Mass Spectrometry-Based Cellular Metabolomics: Current Approaches, Applications, and Future Directions

Development of a highly efficient solubilization method for mass spectrometric analysis of phospholipids in living single cells

Phospholipids are vital constituents of the cell membrane and aid in signal transduction. Phospholipid profiles vary distinctively with the cell type. Notably, specific phospholipid molecules are present in significantly higher or lower concentrations in cancer cells versus normal cells. In this study, live single-cell mass spectrometry (MS) was developed for analyzing phospholipids at the single-cell level. This method facilitates rapid molecular analysis of cells under microscopic observation. For nanoelectrospray ionization, phospholipids were extracted from single cells isolated in a glass capillary through a high-efficiency process. Cell-derived phosphatidylcholines were detected with high sensitivity when trehalose C14 was added as a solubilizing reagent. Trehalose C14 can solubilize cells at low concentrations owing to its low critical micelle concentration, and exerts minimal matrix effects (such as suppressing ionization and causing peak overlap) in the MS analysis of cellular molecules. Analyses of phospholipids in Raji and HEV0070 cells using the developed method revealed specific peaks of phosphatidylcholine and sphingomyelin in the respective cells. The developed technique not only affords phospholipid profiles at the single-cell level, but also holds promise for identifying biomarkers associated with various diseases, particularly cancer.

Understanding PFAS toxicity through cell culture metabolomics: Current applications and future perspectives

Per- and polyfluoroalkyl substances (PFAS), ubiquitous environmental contaminants, pose significant challenges to ecosystems and human health. While cell cultures have emerged as new approach methodologies (NAMs) in ecotoxicity research, metabolomics is an emerging technique used to characterize the small-molecule metabolites present in cells and to understand their role in various biological processes. Integration of metabolomics with cell cultures, known as cell culture metabolomics, provides a novel and robust tool to unravel the complex molecular responses induced by PFAS exposure. In vitro testing also reduces reliance on animal testing, aligning with ethical and regulatory imperatives. The current review summarizes key findings from recent studies utilizing cell culture metabolomics to investigate PFAS toxicity, highlighting alterations in metabolic pathways, biomarker identification, and the potential linkages between metabolic perturbations. Additionally, the paper discusses different types of cell cultures and metabolomics methods used for studies of environmental contaminants and particularly PFAS. Future perspectives on the combination of metabolomics with other advanced technologies, such as single-cell metabolomics (SCM), imaging mass spectrometry (IMS), extracellular flux analysis (EFA), and multi-omics are also explored, which offers a holistic understanding of environmental contaminants. The synthesis of current knowledge and identification of research gaps provide a foundation for future investigations that aim to elucidate the complexities of PFAS-induced cellular responses and contribute to the development of effective strategies for mitigating their adverse effects on human health.

Comparing univariate filtration preceding and succeeding PLS-DA analysis on the differential variables/metabolites identified from untargeted LC-MS metabolomics data

BACKGROUND

PLS-DA of high-dimensional metabolomics data is frequently employed to capture the most pertinent features to sample classification. But the presence of numerous insignificant input features could distort the PLS-DA model, blow up and scramble the selected differential features. Usually, univariate filtration is subsequently complemented to refine the selected features, but often giving unstable results. Whereas by precluding insignificant features through univariate data prefiltration assessed by FDR adjusted p-value, PLS-DA can generate more stable and reliable differential features. We explored and compared these two data analysis procedures to gain insights into the underlying mechanisms responsible for the disparate results.

RESULTS

The effect of univariate data filtration preceding and succeeding PLS-DA analysis on the identified discriminative features/metabolites was investigated using LC-MS data acquired on the samples of human serum and C. elegans extracts, with and without metabolite standards spiked to simulate the treated and control groups of biological samples. It was shown that the univariate data prefiltration before PLS-DA usually gave less but more stable and likely more reliable and meaningful differential features, while PLS-DA applied directly to the original data could be affected by the presence of insignificant features and orthogonal noise. Large number of insignificant variables and orthogonal noise could distort the generated PLS-DA model and affect the p(corr) value, and artificially inflate the calculated VIP values of relevant features due to the increased total number of input features for model construction, thus leading to more false positives selected by the conventional VIP threshold of 1.0.

