Isotope Corrected Chiral and Achiral Nontargeted Metabolomics: An

Subscriber access provided by Washington University | Libraries

Article

Isotope corrected chiral and achiral non-targeted metabolomics (iCAN-Met): An approach for high accuracy and precision metabolomics based on derivatization, and its application to cerebrospinal fluid of patients with Alzheimer’s disease Takahiro Takayama, Hajime Mizuno, Toshimasa Toyo'Oka, Hiroyasu Akatsu, Koichi Inoue, and Kenichiro Todoroki Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b04852 • Publication Date (Web): 04 Mar 2019 Downloaded from http://pubs.acs.org on March 5, 2019

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 11 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

Isotope corrected chiral and achiral non-targeted metabolomics (iCAN-Met): An approach for high accuracy and precision metabolomics based on derivatization, and its application to cerebrospinal fluid of patients with Alzheimer’s disease Takahiro Takayama1, Hajime Mizuno1, Toshimasa Toyo’oka1, Hiroyasu Akatsu2,3, Koichi Inoue4, and Kenichiro Todoroki1* Laboratory of Analytical and Bio-Analytical Chemistry, School of Pharmaceutical Sciences, University of Shizuoka, 52-1 Yada, Suruga-ku, Shizuoka 422-8526, Japan 2 Department of Medicine for Aging Place, Community Health Care/Community-Based Medical Education, Nagoya City University Graduate School of Medical Sciences, Nagoya 467-0001, Japan 3 Department of Neuropathology, Choju Medical Institute, Fukushimura Hospital, Toyohashi 441-8124, Japan 4 Laboratory of Clinical & Analytical Chemistry, College of Pharmaceutical Sciences, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga 525-8577, Japan 1

ABSTRACT: We propose a chiral metabolomics approach based on a data-dependent MS/MS analysis (DDA) using highresolution Q-Tof-MS and 13C-isotope coded derivatization (ICD) reagents, i.e., iDMT-(S)-A and iDMT-(S)-PO. The advantage of the method is the correction of all detected derivatives by parallel derivatization of the isotope-coded and non-coded reagents. The automatic data analysis platform using an MSDIAL and ICD discrimination program, called DINA, was also developed and used for the data analysis process. As a result, a 0.5-2.0% (D-/L-isomer) variation of the isomers was correctly recognized in the automatic data analysis step. Both the semiquantitative comparison and identification efficiency were improved as a result of the high resolution/accuracy of the MS and MS/MS spectra derived from the DDA analysis. This method was used for biomarker discovery in the cerebrospinal fluid (CSF) of patients with Alzheimer’s disease (AD). Forty biomarker candidates were successfully determined, including 14 chiral ones. Keywords: Chiral metabolomics; chiral derivatization reagent; isotope-coded derivatization; high-resolution UPLC-Q-ToF-MS; data-dependent MS/MS scan.

Metabolomics is an important research area for the development of diagnostic methods and therapeutic drugs for various diseases 1-8. Recent insights into the analysis of enantiomers as a result of increased interest in this research area have expanded our understanding of metabolomics. Findings include that the D-amino acid concentrations and their D/L ratio are strongly related to several diseases, for example, the role of plasmic D-Asn and D-Ser in kidney functions and the progression of chronic kidney disease 9, and that D-Ser in the blood samples of patients with schizophrenia results from reduced simulation of the N-methyl-D-aspartate (NMDA) receptor 10, 11. In terms of cancer, DL-2hydroxyglutaric acid and its isomeric ratios were found to be related with acute myeloid leukemia 12. Concentrations of uncommon chiral metabolites such as Damino acids in living bodies are quite low and their variation under abnormal conditions such as disease are also at trace levels 9, 12, 13, 14, 15. Therefore, chiral metabolites as biomarkers may be overlooked because of the low accuracy and sensitivity of common analytical methods. To date, many differential analysis methods for chiral metabolites have been developed 16-21. Some examples include the use of chiral derivatization reagents 16-19, a chiral stationary

phase with liquid chromatography 20, or super-fluids critical phase chromatography 21, which have been found to have high chiral resolution, selectivity, and sensitivity. Derivatization methods targeting functional groups, such as carboxylic acids and amines, are quite effective in metabolomics because they allow for the enhancement of ionization and good chromatographic separation. As such, they expand the coverage of analytes 22-28. In our previous study, DMT-(S)-A for carboxylic acids and DMT-(S)-PO for amines were developed as chiral derivatization reagents for the highly sensitive and selective LC-tandem MS (MS/MS) analyses of chiral and achiral metabolites 29, 30. In addition, we also reported a chiral and achiral non-targeted metabolomics (CAN-Met) method that allows for the comprehensive analysis of several groups of metabolites such as amines and carboxylic acids, by derivatization 31. Furthermore, we developed a discrimination method, the so-called chiral extraction (CHEx), in which we determined whether the detected peaks were derived from a chiral or achiral metabolite 31. The combined use of both CAN-Met and CHEx allowed for the detection of chiral metabolites and the identification of trace amounts of chiral isomers. However, the accurate measurement of the isomer ratios of trace amounts of chiral

ACS Paragon Plus Environment

Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

isomers in quantitative analysis has not yet been fully achieved. For quantitative metabolomics, several approaches have been reported, including standard addition methods using stable-isotope labeled metabolites 32, 33 and parallel derivatization methods using isotope reagents 34-37. Many labeled metabolites have been prepared using the former method, but none corresponded to the individual analysis of chiral metabolites or their derivatives. To overcome these quantitative issues, we developed a novel method via the combination of isotope-coded derivatization (ICD) reagents with data-dependent MS/MS analysis (DDA) derived from Q-Tof-MS. This new method was called isotope corrected chiral and achiral non-targeted metabolomics (iCAN-Met) (Fig. 1). The strategy of our proposed method was based on parallel derivatization by a pair of isotopes (heavy) and non-coded (light) reagents. The aim of this strategy was the overall correction of similarly categorized compounds such as amines or carboxylic acids, in a single analysis, such that accurate and precise data are provided by a single run without the need to use the stable isotope-labeled standards of each metabolite. Furthermore, the DDA based on high-resolution mass enables procurement not only of an accurate monoisotopic mass but also clear fragmentation patterns. Therefore, the accurate annotation of chemical structural information for metabolites can be obtained. The key to the success of our strategy is how we discriminate light and heavy derivatives from other components. In this case, the required data are the retention time, m/z, intensity, and m/z of the product ion. The MSDIAL 38 software developed by Dr. Tsugawa et al. is a powerful data analysis software program for peak picking, deconvolution, and alignment for full MS and MS/MS scanning. This software allows us to obtain the required data index and was thus suitable for the present method. To detect reagent characteristic product ions (CPIs) derived from light- and heavy-derivatives, and determine their ICD mass shifts, we created an Excel macroprogram, discrimination of isotope coded/non-coded peak pair analysis (DINA) (Fig. 2). This program is open source, and its source code is provided in the supplemental information. Using this program, reagent CPIs were extracted, and the peak intensity ratios of the light- and heavy-derivatives were calculated for subsequent multivariate analyses. Conventional programs that meet these objectives and are applicable to the LC-MS systems of individual companies are either not commercially available or are quite expensive. The combination proposed here of DINA with MSDIAL allows for new non-targeting metabolomics using LC-MS (/MS).

