Multiple Analytical Platforms Research Articles

Antibody-drug conjugate bioanalysis using LB-LC-MS/MS hybrid assays: strategies, methodology and correlation to ligand-binding assays.

Antibody-drug conjugates (ADCs) are complex drug constructs with multiple species in the heterogeneous mixture that contribute to their efficacy and toxicity. The bioanalysis of ADCs involves multiple assays and analytical platforms. A series of ligand binding and LC-MS/MS (LB-LC-MS/MS) hybrid assays, through different combinations of anti-idiotype (anti-Id), anti-payload, or generic capture reagents, and cathepsin-B or trypsin enzyme digestion, were developed and evaluated for the analysis of conjugated-payload as well as for species traditionally measured by ligand-binding assays, total-antibody and conjugated-antibody. Hybrid assays are complementary or viable alternatives to ligand-binding assay for ADC bioanalysis and PK/PD modeling. The fit-for-purpose choice of analytes, assays and platforms and an integrated strategy from Discovery to Development for ADC PK and bioanalysis are recommended.

Perinatal deiodinase 2 expression in hepatocytes defines epigenetic susceptibility to liver steatosis and obesity

Thyroid hormone binds to nuclear receptors and regulates gene transcription. Here we report that in mice, at around the first day of life, there is a transient surge in hepatocyte type 2 deiodinase (D2) that activates the prohormone thyroxine to the active hormone triiodothyronine, modifying the expression of ∼165 genes involved in broad aspects of hepatocyte function, including lipid metabolism. Hepatocyte-specific D2 inactivation (ALB-D2KO) is followed by a delay in neonatal expression of key lipid-related genes and a persistent reduction in peroxisome proliferator-activated receptor-γ expression. Notably, the absence of a neonatal D2 peak significantly modifies the baseline and long-term hepatic transcriptional response to a high-fat diet (HFD). Overall, changes in the expression of approximately 400 genes represent the HFD response in control animals toward the synthesis of fatty acids and triglycerides, whereas in ALB-D2KO animals, the response is limited to a very different set of only approximately 200 genes associated with reverse cholesterol transport and lipase activity. A whole genome methylation profile coupled to multiple analytical platforms indicate that 10-20% of these differences can be related to the presence of differentially methylated local regions mapped to sites of active/suppressed chromatin, thus qualifying as epigenetic modifications occurring as a result of neonatal D2 inactivation. The resulting phenotype of the adult ALB-D2KO mouse is dramatic, with greatly reduced susceptibility to diet-induced steatosis, hypertriglyceridemia, and obesity.

Exposome science for public health protection and innovation

The exposome (Wild 2005) represents the totality of exposures from conception onwards, simultaneously identifying, characterizing and quantifying the exogenous and endogenous exposures and modifiable risk factors that predispose to and predict diseases throughout a person’s life span. Unraveling it will help us understand the intricate web of relationships between environmental exposures, lifestyle, genetics and disease. It is thus expected to contribute significantly to the determination of causal associations between environmental factors and human health taking into account genetic susceptibility. Making this vision come true poses significant scientific and technological challenges in terms of both untangling the complex biological networks that regulate our body’s response to external stressors and processing and analyzing the large datasets generated from the use of multiple high throughput analytical platforms (-omics technologies). Exposome research will try to reverse the paradigm of “nature versus nurture” and adopt one defined by complex and dynamic interactions between DNA sequence, epigenetic DNA modifications, gene expression and environmental factors that all combine to influence disease phenotypes. The approach outlined in this lecture brings together and organizes environmental, socio-economic, exposure, biomarker and health effect data; in addition, it includes all the procedures and computational sequences necessary for applying advanced bioinformatics coupling advanced data mining, biological and exposure modeling so as to ensure that environmental exposure-health associations are studied comprehensively. The overall methodology is being verified in a series of population studies across Europe, tackling various levels of environmental exposure, age windows and gender differentiation of exposure, and socio-economic and genetic variability.

Metabolome analysis of 20 taxonomically related benzylisoquinoline alkaloid-producing plants.

