Multiomics and blood-based biomarkers of moyamoya disease: protocol of Moyamoya Omics Atlas (MOYAOMICS)
Background Moyamoya disease (MMD) is a rare and complex cerebrovascular disorder characterized by the progressive narrowing of the internal carotid arteries and the formation of compensatory collateral vessels. The etiology of MMD remains enigmatic, making diagnosis and management challenging. The MOYAOMICS project was initiated to investigate the molecular underpinnings of MMD and explore potential diagnostic and therapeutic strategies. Methods The MOYAOMICS project employs a multidisciplinary approach, integrating various omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, to comprehensively examine the molecular signatures associated with MMD pathogenesis. Additionally, we will investigate the potential influence of gut microbiota and brain-gut peptides on MMD development, assessing their suitability as targets for therapeutic strategies and dietary interventions. Radiomics, a specialized field in medical imaging, is utilized to analyze neuroimaging data for early detection and characterization of MMD-related brain changes. Deep learning algorithms are employed to differentiate MMD from other conditions, automating the diagnostic process. We also employ single-cellomics and mass cytometry to precisely study cellular heterogeneity in peripheral blood samples from MMD patients. Conclusions The MOYAOMICS project represents a significant step toward comprehending MMD’s molecular underpinnings. This multidisciplinary approach has the potential to revolutionize early diagnosis, patient stratification, and the development of targeted therapies for MMD. The identification of blood-based biomarkers and the integration of multiple omics data are critical for improving the clinical management of MMD and enhancing patient outcomes for this complex disease..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
Chinese neurosurgical journal - 10(2024), 1 vom: 08. Feb. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ge, Peicong [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Anmerkungen: |
© The Author(s) 2024 |
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| doi: |
10.1186/s41016-024-00358-3 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
SPR054685311 |
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| 520 | |a Background Moyamoya disease (MMD) is a rare and complex cerebrovascular disorder characterized by the progressive narrowing of the internal carotid arteries and the formation of compensatory collateral vessels. The etiology of MMD remains enigmatic, making diagnosis and management challenging. The MOYAOMICS project was initiated to investigate the molecular underpinnings of MMD and explore potential diagnostic and therapeutic strategies. Methods The MOYAOMICS project employs a multidisciplinary approach, integrating various omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, to comprehensively examine the molecular signatures associated with MMD pathogenesis. Additionally, we will investigate the potential influence of gut microbiota and brain-gut peptides on MMD development, assessing their suitability as targets for therapeutic strategies and dietary interventions. Radiomics, a specialized field in medical imaging, is utilized to analyze neuroimaging data for early detection and characterization of MMD-related brain changes. Deep learning algorithms are employed to differentiate MMD from other conditions, automating the diagnostic process. We also employ single-cellomics and mass cytometry to precisely study cellular heterogeneity in peripheral blood samples from MMD patients. Conclusions The MOYAOMICS project represents a significant step toward comprehending MMD’s molecular underpinnings. This multidisciplinary approach has the potential to revolutionize early diagnosis, patient stratification, and the development of targeted therapies for MMD. The identification of blood-based biomarkers and the integration of multiple omics data are critical for improving the clinical management of MMD and enhancing patient outcomes for this complex disease. | ||
| 700 | 1 | |a Yin, Zihan |4 aut | |
| 700 | 1 | |a Tao, Chuming |4 aut | |
| 700 | 1 | |a Zeng, Chaofan |4 aut | |
| 700 | 1 | |a Yu, Xiaofan |4 aut | |
| 700 | 1 | |a Lei, Shixiong |4 aut | |
| 700 | 1 | |a Li, Junsheng |4 aut | |
| 700 | 1 | |a Zhai, Yuanren |4 aut | |
| 700 | 1 | |a Ma, Long |4 aut | |
| 700 | 1 | |a He, Qiheng |4 aut | |
| 700 | 1 | |a Liu, Chenglong |4 aut | |
| 700 | 1 | |a Liu, Wei |4 aut | |
| 700 | 1 | |a Zhang, Bojian |4 aut | |
| 700 | 1 | |a Zheng, Zhiyao |4 aut | |
| 700 | 1 | |a Mou, Siqi |4 aut | |
| 700 | 1 | |a Zhao, Zhikang |4 aut | |
| 700 | 1 | |a Wang, Shuang |4 aut | |
| 700 | 1 | |a Sun, Wei |4 aut | |
| 700 | 1 | |a Guo, Min |4 aut | |
| 700 | 1 | |a Zheng, Shuai |4 aut | |
| 700 | 1 | |a Zhang, Jia |4 aut | |
| 700 | 1 | |a Deng, Xiaofeng |4 aut | |
| 700 | 1 | |a Liu, Xingju |4 aut | |
| 700 | 1 | |a Ye, Xun |4 aut | |
| 700 | 1 | |a Zhang, Qian |4 aut | |
| 700 | 1 | |a Wang, Rong |4 aut | |
| 700 | 1 | |a Zhang, Yan |4 aut | |
| 700 | 1 | |a Zhang, Shaosen |4 aut | |
| 700 | 1 | |a Wang, Chengjun |4 aut | |
| 700 | 1 | |a Yang, Ziwen |4 aut | |
| 700 | 1 | |a Zhang, Nijia |4 aut | |
| 700 | 1 | |a Wu, Mingxing |4 aut | |
| 700 | 1 | |a Sun, Jian |4 aut | |
| 700 | 1 | |a Zhou, Yujia |4 aut | |
| 700 | 1 | |a Shi, Zhiyong |4 aut | |
| 700 | 1 | |a Ma, Yonggang |4 aut | |
| 700 | 1 | |a Zhou, Jianpo |4 aut | |
| 700 | 1 | |a Yu, Shaochen |4 aut | |
| 700 | 1 | |a Li, Jiaxi |4 aut | |
| 700 | 1 | |a Lu, Junli |4 aut | |
| 700 | 1 | |a Gao, Faliang |4 aut | |
| 700 | 1 | |a Wang, Wenjing |4 aut | |
| 700 | 1 | |a Chen, Yanming |4 aut | |
| 700 | 1 | |a Zhu, Xingen |4 aut | |
| 700 | 1 | |a Zhang, Dong |4 aut | |
| 700 | 1 | |a Zhao, Jizong |4 aut | |
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