Figure 1. Overview of our novel method, iCAN-Met, which is a combination of the ICD correction with DDA scanning of LCHR/MS.

Page 2 of 11

Figure 2. Overview of novel data processing approach based on MSDIAL and DINA to realize the iCAN-Met strategy, followed by isotope coded derivatization-DDA analysis.

We evaluated whether the iCAN-Met method could accurately quantify chiral metabolite biomarkers added in trace amounts to human sera. We applied the proposed method for biomarker discovery of Alzheimer’s disease (AD) in cerebrospinal fluid (CSF) samples. Although many metabolome studies have successfully determined the biomarkers of AD, including the Trp-Kyn pathway metabolites 39- 41 and several amino acids and polyamines 42, 43, 44, comprehensive analytical results that include the isomers of chiral metabolites are rare. Therefore, our approach was applied to obtain new insights into AD metabolism and the determination of its biomarkers. Our iCAN-Met method based on the ICD-DDA strategy could provide an accurate, effective, and practical metabolomics analysis for researchers and medical personnel interested in drug discovery and clinical diagnosis.

EXPERIMENTAL SECTION Figure 1 shows the workflow of the iCAN-Met method for the determination of amines and carboxylic acids. Grouped samples (e.g., groups A and B) were pretreated by deproteinization and were then divided into two portions (group A; A-1, A-2 group B; B-1, B-2). The first (A-1) was mixed with a portion of another group (B-1). This mixture was derivatized with either isotope-labeled reagents (heavy reagents), iDMT-(S)-A (for carboxylic acids), or iDMT-(S)PO (for amines). The other portions (A-2 and B-2) were individually derivatized with either non-isotope-labeled reagents (light reagents), DMT-(S)-A for the carboxylic acids, or DMT-(S)-PO for the amines. The derivatized solutions with heavy and light reagents were mixed and subjected to UPLCQ-ToF-MS as described in the experimental section ‘Instrumental conditions and data analysis’. The obtained data including retention times, m/z values, peak intensities, and m/z values of product ions were selected, deconvoluted, and aligned using the MSDIAL software. The CPIs derived from the light and heavy derivatives were selectively extracted using the DINA program. The obtained peak intensity ratios of their light and heavy derivatives were subjected to a multivariate statistical analysis together with the data obtained by DINA; i.e., a principal component analysis (PCA) for differential analysis and an orthogonal partial least squares discrimination analysis (OPLS-DA) or multiple testing for extraction of the components with significant variation.


Page 3 of 11 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Materials and chemicals 2,4,6-Trichloro-triazine (TCT), 2-chloro-4,6-dimethoxytriazine (CDMT), (3S)-(-)-3-(tertbutoxycarbonylamino)pyrrolidine (3Boc-(S)-A), (3R)-(+)-3(tert-butoxycarbonylamino)pyrrolidine (3Boc-(R)-A), acetoacetic acid lithium salt (AA), DL-3-hydroxybutyric acid (DL-3HA), α-ketoisocaproic acid, hydrochloride (HCl) in ether, N-acetyl-DL-tryptophane (N-Ac-DL-Trp), N-acetyl-DLvaline (N-Ac-DL-Val), 1-(3-dimethylaminopropyl)-3ethylcarbodiimide (EDC), and 1-hydroxy-7-azabenzotriazole (HOAt) were purchased from Tokyo Kasei Co. (Tokyo, Japan). DL-Lactic acid (DL-LA), DL-2-hydroxybutyric acid (DL-2HA), 4-hydroxyphenylacetic acid (4HA), 4hydroxyphenylpyruvic acid (4HP), DL-kynurenine, DL-amino acids, L-amino acids, and D-amino acids were purchased from Sigma-Aldrich (St. Louis, USA). L-Proline, D-proline, propionic acid (PA), fumaric acid (FMA), Nhydroxysuccinimide (HOSu), acetonitrile (CH3CN), methanol (CH3OH), dichloromethane (CH2Cl2), hexane (Hex), ethyl acetate (AcOEt), pyridine, triethylamine (TEA), and LC-MS grade of formic acid (FA) were purchased from Kanto Chemicals (Tokyo, Japan). Butyric acid (BA) was obtained from Dr. Ehrenstofer GmbH (Augsburg, Germany). The ketoisovaleric acid (KVA) sodium salt was obtained from Toronto Research Chemicals (Ontario, Canada). Citric acid (CA), -ketoglutaric acid (KA), succinic acid (SA), and DLmalic acid (MA) were purchased from Wako Pure Chemical Industries, Ltd. (Osaka, Japan). All the ICD reagents were synthesized as described in the Supporting Information. Pooled human sera were obtained from NISSUI, Ltd. (Tokyo, Japan). Water (H2O) was purified using the PURELAB flex 3 ultrapure water purification system (ELGA LabWater, High Wycombe, UK). Each 10 mM stock solution of the carboxylic acid and amino acid was prepared with CH3CN or H2O. These working solutions were prepared by sequential dilutions with CH3CN or H2O just before use. Instrument conditions and data analysis A high-resolution ultra-performance liquid chromatography-electrospray spray ionization tandem mass spectrometry (UPLC-ESI-MS/MS) analysis was performed using an ACQUITY UPLC I-class (Waters, Milford, MA, USA) connected to a Xevo G2-XS Q-Tof mass spectrometer (Waters). An ACQUITY UPLC BEH C18 column (1.7 m, 150 × 2.1 mm i.d., Waters) was used at the flow rate of 0.4 mL/min and 40°C. The elution conditions were as follows: mobile phase, CH3CN/H2O mixture containing 0.1 % (v/v) FA; gradient elution % CH3CN (min), 2 (initial) - 2 (0.5) - 17 (25) - 80 (50) - 98 (50.5) - 98 (52.5) - 2 (53) - 2 (60). The column eluate was introduced into the Q-Tof-MS equipped with an electrospray ionization (ESI) source in the positive-ion mode. The setting of the ESI source and mass spectrometer were as follows: capillary voltage, 2.50 kV; sampling cone, 20; resolving quadrupole LM resolution, 11.0; desolvation gas flow, 800 L/h; cone gas flow, 50 L/h; collision energy, 22-90 eV using charge state recognition; source temperature, 120°C; desolvation temperature, 350°C; detection mode, sensitive; data acquisition, centroid mode from m/z 250 to 1,000 at the scan rate of 0.5 s/scan in the full scan. The mass spectrometer was calibrated using a calibration solution containing sodium formate before the analysis. To maintain mass accuracy during the analysis, the lock mass of leucine enkephalin (m/z 556.2771 [M+H]+) at a concentration of 0.2 ng/mL was used

