Capillary Liquid Chromatography-mass Spectrometry Research Articles

Predictive analysis of breast cancer response to neoadjuvant chemotherapy through plasma metabolomics.

Preoperative chemotherapy is a critical component of breast cancer management, yet its effectiveness is not uniform. Moreover, the adverse effects associated with chemotherapy necessitate the identification of a patient subgroup that would derive the maximum benefit from this treatment. This study aimed to establish a method for predicting the response to neoadjuvant chemotherapy in breast cancer patients utilizing a metabolomic approach. Plasma samples were obtained from 87 breast cancer patients undergoing neoadjuvant chemotherapy at our facility, collected both before the commencement of the treatment and before the second treatment cycle. Metabolite analysis was conducted using capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography-mass spectrometry (LC-MS). We performed comparative profiling of metabolite concentrations by assessing the metabolite profiles of patients who achieved a pathological complete response (pCR) against those who did not, both in initial and subsequent treatment cycles. Significant variances were observed in the metabolite profiles between pCR and non-pCR cases, both at the onset of preoperative chemotherapy and before the second cycle. Noteworthy distinctions were also evident between the metabolite profiles from the initial and the second neoadjuvant chemotherapy courses. Furthermore, metabolite profiles exhibited variations associated with intrinsic subtypes at all assessed time points. The application of plasma metabolomics, utilizing CE-MS and LC-MS, may serve as a tool for predicting the efficacy of neoadjuvant chemotherapy in breast cancer in the future after all necessary validations have been completed.

Salivary metabolomic biomarkers for non-invasive lung cancer detection.

Identifying novel biomarkers for early detection of lung cancer is crucial. Non-invasively available saliva is an ideal biofluid for biomarker exploration; however, the rationale underlying biomarker detection from organs distal to the oral cavity in saliva requires clarification. Therefore, we analyzed metabolomic profiles of cancer tissues compared with those of adjacent non-cancerous tissues, as well as plasma and saliva samples collected from patients with lung cancer (n = 109 pairs). Additionally, we analyzed plasma and saliva samples collected from control participants (n = 83 and 71, respectively). Capillary electrophoresis-mass spectrometry and liquid chromatography-mass spectrometry were performed to comprehensively quantify hydrophilic metabolites. Paired tissues were compared, revealing 53 significantly different metabolites. Plasma and saliva showed 44 and 40 significantly different metabolites, respectively, between patients and controls. Of these, 12 metabolites exhibited significant differences in all three comparisons and primarily belonged to the polyamine and amino acid pathways; N1-acetylspermidine exhibited the highest discrimination ability. A combination of 12 salivary metabolites was evaluated using a machine learning method to differentiate patients with lung cancer from controls. Salivary data were randomly split into training and validation datasets. Areas under the receiver operating characteristic curve were 0.744 for cross-validation using training data and 0.792 for validation data. This model exhibited a higher discrimination ability for N1-acetylspermidine than that for other metabolites. The probability of lung cancer calculated using this model was independent of most patient characteristics. These results suggest that consistently different salivary biomarkers in both plasma and lung tissues might facilitate non-invasive lung cancer screening.

Advanced oxidation and a metrological strategy based on CLC-MS for the removal of pharmaceuticals from pore & surface water

In this work, it is studied the photolysis, electrolysis, and photo-electrolysis of a mixture of pharmaceutics (sulfadiazine, naproxen, diclofenac, ketoprofen and ibuprofen) contained in two very different types of real water matrices (obtained from surface and porewater reservoirs), trying to clarify the role of the matrix on the degradation of the pollutants. To do this, a new metrological approach was also developed for screening of pharmaceuticals in waters by capillary liquid chromatography mass spectrometry (CLC-MS). This allows the detection at concentrations lower than 10 ng mL−1. Results obtained in the degradation tests demonstrate that inorganic composition of the water matrix directly influences on the efficiency of the drugs removal by the different EAOPs and better degradation results were obtained for experiments carried out with surface water. The most recalcitrant drug studied was ibuprofen for all processes evaluated, while diclofenac and ketoprofen were found to be the easiest drugs for being degraded. Photo-electrolysis was found to be more efficient than photolysis and electrolysis, and the increase in the current density was found to attain a slight improvement in the removal although with an associated huge increase in the energy consumption. The main reaction pathways for each drug and technology were also proposed.

