Dansylation Metabolite Assay: A Simple and Rapid Method for Sample Amount Normalization in Metabolomics

Metabolomics involves the comparison of the metabolomes of individual samples from two or more groups to reveal the metabolic differences. In order to measure the metabolite concentration differences accurately, using the same amount of starting materials is essential. In this work, we describe a simple and rapid method for sample amount normalization. It is based on dansylation labeling of the amine and phenol submetabolome of an individual sample, followed by solvent extraction of the labeled metabolites and ultraviolet (UV) absorbance measurement using a microplate reader. A calibration curve of a mixture of 17 dansyl-labeled amino acid standards is used to determine the total concentration of the labeled metabolites in a sample. According to the measured concentrations of individual samples, the volume of an aliquot taken from each sample is adjusted so that the same sample amount is taken for subsequent metabolome comparison. As an example of applications, this dansylation metabolite assay method is shown to be useful in comparative metabolome analysis of two different E. coli strains using a differential chemical isotope labeling LC-MS platform. Because of the low cost of equipment and reagents and the simple procedure used in the assay, this method can be readily implemented. We envisage that, this assay, which is analogous to the bicinchoninic acid (BCA) protein assay widely used in proteomics, will be applicable to many types of samples for quantitative metabolomics

Determination of Total Concentration of Chemically Labeled Metabolites as a Means of Metabolome Sample Normalization and Sample Loading Optimization in Mass Spectrometry-Based Metabolomics

For mass spectrometry (MS)-based metabolomics, it is important to use the same amount of starting materials from each sample to compare the metabolome changes in two or more comparative samples. Unfortunately, for biological samples, the total amount or concentration of metabolites is difficult to determine. In this work, we report a general approach of determining the total concentration of metabolites based on the use of chemical labeling to attach a UV absorbent to the metabolites to be analyzed, followed by rapid step-gradient liquid chromatography (LC) UV detection of the labeled metabolites. It is shown that quantification of the total labeled analytes in a biological sample facilitates the preparation of an appropriate amount of starting materials for MS analysis as well as the optimization of the sample loading amount to a mass spectrometer for achieving optimal detectability. As an example, dansylation chemistry was used to label the amine- and phenol-containing metabolites in human urine samples. LC-UV quantification of the labeled metabolites could be optimally performed at the detection wavelength of 338 nm. A calibration curve established from the analysis of a mixture of 17 labeled amino acid standards was found to have the same slope as that from the analysis of the labeled urinary metabolites, suggesting that the labeled amino acid standard calibration curve could be used to determine the total concentration of the labeled urinary metabolites. A workflow incorporating this LC-UV metabolite quantification strategy was then developed in which all individual urine samples were first labeled with ¹²C-dansylation and the concentration of each sample was determined by LC-UV. The volumes of urine samples taken for producing the pooled urine standard were adjusted to ensure an equal amount of labeled urine metabolites from each sample was used for the pooling. The pooled urine standard was then labeled with ¹³C-dansylation. Equal amounts of the ¹²C-labeled individual sample and the ¹³C-labeled pooled urine standard were mixed for LC-MS analysis. This way of concentration normalization among different samples with varying concentrations of total metabolites was found to be critical for generating reliable metabolome profiles for comparison

Development of Isotope Labeling Liquid Chromatography–Mass Spectrometry for Metabolic Profiling of Bacterial Cells and Its Application for Bacterial Differentiation

Quantitative and comprehensive profiling of cellular metabolites is currently a challenging task in bacterial metabolomics. In this work, a simple and robust method for profiling the amine- and phenol-containing metabolome of bacterial cells is described. The overall workflow consists of methanol-based cell lysis and metabolite extraction with ultrasonication, differential isotope dansylation labeling of cellular metabolites, and analysis of the labeled metabolites by liquid chromatography–mass spectrometry (LC–MS). Over a thousand peak pairs or putative metabolites can be detected from bacterial cells in a 25 min LC–MS run and near 2500 putative metabolites can be found in one bacterium from combined results of multiple analyses. After careful examination and optimization of the sample preparation process, this method is shown to be effective for both Gram-positive and Gram-negative bacteria. An idea of applying LC–ultraviolet (UV) detection to quantify the total amount of labeled metabolites is shown to be effective for normalizing the amounts of metabolites present in different samples for metabolome comparison. The use of differential isotopic labeling allows relative quantification of each individual metabolite, which facilitates comparative metabolomics studies and the generation of a metabolic fingerprint of a bacterium. Finally, this method is demonstrated to be useful for the differentiation of three bacterial species in cultured media and spiked human urine samples

