Computational Mass Spectrometry Imaging of Amyloid Plaque Populations in the Brains of Non-Demented and Alzheimer's Disease Patients

γ-Secretase activity is involved in multiple physiological processes and plays a key role in Alzheimer's disease (AD) pathogenesis. AD-causing mutations in Presenilin (PSEN1/2) (the catalytic core of γ-secretase) and the Amyloid Precursor Protein (APP) consistently reduce the number of γ-secret...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
1. Verfasser: Enzlein, Thomas
Format: Dissertation
Sprache:eng
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:γ-Secretase activity is involved in multiple physiological processes and plays a key role in Alzheimer's disease (AD) pathogenesis. AD-causing mutations in Presenilin (PSEN1/2) (the catalytic core of γ-secretase) and the Amyloid Precursor Protein (APP) consistently reduce the number of γ-secretase cleavages on the APP C99 substrate, leading to production of longer amyloidogenic Aβ peptides. The pathogenic mutant effect points at the shift in Aβ length as fundamental to the disease. However, the chemical identity of these longer Aβ peptides and the associated pathogenic mechanisms remain unknown. In the past the main part of studies centered on Aß1-40 and Aß1-42 but as recent publications have shown, a much higher diversity of molecular entities inside the plaques is involved. A deep understanding which kind of mutation in Presenilin leads to which product profile will help to explain mutation specific disease characteristics. Several studies point to longer Aβ species (≥Aβ1-42) as possible players in starting the aggregation of toxic Aβ-derived species. Mass spectrometry imaging (MSI) technology enables the investigation of the spatial distribution of large numbers of molecules such as Aβ peptide isoforms, lipids, drugs, drug responses etc., with spatial resolution of 10-50 µm and high mass resolution. Because of the high complexity and size of the data generated by a MSI experiment new methods on data handling, reduction and multivariate analysis have to be developed to enable single plaque characterization. This spatially resolved information about the molecular content can be used to characterize the composition of individual plaques and thus points to local differences such as the propensity to form aggregates.