Ultra-performance liquid chromatography coupled to mass spectrometry (UPLC/MS) has been used increasingly for measuring changes of low molecular weight metabolites in biofluids/tissues in response to biological challenges such as drug toxicity and disease processes. Typically samples show high variability in concentration, and the derived metabolic profiles have a heteroscedastic noise structure characterized by increasing variance as a function of increased signal intensity. These sources of experimental and instrumental noise substantially complicate information recovery when statistical tools are used. We apply and compare several preprocessing procedures and introduce a statistical error model to account for these bioanalytical complexities. In particular, the use of total intensity, median fold change, locally weighted scatter plot smoothing, and quantile normalizations to reduce extraneous variance induced by sample dilution were compared. We demonstrate that the UPLC/MS peak intensities of urine samples should respond linearly to variable sample dilution across the intensity range. While all four studied normalization methods performed reasonably well in reducing dilution-induced variation of urine samples in the absence of biological variation, the median fold change normalization is least compromised by the biologically relevant changes in mixture components and is thus preferable. Additionally, the application of a subsequent log-based transformation was successful in stabilizing the variance with respect to peak intensity, confirming the predominant influence of multiplicative noise in peak intensities from UPLC/MS-derived metabolic profile data sets. We demonstrate that variance-stabilizing transformation and normalization are critical preprocessing steps that can benefit greatly metabolic information recovery from such data sets when widely applied chemometric methods are used.

Proton magnetic resonance spectroscopy (1H-MRS) is capable of noninvasively detecting metabolic changes that occur in the brain tissue in vivo. Its clinical utility has been limited so far, however, by analytic methods that focus on independently evaluated metabolites and require prior knowledge about which metabolites to examine. Here, we applied advanced computational methodologies from the field of metabolomics, specifically partial least squares discriminant analysis and orthogonal partial least squares, to in vivo 1H-MRS from frontal lobe white matter of 27 patients with relapsing–remitting multiple sclerosis (RRMS) and 14 healthy controls. We chose RRMS, a chronic demyelinating disorder of the central nervous system, because its complex pathology and variable disease course make the need for reliable biomarkers of disease progression more pressing. We show that in vivo MRS data, when analyzed by multivariate statistical methods, can provide reliable, distinct profiles of MRS-detectable metabolites in different patient populations. Specifically, we find that brain tissue in RRMS patients deviates significantly in its metabolic profile from that of healthy controls, even though it appears normal by standard MRI techniques. We also identify, using statistical means, the metabolic signatures of certain clinical features common in RRMS, such as disability score, cognitive impairments, and response to stress. This approach to human in vivo MRS data should promote understanding of the specific metabolic changes accompanying disease pathogenesis, and could provide biomarkers of disease progression that would be useful in clinical trials.

Finding biomarkers of human neurological diseases is one of the most pressing goals of modern medicine. Most neurological disorders are recognized too late because of the lack of biomarkers that can identify early pathological processes in the living brain. Late diagnosis leads to late therapy and poor prognosis. Therefore, during the past decade, a major endeavor of clinical investigations in neurology has been the search for diagnostic and prognostic biomarkers of brain disease. Recently, a new field of metabolomics has emerged, aiming to investigate metabolites within the cell/tissue/organism as possible biomarkers. Similarly to other “omics” fields, metabolomics offers substantial information about the status of the organism at a given time point. However, metabolomics also provides functional insight into the biochemical status of a tissue, which results from the environmental effects on its genome background. Recently, we have adopted metabolomics techniques to develop an approach that combines both in vitro analysis of cellular samples and in vivo analysis of the mammalian brain. Using proton magnetic resonance spectroscopy, we have discovered a metabolic biomarker of neural stem/progenitor cells (NPCs) that allows the analysis of these cells in the live human brain. We have developed signal-processing algorithms that can detect metabolites present at very low concentration in the live human brain and can indicate possible pathways impaired in specific diseases. Herein, we present our strategy for both cellular and systems metabolomics, based on an integrative processing of the spectroscopy data that uses analytical tools from both metabolomic and spectroscopy fields. As an example of biomarker discovery using our approach, we present new data and discuss our previous findings on the NPC biomarker. Our studies link systems and cellular neuroscience through the functions of specific metabolites. Therefore, they provide a functional insight into the brain, which might eventually lead to discoveries of clinically useful biomarkers of the disease.

No abstract