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ahsinkong新虫 (小有名气)
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[交流]
港科大(广州)公开线上研讨会(英文)11.20,10:00-11:00am 生命科学与生物医学工程领域
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主题：Identification of Diagnostic Signatures for Neurodegenerative Tauopathies by Computational Profiling of LC-MS/MS Data from Human Brain （通过人脑LC-MS/MS数据的计算分析鉴定神经退行性Tauopathies的诊断特征） SPEAKER: Prof. Shaojun TANG DATE: 20 November 2020 (Friday) (GMT+8) TIME: 10:00 am – 11:00 am HOST: Prof. Ricky LEE / Acting Dean of Systems Hub ZOOM ID: 958 5120 9454 （Passcode: 491333） ABSTRACT The aggregation of tau protein in the brain is the hallmark of a diverse group of neurodegenerative diseases, namely tauopathies, with overlapping clinical and pathological phenotypes. Currently, not much is known about the molecular pathology of tau in these tauopathies including Alzheimer’s Disease (AD). To elucidate the role of Tau isoforms and Post Translational Modification (PTM) stoichiometry in tauopathies, we used high-throughput quantitative and qualitative Mass Spectrometry to identify differences and similarities in the post-translational modification state and isoform distribution of tau in post-mortem human tissue from multiple tauopathies. This LC-MS/MS-based approach provides a molecular signature for each tauopathy that allows us to distinguish between the disorders under study. Furthermore, quantitative PTM analysis shows pathological Tau in AD is heterogeneous and reflects disease progression with clinical implications. BIOGRAPHY Dr. Shaojun Tang received his B.Sc. degree in Biotechnology from Sun Yat-Sen University in 2006, M.Sc. in Computer Science and Ph.D. in Bioinformatics from University of Florida in 2012. Subsequently, Dr. Tang conducted his postdoctoral research at Harvard Medical School and became a research Faculty at Georgetown University. Currently, Dr. Tang is an Assistant Professor in the department of Biomedical Sciences at City University of Hong Kong. His research primarily focuses on the development of diagnostic/predictive biomarkers in a wide range of studies, including checkpoint inhibitor cancer immunotherapy and neurodegenerative tauopathies, using multi-'omics' data integration and machine learning approaches.  Thumbnail 960 590.png |
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