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Click the title above to open the Spectroscopy July 2018 Special Issue, Current Trends in Mass Spectrometry, in an interactive PDF format.

CTMS July 2018 Issue PDF

Recent advances in liquid chromatography–mass spectrometry (LC–MS) instrumentation have allowed researchers to tackle new analytical challenges in systems biology and toxicology. Untargeted data-independent acquisition scanning methods have emerged in the fields of proteomics, metabolomics, and lipidomics. DNA adductomics is a developing methodology that can detect DNA adducts (chemical modifications to genomic DNA) of many genotoxicants and reactive oxygen species. The University of Minnesota’s Masonic Cancer Center and Department of Medicinal Chemistry has developed an analysis termed “wide-selected ion monitoring(SIM)/MS

 ” scanning, which is capable of screening for a wide range of DNA adducts. Wide-SIM/MS

 involves sequential, alternating acquisition of high-resolution wide-SIM and corresponding MS

 spectra covering a large mass range. The wide-SIM data analysis approach provides for detection of the parent masses of various compounds (including DNA adducts) present in the sample, whereas the MS

 data allows for detection of the neutral loss of deoxyribose, which identifies a particular compound as a putative DNA adduct. This Wide-SIM/MS

 method has successfully identified DNA adducts from carcinogen exposures and oxidative stress in human prostate and kidney tissues. Wide-SIM/MS

 screening technology can advance our understanding about the role of chemical exposures in DNA damage and disease risk.

	Humans are frequently exposed to low levels of hazardous chemicals in the environment, water, their diet, and medicines, and through occupational exposures. Many of these compounds or their reactive metabolites can bind to macromolecules (1). DNA adducts, the covalent modification of DNA by genotoxicants, can serve as measures of chemical exposure and the biologically effective dose. Epidemiological studies have linked various chemical exposures to the increased risk of different cancers in humans. Examples include:

		the exposure to tobacco-specific nitrosamines is linked with lung cancer (2,3)

		occupational exposure to aromatic amines causes bladder cancer (2,4)

		the usage of traditional Chinese herbs containing aristolochic acid is linked to urothelial cancers (5,6)

		the consumption of grains contaminated with the mycotoxin aflatoxin B

) is a risk factor for liver cancer (7,8)

		DNA adducts of some carcinogens induce mutations in critical genes involved in carcinogenesis, such as 

 and aristolochic acid are two chemicals for which exposures have been definitively linked to DNA adducts, signature mutational spectra in cancer related genes, and the development of cancer in humans (8,13)

	The bioactivation and DNA adduct formation of aristolochic acid and the aromatic amine 4-aminobiphenyl, a bladder carcinogen present in tobacco smoke, are shown in Figure 1. If not repaired, these DNA adducts can induce mutations during cell division (14). DNA adducts of carcinogens are formed at different ring atoms, exocyclic nitrogen and oxygen atoms of the nucleobases, and the phosphate backbone (15). The types of DNA adducts formed are dependent on the structures and chemical properties of the reactive intermediates, and also on the ability of the compounds to intercalate with DNA, which may direct adduct formation to specific nucleophilic sites of the DNA bases (15,16).

 Aristolochic acid-I (AA-I) and 4-aminobiphenyl (4-ABP) are shown as prototypical procarcinogens. AA-I undergoes nitro-reduction through NAD(P)H:quinone oxidoreductase (NQO1), or cytochrome P450. The resulting 

-hydroxyaristolactam-I is bioactivated by sulfotransferases (SULTs) to form an unstable 

-sulfoxy ester, which quickly undergoes heterolytic cleavage to produce the reactive nitrenium/carbenium ion intermediate that forms dA-AL-I and other DNA adducts. 4-ABP undergoes 

-hydroxylation by P450s, then it is further bioactivated by 

-acetyltransferases (NATs) to form the N-acetoxy ester, which leads to the formation of dG-C8-4-ABP through the nitrenium ion intermediate.

Methods of DNA Adduct Detection and Challenges 

	Chemical agents in tobacco smoke, diet, and environmental pollution contribute to damage of the genome. Therefore, chemical analyses of DNA adducts can provide clues about chemicals that contribute to cancer etiology. However, the measurement of DNA adducts in humans is a challenging analytical task, because the levels of DNA adducts usually range between 100 ppb (one adduct per 10

 nucleotides) or less. Moreover, the amount of fresh human tissue is often limited to milligram quantities, and as a result, only low microgram quantities of DNA are available for chemical analyses. Thus, highly sensitive methods are required to screen for DNA adducts. During the past three decades, DNA adducts have been most commonly measured by 

