Lionel Blanchet

Nmr and pattern recognition methods in metabolomics: From data acquisition to biomarker discovery: A review

Metabolomics is the discipline where endogenous and exogenous metabolites are assessed, identified and quantified in different biological samples. Metabolites are crucial components of biological system and highly informative about its functional state, due to their closeness to functional endpoints and to the organisms phenotypes. Nuclear Magnetic Resonance (NMR) spectroscopy, next to Mass Spectrometry (MS), is one of the main metabolomics analytical platforms. The technological developments in the field of NMR spectroscopy have enabled the identification and quantitative measurement of the many metabolites in a single sample of biofluids in a non-targeted and non-destructive manner. Combination of NMR spectra of biofluids and pattern recognition methods has driven forward the application of metabolomics in the field of biomarker discovery. The importance of metabolomics in diagnostics, e.g. in identifying biomarkers or defining pathological status, has been growing exponentially as evidenced by the number of published papers. In this review, we describe the developments in data acquisition and multivariate analysis of NMR-based metabolomics data, with particular emphasis on the metabolomics of Cerebrospinal Fluid (CSF) and biomarker discovery in Multiple Sclerosis (MScl).

Multivariate curve resolution: A review of advanced and tailored applications and challenges

Multivariate curve resolution (MCR) is a widespread methodology for the analysis of process data in many different application fields. This article intends to propose a critical review of the recently published works. Particular attention will be paid to situations requiring advanced and tailored applications of multivariate curve resolution, dealing with improvements in preprocessing methods, multi-set data arrangements, tailored constraints, issues related to non-ideal noise structure and deviation to linearity. These analytical issues are tackling the limits of applicability of MCR methods and, therefore, they can be considered as the most challenging ones.

Fusion of metabolomics and proteomics data for biomarkers discovery: case study on the experimental autoimmune encephalomyelitis.

BackgroundAnalysis of Cerebrospinal Fluid (CSF) samples holds great promise to diagnose neurological pathologies and gain insight into the molecular background of these pathologies. Proteomics and metabolomics methods provide invaluable information on the biomolecular content of CSF and thereby on the possible status of the central nervous system, including neurological pathologies. The combined information provides a more complete description of CSF content. Extracting the full combined information requires a combined analysis of different datasets i.e. fusion of the data.ResultsA novel fusion method is presented and applied to proteomics and metabolomics data from a pre-clinical model of multiple sclerosis: an Experimental Autoimmune Encephalomyelitis (EAE) model in rats. The method follows a mid-level fusion architecture. The relevant information is extracted per platform using extended canonical variates analysis. The results are subsequently merged in order to be analyzed jointly. We find that the combined proteome and metabolome data allow for the efficient and reliable discrimination between healthy, peripherally inflamed rats, and rats at the onset of the EAE. The predicted accuracy reaches 89% on a test set. The important variables (metabolites and proteins) in this model are known to be linked to EAE and/or multiple sclerosis.ConclusionsFusion of proteomics and metabolomics data is possible. The main issues of high-dimensionality and missing values are overcome. The outcome leads to higher accuracy in prediction and more exhaustive description of the disease profile. The biological interpretation of the involved variables validates our fusion approach.

Interpretation and visualization of non-linear data fusion in kernel space: study on metabolomic characterization of progression of multiple sclerosis.

Background In the last decade data fusion has become widespread in the field of metabolomics. Linear data fusion is performed most commonly. However, many data display non-linear parameter dependences. The linear methods are bound to fail in such situations. We used proton Nuclear Magnetic Resonance and Gas Chromatography-Mass Spectrometry, two well established techniques, to generate metabolic profiles of Cerebrospinal fluid of Multiple Sclerosis (MScl) individuals. These datasets represent non-linearly separable groups. Thus, to extract relevant information and to combine them a special framework for data fusion is required. Methodology The main aim is to demonstrate a novel approach for data fusion for classification; the approach is applied to metabolomics datasets coming from patients suffering from MScl at a different stage of the disease. The approach involves data fusion in kernel space and consists of four main steps. The first one is to extract the significant information per data source using Support Vector Machine Recursive Feature Elimination. This method allows one to select a set of relevant variables. In the next step the optimized kernel matrices are merged by linear combination. In step 3 the merged datasets are analyzed with a classification technique, namely Kernel Partial Least Square Discriminant Analysis. In the final step, the variables in kernel space are visualized and their significance established. Conclusions We find that fusion in kernel space allows for efficient and reliable discrimination of classes (MScl and early stage). This data fusion approach achieves better class prediction accuracy than analysis of individual datasets and the commonly used mid-level fusion. The prediction accuracy on an independent test set (8 samples) reaches 100%. Additionally, the classification model obtained on fused kernels is simpler in terms of complexity, i.e. just one latent variable was sufficient. Finally, visualization of variables importance in kernel space was achieved.