SIGNIFICANCE AND NOVELTY

Univariate data filtration preceding PLS-DA was important for the identification of reliable differential features if using a conventional threshold of VIP of 1.0. Presence of insignificant features could distort the PLS-DA model and inflate VIP values. Appropriate VIP threshold is associated with the numbers of input features and the model components. For PLS-DA without univariate prefiltration, threshold of VIP larger than 1.0 is recommended for the selection of discriminative features to reduce the false positives.

Precise prediction of metabolites patterns using machine learning approaches in distinguishing honey and sugar diets fed to mice

As a natural sweetener produced by honey bees, honey was recognized as being healthier for consumption than table sugar. Our previous study also indicated thatmetaboliteprofiles in mice fed honey and mixedsugardiets aredifferent. However, it is still noteworthy about the batch-to-batch consistency of the metabolic differences between two diet types. Here, the machine learning (ML) algorithms were applied to complement and calibrate HPLC-QTOF/MS-based untargeted metabolomics data. Data were generated from three batches of mice that had the same treatment, which can further mine the metabolite biomarkers. Random Forest and Extra-Trees models could better discriminate between honey and mixed sugar dietary patterns under five-fold cross-validation. Finally, SHapley Additive exPlanations tool identified phosphatidylethanolamine and phosphatidylcholine as reliable metabolic biomarkers to discriminate the honey diet from the mixed sugar diet. This study provides us new ideas for metabolomic analysis of larger data sets.

Label-Free Single-Cell SERS Detection and Fluorescence Imaging of Molecular Responses to Endoplasmic Reticulum Stress under Electrical Stimulation

The endoplasmic reticulum (ER) is one of the most important organelles in eukaryotic cells, in which most proteins and lipids are synthesized to regulate complex cellular processes. Generally, the excessive accumulation of unfolded or misfolded proteins can disturb ER homeostasis and induce endoplasmic reticulum stress (ERS). Howbeit, the molecular stress responses within ERS and metastatic behaviors of tumor cells during electrical stimulation (ES) are still poorly investigated and remain a challenge. In this study, by the combined use of fluorescence imaging, ER-targeting plasmonic nanoprobes were developed to trace molecular stress response profiling within the ER during a constant-voltage ES process at ∼1 V based on label-free surface-enhanced Raman spectroscopy (SERS). The excess accumulation of β-misfolded proteins was found after the ES, leading to breaking of the ER homeostasis and further inducing mitochondrial dysfunction. Notably, the excessive stress of ER under ES can destroy the calcium ion balance and induce significant upregulation of calreticulin expression. Importantly, the content ratio of two kinds of cadherin between E-cadherin and N-cadherin was gradually improved with the voltages boosted. Meanwhile, the epithelial adhesion factor expression was ascended with voltages amplified, leading to inhibiting tumor cell migration at low voltages or death under higher voltages (∼1 V). This study provides cellular insights into the ES approach for tumor therapy and also provides a simple and effective method for detecting molecular stress responses in endoplasmic reticulum stress.

Effects of Solvent Evaporation Methods and Short-Term Room Temperature Storage on High-Coverage Cellular Metabolome Analysis

Cellular metabolomics provides insights into the metabolic processes occurring within cells and can help researchers understand how these processes are regulated and how they relate to cellular function, health, and disease. In this technical note, we investigated the effects of solvent evaporation equipment and storage condition on high-coverage cellular metabolomics. We previously introduced a robust CIL LC-MS-based cellular metabolomics workflow that encompasses various steps, including cell harvest, metabolic quenching, cell lysis, metabolite extraction, differential chemical isotope labeling, and LC-MS analysis. This workflow has consistently served as the cornerstone of our collaborative research and service projects. As a core facility catering to users with diverse research needs and financial resources, we have encountered scenarios requiring short-term sample storage. For example, the need often arises to transport samples at room temperature from user sites to our core facility. Herein, we present a study in which we compared different solvent evaporation methods (specifically, the nitrogen blowdown evaporator, SpeedVac concentrator, and lyophilizer) and diverse storage conditions (including dried samples stored in a freezer, samples stored in a freezer with methanol, dried samples stored at room temperature, and samples stored at room temperature with methanol). Our findings indicate that the choice of solvent evaporation equipment did not significantly impact the cellular metabolome. However, we observed a noteworthy change in the metabolome after 7 days of storage when cells were stored with methanol, regardless of whether they were kept at -80 °C or room temperature, in contrast to cells that were dried and frozen. Importantly, we detected no significant alterations in cells that were dried and stored at room temperature. In conclusion, to ensure the production of high-quality CIL LC-MS metabolomics results, we strongly recommend that, in situations where low-temperature storage is not feasible, cell samples should be thoroughly dried before storage or shipment at room temperature.