On-line solid-phase extraction to enhance sensitivity in peptide biomarker analysis by microseparation techniques coupled to mass spectrometry: Capillary liquid chromatography versus capillary electrophoresis

In this study, on-line solid-phase extraction capillary liquid chromatography-mass spectrometry (SPE-CapLC-MS) and on-line solid-phase extraction capillary electrophoresis-mass spectrometry (SPE-CE-MS) were compared for the analysis of the opioid peptide biomarkers dynorphin A (1–7) (DynA), endomorphin 1 (End 1), and methionine-enkephalin (Met). First, a capillary liquid chromatography-mass spectrometry (CapLC-MS) method was established, which allowed limits of detection (LODs) of 0.5 μg/mL for Dyn A and Met, and 0.1 μg/mL for End 1. Then, a column switching setup operated by a 2-position/6-port micro-valve with a C18 enrichment column was assembled for SPE-CapLC-MS. Under optimized conditions, the LODs for the three peptides were lowered up to 1000-fold compared to CapLC-MS, until detecting 0.5 ng/mL concentrations. Repeatability (<0.2 % and <11 % RSD for retention times and peak areas, respectively), linearity (0.5–100 ng/mL), and durability (20 runs) of the enrichment column were appropriate, and the method was applied to analyze human plasma samples. Finally, the established SPE-CapLC-MS method was compared with a valve-free C18-SPE-CE-MS method previously described by our group for the analysis of these opioid peptides, using the same mass spectrometer. Both methods presented an evident difference regarding the need of a valve for the operation and allowed high preconcentration factors and quite similar LODs (until 0.5 and 0.1 ng/mL by SPE-CaLC-MS and SPE-CE-MS, respectively). Some other distinctions related to the instrumental set-up, procedure and method performance were also disclosed and discussed in detail.

Salivary metabolomics with machine learning for colorectal cancer detection.

As the worldwide prevalence of colorectal cancer (CRC) increases, it is vital to reduce its morbidity and mortality through early detection. Saliva‐based tests are an ideal noninvasive tool for CRC detection. Here, we explored and validated salivary biomarkers to distinguish patients with CRC from those with adenoma (AD) and healthy controls (HC). Saliva samples were collected from patients with CRC, AD, and HC. Untargeted salivary hydrophilic metabolite profiling was conducted using capillary electrophoresis–mass spectrometry and liquid chromatography–mass spectrometry. An alternative decision tree (ADTree)‐based machine learning (ML) method was used to assess the discrimination abilities of the quantified metabolites. A total of 2602 unstimulated saliva samples were collected from subjects with CRC (n = 235), AD (n = 50), and HC (n = 2317). Data were randomly divided into training (n = 1301) and validation datasets (n = 1301). The clustering analysis showed a clear consistency of aberrant metabolites between the two groups. The ADTree model was optimized through cross‐validation (CV) using the training dataset, and the developed model was validated using the validation dataset. The model discriminating CRC + AD from HC showed area under the receiver‐operating characteristic curves (AUC) of 0.860 (95% confidence interval [CI]: 0.828‐0.891) for CV and 0.870 (95% CI: 0.837‐0.903) for the validation dataset. The other model discriminating CRC from AD + HC showed an AUC of 0.879 (95% CI: 0.851‐0.907) and 0.870 (95% CI: 0.838‐0.902), respectively. Salivary metabolomics combined with ML demonstrated high accuracy and versatility in detecting CRC.

Detection of Porphyrins in Hair Using Capillary Liquid Chromatography-Mass Spectrometry.

Unlike humans, some animals have evolved a physiological ability to deposit porphyrins, which are pigments produced during heme synthesis in cells, in the skin and associated integument such as hair. Given the inert nature and easiness of collection of hair, animals that present porphyrin-based pigmentation constitute unique models for porphyrin analysis in biological samples. Here we present the development of a simple, rapid, and efficient analytical method for four natural porphyrins (uroporphyrin I, coproporphyrin I, coproporphyrin III and protoporphyrin IX) in the Southern flying squirrel Glaucomys volans, a mammal with hair that fluoresces and that we suspected has porphyrin-based pigmentation. The method is based on capillary liquid chromatography-mass spectrometry (CLC-MS), after an extraction procedure with formic acid and acetonitrile. The resulting limits of detection (LOD) and quantification (LOQ) were 0.006–0.199 and 0.021–0.665 µg mL−1, respectively. This approach enabled us to quantify porphyrins in flying squirrel hairs at concentrations of 3.6–353.2 µg g−1 with 86.4–98.6% extraction yields. This method provides higher simplicity, precision, selectivity, and sensitivity than other methods used to date, presenting the potential to become the standard technique for porphyrin analysis.

