Capillary electrophoresis mass spectrometry based metabolomics

Correspondence:
Buko A . Human Metabolome Technologies, 24 Denby Road, Suite 217, Boston, MA 02134, USA, Phone: +1 617 9870554.

Citation:
Buko AM. Capillary Electrophoresis Mass Spectrometry Based Metabolomics J Appl Bioanal 3(1), 5-20 (2017).

Open-access and Copyright:
©2017 Buko AM. This article is an open access article distributed under the terms of the Creative Commons Attribution License (CC-BY) which permits any use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Funding/Manuscript writing assistance:
The author has no financial support or funding to report and also declares that no writing assistance was utilized in the production of this article.

Competing interest:
The author has declared that no competing interest exists.

Article history:
Received: 19 January 2017, Revised 22 February 2017, Accepted 25 February 2017.

Abstract

Capillary electrophoresis–mass spectrometry (CE-MS) is a powerful orthogonal technique capable of filling in gaps in the identification, quantitation and isomeric resolution of many small hydrophilic and charged metabolites. The metabolome is a large complex mixture of molecules for which not one technique nor a combination of techniques can optimally identify and measure it in it’s entirety. LC-MS, GC-MS and NMR have been the widely used for metabolomics for the past 20 years for a wide range of applications, each technique having shown uniqueness and advantages, for specific applications or target metabolic chemical space. CE-MS captures a unique metabolic chemical space beyond these standard methods providing another window into metabolomics profiling. This review will focus on the recent publications published within 2016 focusing on biotechnology and pharmaceutical applications of CE-MS.

Keywords

Capillary electrophoresis mass spectrometry, metabolomics, metabolite profiling.

Introduction

Since its first formal definition more than a decade ago, metabolomics,  or, the comprehensive analysis of all metabolites present within a biological system, has attracted growing interest in clinical research by academia, industry and government labs. This is most prevalent in biomarker and drug development applications where a considerable change has been witnessed in how new diagnoses, prognoses, and therapeutic options are being discovered and developed using omic technologies. Moreover, many chronic diseases suggest a strong metabolic involvement or even a clear metabolic cause, including cancer. Together with the other omic disciplines, including genomics and proteomics, metabolomics plays a key role in the implementation of personalized medicine; evidence-based medicine designed for individually designed healthcare strategies. In turn, biomarker discovery and the understanding of biochemical pathways typically rely on a multimodal approach. Among these modalities, there continues to be a growing interest in CE-MS based development and implementation in clinical development.

Formulating the problem

Depending on how you classify the metabolome, there is a complex chemical space separated by hydrophilicity, polarity and size, excluding a broad range of metabolites classified as lipids, separately referred to as lipidomics. This class of metabolites also covers a large range of physical properties for which specifically designed platforms work the best. For example, for years liquid chromatography-mass spectrometry (LC-MS) has been used to capture a host of hydrophilic and hydrophobic metabolites, while gas chromatography-mass spectrometry (GC-MS) has been used to capture small molecular weight metabolites. For this review, the metabolome refers to endogenous molecules with a molecular weight typically less than 1000 Kd.To measure, catalogue, and compare the entirety of the metabolic space, the implementation of comprehensive mass spectral databases was needed (e.g., Human Metabolome Database (HMDB), METLIN, MassBank, LIPID MAPS, LipidBlast, NIST 14). These databases have helped drive the field and enable untargeted discovery by CE-MS.The HMDB, for example, lists thousands of metabolites, their physical properties and, for many common metabolites, metabolite concentrations in various biological matrices (blood, urine, saliva, CSF). While METLIN reports structure and mass for thousands of metabolites, small peptides, and xenobiotic metabolites found across the plant and animal kingdom.Because of this diversity in physical properties and size of the metabolome in biological systems, there has been a need for the development of several analytical protocols based on different chromatographic methods to select specific subgroups of metabolites based on these different chemical properties beyond the technical capabilities of LC-MS and GC-MS. These new methods have their own advantages for selecting specific types of molecules: lipids, nucleotides, aminoacids or steroids. Non-mass spectrometric methods such as nuclear magnetic resonance (NMR) and non-chromatographic methods such as matrix-assisted laser desorption- time of flight (MALDI-TOF) imaging are successfully being used for selected metabolomic analyses. The focus of this review is recent publications that use CE-MS to extend the polar metabolome beyond what is observed by LC-MS and GC-MS.

Metabolomics – current methods

Using the appropriate technology can be a critical decision when starting a discovery program. Different methods or technical platforms have advantages within certain chemical spaces, and the choice of platform can effect linear dynamic range, lower level of quantitation, resolution of isomers, baseline biological noise and in-source ion suppression. No universal methods exist so care should me made when making conclusions in evaluating unbiased and untargeted metabolomic data. With the diversity of metabolites captured by any one method, one can expect a range of different responses effecting dynamic range and linearity of response depending upon the specific metabolite properties. The choice of technology can affect the number of false positives, how to assess quality control and the type of statistical analysis in any metabolomics study.
The most commonly used separation method uses high-pressure liquid chromatography (HPLC). HPLC methods vary to accommodate a broader range of metabolites. Ion pairing, ion exchange, hydrophilic interaction chromatography (HILIC) and reverse phase columns can all be used to select specific subtypes of metabolites. Capillary electrophoresis (CE) offers a novel approach with distinct advantages as seen in a growing list of publications.

CE mechanism

Understanding that HPLC and GC methods are not all inclusive, Smith et al. [1] demonstrated the potential of interfacing CE with mass spectrometry (MS) in 1987, although it was not until Soga et al. [2] published in 2002 that CE-MS could be performed with high reproducibility and sensitivity for biological applications. CE works using electrophoretic movement or electroosmotic flow (EOF) to govern transport and separation of metabolites.
EOF is a phenomenon where the electrolyte or running buffer solution itself flows inside the capillary. The flow of the electrolyte solution is the main driving force that pushes samples into the mass spectrometer side of the capillary. A fused-silica capillary contains surface charges of silanol groups present on the inner walls. The silanol groups on the capillary inner wall are ionized presenting an overall negative surface. Opposing ions in the electrolyte solution are attracted to the inner wall surface to achieve a balance of electric charges, resulting in the formation of a double-layer with ionized silanol groups. Under these conditions, a potential difference is created very close to the inner wall. The application of a voltage to both ends of the capillary attracts the positively charged ions of the diffuse double-layer to an anode. In contrast, the silanol groups cannot move due to the fixation on the wall surface and the entire electrolyte solution in the capillary is directed toward the anode with the migration of the positively charged ions, thereby generating a flow.
The degree of mobility of any compound relative to others is due to variations in their ionic radius and size and charge of the electrolyte filling the capillary. A compound or metabolite with a larger ionic radius and smaller charge would have limited mobility compared to small, more polar species. Compounds or metabolites with a smaller ionic radius and higher charge would have high mobility. Hence controlling the electrical gradient across the capillary and pH of the electrolyte solution are two of the most important parameters in controlling metabolite separation into the mass spectrometer.
Hence CE-MS offers a totally different approach to metabolite separation prior to mass spectrometric detection. Earlier reviews go into more detail on how CE works, the theory of electroosmotic flow (EOF) and CE-MS interfaces [3,15,16,17,18].

