Metabolomics aims at studying endogenetic metabolite changes in a biological system to know the influences of genetic and environmental factors. Isotope labelling standards of metabolites are important for quantitative analysis to reduce the matrix effects, but most of metabolites are not commercial available in metabolomics study. Even commercial available, because of so many metabolites, it is not possible to use own isotope labelling standard for each metabolite. Therefore, Isotope labelling reagent that targets a specific functional group in metabolites of interest is a preferable method. The isotope labelled isotopomers of the target metabolites can be used to quantitatively compare their relative contents in different samples. On the other hand, if a full labelled substrate is used, this kind technique can be used to map a global metabolic network estimate flux of metabolic pathways, and assign elemental formulas to unknown metabolites in a given system. In this lecture, we shall summarize the results of stable isotope labelled metabolic profiling and lipid profiling analyses based on HPLC-MS in our laboratory, and also show the derivatization tag for a specific group can be provided not only in the solution, but also on the nanomaterials.

Metabolomics aims at studying endogenetic metabolite changes in a biological system to know the influences of genetic and environmental factors. Isotope labelling standards of metabolites are important for quantitative analysis to reduce the matrix effects, but most of metabolites are not commercial available in metabolomics study. Even commercial available, because of so many metabolites, it is not possible to use own isotope labelling standard for each metabolite. Therefore, Isotope labelling reagent that targets a specific functional group in metabolites of interest is a preferable method. The isotope labelled isotopomers of the target metabolites can be used to quantitatively compare their relative contents in different samples. On the other hand, if a full labelled substrate is used, this kind technique can be used to map a global metabolic network estimate flux of metabolic pathways, and assign elemental formulas to unknown metabolites in a given system. In this lecture, we shall summarize the results of stable isotope labelled metabolic profiling and lipid profiling analyses based on HPLC-MS in our laboratory, and also show the derivatization tag for a specific group can be provided not only in the solution, but also on the nanomaterials.

Metabolomics aims at studying endogenetic metabolite changes in a biological system to know the influences of genetic and environmental factors. Isotope labelling standards of metabolites are important for quantitative analysis to reduce the matrix effects, but most of metabolites are not commercial available in metabolomics study. Even commercial available, because of so many metabolites, it is not possible to use own isotope labelling standard for each metabolite. Therefore, Isotope labelling reagent that targets a specific functional group in metabolites of interest is a preferable method. The isotope labelled isotopomers of the target metabolites can be used to quantitatively compare their relative contents in different samples. On the other hand, if a full labelled substrate is used, this kind technique can be used to map a global metabolic network estimate flux of metabolic pathways, and assign elemental formulas to unknown metabolites in a given system. In this lecture, we shall summarize the results of stable isotope labelled metabolic profiling and lipid profiling analyses based on HPLC-MS in our laboratory, and also show the derivatization tag for a specific group can be provided not only in the solution, but also on the nanomaterials.

The metabolome is the set of small molecular mass compounds (metabolites) found in biological media; and metabolomics, which refers to as the analysis of metabolome in a given biological condition, deals with the large scale detection and quantification of metabolites in biological media. The metabolome is characterized by a huge number of molecules exhibiting a high diversity of chemical structures and abundances. By obtaining information related to both genetic and environmental contributions in biological media, metabolomics offers the possibility to document and highlight interactions between an organism (microorganism, plant or human being) and its environment, in complement to other molecular profiling tools such genomics, transcriptomics and proteomics. Nowadays, metabolomics is widely used, alone and in combination with other omic tools, for biomarker discovery in order to improve our knowledge on the molecular bases of diseases, and for providing with molecular signatures for patient stratification in a personalized medicine perspective. This lecture will deal with medical applications of metabolomics, and with challenges linked to analytical chemistry that still have to be faced so that this technology can meet our expectations: improvement of metabolite detection and quantification, metabolite identification and metabolome annotation, and also the necessity of improving the processing, analysis and management of metabolomics data in order to ensure their interoperability and integration with other kinds of data and metadata in a systems biology perspective.