by a lock spray interface at the flow rate 5 μL/min. Fast DDA was applied to trigger the MS/MS acquisition of the precursor ions with an intensity threshold of 20,000. The top 5 ions with high-intensity peaks were selected to perform the MS/MS at the scan rate of 0.15 s/scan. The data obtained by the fast DDA, including the full scan MS and MS/MS spectra, were transferred to MSDIAL software for peak selection, deconvolution, and alignment. The detailed conditions for data processing of the MSDIAL are described in the Supporting Information. Isotope pair peak-picking using in-house software DINA To extract pairs of peaks corresponding to the light- and heavy-reagent derivatives, we developed and used an in-house VBA-based program, DINA. An overview of data processing is provided in Figure 2. Peak-picking of the isotope pairs of the amine and carboxylic acid derivatives were performed according to the following rules: (1) the difference in both retention times is within 0.1 min, (2) having one of the following CPIs; m/z 209.1053, 226.1315, 195.0987, in which the isotope shifts are 2.0067 Da or corresponding values and their tolerance is within 0.05 Da, (3) isotope shifts of the precursor ions are 2.0067 Da or corresponding values, and their tolerance is within 0.02 Da. A correction factor for each peak was defined as follows: correction factor = InICD/IICD where InICD is each peak height of the non-ICD reagent derivatives, and IICD is each peak height of the corresponding ICD reagent derivative. The resulting data matrix table was used for multiple statistical analyses with EZinfo to determine the various compounds as biomarkers. Other data were analyzed in Excel 2013. The detailed conditions and codes are provided in the Supporting Information. Derivatization reaction Derivatization of carboxylic acids by DMT-(S)-A or iDMT(S)-A The sample solution (50 μL or described volume) containing carboxylic acids was added to 30 μL of 30 mM iDMT-(S)-A or DMT-(S)-A in H2O / CH3CN (1/1 v/v) containing 0.5% TEA, 75 μL of 20 mM EDC in CH3CN, and 75 μL of 20 mM HOAt in H2O/CH3CN (1/4 v/v). This solution was stored at room temperature for 3 h, then evaporated under reduced pressure using an EZ-2 centrifugal evaporator (GeneVac, NY, USA). The resulting residues were dissolved in 100 μL of 0.1% FA in CH3CN/MeOH/H2O (1/1/3 v/v/v). Derivatization of amines by DMT-(S)-PO or iDMT-(S)-PO The sample solution (50 μL or described volume) containing amines was added to 60 μL of 10 mM DMT-(S)PO or iDMT-(S)-PO in CH3CN) and 100 μL of 100 mM TEA in CH3CN. This solution was stored at room temperature for 3 h, then evaporated under reduced pressure using an EZ-2 centrifugal evaporator. The residues were dissolved in 100 μL of 0.1% FA in CH3CN/MeOH/H2O (1/1/3 v/v/v). Repetitive analysis of human serum samples A pooled human serum sample of 100 μL was deproteinized by adding it to 1.9 mL of CH3CN. After a 15min incubation at room temperature, the mixture was centrifuged at 3,000 × g for 10 min. Fifty microliters of the supernatant was then used for the derivatization reaction of DMT-(S)-A and DMT-(S)-PO. The derivatization reactions of iDMT-(S)-A and iDMT-(S)-PO were similarly performed.



Both solutions of the light and heavy derivatives were mixed, and the aliquot (4 μL) was subjected to UPLC-Q-Tof-MS. This analysis was repeated 6 times. The data were processed as previously described. Determination of authentic standards spiked in serum by the iCAN-Met method Pooled human serum samples were similarly pretreated as previously described. For the analysis of the chiral carboxylic acids, 100 μL of the supernatant was spiked at different ratios of the DL-isomers of the carboxylic acid standard mixtures (40 μL, D/L = 0/20, 0.1/20, 0.2/20, 0.4/20 μM of LA, 2-HA, 3-HA, and N-Ac-Trp). Each sample (70 μL) was derivatized with DMT-(S)-A. Subsequently, a mixture of 4 kinds of spiked samples was derivatized by iDMT-(S)-A. Equal volumes of the derivatization reaction solutions of DMT-(S)-A and iDMT(S)-A were mixed and the aliquot (4 μL) was subjected to UPLC-Q-Tof-MS. For the analysis of the chiral amino acids, 100 μL of the supernatant was spiked at different ratios of the DL-isomers of the amino acid standards mixture (40 μL, D/L = 0.1/20, 0.2/20, 0.4/20 μM of Ala, Val, Leu, and Phe). Each sample (70 μL) was derivatized with DMT-(S)-PO. Subsequently, a mixture of 4 kinds of spiked samples was derivatized by iDMT-(S)-PO. Equal volumes of the derivatization reaction solutions of DMT-(S)-PO and iDMT-(S)-PO were mixed and the aliquot (4 μL) was subjected to UPLC-Q-Tof-MS. Both analyses were repeated 3 times, and the data obtained were processed. Preparation of CSF samples from patients with Alzheimer’s disease Postmortem human tissues and CSF samples were collected from bodies at the Choju Medical Institute Fukushimura Hospital. Written informed consent was obtained for the autopsies and permission to use the obtained results for diagnosis, research, and genetic analysis was obtained from the patients’ guardians. The tissues and CSF samples obtained from the Fukushimura Brain Bank were used for the accurate, reliable, and detailed pathological evaluation of AD 45. The volunteers’ tissues and CSF samples were confined to the specific brain region that is associated with the visual pathology of plaques/tangles, from patients that had been diagnosed with gradual pre-mortem memory loss. The CSF samples were dispensed into several tubes and stored at -80°C until employed for analysis after gaining approval from the authorities, including the Ritsumeikan University Ethics Panel. Based on the pathological examinations, the CSF samples were classified into AD and control groups based on the neurofibrillary tangles break stage. Break staging refers to the classification of the degree of pathology in AD; thus, is not a direct diagnosis of clinical AD. Thereafter, 200 L of the CSF samples were deproteinized by adding to 200 μL of CH3CN, followed by centrifuging at 21,500 × g for 5 min at 5°C. Three hundred microliters of each supernatant was then evaporated under reduced pressure with an EZ-2 centrifugal evaporator, and the resulting residue was redissolved in 600 μL of 50% aqueous CH3CN. An aliquot of this solution (45 L) was added to 5 L of 50 M d3-DL-LA (for carboxylic acids analysis using DMT(S)-A) or d3-DL-Ala (for amines analysis using DMT-(S)-PO) and then applied to both derivatization reactions. Each 50 μL of the pretreated CSF sample was derivatized with DMT-(S)-A or DMT-(S)-PO, whereas a mixture of all pretreated CSF

Page 4 of 11

samples was derivatized with iDMT-(S)-A or iDMT-(S)-PO. Equal volumes of the derivatization reaction solutions of the light and heavy reagents were mixed, and the aliquot (4 μL) subjected to UPLC-Q-Tof-MS. For identification of the marker candidates, the CHEx method 31 was applied to all the same pretreated CSF samples and the details are described in the Supporting Information.