Development of Isotope Labeling Liquid Chromatography–Mass Spectrometry for Metabolic Profiling of Bacterial Cells and Its Application for Bacterial Differentiation

Quantitative and comprehensive profiling of cellular metabolites is currently a challenging task in bacterial metabolomics. In this work, a simple and robust method for profiling the amine- and phenol-containing metabolome of bacterial cells is described. The overall workflow consists of methanol-based cell lysis and metabolite extraction with ultrasonication, differential isotope dansylation labeling of cellular metabolites, and analysis of the labeled metabolites by liquid chromatography–mass spectrometry (LC–MS). Over a thousand peak pairs or putative metabolites can be detected from bacterial cells in a 25 min LC–MS run and near 2500 putative metabolites can be found in one bacterium from combined results of multiple analyses. After careful examination and optimization of the sample preparation process, this method is shown to be effective for both Gram-positive and Gram-negative bacteria. An idea of applying LC–ultraviolet (UV) detection to quantify the total amount of labeled metabolites is shown to be effective for normalizing the amounts of metabolites present in different samples for metabolome comparison. The use of differential isotopic labeling allows relative quantification of each individual metabolite, which facilitates comparative metabolomics studies and the generation of a metabolic fingerprint of a bacterium. Finally, this method is demonstrated to be useful for the differentiation of three bacterial species in cultured media and spiked human urine samples

DnsID in MyCompoundID for Rapid Identification of Dansylated Amine- and Phenol-Containing Metabolites in LC–MS-Based Metabolomics

High-performance chemical isotope labeling (CIL) liquid chromatography–mass spectrometry (LC–MS) is an enabling technology based on rational design of labeling reagents to target a class of metabolites sharing the same functional group (e.g., all the amine-containing metabolites or the amine submetabolome) to provide concomitant improvements in metabolite separation, detection, and quantification. However, identification of labeled metabolites remains to be an analytical challenge. In this work, we describe a library of labeled standards and a search method for metabolite identification in CIL LC–MS. The current library consists of 273 unique metabolites, mainly amines and phenols that are individually labeled by dansylation (Dns). Some of them produced more than one Dns-derivative (isomers or multiple labeled products), resulting in a total of 315 dansyl compounds in the library. These metabolites cover 42 metabolic pathways, allowing the possibility of probing their changes in metabolomics studies. Each labeled metabolite contains three searchable parameters: molecular ion mass, MS/MS spectrum, and retention time (RT). To overcome RT variations caused by experimental conditions used, we have developed a calibration method to normalize RTs of labeled metabolites using a mixture of RT calibrants. A search program, DnsID, has been developed in www.MyCompoundID.org for automated identification of dansyl labeled metabolites in a sample based on matching one or more of the three parameters with those of the library standards. Using human urine as an example, we illustrate the workflow and analytical performance of this method for metabolite identification. This freely accessible resource is expandable by adding more amine and phenol standards in the future. In addition, the same strategy should be applicable for developing other labeled standards libraries to cover different classes of metabolites for comprehensive metabolomics using CIL LC–MS

Comparative Proteomic and Metabolomic Analysis of Staphylococcus warneri SG1 Cultured in the Presence and Absence of Butanol

The complete genome of the solvent tolerant Staphylococcus warneri SG1 was recently published. This Gram-positive bacterium is tolerant to a large spectrum of organic solvents including short-chain alcohols, alkanes, esters and cyclic aromatic compounds. In this study, we applied a two-dimensional liquid chromatography (2D-LC) mass spectrometry (MS) shotgun approach, in combination with quantitative 2-MEGA (dimethylation after guanidination) isotopic labeling, to compare the proteomes of SG1 grown under butanol-free and butanol-challenged conditions. In total, 1585 unique proteins (representing 65% of the predicted open reading frames) were identified, covering all major metabolic pathways. Of the 967 quantifiable proteins by 2-MEGA labeling, 260 were differentially expressed by at least 1.5-fold. These proteins are involved in energy metabolism, oxidative stress response, lipid and cell envelope biogenesis, or have chaperone functions. We also applied differential isotope labeling LC-MS to probe metabolite changes in key metabolic pathways upon butanol stress. This is the first comprehensive proteomic and metabolomic study of S. warneri SG1 and presents an important step toward understanding its physiology and mechanism of solvent tolerance

