The term ‘oxidative stress’ was first described by Sies and Cadenas in 1985 as a ‘disturbance in the prooxidant-antioxidant balance in favor of the former, leading to potential damage.’ Sies and Jones have since updated the definition as ‘an imbalance between oxidants and antioxidants in favor of the oxidants, leading to a disruption of redox signaling and control and/or molecular damage.’ These redox processes have shown to important in normal human physiology as well as human disease. A primary molecular target of molecular damage resulting from oxidative stress is polyunsaturated fatty acids (PUFA). One crucial stumbling block to the study of oxidative stress in human physiology and pathophysiology has been the availability of effective biomarkers to not only assess oxidative injury but to also identify the effectiveness of potential therapies to decrease damage. F2-Isoprostanes, a product of lipid peroxidation generated from arachidonic acid, were discovered by Morrow and Roberts in 1990. These molecules fit the magic bullet description of a perfect biomarker as they are chemically stable and ubiquitous in human plasma and urine. Levels of F2-IsoPs in healthy humans were defined and these levels are elevated in disease and lifestyle factors commonly associated with increased oxidative stress. Yet, despite the fact that more than 5000 papers on isoprostanes have been published in the past 30 years, F2-IsoPs are still not utilized as biomarkers in the clinical setting. This presentation will provide an update regarding our current knowledge of F2-IsoPs including information regarding their metabolism and biological activities. Methodological considerations for best practices in the quantification of F2-IsoPs and their clinical utility will also be highlighted.

The term ‘oxidative stress’ was first described by Sies and Cadenas in 1985 as a ‘disturbance in the prooxidant-antioxidant balance in favor of the former, leading to potential damage.’ Sies and Jones have since updated the definition as ‘an imbalance between oxidants and antioxidants in favor of the oxidants, leading to a disruption of redox signaling and control and/or molecular damage.’ These redox processes have shown to important in normal human physiology as well as human disease. A primary molecular target of molecular damage resulting from oxidative stress is polyunsaturated fatty acids (PUFA). One crucial stumbling block to the study of oxidative stress in human physiology and pathophysiology has been the availability of effective biomarkers to not only assess oxidative injury but to also identify the effectiveness of potential therapies to decrease damage. F2-Isoprostanes, a product of lipid peroxidation generated from arachidonic acid, were discovered by Morrow and Roberts in 1990. These molecules fit the magic bullet description of a perfect biomarker as they are chemically stable and ubiquitous in human plasma and urine. Levels of F2-IsoPs in healthy humans were defined and these levels are elevated in disease and lifestyle factors commonly associated with increased oxidative stress. Yet, despite the fact that more than 5000 papers on isoprostanes have been published in the past 30 years, F2-IsoPs are still not utilized as biomarkers in the clinical setting. This presentation will provide an update regarding our current knowledge of F2-IsoPs including information regarding their metabolism and biological activities. Methodological considerations for best practices in the quantification of F2-IsoPs and their clinical utility will also be highlighted.

The term ‘oxidative stress’ was first described by Sies and Cadenas in 1985 as a ‘disturbance in the prooxidant-antioxidant balance in favor of the former, leading to potential damage.’ Sies and Jones have since updated the definition as ‘an imbalance between oxidants and antioxidants in favor of the oxidants, leading to a disruption of redox signaling and control and/or molecular damage.’ These redox processes have shown to important in normal human physiology as well as human disease. A primary molecular target of molecular damage resulting from oxidative stress is polyunsaturated fatty acids (PUFA). One crucial stumbling block to the study of oxidative stress in human physiology and pathophysiology has been the availability of effective biomarkers to not only assess oxidative injury but to also identify the effectiveness of potential therapies to decrease damage. F2-Isoprostanes, a product of lipid peroxidation generated from arachidonic acid, were discovered by Morrow and Roberts in 1990. These molecules fit the magic bullet description of a perfect biomarker as they are chemically stable and ubiquitous in human plasma and urine. Levels of F2-IsoPs in healthy humans were defined and these levels are elevated in disease and lifestyle factors commonly associated with increased oxidative stress. Yet, despite the fact that more than 5000 papers on isoprostanes have been published in the past 30 years, F2-IsoPs are still not utilized as biomarkers in the clinical setting. This presentation will provide an update regarding our current knowledge of F2-IsoPs including information regarding their metabolism and biological activities. Methodological considerations for best practices in the quantification of F2-IsoPs and their clinical utility will also be highlighted.