Inducible nitric oxide synthase activity mediates TNF-α-induced endothelial cell dysfunction

Inducible nitric oxide synthase (iNOS) and vascular endothelial dysfunction have been implicated in the development and progression of atherosclerosis. This study aimed to elucidate the role of iNOS in vascular endothelial dysfunction. Ultrahigh performance liquid chromatography-quadrupole time-of-flight mass spectrometry combined with multivariate data analysis was used to characterize the metabolic changes in human umbilical vein endothelial cells (HUVECs) in response to different treatment conditions. In addition, molecular biology techniques were employed to explain the molecular mechanisms underlying the role of iNOS in vascular endothelial dysfunction. Tumor necrosis factor-α (TNF-α) enhances the expression of iNOS, TXNIP, and the level of reactive oxygen species (ROS) facilitates the entry of nuclear factor-κB (NF-κB) into the nucleus and promotes injury in HUVECs. iNOS deficiency reversed the TNF-α-mediated pathological changes in HUVECs. Moreover, TNF-α increased the expression of tumor necrosis factor receptor-2 (TNFR-2) and the levels of p-IκBα and IL-6 proteins and CD31, ICAM-1, and VCAM-1 protein expression, which was significantly reduced in HUVECs with iNOS deficiency. In addition, treating HUVECs in the absence or presence of TNF-α or iNOS, respectively, enabled the identification of putative endogenous biomarkers associated with endothelial dysfunction. These biomarkers were involved in critical metabolic pathways, including glycosylphosphatidylinositol-anchor biosynthesis, amino acid metabolism, sphingolipid metabolism, and fatty acid metabolism. iNOS deficiency during vascular endothelial dysfunction may affect the expression of TNFR-2, vascular adhesion factors, and the level of ROS via cellular metabolic changes, thereby attenuating vascular endothelial dysfunction.NEW & NOTEWORTHY Inducible nitric oxide synthase (iNOS) deficiency during vascular endothelial dysfunction may affect the expression of tumor necrosis factor receptor-2 and vascular adhesion factors via cellular metabolic changes, thereby attenuating vascular endothelial dysfunction.

Combining Experimental with Computational Infrared and Mass Spectra for High-Throughput Nontargeted Chemical Structure Identification

The inability to identify the structures of most metabolites detected in environmental or biological samples limits the utility of nontargeted metabolomics. The most widely used analytical approaches combine mass spectrometry and machine learning methods to rank candidate structures contained in large chemical databases. Given the large chemical space typically searched, the use of additional orthogonal data may improve the identification rates and reliability. Here, we present results of combining experimental and computational mass and IR spectral data for high-throughput nontargeted chemical structure identification. Experimental MS/MS and gas-phase IR data for 148 test compounds were obtained from NIST. Candidate structures for each of the test compounds were obtained from PubChem (mean = 4444 candidate structures per test compound). Our workflow used CSI:FingerID to initially score and rank the candidate structures. The top 1000 ranked candidates were subsequently used for IR spectra prediction, scoring, and ranking using density functional theory (DFT-IR). Final ranking of the candidates was based on a composite score calculated as the average of the CSI:FingerID and DFT-IR rankings. This approach resulted in the correct identification of 88 of the 148 test compounds (59%). 129 of the 148 test compounds (87%) were ranked within the top 20 candidates. These identification rates are the highest yet reported when candidate structures are used from PubChem. Combining experimental and computational MS/MS and IR spectral data is a potentially powerful option for prioritizing candidates for final structure verification.

