The presenter will describe the use of proteomic profiling approaches to identify biomarker candidates that inform on pathologic processes affecting neuronal function for Alzheimer’s disease progression, and development of targeted assays to qualify a panel of biomarkers. The work was carried out by the Biomarkers Consortium CSF Proteomics Project team, a public-private partnership of government, academia, non-profit, and industry, to develop a targeted proteomic, multiplexed mass spectrometry-based approach for the qualification of candidate AD biomarkers in the well-characterized AD Neuroimaging Initiative (ADNI) cohort. During this work, multiple approaches to statistical analyses were assessed to enable interrogation of analytes which associated with baseline pathology (MCI, AD) vs. Healthy Controls (CN) or associated with progression for MCI patients. These statistical approaches included: (i) univariate association analyses, (ii) univariate prediction models, (iii) exploratory multivariate analyses, and (iv) supervised multivariate analysis.
The proteomic analysis of a large number of candidate markers on the most well characterized cohort of patient samples in AD allowed development of a targeted multiplexed LC-MS/MS assay for several peptides with potential diagnostic or predictive utility, with the most significant differences observed for several peptides that could be used for differentiating or predicting progression vs. non-progression from mild cognitive impairment to AD.
Learning Objectives:
Understand the different types of proteomics approaches Including LCMS, antibody-based, and aptamer based methods
Discuss the evolution and state of the art of the LCMS based proteomics and post-translational modifications
Expand on the use of LCMS approahces for lipidomics and metabolomics
Understand practical considerations for biomarker study design and subsequent biomarker qualification
Explore statistical and pathway analysis approaches for multi-dimensional datasets