BackgroundRecent progress toward the elucidation of benzylisoquinoline alkaloid (BIA) metabolism has focused on a small number of model plant species. Current understanding of BIA metabolism in plants such as opium poppy, which accumulates important pharmacological agents such as codeine and morphine, has relied on a combination of genomics and metabolomics to facilitate gene discovery. Metabolomics studies provide important insight into the primary biochemical networks underpinning specialized metabolism, and serve as a key resource for metabolic engineering, gene discovery, and elucidation of governing regulatory mechanisms. Beyond model plants, few broad-scope metabolomics reports are available for the vast number of plant species known to produce an estimated 2500 structurally diverse BIAs, many of which exhibit promising medicinal properties.ResultsWe applied a multi-platform approach incorporating four different analytical methods to examine 20 non-model, BIA-accumulating plant species. Plants representing four families in the Ranunculales were chosen based on reported BIA content, taxonomic distribution and importance in modern/traditional medicine. One-dimensional 1H NMR-based profiling quantified 91 metabolites and revealed significant species- and tissue-specific variation in sugar, amino acid and organic acid content. Mono- and disaccharide sugars were generally lower in roots and rhizomes compared with stems, and a variety of metabolites distinguished callus tissue from intact plant organs. Direct flow infusion tandem mass spectrometry provided a broad survey of 110 lipid derivatives including phosphatidylcholines and acylcarnitines, and high-performance liquid chromatography coupled with UV detection quantified 15 phenolic compounds including flavonoids, benzoic acid derivatives and hydroxycinnamic acids. Ultra-performance liquid chromatography coupled with high-resolution Fourier transform mass spectrometry generated extensive mass lists for all species, which were mined for metabolites putatively corresponding to BIAs. Different alkaloids profiles, including both ubiquitous and potentially rare compounds, were observed.ConclusionsExtensive metabolite profiling combining multiple analytical platforms enabled a more complete picture of overall metabolism occurring in selected plant species. This study represents the first time a metabolomics approach has been applied to most of these species, despite their importance in modern and traditional medicine. Coupled with genomics data, these metabolomics resources serve as a key resource for the investigation of BIA biosynthesis in non-model plant species.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-015-0594-2) contains supplementary material, which is available to authorized users.

Utilizing human embryonic stem cell-derived cardiomyocytes on multiple analytical platforms for a more comprehensive cardiac safety assessment

Utilizing human embryonic stem cell-derived cardiomyocytes on multiple analytical platforms for a more comprehensive cardiac safety assessment

The human saliva metabolome

Saliva is a clear, watery biofluid produced by the salivary glands to protect and lubricate the oral cavity. While mostly composed of water (99 %), the chemical composition of saliva is known to change quite dramatically in response to a variety of different physiological states, stimuli, insults and stressors. Unfortunately, among the human biofluids typically used in medical testing (such as blood and urine), saliva is rarely used. Given that saliva is the most easily accessible and readily obtained biofluid, this is rather unfortunate. Part of the reluctance to use saliva in medical testing likely has to do with the fact that its chemical composition is not well known. Here, a comprehensive characterization of the human saliva metabolome is presented. Multiple analytical platforms including nuclear magnetic resonance spectroscopy, gas chromatography mass spectrometry, direct flow injection/liquid chromatography mass spectrometry, inductively coupled plasma mass spectrometry, and high performance liquid chromatography were employed to quantify the metabolites that can be commonly detected in human saliva. Using this multiplatform approach, we were able to quantify and/or identify 308 salivary metabolites or metabolite species in human saliva. This experimental work was complemented with computer-aided literature mining that led to the identification and annotation of another 708 salivary metabolites. The combined collection of 853 non-redundant salivary metabolites or metabolite species together with their concentrations, related literature references, and links to their known disease associations are freely available at http://www.hmdb.ca/ .