Early problems

Despite the strong need to broaden the ability to measure the hydrophilic metabolic space, the usage of CE-MS has been relatively slow. Sensitivity, mass spectrometric stability, migration time reproducibility, and correction software for migration time shift have all contributed to slow uptake. In addition, limited commercial CE-MS solutions and the lack of CE-MS availability in core academic, government or industrial laboratories has limited development in the field. Since CE-MS may measure many new molecules, not in LC libraries, the creation of new small molecule libraries and the peak picking and warping software needed for CE alignment has also slowed progress. Lastly, the ability to provide a stable, sensitive, and reliable interface has been a critical issue to overcome.

Early developments

In 2002, Tomoyoshi Soga first developed a metabolome analysis method based on CE-MS, which enabled the simultaneous analysis of several thousand charged metabolites by cationic and anionic methods [1,7,11,14]. Since then, CE-MS began to grow as one of the standard methods in bioscience research. See Soga’s recent review in 2015 [18]. Soga and other labs have clearly demonstrated that most of these early issues and concerns have been overcome by technological improvements, hence the growing list of publications in this field since 2002. CE-MS protocols have been developed to support metabolic measurements, both relative and quantitative, in a large variety of sample types including urine [4,5,8], bacteria [6], CSF [9], neurons [10] and brain [12].

Current advantages and biotechnology applications

Multiple comparative studies reviewed and discussed by Kok et al. [13,17] have shown that the metabolome fraction mapped by CE–MS is usually not covered by other techniques. Importantly, CE also gives complementary information to HILIC [13], as well as, significantly better peak shape in many cases. It is therefore not surprising that CE-MS has already been considered for multiple metabolomics studies covering several applications, from aging [19], amino acid analysis [20,21], bacteria [22-26], Cancer [27-43], CNS [46-53], CVD [54-58], diabetes [59-65], lifestyle [61-71], kidney disease [72,73], liver disease [74-82], the microbiome [83-89] and many other biological areas[90-108].
CE-MS is the most suited technique for analyzing phosphorylated metabolites, amino acids or metabolites from the TCA cycle and glycolytic pathways, all being key metabolites in multiple biochemical processes and gaining remarkable importance in metabolomics, including cancer research. Indeed, isomeric compounds such as citrate/isocitrate, as well as, leucine/isoleucine/also leucine, can be resolved and quantitated in biological matrices [153].
Another great advantage of CE-MS is its high quantification accuracy. CE-MS is less affected by LC-MS associated matrix effects, such as ion suppression and enhancement, due to the lack of mass transfer between solid and liquid phases in CE. CE uses uncoated fused silica capillary columns, the separation occurring by electrical interactions or EOF, unlike LC reverse phase and HILIC methods where peak broadening occurs by mass transfer between the liquid and solid phase. CE provides a flat solvent front compared to the convex front with HPLC separation systems. Peak spreading by CE is only due to longitudinal diffusion across the capillary column thus producing separations with better peak shape, greater dynamic range for certain metabolites over LC-MS methods. CE has also been favourably compared to HILIC separations offering higher flexibility, lower complexity, better RT reproducibility, higher peak capacity and better peak shape [17]. Another challenge to the popularity of CE-MS has been the effect of high salt concentrations on migration time reproducibility. High salt concentrations in the sample can lead to stacking and peak spreading affecting metabolite resolution, identification and mass accuracy. However, this issue can be avoided by determination of an appropriate dilution ratio for general types of samples (blood, tissue, urine, saliva etc.). Alternatively, some sample types may require a pre-test utilizing a small amount of sample. Lastly, one could use some measurement, such as electric conductivity or creatinine concentration in urine, to determine appropriate dilution ratio.
CE-MS systems are currently available from SCIEX CESI 8000 (Redwood City, CA USA) and Agilent 7100 CE (Santa Clara, CA USA) providing both instruments and software as a viable path for individual laboratories. A contract research organization (Human Metabolome Technologies –Tsuruoka, Japan) can also provide CE-MS services, both quantitative and relative measurements for any matrix with protocols for any type of metabolomics interest.
With many technological challenges overcome, the advantages of CE-MS are clearly understood. With the availability of commercial CE-MS instrumentation, development in academic labs and contract labs and with a growing list of publications and reviews [1-108], we saw in 2016 a balance of papers reflecting the diversity and growth of CE-MS in biotechnology and drug development.

Figures and Tables

[Click to enlarge]

Over 200 CEMS publications in 2016

In this paper recent CE–MS applications developed for metabolomics covering the literature from Jan 2016 to Dec 2016 are outlined in Tables 1, 2 and 3. Attention will be paid to CE–MS approaches for the profiling of metabolites in the fields of biomedical, clinical and microbial metabolomics.

Reviews

Reviews provide a short cut to obtaining specific and pertinent information about a topic. They also give a time table context for developing technologies or research areas. Several reviews of CE-MS were published in 2016 summarizing the growth of this field. Table 1  summaries 10 reviews in 2016 on CE-MS, each with a different focus or agenda. Issue 37 of the Journal Electrophoresis [110] provided an entire publication to LC-MS and CE-MS, demonstrating equal status, although the CE-MS applications were not specific to metabolomics. Hankemeier’s laboratory at Leiden University provided 4 separate reviews [111-114] covering different aspects of CE-MS from growing applications to technical developments. Tang et al. [109] provides a discussion comparing HILIC chromatography to capillary electrophoresis with advantages and challenges for both. In Methods in Molecular Biology (2016) a whole chapter is provided [117] on CE-MS in metabolomics with detailed protocols and technical information for those getting into this field.

Technology and protocols

Innovative technologies continue to advance through the ingenuity of researchers, providing reliable improvements in the technologies and protocols. Such advancements are needed in order to provide solutions to increasingly difficult analytical problems seen in the medical and pharmaceutical fields. Over the past year, several publications (Table 2) are noted demonstrating new ideas and concepts designed to expand and improve CE-MS. Untargeted profiling, when including multiple analytical methods, provides the greatest opportunity to discover biomarkers, understand biological pathways and identify new drug targets. The University of San Pablo published 3 papers in 2016 [125-127] using multiple mass spectrometry based untargeted technologies [LC-MS, CE-MS and GC-MS] to create large data rich metabolomics profiles in plasma [125], serum [126] and from cell culture [127] to identify biomarkers in obesity and hypercholesterolemia. D’Orazio et al. [121] compared nano LC and CE-MS to quantitatively measure estrogenic metabolites from mineral water. These nano techniques were used to minimize sample volumes and reagent costs. While the phenyl-LC method provided the better calibration curves, CE-MS, using only electro-osmotic flow (EOF), demonstrated the capability to resolve and measure 11 estrogens including two isobaric compounds.
One of the long-standing issues with CE-MS is the ease and stability of the CE to MS interface. Ramautar [123] and Boizard [120] published on their work to improve the reliability and sensitivity of the MS interface. Ramautar provided data on using a sheathless capillary interface to improve the sensitivity with profiling glioblastoma cell lines, while Boizard designed a beveled capillary tip to improve the stability of the CE-MS interface, examining one sample over 130 times in 4 years. Improvements in sample preparation prior to CE separation was also seen in 2016 with two papers [119,124] using solid phase extraction [SPE] to improve detection sensitivity. Pont et al. [124] utilized on-line SPE with CE-MS to identify biomarkers of Huntington’s disease from mouse plasma. [R6/1]. Yamamoto et al. [119] provide protocols to help stabilize the long-term stability of anionic CE-MS by developing fused silica friendly methods. Lastly, Yamamoto and Sasaki [122] provided multi-step processes to aid in the identification of unknowns in CE-MS using accurate mass and data base mining. Protocols to expand the know metabolome are critical to exploiting the full potential of CE-MS.The heat capacity change is frequently negative (that is the enthalpy change is more negative at higher temperatures), when the bound complex is considered the “reference state.” This arises due to the correlation between heat capacity change and changes in surface hydration associated with binding, causing burial of surface area [15]. If temperature versus enthalpy plot reveals a non-linear (temperature dependent) heat capacity change, that suggests other possibilities including unfolding/refolding of protein at higher temperatures, or coupled equilibria besides binding.

