Ge Peicong, Yin Zihan, Tao Chuming, Zeng Chaofan, Yu Xiaofan, Lei Shixiong, Li Junsheng, Zhai Yuanren, Ma Long, He Qiheng, Liu Chenglong, Liu Wei, Zhang Bojian, Zheng Zhiyao, Mou Siqi, Zhao Zhikang, Wang Shuang, Sun Wei, Guo Min, Zheng Shuai, Zhang Jia, Deng Xiaofeng, Liu Xingju, Ye Xun, Zhang Qian, Wang Rong, Zhang Yan, Zhang Shaosen, Wang Chengjun, Yang Ziwen, Zhang Nijia, Wu Mingxing, Sun Jian, Zhou Yujia, Shi Zhiyong, Ma Yonggang, Zhou Jianpo, Yu Shaochen, Li Jiaxi, Lu Junli, Gao Faliang, Wang Wenjing, Chen Yanming, Zhu Xingen, Zhang Dong, Zhao Jizong
Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
China National Clinical Research Center for Neurological Diseases, Beijing, China.
Chin Neurosurg J. 2024 Feb 8;10(1):5. doi: 10.1186/s41016-024-00358-3.
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.
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.
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.
烟雾病（MMD）是一种罕见且复杂的脑血管疾病，其特征为颈内动脉进行性狭窄以及代偿性侧支血管形成。烟雾病的病因仍然不明，这使得诊断和治疗颇具挑战性。启动了烟雾病组学项目（MOYAOMICS project）来研究烟雾病的分子基础，并探索潜在的诊断和治疗策略。
烟雾病组学项目采用多学科方法，整合各种组学技术，包括基因组学、转录组学、蛋白质组学和代谢组学，以全面检查与烟雾病发病机制相关的分子特征。此外，我们将研究肠道微生物群和脑肠肽对烟雾病发展的潜在影响，评估它们作为治疗策略和饮食干预靶点的适用性。放射组学作为医学成像的一个专业领域，用于分析神经影像数据，以早期检测和表征与烟雾病相关的脑部变化。采用深度学习算法将烟雾病与其他病症区分开来，实现诊断过程的自动化。我们还采用单细胞组学和质谱流式细胞术来精确研究烟雾病患者外周血样本中的细胞异质性。
烟雾病组学项目朝着理解烟雾病的分子基础迈出了重要一步。这种多学科方法有可能彻底改变烟雾病的早期诊断、患者分层以及靶向治疗的发展。基于血液的生物标志物的识别以及多组学数据的整合对于改善烟雾病的临床管理和提高这种复杂疾病的患者治疗效果至关重要。