RESULTS AND DISCUSSION Synthesis and evaluation of isotope-coded chiral derivatization reagents To carry out our iCAN-Met strategy, we synthesized isotope-coded chiral derivatization reagents in which the carbon atoms of two methoxy groups were replaced with 13C; iDMT-(S)-A for the carboxylic acids and iDMT-(S)-PO for the amines. The introduction of a 13C atom into the methoxy groups was performed via the reaction of TCT and 13CH3OH. The synthetic data of the reagents are described in the experimental section of the Supporting Information. Table S1 shows the results of the comparison between the calibration curves obtained from 17 carboxylic acids and 16 amines derivatized with the ICD and non-ICD reagents, respectively. In both calibration curves, the average slope ratios were almost 1.0, and these values did not change in the different sample matrices. This suggests that the difference in the quantification results based on the difference in reagents used was negligible. We then verified the effect of the racemization during derivatization. We derivatized single enantiomers of standard ibuprofen (carboxylic acid) and phenylethylamine (amine) with iDMT-(S)-A and iDMT-(S)-PO. From the LC-MS/MS results, the peak heights of the racemized diastereomers were less than 0.5% compared to those of the original peaks (data not shown). This result indicated that these reagents did not influence the racemization of the analytes even in the quantification of trace amounts. Moreover, the derivative peaks derived from reagents with the insufficient introduction of stable isotopes (i.e., 12C-13C (-1.0033 Da) or 12C-12C (2.0067 Da)) were not observed on the MS spectra. This proves that the stable isotope purities of the used reagents were extremely high. These results indicated that the synthesized reagents could be sufficiently applicable to the iCAN-Met method. Evaluation of iCAN-Met by patient model sample Producing CPIs by collision-induced dissociation (CID) of MS/MS contributes to the selective and accurate identification of the target analytes from a complex matrix, such as serum. However, the data processing has been conventionally carried out by cumbersome manual operations, for which its working time is rate-determining. As mentioned in the Introduction, our DINA program automatically allows for the extraction of reagent CPIs and the calculation of the peak intensity ratios of the light and heavy derivatives according to the procedure shown in Figure 2. Therefore, we combined the DINA program with the iCANMet method to evaluate whether the amine and carboxylic acid metabolites could be extracted with high accuracy from serum samples. Table 1 lists the number of detected peaks, their intensity profiles, and the relative standard deviations.


Page 5 of 11 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table1. Effect of ICD correction on the reproducibility (RSD %) of the peak intensity of carboxylic acids and amines with and without ICD correction. Carboxylic acids Corrected by ICD

Without ICD correction

Amines Corrected by ICD

Without ICD correction

RSD (%) Peak number Cumulative frequency (%) Peak intensity profile Peak number Cumulative frequency (%) Peak intensity profile Efficiency RSD (%) Peak number Cumulative frequency (%) Peak intensity profile Peak number Cumulative frequency (%) Peak intensity profile Efficiency

L 22

20

M 171 511

H 239

H 31 1.10

L 101 1.00

M 171 1.00

H 239 1.00

The number of peaks indicating a peak intensity of “L” < 104, “M” 104-5 × 105, “H” > 5 × 105. “Efficiency” is calculated using the following equation: (number of peak profiles L, M, or H with correction)/(number of peak profiles L, M, or H without correction). When the peak intensities were reduced to 5000 or less, 327 carboxylic acid peaks and 511 amine peaks could be detected. Without the ICD correction, the ratios of the peak number, which indicated less than 20% RSDs (%) by repeated assay (n = 6), were 71% (carboxylic acids) and 79% (amines) of the number of the detected peaks. In contrast, with ICD correction, the results were improved to 82% (carboxylic acids) and 83% (amines). Notably, improvement of the RSDs was confirmed for the low-intensity peaks. These results suggested that the ICD correction using our reagent could significantly contribute to the accurate extraction of the peak fluctuations of the metabolites. Subsequently, we evaluated whether the variation of very low concentration enantiomers was detectable by iCAN-Met. As the patient’s model sample, different ratios of enantiomers (D/L=0/100, 0.5/100, 1/100, and 2/100) of LA, 3-HA, 2-HA, and N-acetyl Trp (carboxylic acids) or Ala, Val, Phe, and Trp (amines) were spiked in human pooled serum. Figures 3A and B show the total ion chromatograms (TICs) and the extracted ion chromatograms (EICs) obtained from the sample analysis. Because the difference in concentration was low (less than 2%), a clear difference in the TICs could not be detected. However, the EICs detected the difference in the ratio of the enantiomers. Figure 4 shows the PCA and S-plot obtained by OPLS-DA, which contain spiked chiral carboxylic acids. Each of the samples was analyzed in triplicate and classified by the different ratios of the enantiomers in the PCA (Fig. 4A-I), even when the differences had a very low variation, such as 0.5 D-/100 L-isomer to 0 D-/100 L-isomer. The D-isomers in the D/L = 2/100 sample were observed to increase in comparison to the D/L = 0/100 sample in the S-plot (Fig. 4AII). Furthermore, Figures 4B-I and -II show the intensity

profiles of 3HA before and after the ICD correction. The differences between D/L = 0/100 to 1/100 and 2/100 were significant at p < 0.01, whereas the differences with 0.5/100 were not significant without the ICD correction (Fig. 4B-I). In contrast, the differences between all groups were significant in all the spiked D-enantiomers according to the analysis of the ICD correction factor (Figs. 4B-II). These significant differences were caused by the improved accuracy of the ICD correction, which lowered the quantification limit of the trace amounts of the enantiomers. In the chiral amine metabolite analysis, the low variation of 4 amino acids was correctly recognized with the PCA and S-Plot (Figs. S1A and B). To analyze the uncommon chiral variants with a very low concentration, such as D-amino acid, highly sensitive detection and accuracy analyses are needed.

Figure 3. TICs and EICs of standard spiked pooled human serum. 1: D/L = 0/100 sample. 2: D/L = 0.5/100 sample. 3: D/L = 1/100 sample. 4: D/L = 2/100 sample. A: carboxylic acids. B: amines. I: TICs. II: XICs of 3HA, 2HA, Val, and Leu.



Figure 4. Results of multivariate analysis of sample model. AI: PCA of derivatives of 327 carboxylic acids in human pooled serum. A-II: S-plot of carboxylic acids compares D/L=2/100 (upper) and D/L=0/100 (bottom) as an example of a twogroup comparison. B-I and II: The intensity profiles of 3HA as an example. **: p < 0.01, NS: Not significant. The figures in the bar charts indicate the RSD% of the n = 3 repeat assay. The results for the amines are shown in the Supplemental Information.