Medical applications

While there were over 200 CE-MS based publications in 2016, this review has focused on those with metabolomics applications and methods. Those 25 publications related to drug development are summarized in Table 3. These publications cover many therapeutic and disease areas, different sample types and study sizes reflecting the diversity and growth in this area.
Oncology has a great unmet need for biomarkers and treatment protocols. Omic technologies, like metabolomics, provides pathways to understanding cancer and treatment effects in both pre-clinical and clinical settings. Colorectal cancer (CRC) is one area of active clinical research. Two publications [131,132] were looking at either serum or colon tissue from CRC patients to develop markers for early detection or a more accurate diagnosis using CE-MS. Hepatocellular carcinoma (HCC) is a primary malignancy of the liver and occurs predominantly in patients with underlying chronic liver disease and cirrhosis, now the third leading cause of cancer deaths worldwide. Hirata et al. [134] and Soga et al. [133] used CE-MS in cells and serum to help understand HCC at the cellular level and discover potential biomarkers. Hirata’s work provides additional support for FBP1 as a therapeutic target, while Soga delivers a series of inflammatory peptides in serum that helps to differentiate Hepatitis B and Hepatitis C effects on HCC patients. Other publications look at diverse samples types; saliva [146], cell culture [151] and brain tissue [152] for biomarkers and pathway analysis of oral cancer, osteosarcoma and glioma. CE-MS provides accurate measurements of critical small molecules associated with aberrant cancer metabolism and inflammation, representing targets for cancer therapeutic development.
While cancer research represents a major investment in the medical community, diseases of the central nervous system (CNS) continue to grow. Bipolar disorder (BD), formerly called manic depression, causes extreme mood swings that include emotional highs (mania or hypomania) and lows (depression). Three groups published using 3 different sample types, plasma [137], CSF [130] and serum [136] to both understand the biology of BD and seek biomarkers for diagnosis. Hashimoto [130] aptly used the specificity of CE-MS to understand the role of mitochondrial dysfunction in BD, finding isocitrate as a diagnostic biomarker in CSF. Likewise, the same group looked at serum TCA cycle metabolites using CE-MS to resolve and identify specific energy metabolites in BD sera. While Fuji [138] used the uniqueness of CE-MS to identify metabolites in brain tissue specific to Schizophrenia.
The remainder of Table 3 lists publications related to lifestyle; alcohol intake [125], physical activity [150]], microbiome [142,145,147] and to various diseases; eye disease [140, 141], inborn errors of metabolism [143], kidney disease [144], diabetes [139], metabolic syndrome [149], osteoporosis [129] and encephalopathy [148]. These publications represent a variety of subjects and sample types [serum plasma, cultured cells, PBMCs, tissue] that are amendable to CE-MS metabolomics profiling, specifically, polar hydrophilic metabolites found in critical pathways such as amino acid metabolism, central energy metabolism, inflammation, stress oxidative pathways, glycolysis, cancer metabolism, and lipid transportation.

Future

The successful implementation and integration of metabolomics in personalized medicine and drug development will rely on the combination of high-throughput analysis, large metabolite coverage, accurate quantitation, high-value data and low-cost analysis. This paradigm can only be achieved using state-of-the-art analytical technologies and computational techniques combining many modalities and data input with growth in technology and implementation. CE-MS is poised to make significant contributions to these fields as the technology and availability to CE-MS continues to increase.
Multi-platform, multi-omics approaches with large studies will develop high-powered algorithms for diagnostic and prognostic studies. Increasing CE databases, improvements in sheathless interfaces and increases in MS detection capability will further push the field forward helping to find solutions to critical questions in drug development and biotechnology.

Conclusion

We demonstrated for the first time that immunocapture-LC/MS could be used for detecting cynomolgus monkey anti-Protein XYZ antibody in the presence of high drug levels. In this approach, ADA was converted to drug-ADA complex and then captured using a biotinylated anti-drug mAb on magnetic beads. After separation from plasma using a magnet, the beads were washed and ADA was eluted followed by trypsin digestion and LC/MS analysis of a surrogate peptide unique to monkey IgG1-4. The assay results were in close agreement with those from an ECL immunoassay and were successfully applied in support of a toxicology study. Potential quantitative correlation between these two different approaches may be explored provided that adequate validation is conducted for the LC/MS assay in the future work.
In this study, the immunocapture-LC/MS assay overcame drug interference and thus presents the potential as an alternative technology platform for ADA detection in the presence of high drug levels. The monkey assay was similar to the human ADA assay we have reported [22] but using a monkey-specific peptide for the detection of monkey ADA. Therefore, we demonstrated that LC/MS approach can be used for evaluation of immunogenicity in different species. It should be noted that in order to perform complete assay validation, monkey specific ADA positive control, which was not available for this study, will be needed.
Owing to LC/MS’s advantages such as high drug tolerance, high specificity, selectivity and reproducibility, wide dynamic range and multiplexing capability, it is expected that immunocapture-LC/MS will become an additional tool in immunogenicity assessment. Further improvements should focus on reducing endogenous interference and increasing assay sensitivity and specificity. LC/MS assay validation must be considered, especially in the regulated bioanalytical arena in support of immunogenicity assessments, using positive controls and stable labeled peptide internal standard. Species specific ADA positive controls will be needed to perform complete assay validation.

Acknowledgement

The authors would like to thanks Drs Christine Grimaldi, Tom Chan, Victor Chen, Edwin Gump for fruitful discussions and valuable input. Special thanks go to Ms. Corina Place for help obtaining the study samples.