LC-MS-based lipid profiling has long been used for the analysis of biological tissues, providing qualitative and quantitative information on the lipid constituents. While LC-MS is the gold standard approach, it generally requires the homogenization and extraction of tissue specimens to achieve sufficient sensitivity and reproducibility. Lipids – being the major constituents of biological membranes – however, show distinct distributions at cellular as well as histological levels. In this regard the biologically relevant information is not the concentration of a given species in the tissue sample block, but their spatial concentration distribution and its relation to the histological distribution of cell types. Unfortunately, there is no single untargeted method to date delivering this information, however the combination of LC-MS and MS imaging can potentially solve this problem. MS imaging methods were originally developed in the 1970s, but the advent of MALDI imaging in the 1990’s has made these methods popular across the analytical community. Currently the MSI field is dominated by MALDI, DESI and SIMS providing spatially resolved MS information with different chemical specificity and spatial resolution. While all MSI methods provide excellent performance for lipids due to their preferential desorption behaviour, the primary information given by MSI is not necessarily quantitative due to suppression effects and the narrow linear range of the methods. Furthermore, calibration strategies using isotope labelled internal standards cannot be applied as deposited standards have different chemical environment compared to the actual analytes. We have developed a hybrid approach for the quantification of lipids in biological tissues utilizing reversed phase LC-MS, desorption electrospray ionization MS imaging and laser capture microdissection (LCM) of tissues. The approach is based on the targeted and untargeted DESI imaging analysis of tissues using triple quadrupole and Q-ToF mass spectrometers respectively, followed by the LCM of histologically homogeneous areas from consecutive tissue sections. The LCM dissected samples were extracted using modified Blight-Dyer method and analysed using a reversed phase untargeted LC-MS method. The concentration values were fitted with function obtained by the linear combination of DESI-MS peaks corresponding to the compound of interest divided by the linear combination of the intensities of the 100 most intensive ion in the spectra, for each individual compound and histological tissue type. The obtained calibration functions were simplified by omitting all components with less than 1% contribution. The study has demonstrated that the combination of MSI and LC-MS is capable of the spatially resolved quantification of lipid species in tissue samples.

LC-MS-based lipid profiling has long been used for the analysis of biological tissues, providing qualitative and quantitative information on the lipid constituents. While LC-MS is the gold standard approach, it generally requires the homogenization and extraction of tissue specimens to achieve sufficient sensitivity and reproducibility. Lipids – being the major constituents of biological membranes – however, show distinct distributions at cellular as well as histological levels. In this regard the biologically relevant information is not the concentration of a given species in the tissue sample block, but their spatial concentration distribution and its relation to the histological distribution of cell types. Unfortunately, there is no single untargeted method to date delivering this information, however the combination of LC-MS and MS imaging can potentially solve this problem. MS imaging methods were originally developed in the 1970s, but the advent of MALDI imaging in the 1990’s has made these methods popular across the analytical community. Currently the MSI field is dominated by MALDI, DESI and SIMS providing spatially resolved MS information with different chemical specificity and spatial resolution. While all MSI methods provide excellent performance for lipids due to their preferential desorption behaviour, the primary information given by MSI is not necessarily quantitative due to suppression effects and the narrow linear range of the methods. Furthermore, calibration strategies using isotope labelled internal standards cannot be applied as deposited standards have different chemical environment compared to the actual analytes. We have developed a hybrid approach for the quantification of lipids in biological tissues utilizing reversed phase LC-MS, desorption electrospray ionization MS imaging and laser capture microdissection (LCM) of tissues. The approach is based on the targeted and untargeted DESI imaging analysis of tissues using triple quadrupole and Q-ToF mass spectrometers respectively, followed by the LCM of histologically homogeneous areas from consecutive tissue sections. The LCM dissected samples were extracted using modified Blight-Dyer method and analysed using a reversed phase untargeted LC-MS method. The concentration values were fitted with function obtained by the linear combination of DESI-MS peaks corresponding to the compound of interest divided by the linear combination of the intensities of the 100 most intensive ion in the spectra, for each individual compound and histological tissue type. The obtained calibration functions were simplified by omitting all components with less than 1% contribution. The study has demonstrated that the combination of MSI and LC-MS is capable of the spatially resolved quantification of lipid species in tissue samples.