Development of an Untargeted Metabolomics Strategy to Study the Metabolic Rewiring of Dendritic Cells upon Lipopolysaccharide Activation

Dendritic cells (DCs) are essential immune cells for defense against external pathogens. Upon activation, DCs undergo profound metabolic alterations whose precise nature remains poorly studied at a large scale and is thus far from being fully understood. The goal of the present work was to develop a reliable and accurate untargeted metabolomics workflow to get a deeper insight into the metabolism of DCs when exposed to an infectious agent (lipopolysaccharide, LPS, was used to mimic bacterial infection). As DCs transition rapidly from a non-adherent to an adherent state upon LPS exposure, one of the leading analytical challenges was to implement a single protocol suitable for getting comparable metabolomic snapshots of those two cellular states. Thus, a thoroughly optimized and robust sample preparation method consisting of a one-pot solvent-assisted method for the simultaneous cell lysis/metabolism quenching and metabolite extraction was first implemented to measure intracellular DC metabolites in an unbiased manner. We also placed special emphasis on metabolome coverage and annotation by using a combination of hydrophilic interaction liquid chromatography and reverse phase columns coupled to high-resolution mass spectrometry in conjunction with an in-house developed spectral database to identify metabolites at a high confidence level. Overall, we were able to characterize up to 171 unique meaningful metabolites in DCs. We then preliminarily compared the metabolic profiles of DCs derived from monocytes of 12 healthy donors upon in vitro LPS activation in a time-course experiment. Interestingly, the resulting data revealed differential and time-dependent activation of some particular metabolic pathways, the most impacted being nucleotides, nucleotide sugars, polyamines pathways, the TCA cycle, and to a lesser extent, the arginine pathway.

Glucose Metabolism: Optimizing Regenerative Functionalities of Mesenchymal Stromal Cells Postimplantation

Mesenchymal stromal cells (MSCs) are considered promising candidates for regenerative medicine applications. Their clinical performance postimplantation, however, has been disappointing. This lack of therapeutic efficacy is most likely due to suboptimal formulations of MSC-containing material constructs. Tissue engineers, therefore, have developed strategies addressing/incorporating optimized cell, microenvironmental, biochemical, and biophysical cues/stimuli to enhance MSC-containing construct performance. Such approaches have had limited success because they overlooked that maintenance of MSC viability after implantation for a sufficient time is necessary for MSCs to develop their regenerative functionalities fully. Following a brief overview of glucose metabolism and regulation in MSCs, the present literature review includes recent pertinent findings that challenge old paradigms and notions. We hereby report that glucose is the primary energy substrate for MSCs, provides precursors for biomass generation, and regulates MSC functions, including proliferation and immunosuppressive properties. More importantly, glucose metabolism is central in controlling in vitro MSC expansion, in vivo MSC viability, and MSC-mediated angiogenesis postimplantation when addressing MSC-based therapies. Meanwhile, in silico models are highlighted for predicting the glucose needs of MSCs in specific regenerative medicine settings, which will eventually enable tissue engineers to design viable and potent tissue constructs. This new knowledge should be incorporated into developing novel effective MSC-based therapies. Impact statement The clinical use of mesenchymal stromal cells (MSCs) has been unsatisfactory due to the inability of MSCs to survive and be functional after implantation for sufficient periods to mediate directly or indirectly a successful regenerative tissue response. The present review summarizes the endeavors in the past, but, most importantly, reports the latest findings that elucidate underlying mechanisms and identify glucose metabolism as the crucial parameter in MSC survival and the subsequent functions pertinent to new tissue formation of importance in tissue regeneration applications. These latest findings justify further basic research and the impetus for developing new strategies to improve the modalities and efficacy of MSC-based therapies.

