Biological samples such as tissues, blood, or tumors are often complex and harbor heterogeneous populations of cells. Separating out specific cell types or subpopulations from such complex mixtures to study their metabolic phenotypes is challenging because experimental procedures for separation may disturb the metabolic state of cells. To address this issue, we developed a method for analysis of cell subpopulations using stable isotope tracing and fluorescence-activated cell sorting followed by liquid chromatography-high-resolution mass spectrometry. To ensure a faithful representation of cellular metabolism after cell sorting, we benchmarked sorted extraction against direct extraction. While peak areas differed markedly with lower signal for amino acids but higher signal for nucleotides, mass isotopomer distributions from sorted cells were generally in good agreement with those obtained from direct extractions, indicating that they reflect the true metabolic state of cells prior to sorting. In proof-of-principle studies, our method revealed metabolic phenotypes specific to T cell subtypes, and also metabolic features of cells in the committed phase of the cell division cycle. Our approach enables studies of a wide range of adherent and suspension cell subpopulations, which we anticipate will be of broad importance in cell biology and biomedicine.

Mammalian cell types exhibit specialized metabolism, and there is ample evidence that various co-existing cell types engage in metabolic cooperation. Moreover, even cultures of a single cell type may contain cells in distinct metabolic states, such as resting or cycling cells. Methods for measuring metabolic activities of such subpopulations are valuable tools for understanding cellular metabolism. Complex cell populations are most commonly separated using a cell sorter, and subpopulations isolated by this method can be analyzed by metabolomics methods. However, a problem with this approach is that the cell sorting procedure subjects cells to stresses that may distort their metabolism. To overcome these issues, we reasoned that the mass isotopomer distributions (MIDs) of metabolites from cells cultured with stable isotope-labeled nutrients are likely to be more stable than absolute metabolite concentrations, because MIDs are formed over longer time scales and should be less affected by short-term exposure to cell sorting conditions. Here, we describe a method based on this principle, combining cell sorting with liquid chromatography-high resolution mass spectrometry (LC-HRMS). The procedure involves analyzing three types of samples: (1) metabolite extracts obtained directly from the complex population; (2) extracts of "mock sorted" cells passed through the cell sorter instrument without gating any specific population; and (3) extracts of the actual sorted populations. The mock sorted cells are compared against direct extraction to verify that MIDs are indeed not altered by the cell sorting procedure itself, prior to analyzing the actual sorted populations. We show example results from HeLa cells sorted according to cell cycle phase, revealing changes in nucleotide metabolism.

AbstractBackgroundAsthma is a chronic inflammatory airway disease influenced by genetic and environmental factors that affects ~300 million people worldwide, leading to ~250,000 deaths annually. Glucocorticoids (GCs) are well-known therapeutics that are used extensively to suppress airway inflammation in asthmatics. The airway epithelium plays an important role in the initiation and modulation of the inflammatory response. While the role of GCs in disease management is well understood, few studies have examined the holistic effects on the airway epithelium.MethodsGene expression data were used to generate a co-transcriptional network, which was interrogated to identify modules of functionally related genes. In parallel, expression data were mapped to the human protein-protein interaction (PPI) network in order to identify modules with differentially expressed genes. A common pathways approach was applied to highlight genes and pathways functionally relevant and significantly altered following GC treatment.ResultsCo-transcriptional network analysis identified pathways involved in inflammatory processes in the epithelium of asthmatics, including the Toll-like receptor (TLR) and PPAR signaling pathways. Analysis of the PPI network identified RXRA, PPARGC1A, STAT1 and IRF9, among others genes, as differentially expressed. Common pathways analysis highlighted TLR and PPAR signaling pathways, providing a link between general inflammatory processes and the actions of GCs. Promoter analysis identified genes regulated by the glucocorticoid receptor (GCR) and PPAR pathways as well as highlighted the interferon pathway as a target of GCs.ConclusionsNetwork analyses identified known genes and pathways associated with inflammatory processes in the airway epithelium of asthmatics. This workflow illustrated a hypothesis generating experimental design that integrated multiple analysis methods to produce a weight-of-evidence based approach upon which future focused studies can be designed. In this case, results suggested a mechanism whereby GCs repress TLR-mediated interferon production via upregulation of the PPAR signaling pathway. These results highlight the role of interferons in asthma and their potential as targets of future therapeutic efforts.