References

1. Olivares JA, Nguyen NT, Yonker CR, Smith RD. Anal Chem 59, 1230-1232 (1987). [CrossRef]
2. Soga T, Ueno Y, Naraoka H, Matsuda K, Tomita M, Nishioka T Pressure-assisted capillary electrophoresis electrospray ionization mass spectrometry for analysis of multivalent anions. Anal Chem 74(24), 6224–6229 (2002).  [CrossRef]
3. Gordon A. Ross Capillary Electrophoresis – Mass Spectrometry Practical Implementation and Applications LCGC Europe January, 2-6 (2001). 
4. Ullsten S, Danielsson R, Bäckström D, Sjöberg P, Bergquist. J. Urine profiling using capillary electrophoresis-mass spectrometry and multivariate data analysis. J Chromatogr A 1117(1), 87–93 (2006).  [CrossRef]
5. Ramautar R, Mayboroda OA, Derks RJ et al. Capillary electrophoresis-time of flight-mass spectrometry using noncovalently bilayer-coated capillaries for the analysis of amino acids in human urine. Electrophoresis 29(12), 2714–2722 (2008).  [CrossRef]
6. Timischl B, Dettmer K, Kaspar H, Thieme M, Oefner PJ. Development of a quantitative, validated capillary electrophoresis-time of flight-mass spectrometry method with integrated high-confidence analyte identification for metabolomics. Electrophoresis 29(10), 2203–2214 (2008).  [CrossRef]
7. Soga T, Igarashi K, Ito C et al. Metabolomic profiling of anionic metabolites by capillary electrophoresis mass spectrometry. Anal Chem 81, 6165–6174 (2009).  [CrossRef]
8. Ramautar R, Busnel JM, Deelder AM, Mayboroda OA. Enhancing the coverage of the urinary metabolome by sheathless capillary electrophoresis-mass spectrometry. Anal Chem 84(2), 885–892 (2012).  [CrossRef]
9. Ramautar R, Shyti R, Schoenmaker B, de Groote L, Derks, RJ, Ferrari MD, van den Maagdenberg AM, Deelder AM, Mayboroda OA. Metabolic profiling of mouse cerebrospinal fluid by sheathless CE-MS. Anal Bioanal Chem 404(10), 2895–2900 (2012).  [CrossRef]
10. Nemes P, Rubakhin SS, Aerts JT, Sweedler JV. Qualitative and quantitative metabolomic investigation of single neurons by capillary electrophoresis electrospray ionization mass spectrometry. Nat Protoc 8(4), 783–799 (2013).  [CrossRef]
11. Hirayama A, Igarashi K, Tomita M, Soga T. Development of quantitative method for determination of γ-glutamyl peptides by capillary electrophoresis tandem mass spectrometry: an efficient approach avoiding matrix effect. J Chromatogr A 1369, 161-169 (2014).  [CrossRef]
12. Aerts JT, Louis KR, Crandall SR, Govindaiah G, Cox CL, Sweedler JV. Patch clamp electrophysiology and capillary electrophoresis-mass spectrometry metabolomics for single cell characterization. Anal Chem 86(6), 3203–3208 (2014).  [CrossRef]
13. Kok MG, Somsen GW, De Jong GJ. Comparison of capillary electrophoresis-mass spectrometry and hydrophilic interaction chromatography-mass spectrometry for anionic metabolic profiling of urine. Talanta 132, 1–7 (2015).  [CrossRef]
14. Soga T, Ohashi Y, Ueno Y, Naraoka H, Tomita M, Nishioka T. Quantitative metabolome analysis using capillary electrophoresis mass spectrometry. J Proteome Res 2(5), 488–494 (2003).  [CrossRef]
15. Wang X, Li K, Adams E et al. Capillary electrophoresis-mass spectrometry in metabolomics: the potential for driving drug discovery and development. Curr Drug Metab 14, 807–813 (2013).  [CrossRef]
16. Ramautar R, Somsen GW, de Jong GJ. CE-MS for metabolomics: developments and applications in the period 2010–2012. Electrophoresis 34, 86–98 (2013).  [CrossRef]
17. Kok M, Somsen GM, de Jong GW. The role of capillary electrophoresis in metabolic profiling studies employing multiple analytical techniques TrAC 61, 223–235 (2014). 
18. Wakayama M, Hirayama A, Soga T. Capillary Electrophoresis-Mass Spectrometry. In: Metabonomics: Methods and Protocols, Bjerrum JT (Ed.).Springer New York, NY 113-122 (2015).  [CrossRef]
19. Nevedomskaya E, Ramautar R, Derks R et al. CE-MS for metabolic profiling of volume-limited urine samples: application to accelerated aging TTD mice. J Proteome Res 8(12), 5559–5567 (2010).  [CrossRef]
20. Soga T, Heiger DN. Amino acid analysis by capillary electrophoresis electrospray ionization mass spectrometry. Anal Chem 72, 1236–1241 (2000).  [CrossRef]
21. Soga T, Kakazu Y, Robert M et al. Qualitative and quantitative analysis of amino acids by capillary electrophoresis-electrospray ionization-tandem mass spectrometry. Electrophoresis 25, 1964–1972 (2004). [CrossRef]
22. Shii N, Nakahigashi K, Baba T et al. Multiple high-throughput analyses monitor the response of E. coli to perturbations. Science 316(5824), 593-597 (2007). [CrossRef]
23. Canuto GA, Castilho-Martins EA, Tavares M, López-Gonzálvez A, Rivas L, Barbas C. CE-ESI-MS metabolic fingerprinting of Leishmania resistance to antimony treatment. Electrophoresis 33(12), 1901–1910 (2012).  [CrossRef]
24. Canuto GA, Castilho-Martins EA, Tavares MF, Rivas L, Barbas C, López-Gonzálvez Á. Multi-analytical platform metabolomic approach to study miltefosine mechanism of action and resistance in Leishmania. Anal Bioanal Chem 406(14), 3459–3476 (2014).  [CrossRef]
25. Rojo D, Canuto GA, Castilho-Martins EA et al. A multiplatform metabolomic approach to the basis of antimonial action and resistance in Leishmania infantum. PLoS ONE 10(7), e0130675 (2015).  [CrossRef]
26. Alves-Ferreira EV, Toledo JS, De Oliveira AH et al. Differential gene expression and infection profiles of cutaneous and mucosal leishmania braziliensis isolates from the same patient. PLoS Negl Trop Dis 9(9), e0004018 (2015).  [CrossRef]
27. Soga T, Ohashi Y, Ueno Y, Naraoka H, Tomita M, Nishioka T. Quantitative metabolome analysis using capillary electrophoresis mass spectrometry. J Proteome Res 2(5), 488–494 (2009).  [CrossRef]
28. Sugimoto M, Wong DT, Hirayama A, Soga T, Tomita M. Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer specific profiles.Metabolomics 6(1), 78–95 (2010).  [CrossRef]
29. Ibáñez C, Simó C, García-Cañas V, Gómez-Martínez A, Ferragut JA, Cifuentes A. CE/LC-MS multiplatform for broad metabolomic analysis of dietary polyphenols effect on colon cancer cells proliferation. Electrophoresis 33(15), 2328–2336 (2012).  [CrossRef]
30. Terashima M, Kusuhara M, Kondo J et al. Metabolome analyses in human gastric cancer tissue using capillary electrophoresis time-of-flight mass spectrometry. J Clin Oncol 30(4_suppl), 61 (2012). 
