Gaining Insights Into Metabolic Networks Using Chemometrics and Bioinformatics: Chronic Kidney Disease as a Clinical Model.

Because of its ability to generate biological hypotheses, metabolomics offers an innovative and promising approach in many fields, including clinical research. However, collecting specimens in this setting can be difficult to standardize, especially when groups of patients with different degrees of disease severity are considered. In addition, despite major technological advances, it remains challenging to measure all the compounds defining the metabolic network of a biological system.

Exploring blood alterations in chronic kidney disease and haemodialysis using metabolomics.

Chronic kidney disease (CKD) is characterized by retention of uremic solutes. Compared to patients with non-dialysis dependent CKD, those requiring haemodialysis (HD) have increased morbidity and mortality. We wished to characterise metabolic patterns in CKD compared to HD patients using metabolomics.

Network principal component analysis: a versatile tool for the investigation of multigroup and multiblock datasets.

Motivation: Complex data structures composed of different groups of observations and blocks of variables are increasingly collected in many domains, including metabolomics. Analysing these high-dimensional data constitutes a challenge, and the objective of this article is to present an original multivariate method capable of explicitly taking into account links between data tables when they involve the same observations and/or variables. For that purpose, an extension of standard principal component analysis called NetPCA was developed.

Implementation of liquid chromatography-high resolution mass spectrometry methods for untargeted metabolomic analyses of biological samples: A tutorial.

Untargeted metabolomics is now widely recognized as a useful tool for exploring metabolic changes taking place in biological systems under different conditions. By its nature, this is a highly interdisciplinary field of research, and mastering all of the steps comprised in the pipeline can be a challenging task, especially for those researchers new to the topic. In this tutorial, we aim to provide an overview of the most widely adopted methods of performing LC-HRMS-based untargeted metabolomics of biological samples.

Combining the advantages of multilevel and orthogonal partial least squares data analysis for longitudinal metabolomics: Application to kidney transplantation.

Kidney transplantation is one of the renal replacement options in patients suffering from end-stage renal disease (ESRD). After a transplant, patient follow-up is essential and is mostly based on immunosuppressive drug levels control, creatinine measurement and kidney biopsy in case of a rejection suspicion. The extensive analysis of metabolite levels offered by metabolomics might improve patient monitoring, help in the surveillance of the restoration of a "normal" renal function and possibly also predict rejection.

Choosing an Optimal Sample Preparation in for Untargeted Metabolomics Approaches.

Untargeted metabolomics aims to provide a global picture of the metabolites present in the system under study. To this end, making a careful choice of sample preparation is mandatory to obtain reliable and reproducible biological information. In this study, eight different sample preparation techniques were evaluated using as a model for Gram-negative bacteria.

Toward a better understanding of chronic kidney disease with complementary chromatographic methods hyphenated with mass spectrometry for improved polar metabolome coverage.

The prevalence of chronic kidney disease (CKD) is increasing worldwide. New technical approaches are needed to improve early diagnosis, disease understanding and patient monitoring, and to evaluate new therapies. Metabolomics, as a prime candidate in the field of CKD research, aims to comprehensively analyze the metabolic complexity of biological systems.

A scoring approach for multi-platform acquisition in metabolomics.

Since the ultimate goal of untargeted metabolomics is the analysis of the broadest possible range of metabolites, some new metrics have to be used by researchers to evaluate and select different analytical strategies when multi-platform analyses are considered. In this context, we aimed at developing a scoring approach allowing to compare the performance of different LC-MS conditions for metabolomics studies. By taking into account both chromatographic and MS attributes of the analytes' peaks (i.

Metabolomics in chronic kidney disease: Strategies for extended metabolome coverage.

Chronic kidney disease (CKD) is becoming a major public health issue as prevalence is increasing worldwide. It also represents a major challenge for the identification of new early biomarkers, understanding of biochemical mechanisms, patient monitoring and prognosis. Each metabolite contained in a biofluid or tissue may play a role as a signal or as a driver in the development or progression of the pathology.

Effective mobility as a robust criterion for compound annotation and identification in metabolomics: Toward a mobility-based library.

Capillary electrophoresis (CE) presents many advantageous features as an analytical technique in metabolomics, such as very low consumption of a sample or the possibility to easily detect very polar and ionizable compounds. However, CE remains an approach only used by a few research groups due to a relatively lower sensitivity and, higher analysis time compared to liquid chromatography. To circumvent these drawbacks, herein we propose a generic CE-mass spectrometry (MS) approach using positive electrospray ionization mode and performing normal- and reverse-polarity CE separations to analyze anionic and acidic compounds.

Applicability of supercritical fluid chromatography - mass spectrometry to metabolomics. I - Optimization of separation conditions for the simultaneous analysis of hydrophilic and lipophilic substances.

The aim of this study was to evaluate the suitability of SFC-MS for the analysis of a wide range of compounds including lipophilic and highly hydrophilic substances (log P values comprised between -6 and 11), for its potential application toward human metabolomics. For this purpose, a generic unified chromatography gradient from 2 to 100% organic modifier in CO was systematically applied. In terms of chemistry, the best stationary phases for this application were found to be the Agilent Poroshell HILIC (bare silica) and Macherey-Nagel Nucleoshell HILIC (silica bonded with a zwitterionic ligand).

ROMANCE: A new software tool to improve data robustness and feature identification in CE-MS metabolomics.

The use of capillary electrophoresis coupled to mass spectrometry (CE-MS) in metabolomics remains an oddity compared to the widely adopted use of liquid chromatography. This technique is traditionally regarded as lacking the reproducibility to adequately identify metabolites by their migration times. The major reason is the variability of the velocity of the background electrolyte, mainly coming from shifts in the magnitude of the electroosmotic flow and from the suction caused by electrospray interfaces.

Metabolomic analysis of urine samples by UHPLC-QTOF-MS: Impact of normalization strategies.

Among the various biological matrices used in metabolomics, urine is a biofluid of major interest because of its non-invasive collection and its availability in large quantities. However, significant sources of variability in urine metabolomics based on UHPLC-MS are related to the analytical drift and variation of the sample concentration, thus requiring normalization. A sequential normalization strategy was developed to remove these detrimental effects, including: (i) pre-acquisition sample normalization by individual dilution factors to narrow the concentration range and to standardize the analytical conditions, (ii) post-acquisition data normalization by quality control-based robust LOESS signal correction (QC-RLSC) to correct for potential analytical drift, and (iii) post-acquisition data normalization by MS total useful signal (MSTUS) or probabilistic quotient normalization (PQN) to prevent the impact of concentration variability.