P-postlabeling (17–19), immunoassay–immunohistochemistry (IHC) (20,21), or mass spectrometry (MS)-based methodologies (15,16). MS-based technologies are the most robust and specific methods for screening of DNA adducts, and MS is the only technique that provides physicochemical data to confirm structure identity. MS methods are also quantitative, when employing the stable isotope dilution method (16). Gas chromatography (GC)–MS has been successfully used to measure free radical–mediated oxidative damage of the deoxyribose moiety and the nucleobases, as well as to measure chemically adducted nucleobases and DNA–protein cross-links (22). However, DNA adducts require chemical derivatization to increase their volatility for GC analysis, and the efficiency of chemical derivatization of adducts is a potential issue for robust measurements. Moreover, the DNA adducts must be stable at the elevated temperatures employed in GC–MS. The online coupling of liquid chromatography (LC) or capillary electrophoresis to MS is the preferred method for the measurement of many DNA adducts, particularly those that are thermally labile. LC with electrospray ionization (ESI)-MS is the predominant platform for DNA adduct analysis (15,16,23–26). The rapid advances in DNA adduct measurements have been driven by the recent developments of LC–MS instrumentation and technologies. The high resolving power and accurate mass measurements possible with quadrupole time-of-flight (Q-TOF) and orbital trap MS detectors, as compared to triple-quadrupole and ion trap (IT) instruments, have significantly reduced background noise and enhanced the selectivity of the analyte detection, and the coupling of nano-flow ultrahigh-pressure liquid chromatography (UHPLC) with a nanoESI source has greatly improved the sensitivity. The detection limit of 10-(2'-deoxyguanosin-

-yl)-7,8,9-trihydroxy-7,8,9,10-tetrahydrobenzo[

]pyrene, a carcinogen present in tobacco smoke and barbequed meats (2), approaches one adduct per 10

 nucleotides (1 adduct per 10 human lung cells) in the lung of smokers (27).

	The analysis of DNA adducts by LC–ESI-MS requires hydrolysis of DNA to modified nucleobases by treatment with acid, base, or elevated temperature, or to modified 2'-deoxyribonucleosides using a cocktail of nucleases. The full-scan high-resolution accurate mass (HRAM) screening of the precursor ions of DNA adducts following acid hydrolysis of DNA has been reported (28); however, tandem MS greatly improves selectivity and sensitivity. The scheme for monitoring DNA adducts at the MS

 scan stage is shown in Figure 2. The most common MS/MS transition feature of DNA adducts is the loss of 2'-deoxyribose (dR, 116 Da, or 116.0473 Da in HRAM) when 2'-deoxynucleosides are subjected to collision-induced dissociation (CID) (Figure 2a) (29). Triple-quadrupole (QqQ) MS instrumentation employing selected reaction monitoring (SRM) or multiple reaction monitoring (MRM) has been the predominant MS platform for targeted DNA adduct analysis over the past two decades because of its fast duty cycle, wide dynamic range, good sensitivity, robustness, and reproducibility (23,30). However, IT-MS has gained popularity in DNA adduct analysis because of its capability of performing multistage MS (MS

 scanning is especially suitable for analyzing modified 2'-deoxynucleosides because DNA adducts typically undergo CID to lose the dR moiety, and the aglycone ions are often detected as the single product ion in the MS

 spectra. In addition, product ion mass spectra of the aglycone adducts [AH

 scan stage provide rich structural information about the structure of the adduct and corroborate the identity of the lesion (vide infra). Moreover, the MS

 scanning removes background signals observed at the MS

 scan stage and can be used for robust quantitative measurement of DNA adducts (32).

 The fragmentation pathways of modified nucleosides analyzed by LC–MS. (a) Most modified nucleosides undergo fragmentation in MS/MS to form the aglycone with the neutral loss of 2'-deoxyribose (dR). Other common fragmentations include (b) the neutral loss of the nucleobase, and (c) the formation of nucleobase ions (34). Copyright MDPI 2017. Adapted with permission from reference 34.

	Many DNA adduct measurements have been performed by targeting from one to several adducts at a time. However, there are many chemicals in the environment and diet, as well as endogenous electrophiles produced in our bodies, that can react with DNA to form adducts (33). Thus, robust and versatile scanning methods have been sought to detect many DNA lesions in an untargeted fashion in a single run. With the emergence of hybrid orbital trap and Q-TOF MS instruments with flexible scanning events, high resolution, and accurate mass measurements, it is possible to screen for many DNA adducts in a single assay (34). The detection of many DNA adducts at a time, termed 