31. Chen JL, Fan J, Yan LS et al. Urine metabolite profiling of human colorectal cancer by capillary electrophoresis mass spectrometry based on MRB. Gastroenterol Res Prac 2012, 8 (2012).  [CrossRef]
32. Yu L, Jiang C, Huang S, Gong X, Wang S, Shen P. Analysis of urinary metabolites for breast cancer patients receiving chemotherapy by CE-MS coupled with on-line concentration. Clin Biochem 46(12), 1065–1073 (2013).  [CrossRef]
33. Kami K, Fujimori T, Sato H et al. Metabolomic profiling of lung and prostate tumor tissues by capillary electrophoresis time-of-flight mass spectrometry. Metabolomics 9(2), 444–453 (2013).  [CrossRef]
34. Alberice JV, Amaral AF, Armitage EG et al.Searching for urine biomarkers of bladder cancer recurrence using a liquid chromatography-mass spectrometry and capillary electrophoresis-mass spectrometry metabolomics approach. J Chromatogr A 1318, 163–170 (2013).  [CrossRef]
35. Qin XY, Wei F, Tanokura M et al.The effect of acyclic retinoid on the metabolomic profiles of hepatocytes and hepatocellular carcinoma cells. PLoS ONE 8(12), e82860 (2013).  [CrossRef]
36. Vincent Z, Urakami K, Maruyama K, Yamaguchi K, Kusuhara M. CD133-positive cancer stem cells from Colo205 human colon adenocarcinoma cell line show resistance to chemotherapy and display a specific metabolomic profile. Genes Cancer 5(7–8), 250–260 (2014). 
37. Chen JL, Fan J, Lu XJ. CE-MS based on moving reaction boundary method for urinary metabolomic analysis of gastric cancer patients. Electrophoresis 35(7), 1032–1039 (2014).  [CrossRef]
38. Glucose and glutamine metabolism in oral squamous cell carcinoma: insight from a quantitative metabolomic approach. Oral Surg Oral Med Oral Pathol Oral Radiol 118(2), 218-225 (2014).  [CrossRef]
39. Zeng J, Yin P, Tan Y et al. Metabolomics study of hepatocellular carcinoma: discovery and validation of serum potential biomarkers by using capillary electrophoresis-mass spectrometry. J Proteome Res 13(7), 3420–3431 (2014).  [CrossRef]
40. Kishikawa T, Otsuka M, Tan PS et al. Decreased miR122 in hepatocellular carcinoma leads to chemoresistance with increased arginine. Oncotarget 6(10), 8339-8352 (2015).  [CrossRef]
41. Uetaki M, Tabata S, Nakasuka F, Soga T, Tomita M. Metabolomic alterations in human cancer cells by vitamin C-induced oxidative stress. Sci Rep 5, 13896 (2015).  [CrossRef]
42. Zhao C, Du H, Xu L et al. Metabolomic analysis revealed glycylglycine accumulation in astrocytes after methionine enkephalin administration exhibiting neuron protective effects. J Pharm Biomed Anal 115, 48–54 (2015).  [CrossRef]
43. Ligat L, Saint-Laurent N, El-Mrani A et al. Pancreatic preneoplastic lesions plasma signatures and biomarkers based on proteome profiling of mouse models. Br J Cancer 113(11), 1590-1598 (2015).  [CrossRef]
44. Knolhoff AM, Nautiyal KM, Nemes P et al. Combining small-volume metabolomic and transcriptomic approaches for assessing brain chemistry. Anal Chem 85(6), 3136–3143 (2013).  [CrossRef]
45. Ibáñez C, Simó C, Martín-Álvarez PJ et al.Toward a predictive model of Alzheimer’s disease progression using capillary electrophoresis–mass spectrometry metabolomics. Anal Chem 84(20), 8532–8540 (2012).  [CrossRef]
46. González-Domínguez R, García A, García-Barrera T, Barbas C, Gómez-Ariza JL. Metabolomic profiling of serum in the progression of Alzheimer’s disease by capillary electrophoresis-mass spectrometry. Electrophoresis 35(23), 3321–3330 (2014).  [CrossRef]
47. Tsuruoka M, Hara J, Hirayama A et al. Capillary electrophoresis-mass spectrometry-based metabolome analysis of serum and saliva from neurodegenerative dementia patients. Electrophoresis 34(19), 2865–2872 (2013).  [CrossRef]
48. Goto T, Kubota Y, Toyoda A. Plasma and Liver Metabolic Profiles in Mice Subjected to Subchronic and Mild Social Defeat Stress. J Proteome Res 14(2), 1025-32 (2015).  [CrossRef]
49. Ogawa S, Hattori K, Sasayama D et al. Reduced cerebrospinal fluid ethanolamine concentration in major depressive disorder. Int J Neuropsychopharmacol 19 (Suppl_1), 71-72 (2016). 
50. Shyti R, Kohler I, Schoenmaker B at al. Plasma metabolic profiling after cortical spreading depression in a transgenic mouse model of hemiplegic migraine by capillary electrophoresis-mass spectrometry. Mol Biosyst 11(5), 1462–1471 (2015).  [CrossRef]
51. Kume S, Yamato M, Tamura Y et al. Potential biomarkers of fatigue identified by plasma metabolome analysis in rats. PLoS ONE 10(3), e0120106 (2015). [CrossRef]
52. Sekiyama K, Waragai M, Akatsu H et al. Disease modifying effect of adiponectin in model of α-synucleinopathies Ann Clin Transl Neurol 1(7), 479-489 (2014). [CrossRef]
53. Koike S, Bundo M, Iwamoto K et al. A snapshot of plasma metabolites in first-episode schizophrenia: a capillary electrophoresis time-of-flight mass spectrometry study. Translational Psychiatry 4, e379–e387 (2014). [CrossRef]
54. Maekawa K, Hirayama A, Iwata Y et al. Global metabolomic analysis of heart tissue in a hamster model for dilated cardiomyopathy. J Mol Cell Cardiol 59, 76–85 (2013). [CrossRef]
55. Shimura D, Nakai G, Jiao Q et al. Metabolomic profiling analysis reveals chamber-dependent metabolite patterns in the mouse heart. Am J Physiol Heart Circ Physiol 305(4), H494–H505 (2013). [CrossRef]
56. Naz S, Calderón Á, García A et al. Unveiling differences between patients with acute coronary syndrome with and without ST elevation through fingerprinting with CE-MS and HILIC-MS targeted analysis. Electrophoresis 36(18), 2303-2313 (2015). [CrossRef]
57. Kouzu H, Miki T, Tanno M et al. Excessive degradation of adenine nucleotides by up-regulated AMP deaminase underlies afterload-induced diastolic dysfunction in the type 2 diabetic heart. J Mol Cell Cardiol 80, 136–145 (2015). [CrossRef]
58. Nakatani K, Watabe T, Masuda D et al. Myocardial energy provision is preserved by increased utilization of glucose and ketone bodies in CD36 knockout mice. Metabolism 64 (9), 1165–1174 (2015). [CrossRef]
59. Moraes EP, Rupérez FJ, Plaza M, Herrero M, Barbas C. Metabolomic assessment with CE-MS of the nutraceutical effect of Cystoseira spp extracts in an animal model. Electrophoresis 32(15), 2055–2062 (2011). [CrossRef]
60. Hirayama A1, Nakashima E, Sugimoto M et al. Metabolic profiling reveals new serum biomarkers for differentiating diabetic nephropathy. Anal Bioanal Chem 404(10), 3101–3109 (2012). [CrossRef]