Mass spectrometry-based quantitation combined with time-dependent metabolomics to discover metabolic features in human neurogenesis using neural constructs generated from neural progenitor cells

Direct studies focusing on the human brain are difficult to plan and conduct due to ethical and practical reasons. The advent of human pluripotent stem cell (hPSC)-derived neurons has revolutionized the research of the human brain and central nervous system, but relevant analytical techniques have been much less explored. Herein, we have designed a novel bioanalytical strategy to discover the characteristics of human neurogenesis using liquid chromatography-mass spectrometry-based quantitation and time-dependent metabolomics in combination with hPSC-derived neural constructs. To examine the growth of neurons in vitro, a quantitative method for the simultaneous measurement of N-acetylaspartic acid (NAA) and N-acetylglutamic acid (NAG) in a culture medium was established. The analysis of endogenous NAA and NAG concentrations over 28 days of neural cell culture not only illustrated the growth and maturation process of neural progenitors, but also confirmed the successful achievement of human neural constructs. Depending on the quantitative results, day 0, 10, 18, and 28 samples representing different growth phases were selected for further investigation of the global metabolic changes in developing human neurons. A versatile non-targeted, time-dependent metabolomics study identified 17 significantly changed metabolites and revealed the altered metabolic pathways including amino acid metabolism (tryptophan, phenylalanine, aspartate and beta-alanine metabolisms), pantothenate and coenzyme A biosynthesis, fatty acid metabolism, and purine and pyrimidine metabolism. The new metabolite profiles and overall metabolic pathways advance our understanding of human neurodevelopment. Additionally, the bioanalytical approach proposed in this study opens an interesting window for the capture and evaluation of the complex metabolic states of human neural cells, which would potentially be utilized in other in vitro models relevant to pathophysiology and treatment of neurological disorders, benefiting biomarker discovery and metabolic mechanism interpretation.

Covalent Bonding and Coulomb Repulsion-Guided AuNP Array: A Tunable and Reusable Substrate for Metabolomic Characterization of Lung Cancer Patient Sera

Surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS) has gained increased attention in the metabolic characterization of human biofluids. However, the stability and reproducibility of nanoparticle-based substrates remain two of the biggest challenges in high-salt environments. Here, by controlling the extent of Coulomb repulsion of 26 nm positively charged AuNPs, a homogeneous layer of covalently bonded AuNPs on a coverslip with tunable interparticle distances down to 16 nm has been successfully fabricated to analyze small biomolecules in human serum. Compared with the self-assembled AuNP array, the covalently bonded AuNP array showed superior performances on stability, reproducibility, and sensitivity in high-salt environments. The stable attachment of AuNPs maintained a detection reproducibility with a RSD less than 12% and enabled the reusability of the array for 10 experiments without significant signal deterioration (<15%) and carryover effects. Moreover, the closely positioned AuNPs allowed the coupling of photoinduced plasmons to generate an enhanced electric field, which promotes the generation of excited electrons to facilitate the desorption/ionization processes instead of the heat dissipation, thus enhancing the detection sensitivity with detection limits down to the femtomole level. Combined with machine learning methods, the AuNP array has been successfully applied to discover seven biomarkers for differentiating early-stage lung cancer patients from healthy controls. It is anticipated that this simple approach of developing robust AuNP arrays can also be extended to other types of NP arrays for wider applications of SALDI-MS technology.

Simultaneous Determination of Methylated Nucleosides by HILIC-MS/MS Revealed Their Alterations in Urine from Breast Cancer Patients

RNA methylation plays a vital role in the pathogenesis of a variety of diseases including cancer, and aberrant levels of modified nucleosides in RNA were revealed to be related to cancer. Urine is a favored source for biomarker discovery due to the non-invasion to patients. Herein, we developed a sensitive hydrophilic interaction liquid chromatography tandem mass spectrometry (HILIC–MS/MS) method combined with stable isotope dilution for accurate quantification of methylated nucleosides in human urine. With this method, we successfully quantified ten methylated nucleosides in urine samples collected from healthy controls and breast cancer patients. We found N⁶-methyladenosine (m⁶A), 2′-O-methyladenosine (A_m), N¹-methyladenosine (m¹A), N⁶,2′-O-dimethyladenosine (m⁶A_m), N¹-methylguanosine (m¹G), 2′-O-methylguanosine (G_m), 5-methylcytidine (m⁵C) and 2′-O-methylcytidine (C_m) were all decreased in early-stage breast cancer patients, and a nomogram prediction model was constructed. Locally advanced breast cancer patients exhibited elevated levels of urinary 2′-O-methylated nucleosides in comparison to early-stage breast cancer patients. Together, we developed a robust method for the simultaneous determination of methylated nucleosides in human urine, and the results revealed an association between the contents of urinary methylated nucleosides and the occurrence of breast cancer, which may stimulate future studies about the regulatory roles of these methylated nucleosides in the initiation and progression of breast cancer.