, by different MS approaches has been recently reviewed (34,35). Four principal scanning techniques are currently in use for DNA adductomics (Figure 3):

		a continuous scan of constant neutral loss (CNL) by monitoring mass transitions of adduct precursors ([M + H]

 data-dependent acquisition (DDA) in quadrupole trap (Q-trap), Q-TOF, or orbital trap

		data-dependent CNL of 116.0 or 116.0473 Da that triggers MS

, with or without a targeted mass inclusion list, in Q-trap, linear ion trap (LIT), or hybrid LIT-orbital trap MS

		data-independent acquisition (DIA) in MS

 (dynamic switching between low and high collision energy in parallel scans) in Q-TOF, and wide-SIM/MS

 Schemes of DNA adductomic approaches using different mass spectrometric scanning strategies focusing on adducted 2'deoxyribonucleosides with precursors ([M + H]

 ). (a) The continuous scanning of the constant neutral loss (CNL) of 116 Da (dR) in QqQ MS. This approach is achieved either by using the CNL scan mode of 116 Da (left), or in pseudo-CNL by setting up 300 SRM transitions of [M + H]

 (right). (b) The data-dependent acquisition (DDA) of top N (the most abundant N ions) or top speed (acquire as many dependent scans as possible within the cycle time) mode in Q-trap, Q-TOF or orbital trap. (c) The CNL (116 or 116.0473 Da) triggering of MS

 in trap-based MS that can perform multi-stage MS scan (Q-trap, LIT, or LIT-orbital trap). Adduct precursors are fragmented if they are selected by the survey scan in DDA mode (left) or if they are in the targeted inclusion list (right). The subsequent MS

 scan is only triggered if the loss of 116 or 116.0473 Da is detected in the MS

 scan. (d) The adductomics approach using data-independent acquisition (DIA) methods in HRAMS. In Q-TOF (left), a full scan of all precursor ions, including presumed adducts, is acquired followed by MS

 (ramping collision energy: low to high). The ions are fragmented to generate the aglycones [M + H – 116]

 and other possible fragment ions. In wide-SIM/MS

 (right) conducted in the Q-orbital trap, all ions, including adduct precursors, are detected in SIM of 30 

 windows in the orbital trap instrument, and their aglycones are detected in the following MS

	DNA adductomics analysis was first achieved by scanning for CNL of 116 Da using fast atom bombardment MS with electric and magnetic sector in 1990 (36). Thereafter, QqQ MS was successfully applied through a "pseudo-CNL" method monitoring over 300 SRM transitions of [M + H]

 (37–39), which has superior sensitivity over the traditional CNL scan mode (40). Our laboratory pioneered the data-dependent constant neutral loss scanning followed by triple-stage mass spectrometry (CNL-MS

) in an LIT MS instrument, using the neutral loss of the dR moiety from the protonated adducts ([M + H - 116]

 scan to trigger the acquisition of the product ion spectra of the aglycone ions at MS

 stage (41). Balbo and Villalta extended the method to HRAM MS (42,43). This method employs either "top N" (the most intense N ions) or "top speed" scan mode (acquire with as many dependent scans as possible within the cycle time) with or without specifying an intensity threshold or charge state. A dynamic exclusion function can be applied to restrict the reoccurrence of the same MS

 scans, thus allowing for more putative adducts precursors to be fragmentated. Background ions detected from solvent blank and negative control samples can be added to an exclusion list to improve the detection efficiency of the adducts. In addition, a targeted inclusion list can be constructed to facilitate the detection of a wide range of DNA adducts and maximize the MS sampling and scanning efficiency.

	The DIA approach in "omics" studies is unbiased and data can be mined post-analyses to search for putative DNA adducts. DIA scanning methods typically employ Q-TOF instrumentation and use a wide Q1 window to sequentially isolate all of the precursors, which are subsequently fragmented in the collision cell, and the resulting fragment ions are detected (44). In some cases, a survey scan is also collected, from which precursor ions can be extracted (45,46). The transition for the loss of 2'-deoxyribose [M + H - dR]

 serves as the foundation for many CNL applications in DNA adductomics. The aglycone ions are often the only fragment ions observed in the MS

 spectra acquired by CID with IT MS instruments. In contrast, in other omics analyses, such as proteomics and metabolomics, multiple MS

 fragment ions of peptides (y and b ions) or metabolites are obtained and used as target and qualifier ions for analyte identification and quantification. Thus, in DNA adductomics, the extraction and coelution of precursor and aglycone peaks of DNA adducts are essential feature criteria for the identification of DNA adducts. An MS

 scan provides spectral data on the adducted aglycone to confirm the identity of the adduct against a mass spectral database of DNA adduct standards or for further characterization of structures of novel adducts.

