Combining NMR and MS with Chemical Derivatization for Absolute

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Combining NMR and MS with Chemical Derivatization for Absolute Quantification with Reduced Matrix Effects Qiang Fei, Dongfang Wang, Paniz Jasbi, Ping Zhang, G. A. Nagana Gowda, Daniel Raftery, and Haiwei Gu Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b05611 • Publication Date (Web): 25 Feb 2019 Downloaded from http://pubs.acs.org on February 25, 2019

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Analytical Chemistry

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Combining NMR and MS with Chemical Derivatization for Absolute Quantification with

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Reduced Matrix Effects

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Qiang Fei†‡, Dongfang Wang‡§, Paniz Jasbi∥, Ping Zhang‡⊥, G. A. Nagana Gowda‡,

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Daniel Raftery*‡#∇, Haiwei Gu*∥

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†College

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‡Northwest

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University of Washington, Seattle, WA 98109, USA

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§Chongqing

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∥

of Chemistry, Jilin University, Changchun 130021, P. R. China Metabolomics Research Center, Department of Anesthesiology and Pain Medicine,

Blood Center, Chongqing 400015, P. R. China

Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University,

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Scottsdale, AZ 85259, USA

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⊥College

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#Department

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∇Fred

of Plant Protection, Southwest University, Chongqing 400715, P. R. China of Chemistry, University of Washington, Seattle, WA 98109, USA

Hutchinson Cancer Research Center, Seattle, WA 98109, USA

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* Corresponding Authors: Daniel Raftery, PhD Department of Anesthesiology and Pain Medicine University of Washington 850 Republican St. Seattle, WA 98109 Tel: 206-543-9709 Fax: 206-616-4819 Email: [email protected] Haiwei Gu, PhD College of Health Solutions Arizona State University 13208 E. Shea Blvd Scottsdale, AZ 85004 Tel: 480-301-6016 Fax: 480-301-7017 Email: [email protected] 1

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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

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ABSTRACT

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Absolute quantitation is a major challenge in metabolomics. Previously, we [G. A. Nagana

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Gowda et al., Anal. Chem. 2018, 20, 2001-2009] showed that nuclear magnetic resonance (NMR)

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spectroscopy can guide absolute quantitation using mass spectrometry (MS); however, this

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method does not account for the matrix effect in MS measurements. To surmount this challenge,

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we have developed a novel method, qNMR-MS, for the absolute quantitation of metabolites using

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MS by combining it with NMR and chemical derivatization. Metabolite concentrations are first

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obtained using NMR for a reference sample. Subsequently, both reference and study samples

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are chemically derivatized with isotope labeled and unlabeled reagents, respectively. The

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derivatized reference sample is then mixed with study samples and measured using MS.

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Comparison of paired isotope unlabeled and labeled MS peaks enables absolute quantitation with

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virtually no matrix effects. As a proof of concept, we applied the qNMR-MS method for absolute

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quantitation of amino acids using propyl-chloroformate (PCF) derivatization. For standards, the

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observed coefficients of determination (R2) of most amino acids were greater than 0.99 across

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concentrations of 0.2 to 20 uM. For human serum, the results of qNMR-MS method are

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comparable to the conventional isotope labeled internal standard (iSTD) method (R2  0.99), with

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an average median coefficient of variation (CV) of 5.45%. The qNMR-MS method is relatively

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simple, highly quantitative, has high cost-efficiency (no iSTD required), and offers new avenues

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for the routine quantitation of amino acids in blood samples and can, in principle, be extended to

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a wide variety of metabolites in different biological samples.

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2


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Metabolomics provides detailed and extensive information about complex biological

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processes and systems.1-10 Metabolomics studies have resulted in a number of important findings

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in systems biology and biomarker discovery, yielding a deeper understanding of cancer

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metabolism11-13 and drug toxicity,14,15 potential methods for improved early disease detection16-19

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and therapeutic monitoring,20,21 as well as applications in environmental science,22 nutrition,23-25

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and other fields related to the study of metabolism.

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A major challenge faced by the field is the lack of methods for broad-based absolute

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metabolite quantitation. This is particularly critical since the measurement of many hundreds to

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thousands of small-molecule intermediates and products has become almost routine. Most

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metabolites, including amino acids, have thus far been measured as relative abundances in the

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vast majority of metabolomics studies,26,27 which severely limits the ability to compare results

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across studies. This situation has, in turn, greatly impacted the usefulness of established

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metabolomics databases, in which the deposited data are generally semi-quantitative and less

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reliable for cross-validation analysis in another study or by different users. Moreover, relative

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abundances cannot be directly used in clinical settings and, as a result, additional research and

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development efforts are needed to quantify potential biomarker candidates prior to possible

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clinical applications. Absolute quantitation creates the possibility and opportunity for constructing

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a universal metabolomics database for a variety of biological systems, and provides the potential

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to bridge gaps between laboratory studies and clinical applications.

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Of the advanced analytical methods that have been employed in metabolomics studies, mass

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spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy are the most heavily

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utilized.6,28,29 In general, NMR is highly quantitative and MS is highly sensitive. These two

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platforms provide both complementary and supplementary information on the identities and

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concentrations of metabolites from a variety of biological samples, including blood.4,30 Recently,

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we developed an optimized protein removal method for NMR, resulting in the quantitation of a 3



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large number of metabolites including amino acids, organic acids, carbohydrates, and

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heterocyclic compounds.31,32 However, NMR’s relatively low sensitivity requires a relatively large

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sample volume (generally ≥200 μL). Hence, NMR quantitation of a large pool of metabolites

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(>100) in blood samples continues to be challenging. Although LC-MS is much more sensitive,17,33

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large-scale external calibration for the quantitation of metabolites is laborious and often lacks

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accuracy due to confounding matrix effects. Although stable isotope labeled internal standards

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(iSTDs) provide superior quantitation in LC-MS,34,35 many iSTDs are not commercially available

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or are very expensive. Similarly, commercial standard kits usually contain structural analogues,

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the quantitative accuracy of which remains controversial given the potential for matrix effects and

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differential MS responses to non-identical chemical structures.36

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To address the absolute quantitation bottleneck in metabolomics, G. A. Nagana Gowda et. al.

