Principal component directed partial least squares analysis for combining nuclear magnetic resonance and mass spectrometry data in metabolomics: Application to the detection of breast cancer

Abstract

Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the two most commonly used analytical tools in metabolomics, and their complementary nature makes the combination particularly attractive. A combined analytical approach can improve the potential for providing reliable methods to detect metabolic profile alterations in biofluids or tissues caused by disease, toxicity, etc. In this paper, 1 H NMR spectroscopy and direct analysis in real time (DART)-MS were used for the metabolomics analysis of serum samples from breast cancer patients and healthy controls. Principal component analysis (PCA) of the NMR data showed that the first principal component (PC1) scores could be used to separate cancer from normal samples. However, no such obvious clustering could be observed in the PCA score plot of DART-MS data, even though DART-MS can provide a rich and informative metabolic profile. Using a modified multivariate statistical approach, the DART-MS data were then reevaluated by orthogonal signal correction (OSC) pretreated partial least squares (PLS), in which the Y matrix in the regression was set to the PC1 score values from the NMR data analysis. This approach, and a similar one using the first latent variable from PLS-DA of the NMR data resulted in a significant improvement of the separation between the disease samples and normals, and a metabolic profile related to breast cancer could be extracted from DART-MS. The new approach allows the disease classification to be expressed on a continuum as opposed to a binary scale and thus better represents the disease and healthy classifications. An improved metabolic profile obtained by combining MS and NMR by this approach may be useful to achieve more accurate disease detection and gain more insight regarding disease mechanisms and biology.

The Emerging Field of Quantitative Blood Metabolomics for Biomarker Discovery in Critical Illnesses

“…More recently however, the coupling of NMR and MS to the quantitative measurement of small molecule metabolites enhances sensitivity and specificity and permits the rapid identification and quantification of metabolites from a single patient sample (10,11,17,20). This improves the likelihood of detecting subtle changes in metabolites and metabolite profiles.…”

“…This is a scientific challenge of the systems biology approach and highlights the need for new knowledge of the relationships and interactions between the components of the system (Figure 2). Monitoring fluctuations of certain metabolites (endogenous low-molecular weight molecules) in body fluids, such as blood (including plasma and serum) and urine, is an important way to detect various human pathologies, including cancer, cardiovascular disease, diabetes, and drug toxicity (6)(7)(8)(9)(10)(11)(12)(13)(14)(15). These data are also needed to construct powerful top-down systems biology tools that link the "omics" disciplines (16).…”

Metabolomik Çalışmalarda Yazılım ve Veritabanı Desteği: LC-MS Verilerinin Değerlendirilmesinde XCMS Kullanımı

“…Metabolomik; kromatografi, moleküler spektroskopi ve kütle spektrometrisi gibi analitik tekniklerin çok değişkenli veri analiz yöntemleriyle birleştirilmesiyle iç içe işleyen biyoloji, kimya ve matematiğin iç içe geçtiği multi-disipliner bir bilimdir 19,20 .…”

Clinical Implementation of Metabolomics

“…Recently, several applications of metabolome analysis based on computer-aided detection and diagnosis (CAD) has been demonstrated (Duda, 2001;Gu et al, 2011;Kell, 2002;Kim et al, 2010;Montoliu et al, 2009;Righi et al, 2009;Serkova et al, 2011;Taylor et al, 2010). The importance of objective indices for diagnosis based on empirical and statistical knowledge are increasing due to the trend called "Evidence Based Medicine (EBM)".…”

“…Recently, various data mining methodologies have been reported for identifying and prioritizing reliable metabolomic markers with high diagnostic capability (Caruana, 2006;Duda, 2001;Gu et al, 2011;Kim et al, 2010;Maeda et al, 2010;Montoliu et al, 2009). In cohort studies, the definite diagnoses of the patients are normally known beforehand.…”