The attempt to describe complex diseases by solely genetic determination has not been successful. There is increasing recognition that the development of disease is often a consequence of interactions between multiple genetic and environmental factors. To date, much of the research on environmental determinants of disease has focused on single exposures generally measured at a single time point. In order to address this limitation, the concept of the exposome has been introduced as a comprehensive approach, studying the full complement of environmental exposures from conception onwards. However, exposures are vast, dynamic, and diverse, and only a small proportion can be reasonably measured due to limitations in technology and feasibility. In addition, the interplay between genes and exposure as well as between different exposures is complicated and multifaceted, which leads to difficulties in linking disease or health outcomes with exposures. The large numbers of collected samples require well-designed logistics. Furthermore, the immense data sets generated from exposome studies require a significant computational investment for both data analysis and data storage. This report summarizes discussions during an international exposome symposium held at Gunma University in Japan regarding the concept of the exposome, challenges in exposome research, and future perspectives in the field.

Statins are the frontline in cholesterol reduction therapies; however, their use in combination with agents that possess complimentary mechanisms of action may achieve further reductions in low-density lipoprotein cholesterol. Thirty-nine patients were treated with either 80 mg simvastatin (n=20) or 10 mg simvastatin plus 10 mg ezetimibe (n=19) for 6 weeks. Dosing was designed to produce comparable low-density lipoprotein cholesterol reductions, while enabling assessment of potential simvastatin-associated pleiotropic effects. Baseline and post-treatment plasma were analyzed for lipid mediators (eg, eicosanoids and endocannabinoids) and structural lipids by liquid chromatography tandem mass spectrometry. After statistical analysis and orthogonal projections to latent structures multivariate modeling, no changes were observed in lipid mediator levels, whereas global structural lipids were reduced in response to both monotherapy (R(2)Y=0.74; Q(2)=0.66; cross-validated ANOVA P=7.0×10(-8)) and combination therapy (R(2)Y=0.67; Q(2)=0.54; cross-validated ANOVA P=2.6×10(-5)). Orthogonal projections to latent structures modeling identified a subset of 12 lipids that classified the 2 treatment groups after 6 weeks (R(2)Y=0.65; Q(2)=0.61; cross-validated ANOVA P=5.4×10(-8)). Decreases in the lipid species phosphatidylcholine (15:0/18:2) and hexosyl-ceramide (d18:1/24:0) were the strongest discriminators of low-density lipoprotein cholesterol reductions for both treatment groups (q<0.00005), whereas phosphatidylethanolamine (36:3e) contributed most to distinguishing treatment groups (q=0.017). Shifts in lipid composition were similar for high-dose simvastatin and simvastatin/ezetimibe combination therapy, but the magnitude of the reduction was linked to simvastatin dosage. Simvastatin therapy did not affect circulating levels of lipid mediators, suggesting that pleiotropic effects are not associated with eicosanoid production. Only high-dose simvastatin reduced the relative proportion of sphingomyelin and ceramide to phosphatidylcholine (q=0.008), suggesting a pleiotropic effect previously associated with a reduced risk of cardiovascular disease.

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease and a leading cause of mortality and morbidity worldwide. The aim of this study was to investigate the sex dependency of circulating metabolic profiles in COPD.Serum from healthy never-smokers (healthy), smokers with normal lung function (smokers), and smokers with COPD (COPD; Global Initiative for Chronic Obstructive Lung Disease stages I-II/A-B) from the Karolinska COSMIC cohort (n=116) was analysed using our nontargeted liquid chromatography-high resolution mass spectrometry metabolomics platform.Pathway analyses revealed that several altered metabolites are involved in oxidative stress. Supervised multivariate modelling showed significant classification of smokers from COPD (p=2.8×10-7). Sex stratification indicated that the separation was driven by females (p=2.4×10-7) relative to males (p=4.0×10-4). Significantly altered metabolites were confirmed quantitatively using targeted metabolomics. Multivariate modelling of targeted metabolomics data confirmed enhanced metabolic dysregulation in females with COPD (p=3.0×10-3) relative to males (p=0.10). The autotaxin products lysoPA (16:0) and lysoPA (18:2) correlated with lung function (forced expiratory volume in 1 s) in males with COPD (r=0.86; p<0.0001), but not females (r=0.44; p=0.15), potentially related to observed dysregulation of the miR-29 family in the lung.These findings highlight the role of oxidative stress in COPD, and suggest that sex-enhanced dysregulation in oxidative stress, and potentially the autotaxin-lysoPA axis, are associated with disease mechanisms and/or prevalence.