Integrating metabolomic data from multiple analytical platforms for a comprehensive characterisation of lemon essential oils

AbstractCitrus cold pressed oils are of great importance to the flavour and fragrance industry. Because of their high added value, careful attention must be paid to ensure the oils' genuineness and authenticity. Characterising their chemical complexity in a holistic perspective constitutes a potent way to relate specific compounds to the organoleptic properties of interest and to assess their quality. In this context, a complete characterisation using untargeted metabolomics represents an analytical challenge. The present study takes advantage of multiblock data modelling to integrate heterogeneous signals collected from GC‐FID, 1H‐NMR, UHPLC‐TOF/MS‐ (negative mode) and UHPLC‐TOF/MS+ (positive mode) platforms to obtain a complete characterisation of 64 samples of cold pressed lemon oil (CPLO). Two statistical approaches (MB‐PLS‐DA and Consensus OPLS‐DA) were used to classify the samples according to their extraction processes [i.e. Sfumatrice, Food Machinery Corporation Inline Extractor (FMC), Brown Oil Extractor (BOE), Pelatrice, or mixed FMC + Pelatrice]. Furthermore, the multiblock strategy allows links between variables from different analytical methods to be drawn easily and facilitates the identification of compounds. Because citrus oils extracted using the Sfumatrice process constitute the reference quality from an organoleptic point of view, these samples were characterised thoroughly and are reported to contain less fatty acids but more sesquiterpenes and furocoumarins compared with products obtained using other extraction processes. Copyright © 2014 John Wiley & Sons, Ltd.

Metabolite and peptide levels in plasma and CSF differentiating healthy controls from patients with newly diagnosed Parkinson's disease.

Parkinson's disease (PD) is a progressive, multi-focal neurodegenerative disease for which there is no effective disease modifying treatment. A critical requirement for designing successful clinical trials is the development of robust and reproducible biomarkers identifying PD in preclinical stages. To investigate the potential for a cluster of biomarkers visualized with multiple analytical platforms to provide a clinically useful tool. Gas Chromatography-Mass Spectrometry (GC-TOFMS) based metabolomics and immunoassay-based protein/peptide analyses on samples from patients with PD diagnosed in Northern Sweden. Low molecular weight compounds from both plasma and cerebrospinal fluid (CSF) from 20 healthy subjects (controls) and 20 PD patients at the time of diagnosis (baseline) were analyzed. In plasma, we found a significant increase in several amino acids and a decrease in C16-C18 saturated and unsaturated fatty acids in patients as compared to control subjects. We also observed an increase in plasma levels of pyroglutamate and 2-oxoisocaproate (ketoleucine) that may be indicative of increased metabolic stress in patients. In CSF, there was a generally lower level of metabolites in PD as compared to controls, with a specific decrease in 3-hydroxyisovaleric acid, tryptophan and creatinine. Multivariate analysis and modeling of metabolites indicates that while the PD samples can be separated from control samples, the list of detected compounds will need to be expanded in order to define a robust predictive model. CSF biomarker immunoassays of candidate peptide/protein biomarkers revealed a significant decrease in the levels of Aβ-38 and Aβ-42, and an increase in soluble APPα in CSF of patients. Furthermore, these peptides showed significant correlations to each other, and positive correlations to the CSF levels of several 5- and 6-carbon sugars. However, combining these metabolites and proteins/peptides into a single model did not significantly improve the statistical analysis. Together, this metabolomics study has detected significant alterations in plasma and CSF levels of a cluster of amino acids, fatty acids and sugars based on clinical diagnosis and levels of known protein and peptide biomarkers.