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introduced a new method (NMR-guided-MS)37 in which metabolites from a single serum specimen

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are quantiﬁed on the basis of a recently developed NMR method32 and then used as a reference

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for absolute metabolite quantitation using MS. While the results of this approach are reasonably

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accurate and reproducible, the limitation of this method is that it cannot correct for matrix effects

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when present. In this study, we further improved our previous method37 and developed a novel

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quantitative approach to combine the advantages of both NMR and MS in order to minimize matrix

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effects and improve quantitation accuracy (qNMR-MS). As an example, we describe the use of

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propyl-chloroformate (PCF) derivatization in this approach to provide absolute concentrations of

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amino acids for the routine measurement of biofluids.38,39 This new approach provides quantitation

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comparable to the internal standard method, and extends to metabolites without iSTDs. In

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addition, the described method is relatively simple and easy to implement and hence offers new

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avenues for the routine quantification of amino acids and other metabolites in human serum

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samples. In principle, the new method can be extended to a variety of metabolites in different

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biological samples. 4


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EXPERIMENTAL METHODS

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Development of Overall Analytical Strategy

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Given the advantages and challenges of NMR and MS, respectively, we developed a novel

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analytical strategy to use the excellent quantitative characteristics of NMR spectroscopy to

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achieve a significant improvement in metabolite quantitation using highly sensitive MS. Figure 1

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shows the overall blueprint of our analytical strategy for qNMR-MS.

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Step 1: Prepare a reference sample with similar matrix to study samples. For example, we

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can mix a small portion of each study sample (>50 in a typical metabolomics study) to obtain this

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reference sample. It is important to note that metabolite concentrations in the reference sample

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need only to be determined once and can then be used in multiple studies and/or in different labs,

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which helps to reduce experimental efforts in quantitation.

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Step 2: Divide the reference sample into two portions (with the same metabolite

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concentrations). The first one will be examined to determine the metabolite concentrations using

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quantitative NMR. The other half will be used in Step 4.

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Step 3: Derivatize each individual sample under investigation with an unlabeled tag. Although

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labeled derivatization reagent can be used for the study samples (then unlabeled derivatization

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reagent will be used for the reference sample in Step 2), Step 3 generally uses more of the

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derivatization reagent than Step 2; therefore, unlabeled derivatization reagent is preferred for the

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study samples to save the cost.

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Step 4: Derivatize the second portion of the reference sample with an isotope labeled reagent (corresponding to the reagent in Step 3).

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Step 5: After derivatization, mix each individual sample with the reference sample in a 1:1

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(v:v) ratio. As a result, the sample matrix effect will be compensated in subsequent MS analysis.

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Step 6: The mixture is then subjected to MS analysis. Given the determined concentrations

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of metabolites in the reference sample from Step 2, the metabolite concentrations in each study

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sample can be easily calculated based on the ratio between the labeled and unlabeled MS peaks.

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Figure 1. The overall analytical strategy to combine NMR and LC-MS for the absolute quantitation

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of amino acids in serum samples.

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Chemicals

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Hydrochloric acid was obtained from EMD Chemicals Inc. (Gibbstown, NJ).

Twenty amino

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acid standards, including DL-alanine, L-arginine, DL-asparagine, L-aspartic acid, DL-cystine, L-

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glutamic acid, L-glutamine, glycine, L-histidine, L-isoleucine, L-leucine, L-lysine, DL-methionine,

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L-phenylalanine, L-proline, L-serine, L-threonine, L-tryptophan, L-tyrosine, and DL-valine, were

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purchased from Sigma-Aldrich (St. Louis, MO). Derivatization reagents propyl-chloroformate and

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ethyl acetate were purchased from Fisher Scientific (Hampton, NH). Other derivatization reagents

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(sodium hydroxide, 1-propanol, 1-propanol-1,1-d2 (>99.5% D, Figure S1), 3-picoline, chloroform,

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iso-octane, hexane, monosodium phosphate (NaH2PO4), disodium phosphate (Na2HPO4), and

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sodium salt of 3-(trimethylsilyl) propionic acid-2,2,3,3-d4 (TSP)) were also purchased from Sigma6


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Aldrich (St. Louis, MO). Four human serum samples were obtained from Innovative Research,

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Inc. (Novi, MI). A mixture of 20 uniformly labeled

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acids and deuterium oxide (D2O) were obtained from Cambridge Isotope Laboratories, Inc.

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(Andover, MA). DI water was obtained using an in-house Synergy Ultrapure Water System from

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Millipore (Billerica, MA).

