Biomarker Core

点击翻译按钮获取中文摘要

1. SUMMARY (Biomarker Core) Biomarkers have enormous value for the detection, management, and treatment of disease, but also for the development of novel therapeutics. The utility of biomarkers is most evident in the management of cardiovascular disease and diabetes, but biomarkers, especially predictive, easily obtainable ones, are still largely absent with respect to neurodegenerative diseases. The best fluid biomarkers currently available for Alzheimer’s disease (AD) include; Aβ, tau, and neurofilament in CSF, a biofluid which is difficult to collect in healthy, at-risk populations or on a repeated basis. Other biomarkers for AD include imaging modalities which are often very expensive or have low sensitivity and specificity at the individual level. The members of this core have considerable experience in unbiased multi-omic screens and data analysis and, over the years, have published numerous studies towards developing new biomarkers for neurodegenerative and other diseases. Enabled by the current ADRC, the core leaders and collaborators have used biospecimens from Stanford ADRC participants and generated extensive preliminary data with unbiased deep immune phenotyping, proteomics, and transcriptomics of human CSF resident cells, and discovered novel Parkinson's disease (PD) biomarkers. Based on this expertise, the mission of this Biomarker Core is to facilitate the discovery of novel biomarkers for AD and PD, as well as new biology underlying the pathological processes that lead to dementia in line with the core mission of the NAPA. This will be achieved by pursuing the collection of genetic and molecular measurements from a broad source of tissues from ADRC participants; the processing and dissemination of this information in useable formats through web portals and other means (i.e., “Deep Phenotyping Database”); the analysis and bioinformatics integration of the collected information with clinical and imaging data, as well as information from public databases; and the development and dissemination of new data analysis algorithms and pipelines.