LC-MS-based lipid profiling has long been used for the analysis of biological tissues, providing qualitative and quantitative information on the lipid constituents. While LC-MS is the gold standard approach, it generally requires the homogenization and extraction of tissue specimens to achieve sufficient sensitivity and reproducibility. Lipids – being the major constituents of biological membranes – however, show distinct distributions at cellular as well as histological levels. In this regard the biologically relevant information is not the concentration of a given species in the tissue sample block, but their spatial concentration distribution and its relation to the histological distribution of cell types. Unfortunately, there is no single untargeted method to date delivering this information, however the combination of LC-MS and MS imaging can potentially solve this problem. MS imaging methods were originally developed in the 1970s, but the advent of MALDI imaging in the 1990’s has made these methods popular across the analytical community. Currently the MSI field is dominated by MALDI, DESI and SIMS providing spatially resolved MS information with different chemical specificity and spatial resolution. While all MSI methods provide excellent performance for lipids due to their preferential desorption behaviour, the primary information given by MSI is not necessarily quantitative due to suppression effects and the narrow linear range of the methods. Furthermore, calibration strategies using isotope labelled internal standards cannot be applied as deposited standards have different chemical environment compared to the actual analytes. We have developed a hybrid approach for the quantification of lipids in biological tissues utilizing reversed phase LC-MS, desorption electrospray ionization MS imaging and laser capture microdissection (LCM) of tissues. The approach is based on the targeted and untargeted DESI imaging analysis of tissues using triple quadrupole and Q-ToF mass spectrometers respectively, followed by the LCM of histologically homogeneous areas from consecutive tissue sections. The LCM dissected samples were extracted using modified Blight-Dyer method and analysed using a reversed phase untargeted LC-MS method. The concentration values were fitted with function obtained by the linear combination of DESI-MS peaks corresponding to the compound of interest divided by the linear combination of the intensities of the 100 most intensive ion in the spectra, for each individual compound and histological tissue type. The obtained calibration functions were simplified by omitting all components with less than 1% contribution. The study has demonstrated that the combination of MSI and LC-MS is capable of the spatially resolved quantification of lipid species in tissue samples.

Peak coalescence causing deviations in mass and intensity of near-isobaric ions is a well-known phenomenon in Fourier-Transform mass spectrometry (FTMS). For example, analysis of lipidomic data requires consideration of near-isobaric ions due to natural abundance of 13C-atoms and series of lipid species differing only by the number of double bonds (DB). We evaluated peak coalescence of lipid species in detail and present a novel workflow based on peak intensity and area to identify and correct for coalescence-induced deviations. Successful application was demonstrated for data recorded at mass resolutions insufficient to resolve the isobaric ions in DB series. Moreover, deviations in the relative isotopic abundance (RIA) could by adjusted by peak intensity/area based correction. In summary, this workflow could be applied for high fidelity identification and accurate quantification of near-isobaric ions suffering from peak coalescence using FTMS.

Peak coalescence causing deviations in mass and intensity of near-isobaric ions is a well-known phenomenon in Fourier-Transform mass spectrometry (FTMS). For example, analysis of lipidomic data requires consideration of near-isobaric ions due to natural abundance of 13C-atoms and series of lipid species differing only by the number of double bonds (DB). We evaluated peak coalescence of lipid species in detail and present a novel workflow based on peak intensity and area to identify and correct for coalescence-induced deviations. Successful application was demonstrated for data recorded at mass resolutions insufficient to resolve the isobaric ions in DB series. Moreover, deviations in the relative isotopic abundance (RIA) could by adjusted by peak intensity/area based correction. In summary, this workflow could be applied for high fidelity identification and accurate quantification of near-isobaric ions suffering from peak coalescence using FTMS.

Peak coalescence causing deviations in mass and intensity of near-isobaric ions is a well-known phenomenon in Fourier-Transform mass spectrometry (FTMS). For example, analysis of lipidomic data requires consideration of near-isobaric ions due to natural abundance of 13C-atoms and series of lipid species differing only by the number of double bonds (DB). We evaluated peak coalescence of lipid species in detail and present a novel workflow based on peak intensity and area to identify and correct for coalescence-induced deviations. Successful application was demonstrated for data recorded at mass resolutions insufficient to resolve the isobaric ions in DB series. Moreover, deviations in the relative isotopic abundance (RIA) could by adjusted by peak intensity/area based correction. In summary, this workflow could be applied for high fidelity identification and accurate quantification of near-isobaric ions suffering from peak coalescence using FTMS.