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Serum Metabolite Extraction

13C,15N-

(97-99% enrichment) standard amino

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Frozen serum samples were thawed at room temperature (25 °C) and homogenized using a

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vortex mixer, and 50 μL of each serum sample was then pipetted into 2 mL Eppendorf vials (Fisher

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Scientific; Hampton, NH). For protein removal, the serum samples were mixed with methanol in

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a 1:2 (v/v) ratio, which was recently shown to extract metabolites with high efficiency.31,32 The

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resulting mixtures were vortexed for 30 s, incubated at −20 °C for 20 min, and centrifuged at

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19100 x g for 30 min to pellet the proteins. The supernatant was dried using an Eppendorf

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Vacufuge-Plus vacuum concentrator. The resulting residue was reconstituted with 50 uL of 0.05

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M HCl solution for derivatization and targeted LC-MS/MS analysis. Sample volumes were 400 μL

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for reference samples. The supernatant was divided into two halves. The first half was

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reconstituted with 200 uL of 0.05 M HCl solution for derivatization and targeted LC-MS/MS

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analysis. The second half was dried and then reconstituted with 200 μL of phosphate buffer (0.1

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M) in D2O containing 50 μM internal reference (TSP) for NMR analysis. For quantitation using the

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internal standard method, 10 μL of extracted serum sample was mixed with 10 μL of

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labeled amino acid standards (5.71 mg in 20 mL 0.05 M HCl solution) and was then diluted to 200

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μL by the addition of DI water. Concentrations were determined by comparing integrated MS

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areas of amino acids to the corresponding iSTDs.

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Derivatization Method

13C, 15N-

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Figure S2 shows the experimental workflow of unlabeled and labeled PCF derivatization in

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schematic form.40 First, 10 μL of each extracted serum sample was added to a 2 mL glass vial, 7



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and then diluted to 200 μL by the addition of DI water. Next, 80 μL of 3-picoline/1-propanol solution

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(23.0:77.0, v/v) was added for derivatization of the study samples, while for the reference sample,

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3-picoline/1-propanol-1,1-d2 solution (23.0:77.0, v/v) was used for isotopic derivatization. Then,

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50 μL of propyl-chloroformate/chloroform/iso-octane (17.4/71.6/11.0, v/v/v) was added to each

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sample. The solutions were vortexed for 1 min, heated at 80 oC for 30 min, and equilibrated for

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10 min at room temperature. Afterward, 250 μL of ethyl acetate was added to extract amino acid

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derivatives, and each vial was vortexed for 1 min and centrifuged at 20800 x g for 10 min.

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Following centrifugation, 200 μL of the supernatant was transferred to a new vial. This solution

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was dried for 40 min using an Eppendorf Vacufuge-Plus vacuum concentrator and reconstituted

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in 100 μL water (0.1% formic acid) for LC-MS/MS analysis.

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NMR Spectroscopy

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NMR experiments were performed at 25 °C on a Bruker AVANCE III 800 MHz spectrometer

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equipped with a cryogenically cooled probe and Z-gradients suitable for inverse detection, as

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described earlier.32 Briefly, the CPMG (Carr−Purcell−Meiboom−Gill) pulse sequence with water

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suppression using presaturation was used for 1H 1D NMR experiments. To enable absolute

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quantitation of metabolites using TSP, NMR experiments were performed with 128 transients and

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a sufficiently long recycle delay (d1=15 s). Chemical shifts were referenced to the internal

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reference (TSP) signal. Raw data were Fourier transformed after zero filling once to a total

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spectrum size of 32K points. Bruker Topspin software package version 3.0 was used for NMR

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data acquisition, processing, and analysis. Metabolite concentrations were determined using

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integrated areas of metabolite peaks with reference to the peak area of TSP reference, after

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taking into account the sample volume and number of protons represented by the peaks used for

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integration.

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LC-MS/MS

8


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For targeted measurements of amino acid derivatives, 4 µL of each prepared sample was

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injected into an Agilent 6410 Triple Quad LC-MS system for analysis using an electrospray

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ionization (ESI) source in positive ionization mode. Chromatographic separation of the

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compounds was achieved on a Waters T3 column (4.6 mm x 150 mm, 5 μm). The mobile phase

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was composed of solvent A (DI water with 0.1% (v/v) formic acid) and solvent B (ACN with 0.1%

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(v/v) formic acid). The elution gradient started at 32% solvent A, which was reduced linearly to

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5% at t=10 min. The percentage of A remained constant (5%) for 2 mins (t=12 min), after which

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the percentage of A was rapidly increased to 32% to prepare for the next injection. The total

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experimental time for each injection was 30 min. The flow rate was 0.3 mL/min, the auto-sampler

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temperature was 4 °C, and the column compartment temperature was set to 50 °C. Multiple-

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reaction-monitoring (MRM) transitions were optimized through direct infusion of derivatives from

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a mixed standard sample containing 50 µM of each amino acid. MRM peaks for amino acid

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derivatives were integrated using MassHunter Workstation Software Quantitative Analysis for

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QQQ B.07.00 (Agilent Technologies).

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Absolute Quantitation Calculation

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For quantitation with good accuracy, the response factor of each amino acid derivative needs

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to be calculated, to at least partially correct errors due to isotope purity, derivatization efficiency,

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etc. Two reference samples (10 μL each) were separately derivatized by the unlabeled reagent

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and isotope labeled reagent, respectively. After derivatization, the two samples of equal volume

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were mixed, and the mixture was then subjected to LC-MS/MS analysis. The response factor (R)

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is the ratio between the labeled and unlabeled MS peak area of each amino acid, as represented

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by Eq. 1:

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𝐴𝑅 ― 𝑙𝑎𝑏𝑒𝑙𝑒𝑑

𝑅 = 𝐴𝑅 ― 𝑢𝑛𝑙𝑎𝑏𝑒𝑙𝑒𝑑

(1)

Where AR-unlabeled and AR-labeled represent the peak areas of amino acids derivatized by the unlabeled and isotope labeled reagents, respectively. 9



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Absolute concentrations of each amino acid in the reference sample were derived by NMR.