Metabolomics and metabolite profiling

During the last 15 years, metabolomics has emerged as the latest of the so-called “omics” research fields. It deals with the analytical characterization of the metabolome, i.e. the low molecular weight metabolite complement of the biological system under investigation. A wide range of biological systems have benefited from metabolomics studies, from relatively simple microbes to plants and complex multiorganism systems such as mammals. The general goal of most metabolomics studies is to generate a snapshot of the metabolic state of a biological sample and to characterize the changes in the abundances of the measured metabolites arising from natural fluctuations or external, experimental biotic or abiotic perturbations. The topical collection in this issue of Analytical and Bioanalytical Chemistry provides a glimpse of the current activities in metabolomics and metabolite profiling. The articles presented elegantly illustrate that the general concept of metabolomics and metabolite profiling has matured significantly during the last decade and that this recent “-omics” toolbox is widely applied in many scientific disciplines, such as diagnostics of human diseases, biomarker discovery, nutrition, food safety, plant science and microbiology. Most state-of-the-art metabolomics studies use liquid chromatography (LC) or gas chromatography (GC) coupled with mass spectrometry (MS) for metabolite profiling, whereas direct infusion MS, NMR spectroscopy and IR spectroscopy are applied for metabolite fingerprinting without prior separation of the sample constituents. Other techniques such as LC with UV detection can be used to complement these techniques, as successfully illustrated by the contribution on carotenoid profiling of this issue. Moreover, the huge chemical diversity of substances contained in biological samples affords multiple analytical platforms in order to cover a wide range of metabolites. When more than one analytical method is applied, the data, which are derived from the metabolomics platforms, have to be integrated. In the present collection this topic has been addressed in the area of food safety with an untargeted GC–MS-based study using two different methods for the profiling of both volatiles and polar small metabolites for the detection of meat spoilage. Independent of the analytical technique employed, two complementary approaches are widely used in current metabolomics: targeted and untargeted analyses. In the targeted approach a set of predefined known substances is monitored, which usually allows absolute quantification and definite identification when limited to metabolites available Published in the topical collection Metabolomics and Metabolite Profiling with guest editors Rainer Schuhmacher, Rudolf Krska, Roy Goodacre, and Wolfram Weckwerth. R. Schuhmacher (*) :R. Krska Department for Agrobiotechnology (IFA-Tulln), Center for Analytical Chemistry, University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad-Lorenz-Strasse 20, 3430 Tulln, Austria e-mail: rainer.schuhmacher@boku.ac.at

Metabolomic profiling of the small for gestational age piglet

Human infants born small for their gestational age (SGA), as associated with intrauterine growth restriction, are at increased risk of morbidity and mortality during early life and beyond. As an animal model, the domestic piglet (Sus scrofa) provides many benefits for studying developmental aspects of the SGA condition. Our objective was to assess global metabolomic profiles of SGA (≤ 0.9kg body weight) and average for gestational age (AGA, 1.3–1.5kg body weight) piglets. Piglets were selected in littermate pairs, weighed daily to assess growth rate, and blood was collected at 15–17 days of age; piglets remained with their mother throughout the study. Following stringent quality assurance procedures on multiple analytical platforms, a total of 323 named biochemicals were identified in plasma samples, with significant effects noted in metabolic pathways involving energy (i.e., TCA cycle), amino acids, nucleotides, and fatty acids. Most notably, de novo synthesis of nicotinamide derivatives was higher (P < 0.05) in SGA piglets, suggesting a deficiency in cofactors important for energy metabolism and biosynthetic reactions. Moreover, changes in glucose metabolism suggested the ability to extract energy from dietary sources may have been compromised in the SGA piglet. We conclude that a significant reduction in the growth potential of SGA piglets is associated with perturbations in multiple metabolic pathways.ACES James Scholar Honors Program, College of ACES, University of Illinois, Urbana‐Champaign

Integrating multiple analytical platforms and chemometrics for comprehensive metabolic profiling: application to meat spoilage detection

Untargeted metabolic profiling has become a common approach to attempt to understand biological systems. However, due to the large chemical diversity in the metabolites it is generally necessary to employ multiple analytical platforms so as to encompass a wide range of metabolites. Thus it is beneficial to find chemometrics approaches which can effectively integrate data generated from multiple platforms and ideally combine the strength of each platform and overcome their inherent weaknesses; most pertinent is with respect to limited chemistries. We have reported a few studies using untargeted metabolic profiling techniques to monitor the natural spoilage process in pork and also to detect specific metabolites associated with contaminations with the pathogen Salmonella typhimurium. One method used was to analyse the volatile organic compounds (VoCs) generated throughout the spoilage process while the other was to analyse the soluble small molecule metabolites (SMM) extracted from the microbial community, as well as from the surface of the spoiled/contaminated meat. In this study, we exploit multi-block principal component analysis (MB-PCA) and multi-block partial least squares (MB-PLS) to combine the VoCs and SMM data together and compare the results obtained by analysing each data set individually. We show that by combining the two data sets and applying appropriate chemometrics, a model with much better prediction and importantly with improved interpretability was obtained. The MB-PCA model was able to combine the strength of both platforms together and generated a model with high consistency with the biological expectations, despite its unsupervised nature. MB-PLS models also achieved the best over-all performance in modelling the spoilage progression and discriminating the naturally spoiled samples and the pathogen contaminated samples. Correlation analysis and Bayesian network analysis were also performed to elucidate which metabolites were correlated strongly in the two data sets and such information could add additional information in understanding the meat spoilage process.