Metabolomics Analysis and Antioxidant Potential of Endophytic Diaporthe fraxini ED2 Grown in Different Culture Media

Endophytic fungi are a promising source of bioactive metabolites with a wide range of pharmacological activities. In the present study, MS-based metabolomics was conducted to study the metabolomes variations of endophytic Diaporthe fraxini ED2 grown in different culture media. Total phenolic content (TPC), total flavonoid content (TFC), 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging, 2,2-azinobis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS), and ferric reducing antioxidant power (FRAP) assays were conducted to assess the antioxidant potential of the fungal extracts. Multivariate data analysis (MVDA) was employed in data analysis and interpretation to elucidate the complex metabolite profile. The supplemented culture medium of D. fraxini fungal extract stimulated the production of metabolites not occurring in the normal culture medium. Antioxidant activity studies revealed the potential of supplemented cultured fungal extract of D. fraxini as a source of antioxidants. The present findings highlight that fungal culture medium supplementation is an effective approach to unravelling the hidden metabolome in plant-associated fungal diversity.

The foundations and development of lipidomics

For over a century, the importance of lipid metabolism in biology was recognized but difficult to mechanistically understand due to the lack of sensitive and robust technologies for identification and quantification of lipid molecular species. The enabling technological breakthroughs emerged in the 1980s with the development of soft ionization methods (Electrospray Ionization and Matrix Assisted Laser Desorption/Ionization) that could identify and quantify intact individual lipid molecular species. These soft ionization technologies laid the foundations for what was to be later named the field of lipidomics. Further innovative advances in multistage fragmentation, dramatic improvements in resolution and mass accuracy, and multiplexed sample analysis fueled the early growth of lipidomics through the early 1990s. The field exponentially grew through the use of a variety of strategic approaches, which included direct infusion, chromatographic separation, and charge-switch derivatization, which facilitated access to the low abundance species of the lipidome. In this Thematic Review, we provide a broad perspective of the foundations, enabling advances, and predicted future directions of growth of the lipidomics field.

Application of Skyline for Analysis of Protein-Protein Interactions In Vivo

Quantitative and qualitative analyses of cell protein composition using liquid chromatography/tandem mass spectrometry are now standard techniques in biological and clinical research. However, the quantitative analysis of protein–protein interactions (PPIs) in cells is also important since these interactions are the bases of many processes, such as the cell cycle and signaling pathways. This paper describes the application of Skyline software for the identification and quantification of the biotinylated form of the biotin acceptor peptide (BAP) tag, which is a marker of in vivo PPIs. The tag was used in the Proximity Utilizing Biotinylation (PUB) method, which is based on the co-expression of BAP-X and BirA-Y in mammalian cells, where X or Y are interacting proteins of interest. A high level of biotinylation was detected in the model experiments where X and Y were pluripotency transcription factors Sox2 and Oct4, or heterochromatin protein HP1γ. MRM data processed by Skyline were normalized and recalculated. Ratios of biotinylation levels in experiment versus controls were 86 ± 6 (3 h biotinylation time) and 71 ± 5 (9 h biotinylation time) for BAP-Sox2 + BirA-Oct4 and 32 ± 3 (4 h biotinylation time) for BAP-HP1γ + BirA-HP1γ experiments. Skyline can also be applied for the analysis and identification of PPIs from shotgun proteomics data downloaded from publicly available datasets and repositories.

