimmunity markers. Conclusions: The new, robust EV Multi-Array analyzer and methodology offer a simple, fast diagnostic tool that reveals novel surface markers for major obstetric syndromes. (© 2024 The Author(s). Acta Obstetricia et Gynecologica Scandinavica published by John Wiley & Sons Ltd on behalf of Nordic Federation of Societies of Obstetrics and Gynecology (NFOG).) |
Databáze: | MEDLINE |
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