The metabolome is the set of small molecular mass compounds (metabolites) found in biological media; and metabolomics, which refers to as the analysis of metabolome in a given biological condition, deals with the large scale detection and quantification of metabolites in biological media. The metabolome is characterized by a huge number of molecules exhibiting a high diversity of chemical structures and abundances. By obtaining information related to both genetic and environmental contributions in biological media, metabolomics offers the possibility to document and highlight interactions between an organism (microorganism, plant or human being) and its environment, in complement to other molecular profiling tools such genomics, transcriptomics and proteomics. Nowadays, metabolomics is widely used, alone and in combination with other omic tools, for biomarker discovery in order to improve our knowledge on the molecular bases of diseases, and for providing with molecular signatures for patient stratification in a personalized medicine perspective. This lecture will deal with medical applications of metabolomics, and with challenges linked to analytical chemistry that still have to be faced so that this technology can meet our expectations: improvement of metabolite detection and quantification, metabolite identification and metabolome annotation, and also the necessity of improving the processing, analysis and management of metabolomics data in order to ensure their interoperability and integration with other kinds of data and metadata in a systems biology perspective.

The metabolome is the set of small molecular mass compounds (metabolites) found in biological media; and metabolomics, which refers to as the analysis of metabolome in a given biological condition, deals with the large scale detection and quantification of metabolites in biological media. The metabolome is characterized by a huge number of molecules exhibiting a high diversity of chemical structures and abundances. By obtaining information related to both genetic and environmental contributions in biological media, metabolomics offers the possibility to document and highlight interactions between an organism (microorganism, plant or human being) and its environment, in complement to other molecular profiling tools such genomics, transcriptomics and proteomics. Nowadays, metabolomics is widely used, alone and in combination with other omic tools, for biomarker discovery in order to improve our knowledge on the molecular bases of diseases, and for providing with molecular signatures for patient stratification in a personalized medicine perspective. This lecture will deal with medical applications of metabolomics, and with challenges linked to analytical chemistry that still have to be faced so that this technology can meet our expectations: improvement of metabolite detection and quantification, metabolite identification and metabolome annotation, and also the necessity of improving the processing, analysis and management of metabolomics data in order to ensure their interoperability and integration with other kinds of data and metadata in a systems biology perspective.

It is widely but erroneously believed that drugs get into cells by passing through the phospholipid bilayer portion of the plasma and other membranes. Much evidence shows, however, that this is not the case, and drugs cross biomembranes by hitchhiking on transporters for other natural molecules to which these drugs are structurally similar 1,2. We present untargeted metabolomics time course analyses of the uptake and secretion of metabolites in human serum by a number of human cell lines. We show how distinct the metabolic footprints of different cell lines are from one another. We subsequently compare the transcriptomes (by RT-qPCR or using published RNA-Seq datasets 3-5) and proteomic expression profiles of cell lines 6,7 with the uptake of substances (by LCMS/MS). By employing mathematical methods we infer the variations in SLC and ABC membrane transporter expression to best explain the variation in metabolite uptake and the relationship to the function of the tissues from which these cell lines are derived. This analyses will also admit the production of quantitative structure-activity relationship (QSAR) models that will further aid in the prediction (and testing) of transporters responsible for the uptake and secretion a number of pharmaceutical drugs. We also utilise the Gini index (coefficient) as a novel means of characterising the variation in individual transporter distributions between these cell lines. Our results show that many transporters exhibit extremely high Gini coefficients, indicating a much higher degree of specialisation than is usually assumed 2.

It is widely but erroneously believed that drugs get into cells by passing through the phospholipid bilayer portion of the plasma and other membranes. Much evidence shows, however, that this is not the case, and drugs cross biomembranes by hitchhiking on transporters for other natural molecules to which these drugs are structurally similar 1,2. We present untargeted metabolomics time course analyses of the uptake and secretion of metabolites in human serum by a number of human cell lines. We show how distinct the metabolic footprints of different cell lines are from one another. We subsequently compare the transcriptomes (by RT-qPCR or using published RNA-Seq datasets 3-5) and proteomic expression profiles of cell lines 6,7 with the uptake of substances (by LCMS/MS). By employing mathematical methods we infer the variations in SLC and ABC membrane transporter expression to best explain the variation in metabolite uptake and the relationship to the function of the tissues from which these cell lines are derived. This analyses will also admit the production of quantitative structure-activity relationship (QSAR) models that will further aid in the prediction (and testing) of transporters responsible for the uptake and secretion a number of pharmaceutical drugs. We also utilise the Gini index (coefficient) as a novel means of characterising the variation in individual transporter distributions between these cell lines. Our results show that many transporters exhibit extremely high Gini coefficients, indicating a much higher degree of specialisation than is usually assumed 2.