 methodology to screen for DNA adducts using a hybrid orbital trap MS system (40). The scheme for wide-SIM/MS

 analyses of DNA adducts is shown in Figure 3d (right) and Figure 4. Our method uses online sample enrichment to remove polar components in the enzymatic digestion matrix, such as salts and non-modified 2'-deoxyribonucleosides, which are present at > 1 million-fold higher levels than DNA adducts. In this configuration, wide-SIM/MS

 screens for bulky hydrophobic DNA adducts that are retained on the trapping column. A UHPLC system operating at 300 nL/min flow rate with a 200 mm x 75 µm reversed-phase C18 fused silica column (with a 10-µm orifice emitter) is interfaced to a nanoESI source. The acquisition method contains 10 alternating wide-SIM scans (30 

 range 330–630) and high-energy collision-induced dissociation (HCD)-MS

 100–630), encompassing all precursor ions of DNA adducts under study. The HCD-MS

approach fragments all precursor ions in the previous wide-SIM scan event. Thus, both precursor ions in wide-SIM and MS

 product ions are detected with the orbital trap MS detector. Potential adducts are identified by extraction and coelution of the precursor ions in the wide-SIM analysis and the aglycones [M + H - 116.0473]

 The detection of DNA adducts in human samples by wide-SIM/MS

. dG-C8-PhIP and several endogenous lipid peroxidation adducts were identified in prostate, and dA-AL-I was detected in renal cortex. Adduct structures were confirmed by the MS

 spectra. Precursor and aglycone ions were extracted with a 5 ppm tolerance. The theoretical 

 of each adduct were dG-C8-PhIP, 490.1946 and 374.1472; dA-AL-I, 543.1623 and 427.1149; OHE-dG, 404.1929 and 288.1455; ONE-dA, 388.1979 and 272.1506; HNE-dC, 382.1973 and 266.1499; and HNE-dG, 422.2034 and 306.1561 (40). Copyright ACS 2017. Adapted with permission from reference 40.

	The orbital trap MS system utilizes automatic gain control (AGC) to tightly regulate the number of ions or charges entering the orbital trap to avoid space-charging effects that would cause a deterioration in mass resolution and a drop-off in sensitivity (31). Increasing the mass resolution in the orbital trap system can improve the specificity of adduct detection; however, the scan rate is proportionally decreased, and therefore, care must be taken to ensure the resolution settings allow for a sufficient data acquisition rate. Increasing the AGC, within the allowable range, extends the intrascan dynamic range (the maximum abundance ratio between the most abundant and the least abundant signal observable within a given spectrum of ions collected in the trap), thus increasing the likelihood to detect adducts that are present at low abundance (47). However, a large AGC value with increased ion injection times can result in under-sampling and an insufficient number of data points for extraction of the chromatographic peaks. In our analyses, the quadrupole was set to transmit a 30 

, which permitted a satisfactory sampling time and intrascan dynamic range while allowing for a sufficient sampling rate. To take full advantage of in-parallel orbital trap detection and curved linear trap (C-trap) filling for the subsequent mass spectral measurement, the maximum fill time (ion injection time) and the transient length of the specified resolution in the orbital trap must be balanced. For resolving power of 60,000 (full width at half maximum [FWHM] at 

 200) in the orbital trap MS system, the transient length is about 128 ms. We chose a maximum injection time of 100 ms for AGC limits of 5 × 10

 to avoid increasing cycle time. Taking into consideration the delay time between scans, the total duty cycle for 20 combined SIM/MS

 scans is about 3 s, which is sufficient to acquire six scans for adduct peaks with an average width of 20 s (FWHM). With these conditions, we showed that DNA adducts of 20 carcinogens derived from chemicals in tobacco smoke, the diet, or lipid peroxidation products spiked in calf thymus DNA (20 µg) before digestion could be identified by wide-SIM/MS

 nucleosides (40). We compared the performance of DIA wide-SIM/MS

 with a targeted inclusion mass list in the orbital trap instrument, the pseudo-CNL and CNL in QqQ MS using the same samples with identical chromatography and ion source conditions. The overall performance of the four approaches were DIA-wide-SIM/MS

 with targeted inclusion > pseudo-CNL > CNL, thereby demonstrating that DIA-wide-SIM/MS

 was the most sensitive, robust, and comprehensive method to screen for bulky DNA adducts. However, isobaric interferences can still occur in the SIM detection at 60,000 resolution. The merging of unresolved peaks can shift the observed 

 outside the 5 ppm mass tolerance window and result in the failure to detect the DNA adduct, particularly when they are present at low abundance. For example, the isotopically labeled internal DNA adduct standard of 4-ABP, [

]-dG-C8-4-ABP, at the lowest spiking level of 0.4 adduct per 10

 nucleotides levels, required a minimum of 200,000 resolution (FWHM at 

 200) to achieve 10% peak valley separation to detect this adduct at 5 ppm mass tolerance (Figure 5).