Despite the attractive nature of the minor enantiomers, they have rarely been targeted because of the limitations of the analysis. This evaluation experiment proved that the proposed method could overcome these difficulties and is able to analyze the minor enantiomers present in complex matrices. Furthermore, our methodology simultaneously allowed for the analysis of amines and carboxylic acids by mixing their derivatization reaction solutions. As already described, the CPIs for the carboxylic acid derivatives were m/z 209.1053 and 226.1315, whereas the CPIs for the amine derivatives were m/z 209.1053 and 195.0987. Therefore, we simultaneously analyzed both chiral carboxylic acids and amines in the biological samples using the difference in these CPIs. Figure S2A shows PCA plots from the analysis of pooled human sera samples spiked with different ratios of minor amine and carboxylic acid enantiomers and the detection of the CPIs with m/z 209.1053. The obtained PCA plots are similar to those in Figure 4 and Figure S1. It was also possible to individually analyze the carboxylic acids or amines from the same LC-MS data by choosing m/z 226.1315 or 195.0897 (Fig. S2B and C) as the CPIs. Our approach of mixing the derivatization reaction solutions can shorten the duration of the LC-MS analysis by half, and the total throughput can be substantially improved. Identification efficiency and sensitivity of Q-Tof-MS platform compared to TQ In our earlier reports, we conducted derivatized metabolomics using the precursor ion scan mode of TQ mass spectrometry. Using this precursor ion scan mode, nontargeted chiral metabolomics have become possible. However, this full-scan-based measurement with a TQ mass spectrometer has the problem of sensitivity and identification accuracy. We adopted Q-Tof-MS with a higher resolution to

Page 6 of 11

solve these problems and achieve highly sensitive and accurate analysis. Table S2 lists the m/z value errors with respect to their theoretical values and limits of detection (LODs, signal-tonoise = 3) for 17 carboxylic acids and 16 amines compared with different mass spectrometry platforms. The errors of m/z to the theoretical values were all less than 7.0 ppm, and the structure of the detected compounds was sufficiently determined from the database search and authentic standards. Using the Orbitrap mass spectrometer instead of Q-Tof-MS provided more accurate and precise mass data analysis. The LOD of the carboxylic acid and amine derivatives in this proposed method is amol to fmol (per column injection), achieving 2 to 400 times higher sensitivity than our previous TQ method using the precursor ion scan mode. Because the quadrupole mass spectrometer has low mass resolution, it is difficult to completely separate metabolites from various background peaks. Therefore, by accurate mass spectrometry with a high resolution mass spectrometer, such as Q-Tof-MS and Orbitrap, it is possible to separate metabolite peaks from the background and enable accurate and highly sensitive detection. Furthermore, the introduced number of reagents corresponds to the number of functional groups (amine and carboxylic acid) in the initial structure. The mass shifts of the ICD product to the non-coded product indicate the number of functional groups in the original structure, as shown in the mass shifts of the derivatives, LA (mono-COOH, 2 Da), FMA (di-COOH, 4 Da), and CA (tri-COOH, 6 Da) (Fig. S3). Moreover, the DDA allows one to obtain almost all of the product ions from the precursor ions in the Q-Tof-MS, and thus the annotation of the detected ion structure is more accurate. Figure S4 shows the annotation example of LA, KCA, MA, Ala, Glu, Lys, and Phe. The fragment ions larger than the reagent’s fragments indicated part of the structure of the reacted metabolites. Furthermore, positional isomers were also discriminated by the fragmentation pattern (Fig. S5). The ions at m/z 268.1492 or 266.1489, and at 279.1452 or 292.1511 indicate the structures of 3HA or 2HA, and Leu and Ile, respectively. Although the derivatization of metabolites has many advantages, such as an increase in the detection sensitivity and isotope labeling for accurate quantification, the identification steps are rather difficult because of the addition of the mass of the derivatization reagent to the metabolites and a database search step for metabolite identification cannot be applied. Thus, this problem has been prevented by the application of derivatization to comprehensive metabolomics. Adoption of the DDA mode in the Q-Tof-MS can provide additional information, such as the MS/MS fragment pattern and number of reactive functional groups, as well as the monoisotopic mass. This solves the problem of conventional derivatized metabolomics. Consequently, the combination of the DDA mode of UPLC-Q-Tof-MS with ICD allowed for both the accurate structure identification and highly sensitive analysis of the metabolites. Application of iCAN-Met for AD CSF and comparison with non-AD We finally applied iCAN-Met to biomarker discovery in the CSF of patients with AD. Table S3 shows the profiles of the patients with and without AD (non-AD). Non-AD refers to


Page 7 of 11 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


a person who is not diagnosed with AD. A total of 20 CSF samples (AD/Non-AD = 10/10) was collected. The sex and age of both groups were almost the same. The pathological diagnosis information was also similar in each group. From the LC-MS/MS analysis of the derivatized CSF samples, 402 carboxylic acid and 629 amine-containing metabolites were successfully detected. We then performed a multivariate analysis such as PCA, and multiple testing to detect the fluctuation in the metabolite peaks between the AD and non-AD samples using the variable of the ICD ratio of each peak. In the PCA score plots obtained from the 1st–2nd principal components shown in Figures S6A and B, no significant differences were observed between the groups.

Table 2. List of marker candidates in the CSF of patients with AD.

m/z

Carboxylic acids p (Mann-Whitney U test)

Fold change

17.42

370.1358

8.14E-03

17.21

345.1593

6.39E-03

14.75

314.1464

13.83 22.20

Retention time (min)

Marker type Asymmetric carbon

Amines p (Mann-Whitney Asymmetric U test) carbon Fold change

Retention time (min)

m/z

1.84

44.15

380.2269

2.30E-03

2.86

1.80

31.89

289.1223

1.30E-02

2.03

7.62E-04

1.71

42.56

433.1867

1.30E-02

2.03

344.1489

3.88E-03

1.70

36.83

553.3707

1.30E-02

1.83

312.1316

4.99E-03

1.42

15.64

413.1784

3.00E-03

1.63

41.35

548.2307

3.88E-03

1.56

38.66

479.1967

1.01E-03

1.36

26.16

370.1419

4.24E-04

1.33

45.28

394.2446

3.88E-03

1.17

O Increase in AD

30.25

440.2046

6.39E-03

0.63

O

32.23

441.1886

1.30E-02

0.81

30.35

340.2055

4.99E-03

0.54

O

22.71

494.2002

1.03E-02

0.78

27.65

397.1620

8.14E-03

0.52

33.84

358.1862

8.14E-03

0.74

31.50

288.1331

6.39E-03

0.51

24.17

369.1877

1.30E-02

0.71

44.78

435.2038

1.03E-02

0.58

30.81

445.1838

1.30E-02

Decrease in AD

0.51

O

O O O

O

H+

The specified m/z values were calculated by the ICD number shift and the charge as an adduct was only. The p-values were calculated by the Mann-Whitney U test with multiple testing correction (false discovery ratio (q) < 0.10). The fold change values were calculated as follows: (Median of ICD factor in patients with AD)/(Median of ICD factor in non-AD patients). The asymmetric carbon was defined as detected peaks picked up by the CHEx method. On the other hand, as shown in Figures 5A-I and B-I, the PCA score plots obtained from the 2nd-3rd and 3rd–4th principal components showed a tendency to divide into the AD and non-AD groups into both cases for the carboxylic acid and amine analyses, even though each contribution ratio was not high.

Figure 5. PCA of the metabolome comparison in the CSF of patients with AD and non-AD patients. A: Results of carboxylcontaining metabolites (2nd to 3rd principal component), a total of 402 peaks. B: Results of amine-containing metabolites (3rd to 4th principal component), a total of 629 peaks.