61. Daimon M, Soga T, Hozawa A et al. Serum glycerophosphate levels are increased in Japanese men with type 2 diabetes. Intern Med 51(6), 545–551 (2012). [CrossRef]
62. Godzien J, García-Martínez D, Martinez-Alcazar P, Ruperez FJ, Barbas C. Effect of a nutraceutical treatment on diabetic rats with targeted and CE-MS non-targeted approaches. Metabolomics 9(1), 188–202 (2013). [CrossRef]
63. Balderas C, Rupérez FJ, Ibañez E et al. Plasma and urine metabolic fingerprinting of type 1 diabetic children. Electrophoresis 34(19), 2882–2890 (2013). [CrossRef]
64. Dudzik D, Zorawski M, Skotnicki M et al. Metabolic fingerprint of gestational diabetes mellitus. J Proteomics 103, 57–71 (2014). [CrossRef]
65. Ciborowski M, Adamska E, Rusak M et al. CE-MS-based serum fingerprinting to track evolution of type 2 diabetes mellitus. Electrophoresis 36(18), 2286-2293 (2015). [CrossRef]
66. Allard E, Bäckström D, Rolf D, Sjöberg PJR, Bergquist J. Comparing capillary electrophoresis−mass spectrometry fingerprints of urine samples obtained after intake of coffee, tea, or water. Anal Chem 80(23), 8946–8955 (2008). [CrossRef]
67. Garcia-Perez I, Lindon JC, Minet E. Application of CE–MS to a metabonomics study of human urine from cigarette smokers and non-smokers. Bioanalysis 6(20), 2733–2749 (2014). [CrossRef]
68. Shima N, Miyawaki I, Bando K et al. Influences of methamphetamine-induced acute intoxication on urinary and plasma metabolic profiles in the rat. Toxicology 287(1–3), 29–37 (2011). [CrossRef]
69. Lee R, West D, Phillips SM, Britz-Mckibbin P. Differential metabolomics for quantitative assessment of oxidative stress with strenuous exercise and nutritional intervention: thiolspecific regulation of cellular metabolism with N-acetyl-Lcysteine pretreatment. Anal Chem 82(7), 2959–2968 (2010). [CrossRef]
70. Ra SG, Maeda S, Higashino R, Imai T, Miyakawa S. Metabolomics of salivary fatigue markers in soccer players after consecutive games. Appl Physiol Nutr Metab 39(10), 1120-1126 (2014). [CrossRef]
71. Kuehnbaum NL, Gillen JB, Kormendi A et al. Multiplexed separations for biomarker discovery in metabolomics: elucidating adaptive responses to exercise training. Electrophoresis 36(18), 2226-2236 (2015). [CrossRef]
72. Akiyama Y, Takeuchi Y, Kikuchi K et al. A metabolomic approach to clarifying the effect of AST-120 on 5/6 nephrectomized rats by capillary electrophoresis with mass spectrometry (CE-MS). Toxins 4(11), 1309–1322 (2012). [CrossRef]
73. Mishima E, Fukuda S, Shima H et al. Alteration of the Intestinal Environment by Lubiprostone Is Associated with Amelioration of Adenine-Induced CKD. J Am Soc Nephrol 26(8), 1787-1794 (2015). [CrossRef]
74. Karasawa T, Saito T, Ueno Y, Sugimoto M, Soga T. Metabolome analysis of erythrocytes from patients with chronic hepatitis C reveals the etiology of ribavirin-induced hemolysis. Int J Med Sci 10(11), 1575–1577 (2013). [CrossRef]
75. Soga T, Sugimoto M, Honma M et al. Serum metabolomics reveals γ-glutamyl dipeptides as biomarkers for discrimination among different forms of liver disease. Hepatol 55(4), 896–905 (2011). [CrossRef]
76. Saito T, Sugimoto M, Igarashi K et al. Dynamics of serum metabolites in patients with chronic hepatitis C receiving pegylated interferon plus ribavirin: a metabolomics analysis. Metabolism 62(11), 1577–1586 (2013). [CrossRef]
77. Ishikawa H, Takaki A, Tsuzaki R et al. L-carnitine prevents progression of non-alcoholic steatohepatitis in a mouse model with upregulation of mitochondrial pathway. PLoS One 9(7), e100627 (2014). [CrossRef]
78. Tsujita T, Peirce V, Baird L et al. Transcription Factor Nrf1 Negatively Regulates the Cystine/Glutamate Transporter and Lipid-Metabolizing Enzymes Mol Cell Biol 34(20), 3800-3816 (2014). [CrossRef]
79. Goto M, Kitamura H, Alam MM. Alcohol dehydrogenase 3 contributes to the protection of liver from nonalcoholic steatohepatitis. Genes to Cells 20(6), 464-480 (2015). [CrossRef]
80. Murakami Y, Kubo S, Tamori A et al. Comprehensive analysis of transcriptome and metabolome analysis in Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma. Sci Rep 5, 16294 (2015). [CrossRef]
81. Ishikawa J, Oshima M, Iwasaki F et al. Hypothermic temperature effects on organ survival and restoration. Sci Rep 5, 9563 (2015). [CrossRef]
82. Zeng J, Huang X, Zhou L et al. Metabolomics Identifies Biomarker Pattern for Early Diagnosis of Hepatocellular Carcinoma: from Diethylnitrosamine Treated Rats to Patients. Sci Rep 5, 16101 (2015). [CrossRef]
83. Matsumoto M, Kibe R, Ooga T et al. Impact of intestinal microbiota on intestinal luminal metabolome. Sci Rep 2, 233 (2012). [CrossRef]
84. Kok MG, Ruijken MM, Swann JR, Wilson ID, Somsen GW, De Jong GJ. Anionic metabolic profiling of urine from antibiotic-treated rats by capillary electrophoresis-mass spectrometry. Anal BioAnal Chem 405(8), 2585–2594 (2013). [CrossRef]
85. Matsumoto M, Kibe R, Ooga T et al. Cerebral low-molecular metabolites influenced by intestinal microbiota: a pilot study. Front Syst Neurosci 7, 9 (2013). [CrossRef]
86. Matsumoto M, Ebata T, Hirooka J et al. Antipruritic effects of the probiotic strain LKM512 in adults with atopic dermatitis. Ann Allergy Asthma Immunol 113(2), 209-216.e7 (2014). [CrossRef]
87. Kibe R, Kurihara S, Sakai Y et al. Upregulation of colonic luminal polyamines produced by intestinal microbiota delays senescence in mice. Sci Rep 4, 4548 (2014). [CrossRef]
88. Rojo D, Gosalbes MJ, Ferrari R et al. Clostridium difficile heterogeneously impacts intestinal community architecture but drives stable metabolome responses. ISME J 9(10), 2206–2220 (2015). [CrossRef]
89. Azuma K, Izumi R, Kawata M et al. Effects of Oral Administration of Chitin Nanofiber on Plasma Metabolites and Gut Microorganisms. Int J Mol Sci. 16(9), 21931-21949 (2015). [CrossRef]
90. Naz S, García A, Barbas C. Multiplatform analytical methodology for metabolic fingerprinting of lung tissue. Anal Chem 85(22), 10941–10948 (2013). [CrossRef]
91. Chalcraft KR, Britz-McKibbin P. Newborn screening of inborn errors of metabolism by capillary electrophoresis-electrospray ionization-mass spectrometry: a second-tier method with improved specificity and sensitivity. Anal Chem 81, 307–314 (2009). [CrossRef]