	Figure 5: Extracted ion chromatograms (EICs) and mass spectra of precursor [

]-dG-C8-4-ABP in pure solvent or in calf thymus DNA digest analyzed by wide-SIM/MS

 at resolutions 60,000 and 500,000 (FWHM at 200 

). Precursor ions were extracted at 5 and 10 ppm mass tolerance. The zoomed-in mass spectrum between m/z 445.20 and 445.22 shows the location of the theoretical 

 of precursor ion (445.2111, red dash line), the mass range of 5 ppm (teal box) and 10 ppm (lavender box) (40). Copyright ACS 2017. Adapted with permission from reference 40.

	We have compiled a list of over 100 DNA adducts with published MS

 fragmentation spectra to facilitate the DNA adductomic analysis by wide-SIM/MS

. The data mining of adducts is performed with LC–MS software by manually extracting theoretical 

 for precursor and aglycone ions at a 5 ppm mass tolerance. The wide-SIM/MS

 scanning technology was used to screen for DNA adducts in human biopsy specimens (Figure 4). A DNA adduct of 2-amino-1-methyl-6-phenylimidazo[4,5-

-(2'-deoxyguanosin-8-yl)-PhIP, was detected in genomic DNA of prostate cancer patients (48). PhIP, formed in cooked meat, is a rodent and possible human prostate carcinogen (49). We also detected a DNA adduct of aristolochic acid, 7-(deoxyadenosin-

-yl)-aristolactam I (dA-AL-I), in the renal cortex of kidney cancer patients who had ingested herbal medicines (50). Aristolochic acid is a renal carcinogen and a component of 

 herbs used in traditional herbal medicines world-wide (6). A number of lipid peroxidation products were also detected in human prostate and kidney tissues by wide-SIM/MS

	Those adducts contained in our data base of DNA adducts with known MS

 fragmentation and neutral losses other than dR (116.0473 Da) can also be mined and broaden the search criteria for different classes of DNA adducts (43). For example, the HCD MS

]PDE produced predominant fragment ions of the B[

]PDE moiety, and the protonated aglycone was barely detected (Figure 6). In a similar vein, the combination of CNL and product ion scan employing Q-trap has been used for the untargeted screening of DNA adducts of B[

]P, phenyl glycidyl ether, and styrene-7,8-oxide, where prominent fragment ions in MS

 from the carcinogen moiety were used for adduct identification (51,52). There is only one report using a Q-TOF with MS

 to screen for DNA adducts related to oxidative stress in lung of rats exposed to magnetite nanoparticles (53).

 EICs, structure, and fragmentation mechanism of dG-N

 scan, the extracted signal of aglycone is 100-fold lower than that of fragment ions derived from the B[a]PDE moiety (40). Copyright ACS 2017. Adapted with permission from reference 40.

	The development of bioinformatic tools dedicated for DNA adduct screening and database searching is critical for the advancement of DNA adductomics. Specific to the work presented here, a workflow that includes feature detection of SIM and MS

 chromatographic peaks, alignment of the precursor and aglycone peaks for the identification of the potential known and unknown adducts in an untargeted approach, and the generation of the accompanying mass spectra would allow for the wide-SIM/MS

 methodology to reach its full potential. Also, a larger DNA adduct database is under construction to facilitate automated adduct identification in future applications. Ion mobility spectrometry can be applied to resolve matrix interferences in DNA adductomics (54). Our experiences suggest that further improvements in ion transmission efficiency, the intrascan dynamic range, and more rapid duty cycles will be required for the detection of some DNA adducts present at low abundance. Biopsy specimens are often unavailable for DNA adduct biomarker research. However, formalin fixed paraffin embedded tissues with clinical diagnosis of disease are an accessible, but underutilized biospecimens for screening DNA adducts (55–57). Also, exfoliated cells in saliva (58,59) and urine (60) have been used to screen for DNA adducts through targeted MS approaches, and are promising biospecimens for screening with DNA adductomics. We expect that the advancements of DNA adductomic technologies over the next decade will provide us with a greater understanding of the chemicals that damage the genome and contribute to the development of disease.

	This research was supported by NIH grants R01 CA220367 (R.J.T.), R01 CA122320 (R.J.T.), R01 ES019564 (R.J.T), R50 CA211256 (P.W.V.), and by the Cancer Center Support Grant CA077598.

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A new type of analysis called “wide-selected ion monitoring (SIM)/MS2” scanning, is capable of screening for a wide range of DNA adducts (chemical modifications to genomic DNA). This method has successfully identified DNA adducts from carcinogen exposures and oxidative stress in human prostate and kidney tissues.