Table 2 lists 9 carboxylic acids and 15 amines extracted as AD biomarker candidates, of which 8 compounds were presumed to be chiral compounds from the results of the CHEx method 31. This result indicated the importance of the analysis of chiral metabolites, which were out-of-target in conventional metabolome studies. A marker extraction was performed by the Mann-Whitney U test with a multiple testing correction (false discovery ratio (q) < 0.10 using the Q-Value method 46) because the scale of the sample was small to find potential biomarkers and not adequate to use a supervised statistical method, such as OPLS-DA. Because the Q-Value method was performed with q < 0.10, 10% false positives were included in the listed metabolites. Table S4 shows the identification results of checking against the human metabolome database (HMDB) and MassBank with the calculated m/z of the biomarker candidate shown in Table 2. The biomarker candidates annotated by MS/MS fragmentation showed as their compound’s name, whereas compounds with only exact mass were expressed as their molecular formulas. Among the 24 extracted biomarkers, 16 kinds of them could be annotated comparing with MS/MS spectra in database, and six of annotated ones were chiral compounds. Figures S7A-C show an example of the chemical structures and the MS/MS spectra of the biomarker candidates identified in Table 2. For example, the ions of m/z 445.1838 (fold change = 0.51, p = 0.01) and 441.1886 (fold change = 0.81, p = 0.01) were identified as kynurenine and Trp, respectively. These act on the Trp-kynurenine metabolism pathway, which is known as a variation pathway of neurodegenerative disorders39. The varied kynurenine peak was a chiral biomarker candidate and was a lower intensity peak compared to the oppositely eluted peak derived from the reaction with DMT-(R)-A in CHEx. The optical configurations of these metabolites were determined as D-kynurenine and L-Trp with an injection of the optically pure authentic standard derivatives (Fig. S8). Because these were chiral enantiomers they could not be identified by optical configuration without standards and the metabolite structures were hard to dissociate by collisioninduced dissociation. In the same pathway, the peak of m/z 397.1620 (fold change = 0.51, p = 0.008) was identified as kynurenic acid from the MS/MS spectra (Fig. S7C) and has been suggested to decrease as a biomarker in several studies 3941. This is because kynurenic acid exerts a neuroprotectivity by acting as an antagonist against the NMDA receptor 47-49. Overstimulation of the NMDA receptor can lead to neuronal death by excitotoxicity, which is a well-known neuropathology in AD. However, the existence of kynurenic acid reduces this effect, acting as an NMDA receptor. These results reinforce the accuracy of iCAN-Met because the pre-reported candidates were determined by iCAN-Met, and the chiral markers were successfully identified. In addition to the above-mentioned compounds, the structures of the biomarker candidates shown in Table 2 were determined from the MS/MS spectra (Figs. S7D and E); the amine metabolite was Ala-Ser (Fig. S7D), and the carboxylic acid metabolite was threonic acid (Fig. S7E). The asymmetric carbons were detected by CHEx and the isomers were detected from the extracted MS chromatogram. No other studies have detected the isomers of these biomarker candidates from samples of AD. Our new biomarker candidates in the CSF of patients with AD could be useful for the development of a novel diagnosis method. However, a major problem for the clinical application

Page 8 of 11

of this method is the invasiveness of collecting CSF samples from patients. Thus, we are working on developing a methodology for the discovery of novel AD markers from non-invasive samples, such as serum or urine. We have successfully determined the novel chiral and achiral biomarkers of AD in CSF samples, and these results suggest that iCAN-Met is a reliable method for real sample analysis. The data obtained regarding newly discovered biomarkers could be used in future investigations of brain tissue, serum, and biological samples to develop a highly accurate and early diagnosis method. In addition to an accurate diagnosis, a novel metabolic pathway could be identified because variations in the chiral metabolites have not been observed in AD samples previous to our results.

CONCLUSIONS This paper presents a novel approach, iCAN-Met, for the accurate determination of biomarkers of chiral and achiral metabolites between different biological sample groups. The method is based on the derivatization of a pair of isotopes (heavy and light reagents) and DDA scanning. To perform an overall correction of the derivatives, MSDIAL and DINA software platforms were adopted for data processing. The proposed method allows for a highly accurate analysis of chiral metabolites with low variation. Although this method could provide structural information about the metabolites within the same run, the identification step remains a manual process. We developed new software adaptable to the derivatization method. Furthermore, we attempted to identify the biomarkers released into the CSF of patients with AD. Forty biomarkers were successfully determined, including novel and/or chiral ones. The CHEx method was applied to determine whether the detected markers were chiral or achiral. As a result, 14 markers were identified as chiral markers, which is almost equivalent to one-third of all markers. Although many metabolomic studies do not take enantiomers into account, our results suggested the importance of chiral separation analysis. The identified markers included known metabolites, such as Trp and kynurenine. In addition to the known markers, we also identified novel chiral metabolite markers, such as threonic acid and Ala-Ser dipeptide. In the future, we hope to evaluate these novel biomarkers using a larger sample size and including other biological fluids, such as blood, urine, and tissue. In conclusion, iCAN-Met is an extremely effective method for reliable chiral and achiral biomarker discovery. It is applicable to metabolome studies for the development of diagnostic methods, as well as pharmaceutical and pathologic physiology, and is not limited to AD studies.

ASSOCIATED CONTENT Supporting Information Additional details of the experiments and four tables; comparison of reactivity of heavy/light reagents, comparison of mass accuracy, limit of detection between Q-Tof-MS and TQ-MS/MS, collected clinical sample information, identification results of AD biomarkers, and eight figures; results of multivariate analysis amine model, result of pre-run mixing method of derivatives of carboxylic acids and amines, relationship of isotope mass shifts and number of reactive sites, annotation example of derivatives with MS/MS


Page 9 of 11 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


fragmentation patterns, annotation example of structural isomers, 1st to 2nd PCA of CSF, MS/MS fragmentation of TrpKynurenine pathway metabolites and novel biomarkers, extracted chromatogram of kynurenine-speculated derivatives (PDF).

AUTHOR INFORMATION Corresponding Author * Kenichiro Todoroki. Tel: +81-54-264-5656, Fax: +81-54264-5654, E-mail: [email protected].

Author Contributions The manuscript was written with contributions from all authors. T.T.*, T.T., H.M., and K.T. conceived the study. T.T.*, T.T., H.M., and K.T. performed the experiments, data analysis, and interpretation. K.I. and H.A. collected, adjusted, and pathologically analyzed subject samples. (*) refers to the first author.

ACKNOWLEDGMENTS We would like to thank Dr. Hiroshi Tsugawa for their technical advice in the development of the data processing method used in this study. We also thank all the patients and relatives of the patients who donated samples to this study. This study was supported by a Grant-in-Aid for JSPS Research Fellow (Number 16J11918) from Japan Society for the Promotion of Science (JSPS).

CONFLICT OF INTEREST The authors declare no competing financial interests.

