Atherosclerotic disease is a major cause of acute cardiovascular events. A deeper understanding of its underlying mechanisms will allow advancing personalized and patient-centered healthcare. Transcriptomic research has proven to be a powerful tool for unravelling the complex molecular pathways that drive atherosclerosis. However, low reproducibility of research findings and lack of standardization of procedures pose significant challenges in this field. In this review, we discuss how transcriptomic research can help in understanding the different phenotypes of the atherosclerotic plaque that contribute to the development and progression of atherosclerosis. We highlight the methodological challenges that need to be addressed to improve research outputs, and emphasize the importance of research protocols harmonization. We also discuss recent advances in transcriptomic research, including bulk or single-cell sequencing, and their added value in plaque phenotyping. Finally, we explore how integrated multiomics data and machine learning improve understanding of atherosclerosis and provide directions for future research.

Transcriptomic research in atherosclerosis: Unravelling plaque phenotype and overcoming methodological challenges / M. Sopić, K. Karaduzovic-Hadziabdic, D. Kardassis, L. Maegdefessel, F. Martelli, A. Meerson, J. Munjas, L.S. Niculescu, M. Stoll, P. Magni, Y. Devaux. - In: JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY PLUS. - ISSN 2772-9761. - 6:(2023), pp. 100048.1-100048.10. [10.1016/j.jmccpl.2023.100048]

Transcriptomic research in atherosclerosis: Unravelling plaque phenotype and overcoming methodological challenges

P. Magni
Penultimo
;
2023

Abstract

Atherosclerotic disease is a major cause of acute cardiovascular events. A deeper understanding of its underlying mechanisms will allow advancing personalized and patient-centered healthcare. Transcriptomic research has proven to be a powerful tool for unravelling the complex molecular pathways that drive atherosclerosis. However, low reproducibility of research findings and lack of standardization of procedures pose significant challenges in this field. In this review, we discuss how transcriptomic research can help in understanding the different phenotypes of the atherosclerotic plaque that contribute to the development and progression of atherosclerosis. We highlight the methodological challenges that need to be addressed to improve research outputs, and emphasize the importance of research protocols harmonization. We also discuss recent advances in transcriptomic research, including bulk or single-cell sequencing, and their added value in plaque phenotyping. Finally, we explore how integrated multiomics data and machine learning improve understanding of atherosclerosis and provide directions for future research.
English
Atherosclerotic plaque; Transcriptomics; Data integration; Machine learning
Settore MED/04 - Patologia Generale
Settore MED/13 - Endocrinologia
Settore MED/05 - Patologia Clinica
Articolo
Esperti anonimi
Ricerca applicata
Pubblicazione scientifica
Goal 3: Good health and well-being
   Comprehensive and personalized assessment of acute coronary syndrome by multiomic approach and artificial intelligence strategy (CardioSCOPE)
   CardioSCOPE
   EUROPEAN COMMISSION
   101086397

   Improving diagnosis and therapy for familial dyslipidaemias: a network of general practitioners and specialised lipid centers
   MINISTERO DELLA SALUTE
   RF-2019-12370896
2023
Elsevier
6
100048
1
10
10
Pubblicato
Periodico con rilevanza internazionale
crossref
Aderisco
info:eu-repo/semantics/article
Transcriptomic research in atherosclerosis: Unravelling plaque phenotype and overcoming methodological challenges / M. Sopić, K. Karaduzovic-Hadziabdic, D. Kardassis, L. Maegdefessel, F. Martelli, A. Meerson, J. Munjas, L.S. Niculescu, M. Stoll, P. Magni, Y. Devaux. - In: JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY PLUS. - ISSN 2772-9761. - 6:(2023), pp. 100048.1-100048.10. [10.1016/j.jmccpl.2023.100048]
open
Prodotti della ricerca::01 - Articolo su periodico
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M. Sopić, K. Karaduzovic-Hadziabdic, D. Kardassis, L. Maegdefessel, F. Martelli, A. Meerson, J. Munjas, L.S. Niculescu, M. Stoll, P. Magni, Y. Devaux...espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1018190
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