Maximizing MS/MS Acquisition for Lipidomics Using Capillary Separation and Orbitrap Tribrid Mass Spectrometer.

Liquid chromatography-mass spectrometry (LC-MS) is a typical strategy for lipidomics analysis. Although capillary LC-MS is a common analytical technique for proteomics analysis, its application to lipidomics has been limited. In this study, we aim at improving lipid identifications achieved in a single LC-MS analysis by a 3-fold approach: capillary LC and nanoelectrospray for enhanced ionization, ion trap for higher sensitivity tandem MS, and parallelization of mass analyzers for increased speed of acquisition on an Orbitrap hybrid system. By applying the methods to a complex lipid mixture of human plasma, we identified and performed relative quantification on over 1500 lipids within a 60 min capillary LC-MS analysis.

Metabolomic Analysis of Small Extracellular Vesicles Derived from Pancreatic Cancer Cells Cultured under Normoxia and Hypoxia.

Extracellular vesicles (EVs) released from cancer cells contribute to various malignant phenotypes of cancer, including metastasis, cachexia, and angiogenesis. Although DNA, mRNAs, miRNAs, and proteins contained in EVs have been extensively studied, the function of metabolites in EVs remains unclear. In this study, we performed a comprehensive metabolomic analysis of pancreatic cancer cells, PANC-1, cultured under different oxygen concentrations, and small EVs (sEVs) released from them, considering the fact that hypoxia contributes to the malignant behavior of cells in pancreatic cancer, which is a poorly diagnosed cancer. sEVs were collected by ultracentrifugation, and hydrophilic metabolites were analyzed using capillary ion chromatography-mass spectrometry and liquid chromatography-mass spectrometry, and lipids were analyzed by supercritical fluid chromatography-tandem mass spectrometry. A total of 140 hydrophilic metabolites and 494 lipids were detected in sEVs, and their profiles were different from those in cells. In addition, the metabolomic profile of sEVs was observed to change under hypoxic stress, and an increase in metabolites involved in angiogenesis was also detected. We reveal the hallmark of the metabolites contained in sEVs and the effect of tumor hypoxia on their profiles, which may help in understanding EV-mediated cancer malignancy.

Bile Metabolites and Risk of Carcinogenesis in Patients With Pancreaticobiliary Maljunction: A Pilot Study.

Pancreaticobiliary maljunction (PBM), a disease with reflux of pancreatic and bile juice in the pancreaticobiliary tract, is a high-risk factor for biliary tract cancer. The aim of this study was to investigate the mechanism of carcinogenesis in PBM using a metabolomics analysis of bile sampled during surgery. Three patients with PBM without biliary tract cancer, four patients with extrahepatic bile duct cancer (EHBC), and three controls with benign disease were enrolled. Metabolomics analysis of bile samples was performed using capillary electrophoresis-mass spectrometry and liquid chromatography-mass spectrometry to discriminate the amino acid and lipidomic profiles. The principal component analysis in the capillary electrophoresis-mass spectrometry and liquid chromatography-mass spectrometry revealed similar metabolites in patients with PBM and those with EHBC; furthermore, there was a clear difference between patients with PBM or EHBC compared to controls. The amino acid profiles revealed the following 20 potential carcinogenic candidates for PBM: isoleucine, phenylalanine, tyrosine, leucine, tryptophan, arginine, lysine, valine, asparagine, methionine, aspartic acid, serine, threonine, histidine, glutamine, alanine, proline, glutamic acid, and pyruvic acid. The lipidomic profiles revealed the following 11 carcinogenic candidates: lysophosphatidylcholine, lysophosphatidylethanolamine, phosphatidyl glycerol, lysophosphatidyl glycerol, triacylglycerol, diacylglycerol, ceramide, sphyngomyeline, fatty acid, hyperforin, and vitamin D. Among these characteristic metabolites, the branched-chain amino acids, methionine and lysophosphatidylcholine are known to be related to carcinogenesis. The bile metabolites were extremely similar in patients with PBM and those with EHBC. Furthermore, amino acid and lipid metabolism was markedly different in patients with PBM or EHBC compared to healthy controls.