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After derivatization, a mixture of a single study sample and the reference sample was analyzed

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by LC-MS/MS and the absolute concentrations of amino acids in the study samples were

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calculated using Eq. 2: 𝐴𝑆𝑖

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𝐶𝑆𝑖 = 𝐴𝑅𝑖 ― 𝑙𝑎𝑏𝑒𝑙𝑒𝑑 × 𝑅 × 𝐶𝑅

(2)

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Where CSi and CR represent the concentrations of a particular amino acid in the study sample

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and reference sample, respectively. ASi and ARi-labeled represent the peak areas of that amino acid

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derivatized by the unlabeled reagent from the study sample and isotope labeled reagent from the

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reference sample, respectively.

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RESULTS

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PCF Derivatization

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This novel qNMR-MS method can be used to analyze a wide variety of biological metabolites;

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here we use amino acids only as a proof of concept, combining NMR, MS, as well as PCF

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derivatization for absolute quantitation. Targeted MS data acquisition was performed using MRM

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in positive mode. Table S1 shows the MRM parameters of 20 amino acids derivatized with the

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unlabeled reagent and isotope labeled reagent, respectively. The MRM parameters of

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labeled standard amino acids derivatized with unlabeled reagents are also included in the table

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alongside precursor ions, product ions, and associated collision energy (CE). LC-MS total ion

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chromatograms of a mixed standard sample of twenty amino acids are shown in Figure S3.

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Separation of these amino acid derivatives was achieved in 5 min, without any chemical

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interference from other compounds in the sample.

13C, 15N-

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Derivatization is an important procedure in qNMR-MS; therefore, to ensure the best

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quantitative performance, we first optimized the PCF derivatization temperature. Figure S4 shows

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the peak areas of twenty amino acid derivatives at different reaction temperatures. The reaction 10


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time was set to 30 min. Peak areas were observed to be similar for most reaction temperatures;

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peak areas were slightly decreased at 40 oC and 60 oC and it was observed that some amino

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acids (such as serine and arginine) exhibited large relative differences when the reaction

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temperatures were set below 80 oC (Figure S5). Given that high temperatures can accelerate

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reactions, and as 80 oC provided acceptable variation values, 80 oC was chosen as the final

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reaction temperature.

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In addition to temperature, we optimized the PCF reaction time to obtain best quantitation.

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Figure S6 shows the peak areas of twenty amino acid derivatives at different reaction times using

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a reaction temperature of 80 oC. No significant differences in peak areas were observed between

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tested reaction times. Figure S7 shows that, with short reaction times, the relative differences of

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some amino acids, such as aspartic acid and serine, became relatively large. When the reaction

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time was longer than 30 min, the relative differences in peak areas of twenty amino acid

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derivatives was less than 20%. Therefore, we chose 30 min as the final reaction time. In addition,

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the extraction efficiency of ethyl acetate and hexane (Figure S8) as well as the effect of pH on

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chemical equilibrium (Figure S9) were also examined. Ethyl acetate was selected as the final

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extract reagent, and basic sodium hydroxide solution was excluded from the PCF derivatization

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procedure. As a result, the PCF derivatization efficiency, determined by the MS intensities of

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amino acids before and after derivatization, was measured to be >99% for all amino acids

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investigated in this study.

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Quantitation of Amino Acids

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We first examined whether qNMR-MS was applicable to amino acid standards. Based on the

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known amino acid concentration range of human blood,41 we prepared standard mixtures of

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twenty amino acids at 0.2 uM, 0.5 uM, 1 uM, 2 uM, 5 uM, 10 uM, 15 uM, and 20 uM to test the

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linearity and accuracy of concentration determinations. Table S2 lists the linear regression

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equations and their coefficients of determination (R2) of the qNMR-MS method for amino acid 11



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quantification using standard samples. The coefficients of determination of most amino acids were

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greater than 0.99, showing the excellent quantitative accuracy of our novel qNMR-MS method.

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In addition, we used human serum to demonstrate the applicability of our new method to

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samples of complicated matrices. One human serum sample was diluted 2, 10, 20,100, and 200

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times to test the concentration linearity of real samples. We also observed similar coefficients of

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determination as in the standard sample (Table S2). Here, the linear equations of serum samples

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were calculated using peak ratios against the dilution factors; therefore, the equations are different

275

between the standards and serum samples.

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To further evaluate this quantitation approach, the concentrations of amino acids in four

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human serum samples were measured using the qNMR-MS method. The reference sample was

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pooled from these 4 study samples, and NMR was used to quantify the amino acid concentrations

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in this reference sample. Seventeen amino acids were quantified. Three amino acids (aspartic

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acid, cysteine, and serine) in the reference sample could not be quantified using NMR because

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either their concentrations were too low or they were overlapped with other peaks. Due to the

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high concentration of amino acids in human serum samples, each sample was diluted typically

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by a factor of 20 prior to derivatization. The median coefficient of variation values (CVs) of these

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four samples using qNMR-MS were 2.0%, 6.2%, 8.3%, and 5.2%, respectively. These results are

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indicative of the high precision and reproducibility of our method.

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The new method was also compared against the conventional internal standard method.

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Correlations of concentrations for all amino acids between the qNMR-MS method and internal

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standard method were close to unity and showed excellent coefficients of determination (R2 ≥

289

0.99) for all four serum samples (Figure 2). As an example, Figure 3 shows a comparison of

290

amino acid concentrations derived by the internal standard method and by our qNMR-MS method

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from a typical serum sample; the data shown here indicate excellent agreement between the two

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quantitation methods. In addition, concentrations of the same amino acids in all serum samples 12


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derived by the qNMR-MS method and the internal standard method were compared individually.

