Silent clues in blood: Proteomic approach for Alzheimer's disease.

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by a prolonged prodromal phase, during which molecular alterations accumulate before the onset of cognitive symptoms. The development of minimally invasive biomarkers for early detection remains a major challenge in clinical diagnosis and therapeutic trials. Blood biomarkers have emerged as a promising alternative to cerebrospinal fluid (CSF) analysis and neuroimaging, although their implementation is limited by the low abundance of brain-derived proteins and the wide dynamic range of the blood proteome. Recent advances in proteomics technologies, including high-resolution liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), immunoprecipitation coupled with mass spectrometry (IP-MS), and ultra-sensitive digital immunoassays, such as the single molecular array (Simoa), have enabled the quantification of protein biomarkers at sub-picogram concentrations. Currently, proteomic approaches have been combined with computational tools, such as artificial intelligence and machine learning, to increase the reliability in detecting differentially abundant blood proteins associated with the preclinical stages of Alzheimer's disease (AD). In this chapter, we discuss the current state of plasma proteomics in Alzheimer's disease, with a focus on biomarker proteins, analytical methodologies, and translational challenges for the clinical application of blood-based protocols. In fact, research focused on blood protein biomarkers may reveal key clues for fully understanding the etiology of AD, enabling earlier diagnosis and monitoring of its progression, thereby paving the way for research targeting a cure.