DNA Adductomic Analysis by Data-Independent Mass Spectrometry

Targeted metabolomics analysis can be performed with standardized assay setups. One great advantage of standardized methods is that they enable comparability between laboratories and across studies. This advantage is especially crucial when focusing on large, longitudinal cohort studies and when comparing data across different centers. We used a standardized targeted kit to demonstrate the accuracy, sensitivity, and reproducibility of the approach. We employed this method to analyze serum samples from type 2 diabetes (T2D) study subjects and healthy controls. We observed that several metabolites were significantly altered between the T2D study subjects and the healthy controls, with these alterations appearing to agree with previous reports of certain metabolites being associated with an increased likelihood of T2D, obesity, or differential changes in response to oral glucose tolerance test. We deemed the results obtained to be highly reproducible. 

	Metabolomics is an innovative scientific field for understanding and exploring the body–cell homeostasis. The entire collection of low-weight molecules that enable the maintenance, growth, and normal function of cells and whole organisms is known as the 

(1), with metabolites acting as markers of biochemical, physiological, and pathological reactions. By monitoring metabolites, the different pathways that develop within a living cell, tissue, or organism can be divulged (2).

	Metabolites have diverse, far-reaching biochemical effects, including the ability to function as co-substrates and regulate post-translational modifications that affect protein activities (3,4). Metabolites also play a role in epigenetic regulations and maintenance of the pluripotency of embryonic stem cells (5–7). Furthermore, hormones and fatty acids can interact with plasma proteins to facilitate their transportation in the bloodstream (8).

	State-of-the-art developments in bioinformatics and analytical techniques, as well as the integration of orthogonal biological approaches, allow the analysis of systems-level effects of metabolites. Subtle changes in metabolic pathways can be detected, providing insights into the mechanisms of many physiological conditions and diseases. For example, plasma trimethylamine N-oxide (TMAO) and urinary taurine have recently been identified as markers of cardiovascular disease (9). Using a targeted metabolic approach covering a wide range of metabolites and a large number of study participants, the research identified sugar metabolites, amino acids, and choline-containing phospholipids to be associated with risk of T2D (10). To correlate a specific metabolite to a specific phenotype, the metabolite must be accurately identified and reproducibly quantified, and its propensity as a biomarker must be validated.

	Biomarker validation in particular can be challenging, because of the difficulties in measuring subtle differences in metabolite concentration. Major advancements in liquid chromatography–mass spectrometry (LC–MS) automation have occurred with the inception of commercial kits, which can reduce costs, increase throughput, provide greater reliability, and improve reproducibility.

	MS is a fitting platform to analyze the metabolome since it is highly sensitive and robust, and provides high reproducibility. The metabolomics sample, containing a mixture of a diverse class of metabolites, can be introduced directly to the mass spectrometer, or undergo a preceding separation step (such as liquid or gas chromatography) before analysis. Thousands of ions can be present in metabolomic samples, so chromatographic separation is often used to minimize signal suppression and allows for greater sensitivity for accurate metabolite identification and quantitation.

	Metabolomics contributes to a paradigm shift toward understanding the cellular metabolism, but has also revealed the importance of the microbiome and its effect on human health and disease (11,12). In fact, metabolomics has identified a number of unexpected biochemical causes for complex, chronic diseases such as cancer, atherosclerosis, and diabetes (13,14).

	Type 2 diabetes (T2D) is defined as an increase of blood glucose levels caused by a pancreatic β-cell dysfunction and insulin resistance without evidence for specific cause (15). A prediabetic state, such as impaired fasting glucose (IFG) or impaired glucose tolerance (IGT), with marginally elevated blood glucose levels may precede T2D for many years (16).

	T2D is among the most prevalent chronic diseases (17), and its rate is expected to continue increasing (18). Complications are more likely to occur with increased duration of the disease, so a means of early detection (even before hyperglycemia) has become a necessity. Technologies are now emerging that allow the assessment of a wide range of metabolites (19–22), presenting opportunities to observe changes in biochemical pathways. Through the monitoring of these pathways, greater insight into the biological mechanisms of early-stage metabolic disorders can be gleaned. By comparing the changes in metabolites between predisposed and healthy individuals, the metabolites involved in disease mechanisms can be discovered (23,24). A change in these metabolite levels can indicate and identify biomarkers, which could enable preventative actions.

	There has been significant progress made in metabolomics through the continual improvements in instrument performance, as well as better experimental designs and innovative sample preparations. LC–MS approaches are becoming even more quantitative with the use of kits that provide standardization, automation, and advantages in interlaboratory reproducibility (20,21,25). The modern challenge is to now move beyond the identification of metabolites and their use as biomarkers. Instead, analysts are attempting to establish the direct physiological roles that metabolites have, and how they are involved in metabolic networks by determining how changes in their concentrations are linked to a variety of phenotypic outcomes. The future of metabolomics looks set to combine traditional biochemical methods with cutting-edge innovations to develop workflows that can assign a biological meaning to varying levels of metabolites, which can then be used to discover the mechanisms of diseases.