Comparative Proteomic and Metabolomic Analysis of Staphylococcus warneri SG1 Cultured in the Presence and Absence of Butanol

The complete genome of the solvent tolerant Staphylococcus warneri SG1 was recently published. This Gram-positive bacterium is tolerant to a large spectrum of organic solvents including short-chain alcohols, alkanes, esters and cyclic aromatic compounds. In this study, we applied a two-dimensional liquid chromatography (2D-LC) mass spectrometry (MS) shotgun approach, in combination with quantitative 2-MEGA (dimethylation after guanidination) isotopic labeling, to compare the proteomes of SG1 grown under butanol-free and butanol-challenged conditions. In total, 1585 unique proteins (representing 65% of the predicted open reading frames) were identified, covering all major metabolic pathways. Of the 967 quantifiable proteins by 2-MEGA labeling, 260 were differentially expressed by at least 1.5-fold. These proteins are involved in energy metabolism, oxidative stress response, lipid and cell envelope biogenesis, or have chaperone functions. We also applied differential isotope labeling LC-MS to probe metabolite changes in key metabolic pathways upon butanol stress. This is the first comprehensive proteomic and metabolomic study of S. warneri SG1 and presents an important step toward understanding its physiology and mechanism of solvent tolerance

Comparative Proteomic and Metabolomic Analysis of Staphylococcus warneri SG1 Cultured in the Presence and Absence of Butanol

The complete genome of the solvent tolerant Staphylococcus warneri SG1 was recently published. This Gram-positive bacterium is tolerant to a large spectrum of organic solvents including short-chain alcohols, alkanes, esters and cyclic aromatic compounds. In this study, we applied a two-dimensional liquid chromatography (2D-LC) mass spectrometry (MS) shotgun approach, in combination with quantitative 2-MEGA (dimethylation after guanidination) isotopic labeling, to compare the proteomes of SG1 grown under butanol-free and butanol-challenged conditions. In total, 1585 unique proteins (representing 65% of the predicted open reading frames) were identified, covering all major metabolic pathways. Of the 967 quantifiable proteins by 2-MEGA labeling, 260 were differentially expressed by at least 1.5-fold. These proteins are involved in energy metabolism, oxidative stress response, lipid and cell envelope biogenesis, or have chaperone functions. We also applied differential isotope labeling LC-MS to probe metabolite changes in key metabolic pathways upon butanol stress. This is the first comprehensive proteomic and metabolomic study of S. warneri SG1 and presents an important step toward understanding its physiology and mechanism of solvent tolerance

MyCompoundID: Using an Evidence-Based Metabolome Library for Metabolite Identification

Identification of unknown metabolites is a major challenge in metabolomics. Without the identities of the metabolites, the metabolome data generated from a biological sample cannot be readily linked with the proteomic and genomic information for studies in systems biology and medicine. We have developed a web-based metabolite identification tool (http://www.mycompoundid.org) that allows searching and interpreting mass spectrometry (MS) data against a newly constructed metabolome library composed of 8 021 known human endogenous metabolites and their predicted metabolic products (375 809 compounds from one metabolic reaction and 10 583 901 from two reactions). As an example, in the analysis of a simple extract of human urine or plasma and the whole human urine by liquid chromatography-mass spectrometry and MS/MS, we are able to identify at least two times more metabolites in these samples than by using a standard human metabolome library. In addition, it is shown that the evidence-based metabolome library (EML) provides a much superior performance in identifying putative metabolites from a human urine sample, compared to the use of the ChemPub and KEGG libraries

MyCompoundID: Using an Evidence-Based Metabolome Library for Metabolite Identification

Identification of unknown metabolites is a major challenge in metabolomics. Without the identities of the metabolites, the metabolome data generated from a biological sample cannot be readily linked with the proteomic and genomic information for studies in systems biology and medicine. We have developed a web-based metabolite identification tool (http://www.mycompoundid.org) that allows searching and interpreting mass spectrometry (MS) data against a newly constructed metabolome library composed of 8 021 known human endogenous metabolites and their predicted metabolic products (375 809 compounds from one metabolic reaction and 10 583 901 from two reactions). As an example, in the analysis of a simple extract of human urine or plasma and the whole human urine by liquid chromatography-mass spectrometry and MS/MS, we are able to identify at least two times more metabolites in these samples than by using a standard human metabolome library. In addition, it is shown that the evidence-based metabolome library (EML) provides a much superior performance in identifying putative metabolites from a human urine sample, compared to the use of the ChemPub and KEGG libraries