Detection of preclinical scrapie from serum by infrared spectroscopy and chemometrics.
In this study we describe a methodology for diagnosing preclinical scrapie infection in hamsters from serum by a combination of Fourier-transform infrared (FT-IR) spectroscopy and chemometrics. Syrian hamsters (Mesocricetus auratus) were orally inoculated with the 263K scrapie agent, or mock-infected, and sera were obtained at 70, 100 and 130 days post infection (dpi) and at the terminal stage of scrapie (160 +/- 10 dpi). The analysis of hamster sera by FT-IR spectroscopy and artificial neural networks (ANN) confirmed results from earlier studies which had indicated the existence of disease-related structural and compositional alterations in the sera of infected donors in the terminal stage of scrapie [Schmitt et al. (2002) Anal Chem 74:3865-3868]. Experimental data from sera of animals in the preclinical stages of scrapie revealed subtle but reproducible spectral variations that permitted the identification of a preclinical scrapie infection at 100 dpi and later, but not at 70 dpi. The IR spectral features that were discriminatory for the preclinical stages differed from those of the terminally ill individuals. In order to reliably identify scrapie-negative as well as preclinical (100 and 130 dpi) and terminal scrapie-positive animals, a hierarchical classification system of independent artificial neural networks (ANN) was established. A "toplevel" ANN was designed which discriminates between animals in the terminal stage of scrapie and preclinical scrapie-positive or control animals. Spectra identified by the "toplevel" ANN as preclinical or controls were then further analyzed by a second classifier, the "sublevel" ANN. Using independent external validation procedures, the toplevel classifier produced an overall classification accuracy of 98%, while the sublevel classifier yielded an accuracy of 93%, indicating that scrapie-specific serum markers were also present for preclinical disease stages. Possible spectral markers responsible for the discrimination capacity of the two different ANNs are discussed.
Lasch
Peter
P
P25 "Biomedical Spectroscopy", Robert-Koch-Institut, Nordufer 20, 13353 Berlin, Germany. LaschP@rki.de
Beekes
Michael
M
Schmitt
Jürgen
J
Naumann
Dieter
D
D000818:Animals; D001185:Artificial Intelligence; D001774:Blood Chemical Analysis / Q000379:methods*; D020650:Combinatorial Chemistry Techniques / Q000379:methods*; D006224:Cricetinae; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D008647:Mesocricetus; D012608:Scrapie / Q000097:blood* / Q000175:diagnosis*; D013055:Spectrophotometry, Infrared / Q000379:methods*