Normalizing and Integrating Metabolomics Data

Metabolomics research often requires the use of multiple analytical platforms, batches of samples, and laboratories, any of which can introduce a component of unwanted variation. In addition, every experiment is subject to within-platform and other experimental variation, which often includes unwanted biological variation. Such variation must be removed in order to focus on the biological information of interest. We present a broadly applicable method for the removal of unwanted variation arising from various sources for the identification of differentially abundant metabolites and, hence, for the systematic integration of data on the same quantities from different sources. We illustrate the versatility and the performance of the approach in four applications, and we show that it has several advantages over the existing normalization methods.

Liquid Chromatography–Mass Spectrometry Calibration Transfer and Metabolomics Data Fusion

Metabolic profiling is routinely performed on multiple analytical platforms to increase the coverage of detected metabolites, and it is often necessary to distribute biological and clinical samples from a study between instruments of the same type to share the workload between different laboratories. The ability to combine metabolomics data arising from different sources is therefore of great interest, particularly for large-scale or long-term studies, where samples must be analyzed in separate blocks. This is not a trivial task, however, due to differing data structures, temporal variability, and instrumental drift. In this study, we employed blood serum and plasma samples collected from 29 subjects diagnosed with small cell lung cancer and analyzed each sample on two liquid chromatography-mass spectrometry (LC-MS) platforms. We describe a method for mapping retention times and matching metabolite features between platforms and approaches for fusing data acquired from both instruments. Calibration transfer models were developed and shown to be successful at mapping the response of one LC-MS instrument to another (Procrustes dissimilarity = 0.04; Mantel correlation = 0.95), allowing us to merge the data from different samples analyzed on different instruments. Data fusion was assessed in a clinical context by comparing the correlation of each metabolite with subject survival time in both the original and fused data sets: a simple autoscaling procedure (Pearson's R = 0.99) was found to improve upon a calibration transfer method based on partial least-squares regression (R = 0.94).

Systematic Evaluation of Extraction Methods for Multiplatform-Based Metabotyping: Application to the Fasciola hepatica Metabolome

Combining data from multiple analytical platforms is essential for comprehensive study of the molecular phenotype (metabotype) of a given biological sample. The metabolite profiles generated are intrinsically dependent on the analytical platforms, each requiring optimization of instrumental parameters, separation conditions, and sample extraction to deliver maximal biological information. An in-depth evaluation of extraction protocols for characterizing the metabolome of the hepatobiliary fluke Fasciola hepatica, using ultra performance liquid chromatography and capillary electrophoresis coupled with mass spectroscopy is presented. The spectrometric methods were characterized by performance, and metrics of merit were established, including precision, mass accuracy, selectivity, sensitivity, and platform stability. Although a core group of molecules was common to all methods, each platform contributed a unique set, whereby 142 metabolites out of 14,724 features were identified. A mixture design revealed that the chloroform:methanol:water proportion of 15:59:26 was globally the best composition for metabolite extraction across UPLC-MS and CE-MS platforms accommodating different columns and ionization modes. Despite the general assumption of the necessity of platform-adapted protocols for achieving effective metabotype characterization, we show that an appropriately designed single extraction procedure is able to fit the requirements of all technologies. This may constitute a paradigm shift in developing efficient protocols for high-throughput metabolite profiling with more-general analytical applicability.

Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis.