Ultrahigh Pressure Capillary Liquid Chromatography-Mass Spectrometry for Metabolomics and Lipidomics

Ultrahigh Pressure Capillary Liquid Chromatography-Mass Spectrometry for Metabolomics and Lipidomics

Capillary ultrahigh-pressure liquid chromatography-mass spectrometry for fast and high resolution metabolomics separations

LC-MS is an important tool for metabolomics due its high sensitivity and broad metabolite coverage. The goal of improving resolution and decreasing analysis time in HPLC has led to the use of 5 – 15 cm long columns packed with 1.7 – 1.9 µm particles requiring pressures of 8 – 12 kpsi. We report on the potential for capillary LC-MS based metabolomics utilizing porous C18 particles down to 1.1 µm diameter and columns up to 50 cm long with an operating pressure of 35 kpsi. Our experiments show that it is possible to pack columns with 1.1 µm porous particles to provide predicted improvements in separation time and efficiency. Using kinetic plots to guide the choice of column length and particle size, we packed 50 cm long columns with 1.7 µm particles and 20 cm long columns with 1.1 µm particles, which should produce equivalent performance in shorter times. Columns were tested by performing isocratic and gradient LC-MS analyses of small molecule metabolites and extracts from plasma. These columns provided approximately 100,000 theoretical plates for metabolite standards and peak capacities over 500 in 100 min for a complex plasma extract with robust interfacing to MS. To generate a given peak capacity, the 1.1 µm particles in 20 cm columns required roughly 75% of the time as 1.7 µm particles in 50 cm columns with both operated at 35 kpsi. The 1.1 µm particle packed columns generated a given peak capacity nearly 3 times faster than 1.7 µm particles in 15 cm columns operated at ~10 kpsi. This latter condition represents commercial state of the art for capillary LC. To consider practical benefits for metabolomics, the effect of different LC-MS variables on mass spectral feature detection was evaluated. Lower flow rates (down to 700 nL/min) and larger injection volumes (up to 1 µL) increased the features detected with modest loss in separation performance. The results demonstrate the potential for fast and high resolution separations for metabolomics using 1.1 µm particles operated at 35 kpsi for capillary LC-MS.

Ultrahigh-Performance capillary liquid chromatography-mass spectrometry at 35 kpsi for separation of lipids

Improvements in sample preparation, separation, and mass spectrometry continue to expand the coverage in LC-MS based lipidomics. While longer columns packed with smaller particles in theory give higher separation performance compared to shorter columns, the implementation of this technology above commercial limits has been sparse due to difficulties in packing long columns and successfully operating instruments at ultrahigh pressures. In this work, a liquid chromatograph that operates up to 35 kpsi was investigated for the separation and identification of lipid species from human plasma. Capillary columns between 15–50 cm long were packed with 1.7 µm BEH C18 particles and evaluated for their ability to separate lipid isomers and complex lipid extracts from human plasma. Putative lipid class identifications were assigned using accurate mass and relative retention time data of the eluting peaks. Our findings indicate that longer columns packed and operated at 35 kpsi outperform shorter columns packed and run at lower pressures in terms of peak capacity and numbers of features identified. Packing columns with relatively high concentration slurries (200 mg/mL) while sonicating the column resulted in 6–34% increase in peak capacity for 50 cm columns compared to lower slurry concentrations and no sonication. For a given analysis time, 50 cm long columns operated at 35 kpsi provided a 20–95% increase in chromatographic peak capacity compared with 15 cm columns operated at 15 kpsi. Analysis times up to 4 h were evaluated, generating peak capacities up to 410 ± 5 (n = 3, measured at 4σ) and identifying 480 ± 85 lipids (n = 2). Importantly, the results also show a correlation between the peak capacity and the number of lipids identified from a human plasma extract. This correlation indicates that ionization suppression is a limiting factor in obtaining sufficient signal for identification by mass spectrometry. The result also shows that the higher resolution obtained by shallow gradients overcomes possible signal reduction due to broader, more dilute peaks in long gradients for improving detection of lipids in LC-MS. Lastly, longer columns operated at shallow gradients allowed for the best separation of both regional and geometrical isomers. These results demonstrate a system that enables the advantages of using longer columns packed and run at ultrahigh pressure for improving lipid separations and lipidome coverage.