92. Takeuchi K, Ohishi M, Endo K et al. Metabolomic analysis of the effects of omeprazole and famotidine on aspirin induced gastric injury. Metabolomics 10(5), 995–1004 (2014). [CrossRef]
93. Ramautar R, Mayboroda OA, Derks RJ et al. Capillary electrophoresis-time of flight-mass spectrometry using noncovalently bilayer-coated capillaries for the analysis of amino acids in human urine. Electrophoresis 29(12), 2714–2722 (2011). [CrossRef]
94. Sugiura Y, Honda K, Kajimura M, Suematsu M. Visualization and quantification of cerebral metabolic fluxes of glucose in awake mice. Proteomics 14(7-8), 829-838 (2014). [CrossRef]
95. Ooga T, Sato H, Nagashima A et al. Metabolomic anatomy of an animal model revealing homeostatic imbalances in dyslipidaemia. Mol Biosyst 7(4), 1217–1223 (2011). [CrossRef]
96. Kinoshita A, Tsukada K, Soga T et al. Roles of hemoglobin Allostery in hypoxia-induced metabolic alterations in erythrocytes: simulation and its verification by metabolome analysis. J Biol Chem 282(14), 10731–10741 (2007). [CrossRef]
97. Lee R, Ptolemy AS, Niewczas L, Britz-Mckibbin P. Integrative metabolomics for characterizing unknown low-abundance metabolites by capillary electrophoresis-mass spectrometry with computer simulations. Anal Chem 79(2), 403–415 (2010). [CrossRef]
98. Andreas NJ, Hyde MJ, Gomez-Romero M et al. Multiplatform characterization of dynamic changes in breast milk during lactation. Electrophoresis 36(18), 2269-2285 (2015). [CrossRef]
99. Rojo D, Hevia A, Bargiela R et al. Ranking the impact of human health disorders on gut metabolism: systemic lupus erythematosus and obesity as study cases. Sci Rep 5, 8310 (2015). [CrossRef]
100. Yamatani H, Takahashi K, Yoshida T, Soga T, Kurachi H. Differences in the fatty acid metabolism of visceral adipose tissue in postmenopausal women. Menopause 21(2), 170–176 (2014). [CrossRef]
101. Sugimoto M, Sakagami H, Yokote Y et al. Non-targeted metabolite profiling in activated macrophage secretion. Metabolomics 8(4), 624–633 (2012). [CrossRef]
102. Soga T, Baran R, Suematsu M et al. Differential metabolomics reveals ophthalmic acid as an oxidative stress biomarker indicating hepatic glutathione consumption. J Biol Chem 281(24), 16768–16776 (2006). [CrossRef]
103. Ramautar R, Van Der Plas AA, Nevedomskaya E et al. Explorative analysis of urine by capillary electrophoresis-mass spectrometry in chronic patients with complex regional pain syndrome. J Proteome Res 8(12), 5559–5567 (2009). [CrossRef]
104. Jeelani G, Sato D, Husain A et al. Metabolic profiling of the protozoan parasite Entamoeba invadens revealed activation of unpredicted pathway during encystation. PLoS ONE 7(5), e37740 (2012). [CrossRef]
105. Nishimura T, Higuchi K, Sai Y, Sugita Y, Yoshida Y, Tomi M, Wada M, Wakayama T, Tamura A, Tsukita S, Soga T, Nakashima E. Fetal growth retardation and lack of hypotaurine in ezrin knockout mice. PLoS One 9(8), e105423 (2014). [CrossRef]
106. Zeng J, Kuang H, Hu C et al. Effect of bisphenol A on rat metabolic profiling studied by using capillary electrophoresis time-of-flight mass spectrometry. Environ Sci Technol 47(13), 7457–7465 (2013). [CrossRef]
107. Takeuchi K, Ohishi M, Ota S et al. Metabolic profiling to identify potential serum biomarkers for gastric ulceration induced by nonsteroid anti-inflammatory drugs. J Proteome Res 12(3), 1399–1407 (2013). [CrossRef]
108. Takeuchi K, Ohishi M, Endo K et al. Hydroxyproline, a serum biomarker candidate for gastric ulcer in rats: a comparison study of metabolic analysis of gastric ulcer models induced by ethanol, stress, and aspirin. Biomark. Insights 9, 61–66 (2014). [CrossRef]
109. Dao-Quan Tang, Ll Zou, Xiao-Xing Yin ,Choon Nam Ong. HILIC-MS for Metabolomics: An attractive and complementary approach to RPLC-MS. Mass Spectrom Rev 35(5), 574–600 (2016). [CrossRef]
110. Klampfl CW. Capillary Electrophoresis-/Liquid Chromatography-Mass Spectrometry. Electrophoresis. 37(7-8), 879 (2016). [CrossRef]
111. Ramautar R.Chapter one: Capillary Electrophoresis-Mass Spectrometry for Clinical Metabolomic.Adv Clin Chem 74, 1-34 (2016). [CrossRef]
112. Zhang W, Hankemeier T, Ramautar R. Next-generation capillary electrophoresis-mass spectrometry approaches in metabolomics. Curr Opin Biotechnol 43, 1-7 (2016). [CrossRef]
113. Ramautar R.CE-MS in metabolomics: status quo and the way forward. Bioanalysis 8(5), 371–374 (2016). [CrossRef]
114. Kohler I, Giera M. Recent advances in liquid-phase separations for clinical metabolomics. J Sep Sci 40(1), 93-108 (2016). [CrossRef]
115. Iadarola P, Fumagalli M, Bardoni AM, Salvini R, Viglio S.Recent applications of CE- and HPLC-MS in the analysis of human fluids. Electrophoresis 37(1), 212-230 (2016). [CrossRef]
116. Garcia A, Godzien J, Lopez-Gonzalvez A and Barbas J. Capillary electrophoresis mass spectrometry as a tool for untargeted metabolomics Bioanalysis, 9(1), 99-130 (2017). [CrossRef]
117. Maier TV, Schmitt-Kopplin P. Capillary Electrophoresis in Metabolomics. In: Capillary Electrophoresis: Methods and Protocols, Schmitt-Kopplin P (Ed.).Springer New York, NY 437-470 (2016). [CrossRef]
118. Hernández-Mesa M, Cruces-Blanco C, García-Campaña AM., Capillary electrophoresis-tandem mass spectrometry combined with molecularly imprinted solid phase extraction as useful tool for the monitoring of 5-nitroimidazoles and their metabolites in urine samples. Talanta 163, 111-120 (2017). [CrossRef]
119. Yamamoto M, Ly R, Gill B et al. Robust and High-Throughput Method for Anionic Metabolite Profiling: Preventing Polyimide Aminolysis and Capillary Breakages under Alkaline Conditions in Capillary Electrophoresis-Mass Spectrometry. Anal Chem 88(21), 10710-10719 (2016). [CrossRef]
120. Boizard F, Brunchault V, Moulos P et al. A capillary electrophoresis coupled to mass spectrometry pipeline for long term comparable assessment of the urinary metabolome. Sci Rep 6, 34453 (2016). [CrossRef]
121. D’Orazio G, Hernández-Borges J, Asensio-Ramos M, Rodríguez-Delgado MÁ, Fanali S. Capillary electrochromatography and nano-liquid chromatography coupled to nano-electrospray ionization interface for the separation and identification of estrogenic compounds. Electrophoresis 37(2), 356-62 (2016). [CrossRef]
