Chemometric and statistical analyses of ToF-SIMS spectra of increasingly complex biological samples

Characterizing and classifying molecular variations within biological samples are critical for determining the fundamental mechanisms of biological processes. Toward these ends, time-of-flight secondary ion mass spectrometry (ToF-SIMS) was used to examine increasingly complex samples of biological relevance. The large, multivariate datasets were analyzed using five common statistical and chemometric techniques: principal component analysis (PCA), linear discriminant analysis (LDA), partial least-squares discriminant analysis (PLSDA), soft independent modeling of class analogy (SIMCA), and decision-tree analysis by recursive partitioning. PCA was found to provide insight into both the relative groupings of samples and the molecular basis for those groupings. For monosaccharide, pure protein, and complex protein mixture samples, LDA, PLSDA, and SIMCA all produced excellent classification. For mouse embryo tissues, however, SIMCA did not classify samples as accurately. The decision-tree analysis was the least successful for all tested samples, providing neither as accurate a classification nor chemical insight. Based on these results we conclude that as the complexity of samples increases, so must the sophistication of the multivariate technique used to classify the samples. PCA is a preferred first step for understanding ToF-SIMS data that can be followed by either LDA or PLSDA for effective classification. This study demonstrates the strength of the combination of ToF-SIMS and multivariate analysis to classify increasingly complex biological samples. Applying these techniques to information-rich mass spectral data opens the possibilities for new applications including classification of subtly different biological samples that may provide insights into cellular processes, disease progress, and disease diagnosis. Copyright (C) 2008 John Wiley & Sons, Ltd.