REFERENCES (1) Vinayavekhin, N.; Homan, E. A.; Saghatelian, A. Exploring disease through metabolomics. ACS Chem. Biol. 2010, 5, 91-103. (2) Nishiumi, S.; Kobayashi, T.; Ikeda, A.; Yoshie, T.; Kibi, M., Izumi, Y.; Okuno, T.; Hayashi, N.; Kawano, S.; Takenawa, T.; Azuma, T.; Yoshida, M. A novel serum metabolomics-based diagnostic approach for colorectal cancer. PLoS One 2012, 7, e40459. (3) Yoshida, R.; Tamura, T.; Takaoka, C.; Harada, K.; Kobayashi, A.; Mukai, Y.; Fukusaki, E. Metabolomics-based systematic prediction of yeast lifespan and its application for semi-rational screening of ageing-related mutants. Aging Cell 2010, 9, 616-625. (4) Sreekumar, A.; Poisson, L. M.; Rajendiran, T. M.; Khan, A. P.; Cao, Q.; Yu, J.; Laxman, B.; Mehra, R.; Lonigro, R. J.; Li, Y.; Nyati, M. K.; Ahsan, A.; Kalyana-Sundaram, S.; Han, B.; Cao, X.; Byun, J.; Omenn, G. S.; Ghosh, D.; Pennathur, S.; Alexander, D. C.; Berger, A.; Shuster, J. R.; Wei, J. T.; Varambally, S.; Beecher, C.; Chinnaiyan, A. M. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 2009, 457, 910-914. (5) Gilbert, J. A.; Quinn, R. A.; Debelius, J.; Xu, Z. J. Z.; Morton, J.; Garg, N.; Jansson, J. K.; Dorrestein, P. C.; Knight, R. Microbiomewide association studies link dynamic microbial consortia to disease. Nature 2016, 535, 94-103 (6) Holmes, E.; Wilson, I. D.; Nicholson, J. K. Metabolic phenotyping in health and disease. Cell 2008, 134, 714–717. (7) Martin-Lorenzo, M.; Gonzalez-Calero, L.; Ramos-Barron, A.; Sanchez-Niño, M. D.; Gomez-Alamillo, C.; García-Segura J. M.; Ortiz, A.; Arias, M.; Vivanco, F.; Alvarez-Llamas, G. Urine metabolomics insight into acute kidney injury point to oxidative stress disruptions in energy generation and H2S availability. J. Mol. Med. (Berl) 2017, 12, 1399–1409.

(8) Schnackenberg, L. K.; Beger, R. D. The role of metabolic biomarkers in drug toxicity studies. Toxicol. Mech. Methods 2008, 18, 301–311. (9) Kimura, T.; Hamase, K.; Miyoshi, Y.; Yamamoto, R.; Yasuda, K.; Mita, M.; Rakugi, H.; Hayashi, T.; Isaka, Y. Chiral amino acid metabolomics for novel biomarker screening in the prognosis of chronic kidney disease. Sci. Rep. 2016, 6, 26137. (10) El-Tallawy, H. N.; Saleem, T. H.; El-Ebidi, A. M. A. A.; Hassan, M. H.; Gabra, R. H.; Farghaly, W. M. A.; El-Maali, N. A.; Sherkawy, H. S. Clinical and biochemical study of D-serine metabolism among schizophrenia patients. Neuropsychiatr. Dis. Treat. 2017, 13, 1057–1063. (11) Weatherly, C. A.; Du, S.; Parpia, C.; Santos, P. T.; Hartman, A. L.; Armstrong, D. W. D-Amino Acid levels in perfused mouse brain tissue and blood: A comparative study. ACS Chem. Neurosci. 2017, 8, 1251–1261. (12) Poinsignon, V.; Mercier, L.; Nakabayashi, K.; David, M. D.; Lalli, A.; Penard- Lacronique, V.; Quivoron, C.; Saada, V.; De Botton, S.; Broutin, S.; Paci, A. J. Quantitation of isocitrate dehydrogenase (IDH)-induced D and L enantiomers of 2-hydroxyglutaric acid in biological fluids by a fully validated liquid tandem mass spectrometry method, suitable for clinical applications. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2016, 1022, 290– 297. (13) Snyder, S. H.; Kim, P. M. D-amino acids as putative neurotransmitters: Focus on D-serine. Neurochem. Res. 2005, 25, 553–560. (14) Miyoshi, Y.; Koga, R.; Oyama, T.; Han, H.; Ueno, K.; Masuyama, K.; Itoh, Y.; Hamase, K. HPLC analysis of naturally occurring free D-amino acids in mammals. J. Pharm. Biomed. Anal. 2012, 69, 42–49. (15) Lin, C.-H.; Yang, H.-T.; Chiu, C.-C.; Lane, H.-Y. Blood levels of D-amino acid oxidase vs. D-amino acids in reflecting cognitive aging. Sci. Rep. 2017, 7, 14849. (16) Bhushan, R.; Vashistha, V. K. Synthesis of variants of Marfey's reagent having d-amino acids as chiral auxiliaries and liquid-chromatographic enantioseparation of (RS)-Mexiletine in spiked plasma: assessment and comparison with L-amino acid analogs. J. Chromatogr. A 2015, 1379, 43–50. (17) Ogawa, S.; Tadokoro, H.; Sato, M.; Higashi, T. Enantioselective determination of ibuprofen in saliva by liquid chromatography/tandem mass spectrometry with chiral electrospray ionization-enhancing and stable isotope-coded derivatization. J. Pharm. Biomed. Anal. 2014, 98, 387–392. (18) Nagao, R.; Tsutsui, H.; Mochizuki, T.; Takayama, T.; Kuwabara, T.; Min, J. Z.; Inoue, K.; Todoroki, K.; Toyo'oka, T. Novel chiral derivatization reagents possessing a pyridylthiourea structure for enantiospecific determination of amines and carboxylic acids in high-throughput liquid chromatography and electrospray-ionization mass spectrometry for chiral metabolomics identification. J. Chromatogr. A 2013, 1296, 111– 118. (19) Sakamoto, T.; Kuwabara, R.; Takahashi, S.; Onozato, M.; Ichiba, H.; Iizuka, H.; Fukushima, T. Determination of D-serine in human serum by LC-MS/MS using a triazole-bonded column after pre-column derivatization with (S)-4-(3isothiocyanatopyrrolidin-1-yl)-7- (N, N-dimethylaminosulfonyl)2,1,3-benzoxadiazole. Anal. Bioanal. Chem. 2016, 408, 517–526. (20) Konya, Y.; Bamba, T.; Fukusaki, E. Extra-facile chiral separation of amino acid enantiomers by LC-TOFMS analysis. J. Biosci. Bioeng. 2016, 121, 349-353. (21) Taylor, L. T. J. Super. Fluids 2009, 47, 566-573 (22) Nishio, T.; Higashi, T.; Funaishi, A.; Tanaka, J.; Shimada, K. Development and application of electrospray-active derivatization reagents for hydroxysteroids. J. Pharm. Biomed. Anal. 2007, 44, 786–795. (23) Bian, X.; Sun, B.: Zheng, P.; Li, N.; Wu, J. L. Derivatization enhanced separation and sensitivity of long chain-free fatty acids: Application to asthma using targeted and non-targeted liquid