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As shown in Figure 4 and Figure S10, most of the amino acids exhibited excellent agreement

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between the qNMR-MS method and internal standard method (R2 > 0.9). Notably, as we observed

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in a previous study,33 quantitation using iSTDs also requires extra care; e.g., a large variation can

297

be produced if the concentration of iSTD is very different from that of the corresponding compound

298

under investigation, which was observed once again in this study. However, our qNMR-MS

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method is less affected by this concentration difference between unlabeled and labeled

300

compounds, since the reference sample, pooled from all the study samples, has less chance of

301

having concentrations dramatically different from that of the study samples. In addition, the

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response factor (R) in Eq. 1 was designed to at least partially correct errors due to isotope purity,

303

derivatization efficiency, etc., which helps to further improve quantitative performance. As

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evidenced by the coefficient of determination, almost all observed variance in the internal

305

standard method is accounted for by our novel quantitation method. For example, the R2 value

306

obtained from linear regression analysis of glycine (Figure 4c) indicates that roughly 94% of the

307

variance observed in our qNMR-MS is accounted for by the iSTD method. This shows the qNMR-

308

MS method to be as robust a technique as the conventional internal standard method.

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Figure 2. Linear regression equations for concentrations of all amino acids in four human serum

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samples between the qNMR-MS and internal standard methods. Coefficients of determination (R2)

312

show excellent agreement between methods with almost no discrepancy between observed

313

values.

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14


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Figure 3. Comparison of the concentrations of 17 amino acids in the 4th human serum sample,

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derived from the qNMR-MS and internal standard methods.

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Figure 4. Linear regression equations for four amino acids in human serum samples (see Figure

321

S10 for data on 13 other amino acids). (a) Asparagine, (b) Glutamic Acid, (c) Glycine, and (d)

322

Proline. Coefficients of determination (R2) values show excellent agreement between qNMR-MS

323

and internal standard methods.

16


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324

In order to evaluate potential matrix effects, results of our previous NMR-guided-MS method37

325

and the qNMR-MS method were compared. These two methods were used to establish

326

regression curves, and the coefficients of determination are shown in Figure 5 and Table S3.

327

The standard sample has no other components except amino acids, which have less matrix

328

effects in MS measurements than human serum samples that are well-known to have complicated

329

matrices. Both methods achieved good results for standard samples, and average R2 values for

330

the NMR-guided-MS and qNMR-MS methods were observed to be 0.9952 and 0.9965,

331

respectively. Because of the complex matrix effects present in human serum samples, however,

332

the NMR-guided-MS method produced somewhat poorer results, (average R2 = 0.9583). The

333

qNMR-MS method compensated for any matrix effects (average R2 = 0.9953) and achieved

334

results comparable to those obtained from standard samples.

335 336

Figure 5. Coefficients of determination (R2) for quantitating amino acids in human serum and

337

standard samples. Each sample was tested using two quantitation calculation methods: NMR-

338

guided-MS and qNMR-MS. NMR-guided-MS uses the peak area of each amino acid derivative to

339

quantify, while qNMR-MS uses the peak area ratio between labeled and unlabeled MS peaks of

340

each amino acid derivative to quantify metabolites.

341 17



342

Page 18 of 26

DISCUSSION

343

To significantly improve quantitation in metabolomics, we developed a novel approach

344

(qNMR-MS) which enables absolute metabolite quantitation, exemplified here using amino acids.

345

This method requires only a reference sample to obtain amino acid concentrations via NMR. After

346

derivatization with unlabeled and labeled tags, each study sample and the reference sample are

347

mixed together and subject to LC-MS/MS analysis. Based on the concentrations from the

348

reference sample using NMR, all study samples were quantitated by LC-MS/MS, comparing the

349

corresponding isotope labeled and unlabeled peak areas. Our method combines the advantages

350

of NMR, MS, and chemical derivatization to enable absolute quantitation with high sensitivity.

351

Importantly, our novel qNMR-MS method can significantly reduce the interference of matrix

352

effects.

353

For improved absolute quantification results, it is important that the reference and study

354

samples have similar matrices. This reference sample can be a commercially-available pooled

355

blood sample with enough volume for both NMR and MS analysis, or more ideally, a pooled study

356

sample. The reference sample can be concentrated or diluted, so that metabolite concentrations

357

are similar to those in the study sample, thereby enabling the accurate determination of metabolite

358

concentrations based on MS intensities of similar magnitude. In addition, 2D NMR methods42 can

359

be used for quantitation in case peak overlap is a problem.

360

After optimized PCF derivatization in this study, structures of the derivatized amino acids are

361

different from the amino acids themselves and, as a result, their physical and chemical

362

characteristics, extraction efficiency, LC retention, and MS/MS characteristics are favorable for

363

analysis. In particular, PCF derivatization reduces the polarity of amino acids, which prevents

364

coelution with other highly polar compounds in complex matrices. The derivatization process

365

increases the molecular weight of relatively low-weight molecules. As a result, the interference of

18


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366

matrix materials is reduced by increasing the retention time during reversed-phase

367

chromatographic separation.