Discrimination between Metastasis and Glioblastoma Multiforme Based on Morphometric Analysis of MR Images

BACKGROUND AND PURPOSE: Solitary MET and GBM are difficult to distinguish by using MR imaging. Differentiation is useful before any metastatic work-up or biopsy. Our hypothesis was that MET and GBM tumors differ in morphology. Shape analysis was proposed as an indicator for discriminating these 2 types of brain pathologies. The purpose of this study was to evaluate the accuracy of this approach in the discrimination of GBMs and brain METs. MATERIALS AND METHODS: The dataset consisted of 33 brain MR imaging sets of untreated patients, of which 18 patients were diagnosed as having a GBM and 15 patients, as having solitary metastatic brain tumor. The MR imaging was segmented by using the K-means algorithm. The resulting set of classes (also called “clusters”) represented the variety of tissues observed. A morphology-based approach allowed discrimination of the 2 types of tumors. This approach was validated by a leave-1-patient-out procedure. RESULTS: A method was developed for the discrimination of GBMs and solitary METs. Two masses out of 33 were wrongly classified; the overall results were accurate in 93.9% of the observed cases. CONCLUSIONS: A semiautomated method based on a morphologic analysis was developed. Its application was found to be useful in the discrimination of GBM from solitary MET.

Confirmation of brand identity of a trappist beer by mid-infrared spectroscopy coupled with multivariate data analysis

Authentication of foods is of importance both to consumers and producers for e.g. confidence in label descriptions and brand protection, respectively. The authentication of beers has received limited attention and in most cases only small data sets were analysed. In this study, Fourier-transform infrared attenuated total reflectance (FT-IR ATR) spectroscopy was applied to a set of 267 beers (53 different brands) to confirm claimed identity for samples of a single beer brand based on their spectral profiles. Skewness-adjusted robust principal component analysis (ROBPCA) was deployed to detect outliers in the data. Subsequently, extended canonical variates analysis (ECVA) was used to reduce the dimensionality of the data while simultaneously achieving maximum class separation. Finally, the reduced data were used as inputs to various linear and non-linear classifiers. Work focused on the specific identification of Rochefort 8° (a Trappist beer) and both direct and indirect (using an hierarchical approach) identification strategies were studied. For the classification problems Rochefort vs. non-Rochefort, Rochefort 8° vs. non-Rochefort 8° and Rochefort 8° vs. Rochefort 6° and 10°, correct prediction abilities of 93.8%, 93.3% and 97.3%, respectively were achieved.

Quantifying small molecule phenotypic effects using mitochondrial morpho-functional fingerprinting and machine learning

In primary fibroblasts from Leigh Syndrome (LS) patients, isolated mitochondrial complex I deficiency is associated with increased reactive oxygen species levels and mitochondrial morpho-functional changes. Empirical evidence suggests these aberrations constitute linked therapeutic targets for small chemical molecules. However, the latter generally induce multiple subtle effects, meaning that in vitro potency analysis or single-parameter high-throughput cell screening are of limited use to identify these molecules. We combine automated image quantification and artificial intelligence to discriminate between primary fibroblasts of a healthy individual and a LS patient based upon their mitochondrial morpho-functional phenotype. We then evaluate the effects of newly developed Trolox variants in LS patient cells. This revealed that Trolox ornithylamide hydrochloride best counterbalanced mitochondrial morpho-functional aberrations, effectively scavenged ROS and increased the maximal activity of mitochondrial complexes I, IV and citrate synthase. Our results suggest that Trolox-derived antioxidants are promising candidates in therapy development for human mitochondrial disorders.