	Sample preparation for metabolite measurements with the AbsoluteIDQ p400 HR kit (Biocrates Life Sciences AG) was performed according to the manufacturer's protocol. The kit includes a 96-well plate, calibration standards, quality controls samples, internal standards, test-mixes for system suitability test, acquisition and quantitation methods, protocol for sample preparation, and the MetIDQ software package. Briefly, 10 µL of plasma sample was pipetted onto the 96-well plate containing the internal standards. Following derivatization, metabolites and internal standards were extracted and diluted for subsequent analysis. A total of six samples (three serum samples from T2D study subjects and three serum samples from healthy controls) were analyzed.

	Metabolites were measured with a targeted metabolomics approach, using the AbsoluteIDQ p400 HR kit and a Thermo Scientific Vanquish ultrahigh-pressure liquid chromatography (UHPLC) system, coupled to a Thermo Scientific Q Exactive HF Orbitrap mass spectrometer. The method allows for the quantitation of up to 408 endogenous metabolites across 11 different metabolite classes. Amino acids and biogenic amines were analyzed by liquid chromatography–mass spectrometry (LC–MS), whereas acylcarnitines, phosphatidylcholines, lysophosphatidylcholines, diglycerides, triglycerides, sphingomyelins, ceramides, cholesteryl esters, and hexoses were analyzed by flow injection analysis–mass spectrometry (FIA–MS). The automated technical validation of the analyzed kit plate to approve the validity of the run and provide verification of the actual performance of the applied quantitative procedure including instrumental analysis was performed using MetIDQ software provided with the kit. This validation was achieved based on the obtained results; defined acceptance criteria for blank, zero samples, calibration standards, and curves; low-, medium-, and high-level quality control (QC) samples; and measured signal intensity of internal standards over the plate.

	Metabolites were quantified according to the manufacturer's protocol, using the MetIDQ software for targeted metabolomic data processing and management. Statistical analysis was performed with the MetIDQ StatPack module.

 Data from the low-level QC samples in LC–MS showing the obtained accuracy (measured vs. theoretical values). Medium- and high-level QC measurements were also performed and evaluated.

	Figure 1 shows data from the low-level QC sample of the kit analyzed using LC–MS. The measured values were compared with theoretical values to determine the accuracy of the method. The accuracy range was 86–122% for the low-level QC sample. Similar results were obtained for the medium- and high-level QC samples. The accuracy range was 89–114% for the medium-level QC sample, and 95–118% for the high-level QC sample.

 Data from the low-level QC sample in FIA–MS showing the obtained accuracy (measured vs. theoretical values). Medium- and high-level QC measurements were also performed and evaluated.

	Figure 2 shows the data from the low QC sample analyzed using FIA–MS. Again, the measured values were compared with the theoretical values, and the accuracy of the method was determined. The accuracy range was 65–138% for the low-level QC sample. Overall, for all spiked QC samples (low-, medium- and high-level samples), the accuracy of the FIA–MS analysis for 84% of the metabolite measurements was well within the 70–130% range (Figure 3).

 Summary from all QC samples showing the percent difference between the measured and theoretical values for all analytes in the QC samples in FIA–MS. Data are displayed for two low-, two medium-, and three high-level QC samples (a total of seven QC samples).

	The low- and high-level QC samples were extracted in triplicate and the medium-level QC samples were extracted four times, then subsequently analyzed to assess the reproducibility of the method. Relative standard deviation (RSD) values were calculated for all metabolites present in the QC samples; the results are displayed in Figure 4. For the LC–MS analysis, the RSD was within 15%, whereas, for the FIA–MS analysis, it was within 20% for all metabolites. In both LC–MS and FIA–MS, excellent reproducibility was observed, and the RSD values for repeated analyses were well within 10% for 90% of the analytes in both the LC–MS and FIA–MS analyses.

 Pie charts showing the calculated RSD values from repeated analyses (well replicates) of the QC samples (low-, medium- and high-level combined) The tables show the details of how the pie charts were constructed.

	The method was employed to analyze serum samples from T2D study subjects aiming to identify changes in metabolite concentrations between the two study groups. Each serum sample (a total of six samples; three serum samples from T2D study subjects and three serum samples from healthy controls) was extracted using the kit in triplicate. The reproducibility of the analytical method was evaluated by calculating the RSD values for all metabolites detected in the samples. The data were combined for both LC–MS and FIA–MS. The RSD for the vast majority (90%) of metabolites was <20%, whereas for 81% of all metabolites detected the RSD values were <15%. These results demonstrated the very good reproducibility of the method for both LC–MS and FIA–MS (Figure 5).