368

qNMR-MS provides an alternative to the use of individual iSTDs. While our method also

369

utilizes isotope labeled iSTDs, they are introduced through a derivatization process, as has been

370

exploited previously by others.38,39,43 Similarly, our qNMR-MS method also has advantages similar

371

to those of the internal standard method,44 in that the amino acids in the reference sample

372

derivatized by the isotope labeled reagent can compensate for variability in MS detection of study

373

samples. Since amino acids derivatized by isotope labeled reagents are almost identical in

374

structure to and co-elute with amino acids derivatized by unlabeled reagents, the degree of

375

ionization suppression or enhancement caused by the co-eluting matrix components should, in

376

theory, be the same for both. Therefore, while absolute response may be affected, the peak area

377

ratio between labeled and unlabeled MS peaks of amino acids should be unaffected and the

378

bioanalytical method is accurate, precise, and robust. Thus, the qNMR-MS method is a valid

379

approach for correcting matrix effects.

380

In order to further assess the relative magnitude of matrix effects on real samples, we

381

compared the qNMR-MS results to our previous work (NMR-guided-MS)37 that uses peak-area

382

signals to determine concentrations. The qNMR-MS method described herein can decrease

383

confounding matrix effects, significantly improving the quantitative performance of some amino

384

acids such as glycine, histidine, and valine. For example, although glutamine typically shows very

385

poor correlation due to glutamine cyclization45 as described in our previous work, the quantitation

386

of glutamine was significantly improved using our qNMR-MS method. The coefficient of

387

determination of glutamine was 0.89, as opposed to 0.31 as reported in our previous work. These

388

results indicate that matrix effects were substantially reduced by our novel qNMR-MS method.

389

The qNMR-MS method developed here has been applied to the quantitation of amino acids

390

to show the merit of this approach; in theory, other chemical classes can be analyzed using 19



Page 20 of 26

391

qNMR-MS by substituting appropriate derivatization reactions. Previous work on semi-

392

quantitative assays46 has shown derivatization of amines by 1-1’-13C2 acetic anhydride to enhance

393

their detection with minimal sample pretreatment. Others have shown a 10-fold increase in

394

detection of fatty acids following derivatization by N-[4-(aminomethyl)phenyl]pyridinium (AMPP).47

395

Also, application of a chemoselective smart isotope tag such as

396

to increase detection of the carboxyl-containing metabolome by NMR and MS methods.48

397

Therefore, a wide variety of chemical classes can be quantified using our approach by applying

398

various derivatization methods suited to the detection of particular chemical classes. In principle,

399

qNMR-MS is able to have a very wide coverage; for example, carboxyl- and amino-containing

400

compounds cover ~75% of the metabolome. In addition, NMR quantitation approaches can be

401

optimized for various types of samples, such as urine. The application of various derivatization

402

techniques can significantly increase the coverage of our novel qNMR-MS method beyond amino

403

acids, allowing for the comparison of measurements between studies and, potentially, the creation

404

of a universal, quantitative metabolomics database.

15N-cholamine

has been shown

405 406

CONCLUSIONS

407

In summary, we describe a new quantitation method, qNMR-MS, which combines the

408

advantages of NMR and MS, as well as chemical derivatization to quantify amino acids with

409

minimal matrix effects. First, the concentrations of amino acids in a reference sample are

410

determined via NMR. PCF is used to derivatize amino acids in serum samples and the reference

411

sample, and then absolute concentrations of the amino acids can be calculated as the ratio

412

between the MS peak areas of the unlabeled study samples and isotopic labeled reference

413

sample, using the concentrations provided by NMR. This qNMR-MS method provides excellent

414

quantitation, and can significantly reduce matrix effects in MS analysis. This method can achieve

415

quantitative results comparable to the internal standard method and thus, when iSTDs are not 20


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416

commercially available or very expensive, the qNMR-MS method is a viable alternative. Based

417

on its quantitation accuracy, small sample usage, and reduction of matrix effects, this qNMR-MS

418

approach should be a highly useful method for metabolomics research. Importantly, the analytical

419

development and application of this novel and useful quantitative tool offers new avenues for the

420

routine quantitation of amino acids in blood samples and can, in principle, be extended to a wide

421

variety of metabolites in different biological samples.

422

21



Page 22 of 26

423

ASSOCIATED CONTENT

424

Supporting Information

425

The supporting tables and figures are available free of charge via the Internet at http://

426

pubs.acs.org/.

427 428

AUTHOR INFORMATION

429

Corresponding Authors:

430

*Phone: 206-543-9709. Fax: 206-616-4819. E-mail: [email protected].

431

*Phone: 480-301-6016. Fax: 480-301-7017. E-mail: [email protected]

432 433

Notes

434

The authors declare no potential conflicts of interest.

435 436

ACKNOWLEDGMENTS

437

This work was supported by the NIH (P30 DK035816, P30 CA015704, 1R01ES030197-01), the

438

University of Washington and the College of Health Solutions at Arizona State University.