Lipidomic and proteomic evaluation of extracellular membrane vesicles from Streptococcus mutans wild‐type, ΔsrtA and Δsfp strains

BackgroundExtracellular membrane vesicles (EMVs) have long been studied in Gram‐negative bacteria. It is more recently established that Gram‐positive bacteria are capable of producing EMVs despite their thick cell wall. Our group showed that the cariogenic bacterium Streptococcus mutans produces EMVs that carry protein and eDNA. EMV protein content is altered in a deletion mutant lacking the transpeptidase, Sortase A. In Bacillus subtilis, EMVs are disrupted by the lipoprotein surfactin and accumulate in a Δsfp mutant. Our current objective is to isolate EMVs from wild‐type, ΔsrtA and Δsfp strains in order to compare their respective lipidomic and proteomic profiles, as well as to compare each strain's EMVs with corresponding cytoplasmic membrane preparations.MethodsEMVs were isolated from four separate replicates of each strain grown in Terleckyj's defined media (TDM). Filtered culture supernatants were concentrated ~50 fold, and EMVs were harvested by high‐speed centrifugation and density gradient isolation. Cytoplasmic membranes were isolated from protoplasts derived from bacterial cells from the same cultures. Lipids were analyzed by capillary‐liquid chromatography mass spectrometry (Cap‐LC‐MS). Proteins were analyzed by Nano‐Liquid chromatography tandem mass spectrometry (Nano‐LC/MS/MS). MV size, concentration, and polydisperity were assessed by transmission electron microscopy, nanoparticle tracking analysis, and dynamic light scattering.ResultsEMVs from the ΔsrtA mutant were enlarged compared to the other strains. Lipid composition of EMV membranes consisted primarily of glycerophospholipids, with some glycerolipids and sphingolipids. Glycerophosphoinsitol (PI) was underrepresented in both mutant strains compared to the WT. Glycerophosphocholine (PC) was overrepresented in the Δsfp mutant compared to the other two strains. Cardiolipin (CL) was overrepresented in EMVs from the WT strain compared to its corresponding cytoplasmic membrane preparations. Proteomics analyses are underway.ConclusionsLipidomic and proteomic analyses will provide information regarding S. mutans vesiculogenesis and potential functional differences in EMVs produced by WT and mutant strains.Support or Funding InformationNIH R21DE025348 NIH R01DE008007This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

A simple poly(styrene-co-divinylbenzene)-coated glass blood spot method for monitoring of seven antidepressants using capillary liquid chromatography-mass spectrometry

A simple, rapid, selective and sensitive monitoring method for the simultaneous determination of the widely-prescribed antidepressants agomelatine, bupropion, citalopram, fluoxetine, mirtazapine, paroxetine, trazodone in just a human blood drop is here developed and validated. This methodology is based on the use of lab manufactured poly(styrene-co-divinylbenzene)-coated glass (PS-DVB) blood spot for the extraction of the analytes and their subsequent separation and detection by capillary liquid chromatography-mass spectrometry (CLC-MS). Briefly, 10 mm-side squares were punched out from blood spots collected on glass substrate coated by 10 µg of the PS-DVB polymer and eluted with 1.0 mL of 2.0% acetic acid in methanol. The analytes were then separated and detected in less than 20 min by capillary CLC-MS using a Jupiter 4 µm Proteo 90 Å column and water: acetonitrile (20:80 v/v) and ammonium acetate (5 mM, pH 3.0) as mobile phase. Limit of detection (LOD) ranged from 0.018 to 0.038 µg mL−1, and remarkable precision values for the responses and retention times lower than 5.89% and 1.92% were calculated, respectively. Moreover, accuracy values ranging between 85% and 104% were obtained.