It is widely but erroneously believed that drugs get into cells by passing through the phospholipid bilayer portion of the plasma and other membranes. Much evidence shows, however, that this is not the case, and drugs cross biomembranes by hitchhiking on transporters for other natural molecules to which these drugs are structurally similar 1,2. We present untargeted metabolomics time course analyses of the uptake and secretion of metabolites in human serum by a number of human cell lines. We show how distinct the metabolic footprints of different cell lines are from one another. We subsequently compare the transcriptomes (by RT-qPCR or using published RNA-Seq datasets 3-5) and proteomic expression profiles of cell lines 6,7 with the uptake of substances (by LCMS/MS). By employing mathematical methods we infer the variations in SLC and ABC membrane transporter expression to best explain the variation in metabolite uptake and the relationship to the function of the tissues from which these cell lines are derived. This analyses will also admit the production of quantitative structure-activity relationship (QSAR) models that will further aid in the prediction (and testing) of transporters responsible for the uptake and secretion a number of pharmaceutical drugs. We also utilise the Gini index (coefficient) as a novel means of characterising the variation in individual transporter distributions between these cell lines. Our results show that many transporters exhibit extremely high Gini coefficients, indicating a much higher degree of specialisation than is usually assumed 2.

The reporting of retention times in metabolomics is hampered by a lack of standardization. Different columns and solvent systems are used for analysis of metabolites, but even when using the same stationary phase, column dimensions, mobile phase and settings, absolute retention times can still vary greatly. This is due to the fact that retention is affected by a range of minor parameters such as LC system dead volume, inconsistencies in flow rate, gradient, temperature, mobile phase pH, etc. Retention time indexing (RTI), routinely employed in gas chromatography (e.g., Kovats index), allows compensation for such drifts. Different systems have been reported for RTI in liquid chromatography, but none of them have been applied to metabolomics as they have with GC. Recently, a more universal RTI system has been reported based on a homologous series of N-alkylpyridinyl sulfonates (NAPS). The employed substances ionize in both positive and negative ionization mode and are UV-active. We employed this system for indexing of >500 metabolite standards separated using a generic reversed phase method typically used in metabolomics. We systematically altered different parameters such as flowrate and temperature in steps to mimic differences between systems and then checked differences in the calculated retention indices. Through this we have been able to explore the limits of retention indexing in LC-MS and the possibility of standardizing retention data in metabolomics.

The reporting of retention times in metabolomics is hampered by a lack of standardization. Different columns and solvent systems are used for analysis of metabolites, but even when using the same stationary phase, column dimensions, mobile phase and settings, absolute retention times can still vary greatly. This is due to the fact that retention is affected by a range of minor parameters such as LC system dead volume, inconsistencies in flow rate, gradient, temperature, mobile phase pH, etc. Retention time indexing (RTI), routinely employed in gas chromatography (e.g., Kovats index), allows compensation for such drifts. Different systems have been reported for RTI in liquid chromatography, but none of them have been applied to metabolomics as they have with GC. Recently, a more universal RTI system has been reported based on a homologous series of N-alkylpyridinyl sulfonates (NAPS). The employed substances ionize in both positive and negative ionization mode and are UV-active. We employed this system for indexing of >500 metabolite standards separated using a generic reversed phase method typically used in metabolomics. We systematically altered different parameters such as flowrate and temperature in steps to mimic differences between systems and then checked differences in the calculated retention indices. Through this we have been able to explore the limits of retention indexing in LC-MS and the possibility of standardizing retention data in metabolomics.

The reporting of retention times in metabolomics is hampered by a lack of standardization. Different columns and solvent systems are used for analysis of metabolites, but even when using the same stationary phase, column dimensions, mobile phase and settings, absolute retention times can still vary greatly. This is due to the fact that retention is affected by a range of minor parameters such as LC system dead volume, inconsistencies in flow rate, gradient, temperature, mobile phase pH, etc. Retention time indexing (RTI), routinely employed in gas chromatography (e.g., Kovats index), allows compensation for such drifts. Different systems have been reported for RTI in liquid chromatography, but none of them have been applied to metabolomics as they have with GC. Recently, a more universal RTI system has been reported based on a homologous series of N-alkylpyridinyl sulfonates (NAPS). The employed substances ionize in both positive and negative ionization mode and are UV-active. We employed this system for indexing of >500 metabolite standards separated using a generic reversed phase method typically used in metabolomics. We systematically altered different parameters such as flowrate and temperature in steps to mimic differences between systems and then checked differences in the calculated retention indices. Through this we have been able to explore the limits of retention indexing in LC-MS and the possibility of standardizing retention data in metabolomics.