439

22


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441

REFERENCES

442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485

(1) Lindon, J. C.; Nicholson, J. K. The emergent role of metabolic phenotyping in dynamic patient stratification. Expert Opinion on Drug Metabolism & Toxicology 2014, 10, 915-919. (2) Zampieri, M.; Sekar, K.; Zamboni, N.; Sauer, U. Frontiers of high-throughput metabolomics. Current opinion in chemical biology 2017, 36, 15-23. (3) Gowda, G. A. N.; Raftery, D. Biomarker Discovery and Translation in Metabolomics. Current Metabolomics 2013, 1, 227-240. (4) Gu, H.; Gowda, G. A. N.; Raftery, D. Metabolic profiling: are we en route to better diagnostic tests for cancer? Future Oncology 2012, 8, 1207-1210. (5) Xia, J.; Broadhurst, D. I.; Wilson, M.; Wishart, D. S. Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics 2013, 9, 280-299. (6) Markley, J. L.; Brüschweiler, R.; Edison, A. S.; Eghbalnia, H. R.; Powers, R.; Raftery, D.; Wishart, D. S. The future of NMR-based metabolomics. Current Opinion in Biotechnology 2017, 43, 34-40. (7) Fiehn, O. Metabolomics by gas chromatography–mass spectrometry: Combined targeted and untargeted profiling. Current protocols in molecular biology 2016, 114, 30.34. 31-30.34. 32. (8) Yan, M.; Xu, G. Current and future perspectives of functional metabolomics in disease studies–A review. Anal. Chim. Acta 2018, 1037, 41-54. (9) McCartney, A.; Vignoli, A.; Biganzoli, L.; Love, R.; Tenori, L.; Luchinat, C.; Di Leo, A. Metabolomics in breast cancer: A decade in review. Cancer Treatment Reviews 2018, 67, 88-96. (10) Cui, L.; Lu, H.; Lee, Y. H. Challenges and emergent solutions for LC-MS/MS based untargeted metabolomics in diseases. Mass Spectrometry Reviews 2018, 37, 772-792. (11) Carroll, P. A.; Diolaiti, D.; McFerrin, L.; Gu, H.; Djukovic, D.; Du, J.; Cheng, P. F.; Anderson, S.; Ulrich, M.; Hurley, J. B.; Raftery, D.; Ayer, D. E.; Eisenman, R. N. Deregulated Myc Requires MondoA/Mlx for Metabolic Reprogramming and Tumorigenesis. Cancer Cell 2015, 27, 271-285. (12) Benjamin, D. I.; Cravatt, B. F.; Nomura, D. K. Global profiling strategies for mapping dysregulated metabolic pathways in cancer. Cell Metab 2012, 16, 565-577. (13) Gu, H.; Carroll, P. A.; Du, J.; Zhu, J.; Neto, F. C.; Eisenman, R. N.; Raftery, D. Quantitative method to investigate the balance between metabolism and proteome biomass: starting from glycine. Angew. Chem. Int. Ed. 2016, 55, 15646-15650. (14) Kumar, B.; Prakash, A.; Ruhela, R. K.; Medhi, B. Potential of metabolomics in preclinical and clinical drug development. Pharmacological Reports 2014, 66, 956-963. (15) Wishart, D. S. Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov 2016, 15, 473-484. (16) Roberts, L. D.; Gerszten, R. E. Toward New Biomarkers of Cardiometabolic Diseases. Cell Metabolism 2013, 18, 43-50. (17) Zhu, J.; Djukovic, D.; Deng, L.; Gu, H.; Himmati, F.; Chiorean, E. G.; Raftery, D. Colorectal Cancer Detection Using Targeted Serum Metabolic Profiling. Journal of Proteome Research 2014, 13, 4120-4130. (18) Jacob, M.; Malkawi, A.; Albast, N.; Al Bougha, S.; Lopata, A.; Dasouki, M.; Rahman, A. M. A. A targeted metabolomics approach for clinical diagnosis of inborn errors of metabolism. Anal. Chim. Acta 2018, 1025, 141-153. (19) Watson, E.; Reid, G. Metabolomics as a clinical testing method for the diagnosis of vaginal dysbiosis. American Journal of Reproductive Immunology 2018, e12979. (20) Halama, A.; Riesen, N.; Moller, G.; de Angelis, M. H.; Adamski, J. Identification of biomarkers for apoptosis in cancer cell lines using metabolomics: tools for individualized medicine. Journal of Internal Medicine 2013, 274, 425-439. 23



Page 24 of 26

486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506

(21) Nicholson, J. K.; Holmes, E.; Kinross, J. M.; Darzi, A. W.; Takats, Z.; Lindon, J. C. Metabolic phenotyping in clinical and surgical environments. Nature 2012, 491, 384-392. (22) Bundy, J. G.; Davey, M. P.; Viant, M. R. Environmental metabolomics: a critical review and future perspectives. Metabolomics 2009, 5, 3-21. (23) Barton, S.; Navarro, S. L.; Buas, M. F.; Schwarz, Y.; Gu, H.; Djukovic, D.; Raftery, D.; Kratz, M.; Neuhouser, M. L.; Lampe, J. W. Targeted plasma metabolome response to variations in dietary glycemic load in a randomized, controlled, crossover feeding trial in healthy adults. Food & function 2015, 6, 29492956. (24) Christopher, K. B. Nutritional metabolomics in critical illness. Current Opinion in Clinical Nutrition & Metabolic Care 2018, 21, 121-125. (25) Lampe, J. W.; Huang, Y.; Neuhouser, M. L.; Tinker, L. F.; Song, X.; Schoeller, D. A.; Kim, S.; Raftery, D.; Di, C.; Zheng, C. Dietary biomarker evaluation in a controlled feeding study in women from the Women’s Health Initiative cohort, 2. The American journal of clinical nutrition 2016, 105, 466-475. (26) Khachik, F.; Spangler, C. J.; Smith, J. C.; Canfield, L. M.; Steck, A.; Pfander, H. Identification, Quantification, and Relative Concentrations of Carotenoids and Their Metabolites in Human Milk and Serum. Anal. Chem. 1997, 69, 1873-1881. (27) Lee, Y.; Khan, A.; Hong, S.; Jee, S. H.; Park, Y. H. A metabolomic study on high-risk stroke patients determines low levels of serum lysine metabolites: a retrospective cohort study. Molecular BioSystems 2017, 13, 1109-1120. (28) Pan, Z. Z.; Raftery, D. Comparing and combining NMR spectroscopy and mass spectrometry in metabolomics. Analytical and Bioanalytical Chemistry 2007, 387, 525-527.