Defining NASH from a Multi-Omics Systems Biology Perspective

Non-alcoholic steatohepatitis (NASH) is a chronic liver disease affecting up to 6.5% of the general population. There is no simple definition of NASH, and the molecular mechanism underlying disease pathogenesis remains elusive. Studies applying single omics technologies have enabled a better understanding of the molecular profiles associated with steatosis and hepatic inflammation—the commonly accepted histologic features for diagnosing NASH, as well as the discovery of novel candidate biomarkers. Multi-omics analysis holds great potential to uncover new insights into disease mechanism through integrating multiple layers of molecular information. Despite the technical and computational challenges associated with such efforts, a few pioneering studies have successfully applied multi-omics technologies to investigate NASH. Here, we review the most recent technological developments in mass spectrometry (MS)-based proteomics, metabolomics, and lipidomics. We summarize multi-omics studies and emerging omics biomarkers in NASH and highlight the biological insights gained through these integrated analyses.

A novel ratiometric MALDI-MS quantitation strategy for alkaline phosphatase activity with a homogeneous reaction and a tunable dynamic range

A unique ratiometric MALDI-MS strategy is proposed for the convenient and reliable quantitation of alkaline phosphatase based on the homogeneous enzymatic cleavage of a coded phosphopeptide (CPP)-triggered double-signal output. The dynamic range can be tuned by simply adjusting the primary concentration of CPP. The proposed strategy is also capable of being challenged by real human serum, and thus it may offer a wonderful approach for the convenient identification and quantitation of various enzyme activities in clinical diagnosis.

Metabolic Phenotypes in Asthmatic Adults: Relationship with Inflammatory and Clinical Phenotypes and Prognostic Implications

Bronchial asthma is a chronic disease that affects individuals of all ages. It has a high prevalence and is associated with high morbidity and considerable levels of mortality. However, asthma is not a single disease, and multiple subtypes or phenotypes (clinical, inflammatory or combinations thereof) can be detected, namely in aggregated clusters. Most studies have characterised asthma phenotypes and clusters of phenotypes using mainly clinical and inflammatory parameters. These studies are important because they may have clinical and prognostic implications and may also help to tailor personalised treatment approaches. In addition, various metabolomics studies have helped to further define the metabolic features of asthma, using electronic noses or targeted and untargeted approaches. Besides discriminating between asthma and a healthy state, metabolomics can detect the metabolic signatures associated with some asthma subtypes, namely eosinophilic and non-eosinophilic phenotypes or the obese asthma phenotype, and this may prove very useful in point-of-care application. Furthermore, metabolomics also discriminates between asthma and other “phenotypes” of chronic obstructive airway diseases, such as chronic obstructive pulmonary disease (COPD) or Asthma–COPD Overlap (ACO). However, there are still various aspects that need to be more thoroughly investigated in the context of asthma phenotypes in adequately designed, homogeneous, multicentre studies, using adequate tools and integrating metabolomics into a multiple-level approach.

Integrated mass spectrometry-based multi-omics for elucidating mechanisms of bacterial virulence

Despite being considered the simplest form of life, bacteria remain enigmatic, particularly in light of pathogenesis and evolving antimicrobial resistance. After three decades of genomics, we remain some way from understanding these organisms, and a substantial proportion of genes remain functionally unknown. Methodological advances, principally mass spectrometry (MS), are paving the way for parallel analysis of the proteome, metabolome and lipidome. Each provides a global, complementary assay, in addition to genomics, and the ability to better comprehend how pathogens respond to changes in their internal (e.g. mutation) and external environments consistent with infection-like conditions. Such responses include accessing necessary nutrients for survival in a hostile environment where co-colonizing bacteria and normal flora are acclimated to the prevailing conditions. Multi-omics can be harnessed across temporal and spatial (sub-cellular) dimensions to understand adaptation at the molecular level. Gene deletion libraries, in conjunction with large-scale approaches and evolving bioinformatics integration, will greatly facilitate next-generation vaccines and antimicrobial interventions by highlighting novel targets and pathogen-specific pathways. MS is also central in phenotypic characterization of surface biomolecules such as lipid A, as well as aiding in the determination of protein interactions and complexes. There is increasing evidence that bacteria are capable of widespread post-translational modification, including phosphorylation, glycosylation and acetylation; with each contributing to virulence. This review focuses on the bacterial genotype to phenotype transition and surveys the recent literature showing how the genome can be validated at the proteome, metabolome and lipidome levels to provide an integrated view of organism response to host conditions.