 Pie chart showing the observed %RSD values in the serum samples. Each serum sample (a total of six samples; three serum samples from T2D study subjects and three serum samples from healthy controls) was extracted on the kit in triplicate. The data are combined for both LC–MS and FIA–MS.

	Figure 6 shows representative examples of metabolites whose concentrations were significantly altered (p < 0.001) between the two groups. For some metabolites, the concentrations were higher in the T2D study subjects, whereas, for other metabolites, the concentration was lower in the T2D subjects when compared to the healthy controls. The 

-test are also indicated in each graph. For instance, we observed increased concentrations of diglycerides DG (41:1) (

-test] = 5.09E-09), cholesterol ester CE (22:6) (

-test] = 2.85E-07), sphingomyelin SM (36:1) (

-test] = 1.00E-06), triglyceride TG (56:7) (

-test] = 6.00E-06), acylcarnitine AC (2:0) (p-value [t-test] = 1.60E-05), phosphatidylcholines PC (38:6) (p-value [t-test] = 1.03E-04), and PC (40:6) (

-test] = 2.91E-04), in the T2D serum samples compared to the control subjects. The concentration of triglyceride TG (52:6) (

-test] = 2.75E-04), cholesterol ester CE (18:3) (

-test] = 6.30E-04), and phosphatidylcholines PC (40:4) (

-test] = 9.97E-04), were lower in the T2D serum samples.

 Representative examples of metabolites whose concentrations were significantly altered (p < 0.001) between the two groups (T2D study subjects vs. healthy controls). The 

-test analysis are indicated in each graph.

	Summary Table I shows all metabolites with p < 0.001 (

-test analysis) and the calculated average concentrations along with the fold change. The fold change was calculated as the concentration in T2D serum samples/concentration in healthy control serum samples. The 

-value based on Mann-Whitney U test is also shown in Table I.

	The accuracy and reproducibility of the standardized method was assessed by examining the QC samples. The findings indicate that the accuracy in the spiked QC samples for amino acids and biogenic amines, analyzed by LC–MS, was in the range of 86–118% for 41 of 42 metabolites examined, and within 86–122% for all metabolites. In terms of the method reproducibility, the RSD in LC–MS was within 15%, for all metabolites examined. For the spiked QC samples analyzed using FIA–MS, the accuracy for 84% of metabolite measurements was well within 70–130%. The RSD for the spiked QC samples in FIA–MS was below 20%.

	Six serum samples obtained from T2D study subjects and healthy controls were analyzed using the metabolite analysis kit and MS for identifying changes in metabolite concentration between different study groups. We evaluated the reproducibility of the analytical method by performing a triplicate analysis of the six serum samples. The results indicated the high reproducibility of the analytical method. For 81% of the analytes in both LC–MS and FIA–MS, the obtained RSD was below 15% and for 90% the RSD was below 20%.

	Several metabolites-that is, diglycerides DG (41:1), cholesterol ester CE (22:6), sphingomyelin SM (36:1), triglycerides TG (56:7), TG (52:6), acylcarnitine AC (2:0), phosphatidylcholines PC (38:6), PC (36:6), PC(40:6), serotonin, and isoleucine-were observed to be significantly altered between the two groups.

	Acetylcarnitine AC (2:0), serotonin, isoleucine, and phosphatidylcholines PC (38:6), PC (36:6), and PC (40:6) have been previously reported to be associated with higher likelihood of T2D and obesity, or they have shown differential changes in response to oral glucose tolerance test (10, 26–28) in agreement with our findings. Our results demonstrate the effectiveness of the analytical method for reliable, accurate, and reproducible quantitation as well as for monitoring and quantifying changes between different study groups.

	(1) R. Goodacre, S. Vaidyanathan, W.B. Dunn, G.G. Harrigan, and D.B. Kell, 

	(2) S.B. Milne, T.P. Mathews, D.S. Myers, P.T. Ivanova, and H.A. Brown, 

	(3) K.E. Wellen, G. Hatzivassiliou, U.M. Sachdeva, T.V. Bui, J.R. Cross, and C.B. Thompson, 

An important advantage of standardized methods is that they enable comparability between laboratories and across studies. In
this work, the author used a standardized targeted kit to demonstrate the accuracy, sensitivity, and reproducibility of the approach, analyzing serum samples obtained from type 2 diabetes study subjects and healthy controls.

Special Issues-07-01-2018

CTMS July 2018 Issue PDF

DNA Adductomic Analysis by Data-Independent Mass Spectrometry

Targeted Metabolomics Analysis of Type 2 Diabetes Serum Samples Using Mass Spectrometry and a Standardized Quantitative Method

High-Throughput Analysis of Volatile Compounds in Air, Water, and Soil Using SIFT-MS

Review of the 66th Conference on Mass Spectrometry and Allied Topics