Regularized MANOVA (rMANOVA) in untargeted metabolomics.

Many advanced metabolomics experiments currently lead to data where a large number of response variables were measured while one or several factors were changed. Often the number of response variables vastly exceeds the sample size and well-established techniques such as multivariate analysis of variance (MANOVA) cannot be used to analyze the data. ANOVA simultaneous component analysis (ASCA) is an alternative to MANOVA for analysis of metabolomics data from an experimental design. In this paper, we show that ASCA assumes that none of the metabolites are correlated and that they all have the same variance. Because of these assumptions, ASCA may relate the wrong variables to a factor. This reduces the power of the method and hampers interpretation. We propose an improved model that is essentially a weighted average of the ASCA and MANOVA models. The optimal weight is determined in a data-driven fashion. Compared to ASCA, this method assumes that variables can correlate, leading to a more realistic view of the data. Compared to MANOVA, the model is also applicable when the number of samples is (much) smaller than the number of variables. These advantages are demonstrated by means of simulated and real data examples. The source code of the method is available from the first author upon request, and at the following github repository: https://github.com/JasperE/regularized-MANOVA.

Simultaneous analysis of plasma and CSF by NMR and hierarchical models fusion

AbstractBecause cerebrospinal fluid (CSF) is the biofluid which interacts most closely with the central nervous system, it holds promise as a reporter of neurological disease, for example multiple sclerosis (MScl). To characterize the metabolomics profile of neuroinflammatory aspects of this disease we studied an animal model of MScl—experimental autoimmune/allergic encephalomyelitis (EAE). Because CSF also exchanges metabolites with blood via the blood–brain barrier, malfunctions occurring in the CNS may be reflected in the biochemical composition of blood plasma. The combination of blood plasma and CSF provides more complete information about the disease. Both biofluids can be studied by use of NMR spectroscopy. It is then necessary to perform combined analysis of the two different datasets. Mid-level data fusion was therefore applied to blood plasma and CSF datasets. First, relevant information was extracted from each biofluid dataset by use of linear support vector machine recursive feature elimination. The selected variables from each dataset were concatenated for joint analysis by partial least squares discriminant analysis (PLS-DA). The combined metabolomics information from plasma and CSF enables more efficient and reliable discrimination of the onset of EAE. Second, we introduced hierarchical models fusion, in which previously developed PLS-DA models are hierarchically combined. We show that this approach enables neuroinflamed rats (even on the day of onset) to be distinguished from either healthy or peripherally inflamed rats. Moreover, progression of EAE can be investigated because the model separates the onset and peak of the disease. FigureGraphical representation of Hierarchical Models Fusion applied to concatenated plasma and CSF datasets.

Monitoring and interpretation of photoinduced biochemical processes by rapid-scan FTIR difference spectroscopy and hybrid hard and soft modeling.

Natural photochemical processes often require special instrumentation to monitor them at a suitable time scale. Rapid-scan FTIR difference spectroscopy is one of the preferred techniques to obtain rich structural information in the scale of milliseconds about photochemical processes of complex natural systems. The difference spectra obtained by this technique enhance the fine spectroscopic changes undergone during the process but require powerful data analysis methodologies to take full advantage of the information provided. Hybrid hard- and soft-modeling methodologies allow for coping with difficulties linked to the nature of the time-resolved measurement and to the complexity of the kinetic model describing the natural photochemical process. Thus, this methodology presents the following advantages: (a) handles difference spectra, taking into account the consequences of the lack of measurement about the initial stage of the process, (b) models events of the process that may be defined by a kinetic model (by hard modeling) and events that do not obey a mechanistic behavior (by soft modeling), (c) adapts to the photoaccumulation/relaxation stages of reversible photochemical processes, and (d) works simultaneously with series of experiments performed in different conditions and showing different kinetic behavior. The results of this data treatment provide complete kinetic information on the photochemical processes, e.g., rate constants, and a global picture of the difference spectra and the concentration profiles linked to each of the events (hard or soft modeled) contributing to the measured signal. The performance of the combination of time-resolved differential FTIR and hybrid hard and soft modeling is shown in a complex case study related to the photosynthetic activity of the reaction center of the purple bacteria Rhodobacter sphaeroides.