507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531

(29) Khamis, M. M.; Adamko, D. J.; El ‐ Aneed, A. Mass spectrometric based approaches in urine metabolomics and biomarker discovery. Mass spectrometry reviews 2017, 36, 115-134. (30) Gowda, G. A. N.; Zhang, S. C.; Gu, H. W.; Asiago, V.; Shanaiah, N.; Raftery, D. Metabolomics-based methods for early disease diagnostics. Expert Review of Molecular Diagnostics 2008, 8, 617-633. (31) Gowda, G. A. N.; Raftery, D. Quantitating Metabolites in Protein Precipitated Serum Using NMR Spectroscopy. Anal. Chem. 2014, 86, 5433-5440. (32) Nagana Gowda, G. A.; Gowda, Y. N.; Raftery, D. Expanding the Limits of Human Blood Metabolite Quantitation Using NMR Spectroscopy. Anal. Chem. 2015, 87, 706-715. (33) Gu, H.; Zhang, P.; Zhu, J.; Raftery, D. Globally Optimized Targeted Mass Spectrometry: Reliable Metabolomics Analysis with Broad Coverage. Anal. Chem. 2015, 87, 12355-12362. (34) Arrivault, S.; Guenther, M.; Fry, S. C.; Fuenfgeld, M. M. F. F.; Veyel, D.; Mettler-Altmann, T.; Stitt, M.; Lunn, J. E. Synthesis and Use of Stable-Isotope-Labeled Internal Standards for Quantification of Phosphorylated Metabolites by LC–MS/MS. Anal. Chem. 2015, 87, 6896-6904. (35) Li, P.; Li, Z.; Beck, W. D.; Callahan, P. M.; Terry, A. V.; Bar-Peled, M.; Bartlett, M. G. Bio-generation of stable isotope-labeled internal standards for absolute and relative quantitation of phase II drug metabolites in plasma samples using LC–MS/MS. Analytical and Bioanalytical Chemistry 2015, 407, 40534063. (36) Stokvis, E.; Rosing, H.; Beijnen, J. H. Stable isotopically labeled internal standards in quantitative bioanalysis using liquid chromatography/mass spectrometry: necessity or not? Rapid Communications in Mass Spectrometry 2005, 19, 401-407. (37) Nagana Gowda, G. A.; Djukovic, D.; Bettcher, L. F.; Gu, H.; Raftery, D. NMR-Guided Mass Spectrometry for Absolute Quantitation of Human Blood Metabolites. Anal. Chem. 2018, 90, 2001-2009. (38) Han, J.; Higgins, R.; Lim, M. D.; Atkinson, K.; Yang, J.; Lin, K.; Borchers, C. H. Isotope-labeling derivatization with 3-nitrophenylhydrazine for LC/multiple-reaction monitoring-mass-spectrometrybased quantitation of carnitines in dried blood spots. Anal. Chim. Acta 2018, 1037, 177-187. 24


Page 25 of 26 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

532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558


(39) 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. (40) Kaspar, H. Amino acid analysis in biological fluids by GC-MS. Dissertation, Universität Regensburg, 2009. (41) ElBaz, F. M.; Zaki, M. M.; Youssef, A. M.; ElDorry, G. F.; Elalfy, D. Y. Study of plasma amino acid levels in children with autism: An Egyptian sample. Egyptian Journal of Medical Human Genetics 2014, 15, 181186. (42) Hu, K.; Westler, W. M.; Markley, J. L. Simultaneous Quantification and Identification of Individual Chemicals in Metabolite Mixtures by Two-Dimensional Extrapolated Time-Zero 1H−13C HSQC (HSQC0). J. Am. Chem. Soc. 2011, 133, 1662-1665. (43) Yang, W. C.; Regnier, F. E.; Jiang, Q.; Adamec, J. In vitro stable isotope labeling for discovery of novel metabolites by liquid chromatography-mass spectrometry: Confirmation of gamma-tocopherol metabolism in human A549 cell. J. Chromatogr. A 2010, 1217, 667-675. (44) Wang, S.; Cyronak, M.; Yang, E. Does a stable isotopically labeled internal standard always correct analyte response?: A matrix effect study on a LC/MS/MS method for the determination of carvedilol enantiomers in human plasma. Journal of Pharmaceutical and Biomedical Analysis 2007, 43, 701-707. (45) Nagana Gowda, G. A.; Gowda, Y. N.; Raftery, D. Massive Glutamine Cyclization to Pyroglutamic Acid in Human Serum Discovered Using NMR Spectroscopy. Anal. Chem. 2015, 87, 3800-3805. (46) Shanaiah, N.; Desilva, M. A.; Gowda, G. A. N.; Raftery, M. A.; Hainline, B. E.; Raftery, D. Class selection of amino acid metabolites in body Fluids using chemical derivatization and their enhanced 13C NMR. Proceedings of the National Academy of Sciences of the United States of America 2007, 104, 11540-11544. (47) Bollinger, J. G.; Rohan, G.; Sadilek, M.; Gelb, M. H. LC/ESI-MS/MS detection of FAs by charge reversal derivatization with more than four orders of magnitude improvement in sensitivity. Journal of Lipid Research 2013, 54, 3523-3530. (48) Tayyari, F.; Gowda, G. A. N.; Gu, H.; Raftery, D. N-15-Cholamine-A Smart Isotope Tag for Combining NMR- and MS-Based Metabolite Profiling. Anal. Chem. 2013, 85, 8715-8721.

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Combining NMR and MS with Chemical Derivatization for Absolute

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