Today’s lipidomics approaches allow efficient and reliable quantification of lipids, thereby covering a broad spectrum of lipid classes at increasingly high molecular resolution. In association analyses based on lipidomics data sets from large human cohorts, numerous of these measured lipid species have been found to correlate with genetic variants and health-related phenotypes or risk factors. While, in principle, these association results may unravel new insights into disease pathomechanisms, functional result interpretation in lipidomics currently faces particular challenges: Existing biological knowledge about lipids is often available on a rather general level (for the lipid class) or is limited to a relatively small number of concrete lipids (with specific molecular structure and stereochemistry). Typically less is known about the functions and biological context of lipids or subclasses of lipids at the structural resolution accessible through modern lipidomics techniques. To allow basic embedding of lipidomics results into their biological context, we propose a data-driven approach leveraging the multitude of biological links as revealed in recent omics studies. Using Neo4j, a graph database, we store and integrate results from large-scale, publicly available cross-omics analyses (e.g. genome-wide association studies with health-related phenotypes, lipids/metabolites, proteins, and transcripts) into an accessible network structure, interconnecting entities from diverse biological levels with measured metabolites and lipid species. Additionally, we integrate known gene-transcript-protein relations and pathway annotations to extend and consolidate the data-derived network. As a prototype of the described integrative network-based framework, we have set up a Neo4j database which mainly focuses on the molecular networks (including genes, transcripts, proteins, metabolites/lipids) related to phenotypes in Alzheimer’s disease. A web-based interface facilitates exploration of a metabolite’s molecular neighbourhood within the integrative multi-omics network and enables further exploration of the network’s topology. Future challenges for expanding and optimising these networks mainly lie in the correct mapping of lipidomics measures onto lipid classes or specific lipids as represented in known biochemical networks and in the complex mapping (harmonisation) between lipid measures from different analytical platforms.

Today’s lipidomics approaches allow efficient and reliable quantification of lipids, thereby covering a broad spectrum of lipid classes at increasingly high molecular resolution. In association analyses based on lipidomics data sets from large human cohorts, numerous of these measured lipid species have been found to correlate with genetic variants and health-related phenotypes or risk factors. While, in principle, these association results may unravel new insights into disease pathomechanisms, functional result interpretation in lipidomics currently faces particular challenges: Existing biological knowledge about lipids is often available on a rather general level (for the lipid class) or is limited to a relatively small number of concrete lipids (with specific molecular structure and stereochemistry). Typically less is known about the functions and biological context of lipids or subclasses of lipids at the structural resolution accessible through modern lipidomics techniques. To allow basic embedding of lipidomics results into their biological context, we propose a data-driven approach leveraging the multitude of biological links as revealed in recent omics studies. Using Neo4j, a graph database, we store and integrate results from large-scale, publicly available cross-omics analyses (e.g. genome-wide association studies with health-related phenotypes, lipids/metabolites, proteins, and transcripts) into an accessible network structure, interconnecting entities from diverse biological levels with measured metabolites and lipid species. Additionally, we integrate known gene-transcript-protein relations and pathway annotations to extend and consolidate the data-derived network. As a prototype of the described integrative network-based framework, we have set up a Neo4j database which mainly focuses on the molecular networks (including genes, transcripts, proteins, metabolites/lipids) related to phenotypes in Alzheimer’s disease. A web-based interface facilitates exploration of a metabolite’s molecular neighbourhood within the integrative multi-omics network and enables further exploration of the network’s topology. Future challenges for expanding and optimising these networks mainly lie in the correct mapping of lipidomics measures onto lipid classes or specific lipids as represented in known biochemical networks and in the complex mapping (harmonisation) between lipid measures from different analytical platforms.

Today’s lipidomics approaches allow efficient and reliable quantification of lipids, thereby covering a broad spectrum of lipid classes at increasingly high molecular resolution. In association analyses based on lipidomics data sets from large human cohorts, numerous of these measured lipid species have been found to correlate with genetic variants and health-related phenotypes or risk factors. While, in principle, these association results may unravel new insights into disease pathomechanisms, functional result interpretation in lipidomics currently faces particular challenges: Existing biological knowledge about lipids is often available on a rather general level (for the lipid class) or is limited to a relatively small number of concrete lipids (with specific molecular structure and stereochemistry). Typically less is known about the functions and biological context of lipids or subclasses of lipids at the structural resolution accessible through modern lipidomics techniques. To allow basic embedding of lipidomics results into their biological context, we propose a data-driven approach leveraging the multitude of biological links as revealed in recent omics studies. Using Neo4j, a graph database, we store and integrate results from large-scale, publicly available cross-omics analyses (e.g. genome-wide association studies with health-related phenotypes, lipids/metabolites, proteins, and transcripts) into an accessible network structure, interconnecting entities from diverse biological levels with measured metabolites and lipid species. Additionally, we integrate known gene-transcript-protein relations and pathway annotations to extend and consolidate the data-derived network. As a prototype of the described integrative network-based framework, we have set up a Neo4j database which mainly focuses on the molecular networks (including genes, transcripts, proteins, metabolites/lipids) related to phenotypes in Alzheimer’s disease. A web-based interface facilitates exploration of a metabolite’s molecular neighbourhood within the integrative multi-omics network and enables further exploration of the network’s topology. Future challenges for expanding and optimising these networks mainly lie in the correct mapping of lipidomics measures onto lipid classes or specific lipids as represented in known biochemical networks and in the complex mapping (harmonisation) between lipid measures from different analytical platforms.