Gold Nanocluster Prospecting via Capillary Liquid Chromatography-Mass Spectrometry: Discovery of Three Quantized Gold Clusters in a Product Mixture of “2 nm Gold Nanoparticles”

A nonaqueous reversed phase liquid chromatography-mass spectrometry (LC-MS) method has been developed for extremely hydrophobic MPCs (monolayer-protected clusters), and has been applied to the efficient separation of gold–dodecanethiolate (ddt) assemblies, leading to the identification of three dodecanethiolate-protected gold clusters, Au130(ddt)50, Au137(ddt)56, and Au144(ddt)60, as prominent components of a commercial product of nominally 2 nm (core-diameter) protected gold nanoparticles obtained from nanoComposix, Inc. Various components were separated, according to hydrophobic character, using a linear gradient of methanol–dichloromethane mobile phases, on a C18 HPLC column. Varying concentrations of mobile-phase modifier (triethylammonium acetate) were compared for effect on chromatographic peak shape and cluster retention. Positive electrospray ionization (ESI) was used to ionize all components in the sample. LC separation prior to inline + ESI-MS detection facilitated sample analysis via production of simplified mass spectra for each eluting cluster species and provided insight into the relative polarity of the clusters shown here. UV–vis detection facilitated method development and allowed determination of nonionizing, and/or polydisperse components.

Reliability of plasma polar metabolite concentrations in a large-scale cohort study using capillary electrophoresis-mass spectrometry.

BackgroundCohort studies with metabolomics data are becoming more widespread, however, large-scale studies involving 10,000s of participants are still limited, especially in Asian populations. Therefore, we started the Tsuruoka Metabolomics Cohort Study enrolling 11,002 community-dwelling adults in Japan, and using capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography–mass spectrometry. The CE-MS method is highly amenable to absolute quantification of polar metabolites, however, its reliability for large-scale measurement is unclear. The aim of this study is to examine reproducibility and validity of large-scale CE-MS measurements. In addition, the study presents absolute concentrations of polar metabolites in human plasma, which can be used in future as reference ranges in a Japanese population.MethodsMetabolomic profiling of 8,413 fasting plasma samples were completed using CE-MS, and 94 polar metabolites were structurally identified and quantified. Quality control (QC) samples were injected every ten samples and assessed throughout the analysis. Inter- and intra-batch coefficients of variation of QC and participant samples, and technical intraclass correlation coefficients were estimated. Passing-Bablok regression of plasma concentrations by CE-MS on serum concentrations by standard clinical chemistry assays was conducted for creatinine and uric acid.Results and conclusionsIn QC samples, coefficient of variation was less than 20% for 64 metabolites, and less than 30% for 80 metabolites out of the 94 metabolites. Inter-batch coefficient of variation was less than 20% for 81 metabolites. Estimated technical intraclass correlation coefficient was above 0.75 for 67 metabolites. The slope of Passing-Bablok regression was estimated as 0.97 (95% confidence interval: 0.95, 0.98) for creatinine and 0.95 (0.92, 0.96) for uric acid. Compared to published data from other large cohort measurement platforms, reproducibility of metabolites common to the platforms was similar to or better than in the other studies. These results show that our CE-MS platform is suitable for conducting large-scale epidemiological studies.

Knowledge integration strategies for untargeted metabolomics based on MCR-ALS analysis of CE-MS and LC-MS data

In this work, two knowledge integration strategies based on multivariate curve resolution alternating least squares (MCR-ALS) were used for the simultaneous analysis of data from two metabolomic platforms. The benefits and the suitability of these integration strategies were demonstrated in a comparative study of the metabolite profiles from yeast (Saccharomyces cerevisiae) samples grown in non-fermentable (acetate) and fermentable (glucose) carbon source. Untargeted metabolomics data acquired by capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography-mass spectrometry (LC-MS) were jointly analysed. On the one hand, features obtained by independent MCR-ALS analysis of each dataset were joined to obtain a biological interpretation based on the combined metabolic network visualization. On the other hand, taking advantage of the common spectral mode, a low-level data fusion strategy was proposed merging CE-MS and LC-MS data before the MCR-ALS analysis to extract the most relevant features for further biological interpretation. Then, results obtained by the two presented methods were compared. Overall, the study highlights the ability of MCR-ALS to be used in any of both knowledge integration strategies for untargeted metabolomics. Furthermore, enhanced metabolite identification and differential carbon source response detection were achieved when considering a combination of LC-MS and CE-MS based platforms.