Biological systems are increasingly being studied in a holistic manner, using omics approaches, to provide quantitative and qualitative descriptions of the diverse collection of cellular components. Among the omics approaches, metabolomics, which deals with the quantitative global profiling of small molecules or metabolites, is being used extensively to explore the dynamic response of living systems, such as organelles, cells, tissues, organs and whole organisms, under diverse physiological and pathological conditions. This technology is now used routinely in a number of applications, including basic and clinical research, agriculture, microbiology, food science, nutrition, pharmaceutical research, environmental science and the development of biofuels. Of the multiple analytical platforms available to perform such analyses, nuclear magnetic resonance and mass spectrometry have come to dominate, owing to the high resolution and large datasets that can be generated with these techniques. The large multidimensional datasets that result from such studies must be processed and analyzed to render this data meaningful. Thus, bioinformatics tools are essential for the efficient processing of huge datasets, the characterization of the detected signals, and to align multiple datasets and their features. This paper provides a state-of-the-art overview of the data processing tools available, and reviews a collection of recent reports on the topic. Data conversion, pre-processing, alignment, normalization and statistical analysis are introduced, with their advantages and disadvantages, and comparisons are made to guide the reader.

Canonical correlation analysis of multiple sensory directed metabolomics data blocks reveals corresponding parts between data blocks

Multiple analytical platforms are frequently used in metabolomics studies. The resulting multiple data blocks contain, in general, similar parts of information which can be disclosed by chemometric methods. The metabolites of interest, however, are usually just a minor part of the complete data block and are related to a response of interest such as quality traits. Concatenation of data matrices is frequently used to simultaneously analyze multiple data blocks. Two main problems may occur with this approach: 1) the number of variables becomes very large in relation to the number of observations which may deteriorate model performance, and 2) scaling issues between the data blocks need to be resolved. Therefore, a method is proposed that circumvents direct concatenation of two data matrices but does uncover the shared and distinct parts of the data sets in relation to quality traits. The relevant part of the data blocks with respect to the quality trait of interest is revealed by partial least squares regression on each of the data blocks. The score vectors of both models that are predictive for the quality trait are then used in a canonical correlation analysis. Highly correlating score vectors indicate parts of the data blocks that are closely related. By inspecting the relevant loading vectors, the metabolites of interest are revealed.

Serum and Urine Metabolite Profiling Reveals Potential Biomarkers of Human Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common malignancy in the world with high morbidity and mortality rate. Identification of novel biomarkers in HCC remains impeded primarily because of the heterogeneity of the disease in clinical presentations as well as the pathophysiological variations derived from underlying conditions such as cirrhosis and steatohepatitis. The aim of this study is to search for potential metabolite biomarkers of human HCC using serum and urine metabolomics approach. Sera and urine samples were collected from patients with HCC (n = 82), benign liver tumor patients (n = 24), and healthy controls (n = 71). Metabolite profiling was performed by gas chromatography time-of-flight mass spectrometry and ultra performance liquid chromatography-quadrupole time of flight mass spectrometry in conjunction with univariate and multivariate statistical analyses. Forty three serum metabolites and 31 urinary metabolites were identified in HCC patients involving several key metabolic pathways such as bile acids, free fatty acids, glycolysis, urea cycle, and methionine metabolism. Differentially expressed metabolites in HCC subjects, such as bile acids, histidine, and inosine are of great statistical significance and high fold changes, which warrant further validation as potential biomarkers for HCC. However, alterations of several bile acids seem to be affected by the condition of liver cirrhosis and hepatitis. Quantitative measurement and comparison of seven bile acids among benign liver tumor patients with liver cirrhosis and hepatitis, HCC patients with liver cirrhosis and hepatitis, HCC patients without liver cirrhosis and hepatitis, and healthy controls revealed that the abnormal levels of glycochenodeoxycholic acid, glycocholic acid, taurocholic acid, and chenodeoxycholic acid are associated with liver cirrhosis and hepatitis. HCC patients with alpha fetoprotein values lower than 20 ng/ml was successfully differentiated from healthy controls with an accuracy of 100% using a panel of metabolite markers. Our work shows that metabolomic profiling approach is a promising screening tool for the diagnosis and stratification of HCC patients.