High-Throughput Non-targeted Chemical Structure Identification Using Gas-Phase Infrared Spectra

The high-throughput identification of unknown metabolites in biological samples remains challenging. Most current non-targeted metabolomics studies rely on mass spectrometry, followed by computational methods that rank thousands of candidate structures based on how closely their predicted mass spectra match the experimental mass spectrum of an unknown. We reasoned that the infrared (IR) spectra could be used in an analogous manner and could add orthologous structure discrimination; however, this has never been evaluated on large data sets. Here, we present results of a high-throughput computational method for predicting IR spectra of candidate compounds obtained from the PubChem database. Predicted spectra were ranked based on their similarity to gas-phase experimental IR spectra of test compounds obtained from the NIST. Our computational workflow (IRdentify) consists of a fast semiempirical quantum mechanical method for initial IR spectra prediction, ranking, and triaging, followed by a final IR spectra prediction and ranking using density functional theory. This approach resulted in the correct identification of 47% of 258 test compounds. On average, there were 2152 candidate structures evaluated for each test compound, giving a total of approximately 555,200 candidate structures evaluated. We discuss several variables that influenced the identification accuracy and then demonstrate the potential application of this approach in three areas: (1) combining IR and mass spectra rankings into a single composite rank score, (2) identifying the precursor and fragment ions using cryogenic ion vibrational spectroscopy, and (3) the incorporation of a trimethylsilyl derivatization step to extend the method compatibility to less-volatile compounds. Overall, our results suggest that matching computational with experimental IR spectra is a potentially powerful orthogonal option for adding significant high-throughput chemical structure discrimination when used with other non-targeted chemical structure identification methods.

Lipid Profiling of 20 Mammalian Cells by Capillary Microsampling Combined with High-Resolution Spectral Stitching Nanoelectrospray Ionization Direct-Infusion Mass Spectrometry

Studies of cellular metabolism can provide profound insights into the underlying molecular mechanisms and metabolic function. To date, the majority of cellular metabolism studies based on chromatography-mass spectrometry (MS) require population cells to obtain informative metabolome. These methods are not only time-consuming but also not suitable for amount-limited cell samples such as circulating tumor cells, stem cells, and neurons. Therefore, it is extremely essential to develop analytical methods enabling to detect metabolome from tens of cells in a high-throughput and high-sensitivity way. In this work, a novel platform for rapid and sensitive detection of lipidome in 20 mammalian cells was proposed using capillary microsampling combined with high-resolution spectral stitching nanoelectrospray ionization direct-infusion MS. It can be used to collect cells rapidly and accurately via the capillary microprobe, extract lipids directly in a 96-well plate using a spray solvent, and detect more than 500 lipids covering 19 lipid subclasses within 3 min. This novel platform was successfully applied to study the lipid features of different cancer cell types and subtypes as well as target cells from tissue samples. This study provides a strategy for determining the lipid species with rich information in tens of cells and demonstrates great potential for clinical applications.

Stress-related hormone reduces autophagy through the regulation of phosphatidylethanolamine in breast cancer cells

Background

An increasing number of studies indicate that adrenergic signaling plays a fundamental role in tumor progression and metastasis induced by chronic stress. However, despite the growing attention, an understanding of the mechanisms linking chronic stress and cancer is still insufficient.

Methods

Western blot analysis and transmission electron microscopy (TEM) were used to observe the changes in autophagy level in a breast cancer cell line (MCF-7) after epinephrine treatment. Non-targeted metabolomics was also used to detect MCF-7 metabolites after epinephrine treatment. The xenograft model was used to detect the level of autophagy after epinephrine intervention.

Results

The results showed that epinephrine treatment reduced the autophagy level of breast cancer cells. Epinephrine changed the level of phosphatidylethanolamine (PE) in breast cancer cells as detected by non-targeted metabolomics. Epinephrine also changed autophagy in breast cancer cells by decreasing the level of PE in cells. When autophagy decreased, the invasion and migration of breast cancer cells increased in vitro, and the progression of breast cancer accelerated in vivo.

Conclusions

These findings suggest that stress-related hormones affect the tumor progression of breast cancer. Therefore, strengthening the emotional management strategies of patients during the process of antitumor treatment as a supplement to the existing treatments may be beneficial.