chromatography-mass spectrometry approach. Anal. Chim. Acta. 2017, 989, 59–70 (24) Toyo’oka, T. Derivatization-based High-throughput Bioanalysis by LC-MS. Anal. Sci. 2017, 33, 555–564. (25) Li, S.; Jin, Y.; Tang, Z.; Lin, S.; Liu, H.; Jiang, Y.; Cai, Z. A novel method of liquid chromatography-tandem mass spectrometry combined with chemical derivatization for the determination of ribonucleosides in urine. Anal. Chim. Acta 2015, 864, 30–38. (26) Ogawa, S.; Tomaru, K.; Matsumoto, N.; Watanabe, S.; Higashi, T. LC/ESI-MS/MS method for determination of salivary eicosapentaenoic acid concentration to arachidonic acid concentration ratio. Biomed. Chromatogr. 2016, 30, 29–34. (27) Toyo’oka, T. Diagnostic approach to disease using noninvasive samples based on derivatization and LC-ESI-MS/MS. Biol. Pharm. Bull. 2016, 39, 1397–1411. (28) Qi, B.-L.; Liu, P.; Wang, Q.-Y.; Cai, W.-J.; Yuan, B.-F.; Feng, Y.Q. TrAC. Anal. Chem. 2014, 59, 121–132. (29) Takayama, T.; Kuwabara, T.; Maeda, T.; Noge, I.; Kitagawa, Y.; Inoue, K.; Todoroki, K.; Min, JZ.; Toyo’oka, T. Profiling of chiral and achiral carboxylic acid metabolomics: synthesis and evaluation of triazine-type chiral derivatization reagents for carboxylic acids by LC-ESI-MS/MS and the application to saliva of healthy volunteers and diabetic patients. Anal. Bioanal. Chem. 2015, 407, 1003–1014. (30) Mochizuki, T.; Takayama, T.; Todoroki, K.; Inoue, K.; Min, JZ.; Toyo’oka, T. Towards the chiral metabolomics: Liquid chromatography-mass spectrometry based DL-amino acid analysis after labeling with a new chiral reagent, (S)-2,5dioxopyrrolidin-1-yl-1-(4,6-dimethoxy-1,3,5-triazin-2yl)pyrrolidine-2-carboxylate, and the application to saliva of healthy volunteers. Anal. Chim. Acta. 2015, 875, 73–82. (31) Takayama, T.; Mochizuki, T.; Todoroki, K.; Min, J. Z.; Mizuno, H.; Inoue, K.; Akatsu, H.; Noge, I.; Toyo'oka, T. A novel approach for LC-MS/MS-based chiral metabolomics fingerprinting and chiral metabolomics extraction using a pair of enantiomers of chiral derivatization reagents. Anal. Chim. Acta. 2015, 898, 73-84. (32) Mizuno, H.; Ueda, K.; Kobayashi, Y.; Tsuyama, N.; Todoroki, K.; Min, J. Z.; Toyo’oka, T. The great importance of normalization of LC-MS data for highly-accurate non-targeted metabolomics. Biomed. Chromatogr. 2017, 31, 1–7. (33) Boysen, A. K.; Heal, K. R.; Carlson, L. T.; Ingalls, A. E. Bestmatched internal standard normalization in liquid chromatography-mass spectrometry metabolomics applied to environmental samples. Anal. Chem. 2018, 90, 1363–1369. (34) Higashi, T.; Ogawa, S. J. Isotope-coded ESI-enhancing derivatization reagents for differential analysis, quantification and profiling of metabolites in biological samples by LC/MS: A review. Pharm. Biomed. Anal. 2016, 130, 181–193. (35) Guo, N.; Peng, C. Y.; Zhu, Q. F.; Yuan, B. F.; Feng, Y. Q. Profiling of carbonyl compounds in serum by stable isotope labeling - Double precursor ion scan - Mass spectrometry analysis. Anal. Chim. Acta. 2017, 967, 42–51. (36) Luo, X.; Li, L. Metabolomics of small numbers of cells: Metabolomic profiling of 100, 1000, and 10000 human breast cancer cells. Anal. Chem. 2017, 89, 11664–11671. (37) Chen, D.; Han, W.; Su, X.; Li, L.; Li, L. Overcoming sample matrix effect in quantitative blood metabolomics using chemical isotope labeling liquid chromatography mass spectrometry. Anal. Chem. 2017, 89, 9424–9431. (38) Tsugawa, H.; Cajka, T.; Kind, T.; Ma, Y.; Higgins, B.; Ikeda, K.; Kanazawa, M.; VanderGheynst, J.; Fiehn, O.; Arita, M. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat. Methods 2015, 12, 523–526. (39) Gulaj, E.; Pawlak, K.; Bien, B.; Pawlak, D. Kynurenine and its metabolites in Alzheimer's disease patients. Adv. Med. Sci. 2010, 55, 204–211.

Page 10 of 11

(40) Lim, C. K.; Fernández-Gomez, F. J.; Braidy, N.; Estrada, C.; Costa, C.; Costa, S.; Bessede, A.; Fernandez-Villalba, E.; Zinger, A.; Herrero, M. T.; Guillemin, G. J. Prog. Neurobiol. 2017, 155, 76–95. (41) Maddison, D. C.; Giorgini, F. The kynurenine pathway and neurodegenerative disease. Semin. Cell Dev. Biol. 2015, 40, 134– 141. (42) Tsuruoka, M.; Hara, J.; Hirayama, A.; Sugimoto, M.; Soga, T.; Shankle, W. R.; Tomita, M. Electrophoresis 2013, 34, 2865-72. (43) Mapstone, M.; Lin, F.; Nalls, M. A.; Cheema, A. K.; Singleton, A. B.; Fiandaca, M. S.; Federoff, H. Capillary electrophoresis-mass spectrometry-based metabolome analysis of serum and saliva from neurodegenerative dementia patients. J. Neurobiol. Aging 2017, 51, 148–155. (44) Graham, S. F.; Chevallier, O. P.; Elliott, C. T.; Hölscher, C.; Johnston, J.; McGuinness, B.; Kehoe, P. G.; Passmore, A. P.; Green, B. D. Untargeted metabolomic analysis of human plasma indicates differentially affected polyamine and L-arginine metabolism in mild cognitive impairment subjects converting to Alzheimer's disease. PLoS One 2015, 10, e0119452. (45) Akatsu, H. [Collaboration between Fukushimura Brain Bank, based in a private geriatric hospital and a national/international network]. Brain Nerve 2010, 62, 10431052. (46) Storey JD.; Tibshirani R. Statistical significance for genome wide studies, Proc Natl Acad. Sci. U S A., 2003, 100, 9440–5 (47) Kaminski, R. M.; Zielinska, E.; Dekundy, A.; van Luijtelaar, G.; Turski, W. Deficit of endogenous kynurenic acid in the frontal cortex of rats with a genetic form of absence epilepsy. Pol. J. Pharmacol. 2003, 55, 741-746. (48) Moroni, F.; Cozzi, A.; Peruginelli, F.; Carpendeo R, Pellegrini-Giampietro D. E. Neuroprotective effects of kynurenine3-hydroxylase inhibitors in models of brain ischemia. Adv. Exp. Med. Biol. 1999, 467, 199-206. (49) Scharfman, H. E.; Goodman, J. H.; Schwarcz, R. Electrophysiological effects of exogenous and endogenous kynurenic acid in the rat brain: studies in vivo and in vitro. Amino Acids. 2000, 19, 283-297.


Page 11 of 11 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Isotope corrected chiral and achiral non-targeted metabolomics (iCAN-Met): A novel approach for high accuracy and precision metabolomics based on derivatization, and its application to cerebrospinal fluid of patients with Alzheimer’s disease


Isotope Corrected Chiral and Achiral Nontargeted Metabolomics: An

Recommend Documents