A proper metabolomics strategy supports efficient food quality improvement: A case study on tomato sensory properties

In agricultural and food products, typical quality parameters are sensory properties, shelf-life, safety, health, nutritional value, crop yield per area and disease resistance. It is known that these parameters are importantly determined by the metabolites in the crops and food products. Metabolomics is the state-of-the-art routine technique that can effectively facilitate the improvement of the food chain quality by analyzing key metabolites as efficient quality predictors, to deduce production improvement strategies and for screening and identifying traits for breeding. The aim of this paper is to show such a metabolomics strategy with a special focus on the combination of multiple analytical platforms for sufficient metabolite coverage and a validated multivariate data analysis to reliably determine key metabolites. As a demonstrator for the metabolomics strategy, it was applied to determine the key tomato metabolites with respect to selected sensory attributes. From a literature-based validation study and a comparison to standard used markers, the relevance of the found metabolites was shown.

Quantitative Proteomics and Metabolomics Analysis of Normal Human Cerebrospinal Fluid Samples

The analysis of cerebrospinal fluid (CSF) is used in biomarker discovery studies for various neurodegenerative central nervous system (CNS) disorders. However, little is known about variation of CSF proteins and metabolites between patients without neurological disorders. A baseline for a large number of CSF compounds appears to be lacking. To analyze the variation in CSF protein and metabolite abundances in a number of well-defined individual samples of patients undergoing routine, non-neurological surgical procedures, we determined the variation of various proteins and metabolites by multiple analytical platforms. A total of 126 common proteins were assessed for biological variations between individuals by ESI-Orbitrap. A large spread in inter-individual variation was observed (relative standard deviations [RSDs] ranged from 18 to 148%) for proteins with both high abundance and low abundance. Technical variation was between 15 and 30% for all 126 proteins. Metabolomics analysis was performed by means of GC-MS and nuclear magnetic resonance (NMR) imaging and amino acids were specifically analyzed by LC-MS/MS, resulting in the detection of more than 100 metabolites. The variation in the metabolome appears to be much more limited compared with the proteome: the observed RSDs ranged from 12 to 70%. Technical variation was less than 20% for almost all metabolites. Consequently, an understanding of the biological variation of proteins and metabolites in CSF of neurologically normal individuals appears to be essential for reliable interpretation of biomarker discovery studies for CNS disorders because such results may be influenced by natural inter-individual variations. Therefore, proteins and metabolites with high variation between individuals ought to be assessed with caution as candidate biomarkers because at least part of the difference observed between the diseased individuals and the controls will not be caused by the disease, but rather by the natural biological variation between individuals.

Quantitative Proteomics and Metabolomics Analysis of Normal Human Cerebrospinal Fluid Samples*

The analysis of cerebrospinal fluid (CSF) is used in biomarker discovery studies for various neurodegenerative central nervous system (CNS) disorders. However, little is known about variation of CSF proteins and metabolites between patients without neurological disorders. A baseline for a large number of CSF compounds appears to be lacking. To analyze the variation in CSF protein and metabolite abundances in a number of well-defined individual samples of patients undergoing routine, non-neurological surgical procedures, we determined the variation of various proteins and metabolites by multiple analytical platforms. A total of 126 common proteins were assessed for biological variations between individuals by ESI-Orbitrap. A large spread in inter-individual variation was observed (relative standard deviations [RSDs] ranged from 18 to 148%) for proteins with both high abundance and low abundance. Technical variation was between 15 and 30% for all 126 proteins. Metabolomics analysis was performed by means of GC-MS and nuclear magnetic resonance (NMR) imaging and amino acids were specifically analyzed by LC-MS/MS, resulting in the detection of more than 100 metabolites. The variation in the metabolome appears to be much more limited compared with the proteome: the observed RSDs ranged from 12 to 70%. Technical variation was less than 20% for almost all metabolites. Consequently, an understanding of the biological variation of proteins and metabolites in CSF of neurologically normal individuals appears to be essential for reliable interpretation of biomarker discovery studies for CNS disorders because such results may be influenced by natural inter-individual variations. Therefore, proteins and metabolites with high variation between individuals ought to be assessed with caution as candidate biomarkers because at least part of the difference observed between the diseased individuals and the controls will not be caused by the disease, but rather by the natural biological variation between individuals.