122. Yamamoto H, Sasaki K. Metabolomics-based approach for ranking the candidate structures of unidentified peaks in capillary electrophoresis time-of-flight mass spectrometry Electrophoresis DOI:10.1002/elps.201600328 [CrossRef]
123. Gulersonmez MC, Lock S, Hankemeier T, Ramautar R. Sheathless capillary electrophoresis-mass spectrometry for anionic metabolic profiling. Electrophoresis 37, 1007–1014 (2016). [CrossRef]
124. Pont L, Benavente F, Jaumot J et al. Metabolic profiling for the identification of Huntington biomarkers by on-line solid phase extraction capillary electrophoresis mass spectrometry combined with advanced data analysis tools. Electrophoresis 37(5–6), 795–808 (2016). [CrossRef]
125. González-Peña D, Dudzik D, Colina-Coca C et al. Multiplatform metabolomic fingerprinting as a tool for understanding hypercholesterolemia in Wistar rats. Eur J Nutr. 55(3), 997–1010 (2016). [CrossRef]
126. Mastrangelo A, Martos-Moreno GÁ, García A et al. Insulin resistance in prepubertal obese children correlates with sex dependent early onset metabolomic alterations. Int J Obes 40(10), 1494–1502 (2016). [CrossRef]
127. Mastrangelo A, Panadero MI, Pérez LM et al. New insight on obesity and adipose-derived stem cells using comprehensive metabolomics. Biochem J 473(14), 2187–2203 (2016). [CrossRef]
128. Harada S, Takebayashi T, Kurihara A et al. Metabolomic profiling reveals novel biomarkers of alcohol intake and alcohol-induced liver injury in community-dwelling men. Environ Health Prev Med 21(1), 18–26 (2016). [CrossRef]
129. Miyamoto T, Hirayama A, Sato Y et al. A serum metabolomics-based profile in low bone mineral density postmenopausal women. Bone 95, 1-4 (2017). [CrossRef]
130. Yoshimi N, Futamura T, Bergen SE et al. Cerebrospinal fluid metabolomics identifies a key role of isocitrate dehydrogenase in bipolar disorder: evidence in support of mitochondrial dysfunction hypothesis. Mol Psychiatry 21, 1504–1510 (2016). [CrossRef]
131. Gao P, Zhou C, Zhao L, Zhang G, Zhang Y. Tissue amino acid profile could be used to differentiate advanced adenoma from colorectal cancer. J Pharm Biomed Anal 118, 349-355 (2016). [CrossRef]
132. Uchiyama K, Yagi N, Mizushima K et al. Serum metabolomics analysis for early detection of colorectal cancer. DOI: 10.1007/s00535-016-1261-6. [CrossRef]
133. Saito T, Sugimoto M, Okumoto K et al. Serum metabolome profiles characterized by patients with hepatocellular carcinoma associated with hepatitis B and C. World J Gastroenterol. 22(27), 6224-6234 (2016). [CrossRef]
134. Hirata H, Sugimachi K, Komatsu H et al. Decreased Expression of Fructose-1,6-bisphosphatase Associates with Glucose Metabolism and Tumor Progression in Hepatocellular Carcinoma. Cancer Res 76(11), 3265-3276 (2016). [CrossRef]
135. Ochiai Y, Uchida Y, Ohtsuki S et al. The blood-brain barrier fatty acid transport protein 1 (FATP1/SLC27A1) supplies docosahexaenoic acid to the brain, and insulin facilitates transport. DOI: 10.1111/jnc.139432016
136. Yoshimia N, Futamura T, Kakumoto K et al. Blood metabolomics analysis identifies abnormalities in the citric acid cycle, urea cycle, and amino acid metabolism in bipolar disorder BBA Clin 5, 151–158 (2016). [CrossRef]
137. Kageyama Y, Kasahara T, Morishita H et al. Search for plasma biomarkers in drug-free patients with bipolar disorder and schizophrenia using metabolome analysis. Psychiatry Clin Neurosci 71(2), 115-123 (2016). [CrossRef]
138. Fujii T, Hattori K, Miyakawa T, Ohashi Y, Sato H, Kunugi H. Metabolic profile alterations in the postmortem brains of patients with schizophrenia using capillary electrophoresis-mass spectrometry. DOI: 10.1016/j.schres.2016.11.011 [CrossRef]
139. Nozawa S, Sato T, Katayama K et al. Metabolic analysis of canine peripheral blood mononuclear cells treated ex vivo with dexamethasone. Vet J 207, 184-187 (2016). [CrossRef]
140. Barbas-Bernardos C, Armitage EG, García A et al. Looking into aqueous humor through metabolomics spectacles – exploring its metabolic characteristics in relation to myopia. J Pharm Biomed Anal 127, 18–25 (2016). [CrossRef]
141. Hamuro J, Ueno M, Asada K et al. Metabolic Plasticity in Cell State Homeostasis and Differentiation of Cultured Human Corneal Endothelial Cell Invest Ophthalmol Vis Sci 57(10), 4452-4463 (2016). 
142. Serrano-Villar S, Rojo D, Martínez-Martínez M et al. Gut bacteria metabolism impacts immune recovery in HIVinfected individuals. EBioMedicine 8, 203–216 (2016). [CrossRef]
143. Yoshimatsu H, Yonezawa A, Yamanishi K et al. Disruption of Slc52a3 gene causes neonatal lethality with riboflavin deficiency in mice. Sci Rep 6, 27557 (2016). [CrossRef]
144. Kimura T, Yasuda K, Yamamoto R et al. Identification of biomarkers for development of end-stage kidney disease in chronic kidney disease by metabolomic profiling. Sci Rep 6, 26138 (2016). [CrossRef]
145. Aoki-Yoshida A, Aoki R, Moriya N et al. Omics Studies of the Murine Intestinal Ecosystem Exposed to Subchronic and Mild Social Defeat Stress J Proteome Res 15 (9), 3126–3138 (2016).
146. Ishikawa S, Sugimoto M, Kitabatake K et al. Identification of salivary metabolomic biomarkers for oral cancer screening. Sci Rep 6, 31520 (2016). [CrossRef]
147. Washio J, Ogawa T, Suzuki K, Tsukiboshi Y, Watanabe M, Takahashi N. Amino acid composition and amino acid-metabolic network in supragingival plaque. Biomed Res 37(4), 251-257 (2016). [CrossRef]
148. Torii Y, Kawano Y, Sato H et al. Quantitative metabolome profiling reveals the involvement of the kynurenine pathway in influenza-associated encephalopathy. DOI: 10.1007/s11306-016-1011-z. [CrossRef]
149. Iida M, Harada S, Kurihara A et al. Profiling of plasma metabolites in postmenopausal women with metabolic syndrome. Menopause 23(7), 749-758 (2016). [CrossRef]
150. Fukai K, Harada S, Iida M et al. Metabolic Profiling of Total Physical Activity and Sedentary Behavior in Community-Dwelling Men PLoS One 11(10), e0164877 (2016). [CrossRef]
151. Chano T, Avnet S, Kusuzaki K et al. Tumour-specific metabolic adaptation to acidosis is coupled to epigenetic stability in osteosarcoma cells. Am J Cancer Res 6(4), 859–875 (2016). 
152. Gao P, Yang C, Nesvick CL et al. Hypotaurine evokes a malignant phenotype in glioma through aberrant hypoxic signaling. Oncotarget 7(12), 15200-14 (2016). 

All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 4.0 License

---