Early-life exposures are believed to influence the incidence of pediatric acute lymphoblastic leukemia (ALL). Archived neonatal blood spots (NBS), collected within the first days of life, offer a means to investigate small molecules that reflect early-life exposures. Using untargeted metabolomics, we compared abundances of small-molecule features in extracts of NBS punches from 332 children that later developed ALL and 324 healthy controls. Subjects were stratified by early (1-5 y) and late (6-14 y) diagnosis. Mutually-exclusive sets of metabolic features - representing putative lipids and fatty acids - were associated with ALL, including 9 and 19 metabolites in the early- and late-diagnosis groups, respectively. In the late-diagnosis group, a prominent cluster of features with apparent 18:2 fatty-acid chains suggested that newborn exposure to the essential nutrient, linoleic acid, increased ALL risk. Interestingly, abundances of these putative 18:2 lipids were greater in infants who were fed formula rather than breast milk (colostrum) and increased with the mother's pre-pregnancy body mass index. These results suggest possible etiologic roles of newborn nutrition in late-diagnosis ALL.

The concept of the exposome was introduced over 15 years ago to reflect the important role that the environment exerts on health and disease. While originally viewed as a call-to-arms to develop more comprehensive exposure assessment methods applicable at the individual level and throughout the life course, the scope of the exposome has now expanded to include the associated biological response. In order to explore these concepts, a workshop was hosted by the Gunma University Initiative for Advanced Research (GIAR, Japan) to discuss the scope of exposomics from an international and multidisciplinary perspective. This Global Perspective is a summary of the discussions with emphasis on (1) top-down, bottom-up, and functional approaches to exposomics, (2) the need for integration and standardization of LC- and GC-based high-resolution mass spectrometry methods for untargeted exposome analyses, (3) the design of an exposomics study, (4) the requirement for open science workflows including mass spectral libraries and public databases, (5) the necessity for large investments in mass spectrometry infrastructure in order to sequence the exposome, and (6) the role of the exposome in precision medicine and nutrition to create personalized environmental exposure profiles. Recommendations are made on key issues to encourage continued advancement and cooperation in exposomics.

Human blood is a self-regenerating lipid-rich biological fluid that is routinely collected in hospital settings. The inventory of lipid molecules found in blood plasma (plasma lipidome) offers insights into individual metabolism and physiology in health and disease. Disturbances in the plasma lipidome also occur in conditions that are not directly linked to lipid metabolism; therefore, plasma lipidomics based on MS is an emerging tool in an array of clinical diagnostics and disease management. However, challenges exist in the translation of such lipidomic data to clinical applications. These relate to the reproducibility, accuracy, and precision of lipid quantitation, study design, sample handling, and data sharing. This position paper emerged from a workshop that initiated a community-led process to elaborate and define a set of generally accepted guidelines for quantitative MS-based lipidomics of blood plasma or serum, with harmonization of data acquired on different instrumentation platforms across independent laboratories as an ultimate goal. We hope that other fields may benefit from and follow such a precedent.

We achieve automated and reliable annotation of lipid species and their molecular structures in high-throughput data from chromatography-coupled tandem mass spectrometry using decision rule sets embedded in Lipid Data Analyzer (LDA; http://genome.tugraz.at/lda2). Using various low- and high-resolution mass spectrometry instruments with several collision energies, we proved the method's platform independence. We propose that the software's reliability, flexibility, and ability to identify novel lipid molecular species may now render current state-of-the-art lipid libraries obsolete.