title: Multivariate analysis of 3D ToF-SIMS images: method validation and application to cultured neuronal networks.
abstract: Advanced data analysis tools are crucial for the application of ToF-SIMS analysis to biological samples. Here, we demonstrate that by using a training set approach principal components analysis (PCA) can be performed on large 3D ToF-SIMS images of neuronal cell cultures. The method readily provides access to sample component information and significantly improves the images' signal-to-noise ratio (SNR).
authors.affiliation: Laboratory of Biophysics and Surface Analysis, School of Pharmacy, Boots Science Building, University of Nottingham, University Park, Nottingham NG72RD, UK.
authors.forename: S
authors.initials: S
authors.lastname: Van Nuffel
authors.forename: C
authors.initials: C
authors.lastname: Parmenter
authors.forename: D J
authors.initials: DJ
authors.lastname: Scurr
authors.forename: N A
authors.initials: NA
authors.lastname: Russell
authors.forename: M
authors.initials: M
authors.lastname: Zelzer
mesh_terms: D000818:Animals; D002478:Cells, Cultured; D021621:Imaging, Three-Dimensional / Q000379:methods*; D015999:Multivariate Analysis; D009415:Nerve Net / Q000166:cytology*; D009474:Neurons / Q000166:cytology*; D025341:Principal Component Analysis; D051381:Rats; D059629:Signal-To-Noise Ratio; D018629:Spectrometry, Mass, Secondary Ion