The use of metabolome analysis to identify the cause of an unexplained disease of Japanese gentians (Gentiana triflora)

Gentian spotted bleaching disease (GSBD), a novel disease of unknown etiology, affects Gentiana triflora plants that are cultivated as ornamental flowers in Japan. This disease leads to the production of necrotic leaf spots, a delay in flowering, and has thus become a serious problem for gentian production. The objective of this study was to identify the cause of GSBD in G. triflora by analyzing differences between healthy and GSBD-affected leaves. Selected metabolite concentrations in healthy and GSBD-affected leaves were quantified using capillary electrophoresis and liquid chromatography-mass spectrometry, and statistically significant differences in metabolite concentrations were assessed. GSBD-affected metabolic pathways were identified followed by examination of pathway-related gene expression and enzyme activities. Furthermore, the effects of root hypoxia on metabolite concentrations and gene expression were investigated. We found that concentrations of Calvin cycle intermediates and ribulose 1,5-bisphosphate carboxylase/oxygenase (RubisCO) activity were significantly lower in GSBD-affected leaves, whereas sucrose cleavage and Ala accumulation were enhanced. Since these metabolic changes are frequently observed in plants exposed to hypoxia, the expression of hypoxia-responsive genes was investigated. Expression levels of hypoxia-responsive genes were higher in GSBD-affected plants than in the controls. Furthermore, root hypoxia induced similar symptoms and metabolic changes as those observed in GSBD-affected plants. Our results indicate that GSBD was likely induced by root hypoxia and that metabolome analysis is an effective tool for identifying the cause of plant disease with unknown etiologies.

Micro-electromembrane extraction using multiple free liquid membranes and acceptor solutions – Towards selective extractions of analytes based on their acid-base strength

This work investigated selective micro-electromembrane extractions (μ-EMEs) of the colored indicators metanil yellow and congo red (visual proof-of-principle) and the small drug substances nortriptyline, papaverine, mianserin, and citalopram (model analytes) based on their acid-base strength. With two free liquid membranes (FLMs), the target analytes were extracted from aqueous donor solution, across FLM 1 (1-pentanol, 1-ethyl-2-nitrobenzene (ENB) or 4-nitrocumene (4-NC)), into aqueous acceptor solution 1, further across FLM 2 (1-pentanol, ENB or 4-NC), and finally into aqueous acceptor solution 2. All phases had volumes between 1.0 and 1.5 μL and extractions were promoted by 200–300 V d.c. applied across the five-phase μ-EME system formed in a perfluoroalkoxy capillary tubing. The anode was located in acceptor solution 2 and the cathode was located in donor solution for μ-EMEs of acidic analytes, and locations of the electrodes were vice versa for μ-EMEs of basic analytes. After μ-EME, donor solution and acceptor solution 1 and 2 were analyzed by capillary electrophoresis or liquid chromatography-mass spectrometry. The model analytes migrated efficiently in the proposed μ-EME system, their migration behavior was controlled by pH in aqueous solutions and their selective fractionation into acceptor solution 1 and 2 was demonstrated based on their acid-base strength. Under optimal conditions, acceptor solution 2 contained 60% nortriptyline (pKa = 10.5) and less than 1% papaverine (pKa = 6.0) and acceptor solution 1 contained 17% nortriptyline and 27% papaverine after 15 min of μ-EME. The five-phase μ-EME system was also compatible with human plasma samples. Work is in progress to further increase the fractionation capability, and to implement the concept into microfluidic platforms.

Classification of congenital disorders of glycosylation based on analysis of transferrin glycopeptides by capillary liquid chromatography-mass spectrometry

In this work, we describe a multivariate data analysis approach for data exploration and classification of the complex and large data sets generated to study the alteration of human transferrin (Tf) N-glycopeptides in patients with congenital disorders of glycosylation (CDG). Tf from healthy individuals and two types of CDG patients (CDG-I and CDG-II) is purified by immunoextraction from serum samples before trypsin digestion and separation by capillary liquid chromatography mass spectrometry (CapLC-MS). Following a targeted data analysis approach, partial least squares discriminant analysis (PLS-DA) is applied to the relative abundance of Tf glycopeptide glycoforms obtained after integration of the extracted ion chromatograms of the different samples. The performance of PLS-DA for classification of the different samples and for providing a novel insight into Tf glycopeptide glycoforms alteration in CDGs is demonstrated. Only six out of fourteen of the detected glycoforms are enough for an accurate classification. This small glycoform set may be considered a sensitive and specific novel biomarker panel for CDGs.