Alzheimer disease and familial British dementia are neurodegenerative diseases that are characterized by the presence of numerous amyloid plaques in the brain. These lesions contain fibrillar deposits of the β-amyloid peptide (Aβ) and the British dementia peptide (ABri), respectively. Both peptides are toxic to cells in culture, and there is increasing evidence that early “soluble oligomers” are the toxic entity rather than mature amyloid fibrils. The molecular mechanisms responsible for this toxicity are not clear, but in the case of Aβ, one prominent hypothesis is that the peptide can induce oxidative damage via the formation of hydrogen peroxide. We have developed a reliable method, employing electron spin resonance spectroscopy in conjunction with the spin-trapping technique, to detect any hydrogen peroxide generated during the incubation of Aβ and other amyloidogenic peptides. Here, we monitored levels of hydrogen peroxide accumulation during different stages of aggregation of Aβ-(1–40) and ABri and found that in both cases it was generated as a short “burst” early on in the aggregation process. Ultrastructural studies with both peptides revealed that structures resembling “soluble oligomers” or “protofibrils” were present during this early phase of hydrogen peroxide formation. Mature amyloid fibrils derived from Aβ-(1–40) did not generate hydrogen peroxide. We conclude that hydrogen peroxide formation during the early stages of protein aggregation may be a common mechanism of cell death in these (and possibly other) neurodegenerative diseases.

In the biomedical field, infrared (IR) spectroscopic studies can involve the processing of data derived from many samples, divided into classes such as category of tissue (e.g., normal or cancerous) or patient identity. We require reliable methods to identify the class-specific information on which of the wavenumbers, representing various molecular groups, are responsible for observed class groupings. Employing a prostate tissue sample divided into three regions (transition zone, peripheral zone, and adjacent adenocarcinoma), and interrogated using synchrotron Fourier-transform IR microspectroscopy, we compared two statistical methods: (a) a new “cluster vector” version of principal component analysis (PCA) in which the dimensions of the dataset are reduced, followed by linear discriminant analysis (LDA) to reveal clusters, through each of which a vector is constructed that identifies the contributory wavenumbers; and (b) stepwise LDA, which exploits the fact that spectral peaks which identify certain chemical bonds extend over several wavenumbers, and which following classification via either one or two wavenumbers, checks whether the resulting predictions are stable across a range of nearby wavenumbers. Stepwise LDA is the simpler of the two methods; the cluster vector approach can indicate which of the different classes of spectra exhibit the significant differences in signal seen at the “prominent” wavenumbers identified. In situations where IR spectra are found to separate into classes, the excellent agreement between the two quite different methods points to what will prove to be a new and reliable approach to establishing which molecular groups are responsible for such separation.