Deuterium Oxide Labeling for Global Omics Relative Quantification

Jun 19, 2019 - Hypoxia is a status of low oxygen in a tissue and is involved in many .... Application of DOLGOReQ to HeLa lipidome combined with ...
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Article Cite This: Anal. Chem. 2019, 91, 8853−8863

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Deuterium Oxide Labeling for Global Omics Relative Quantification: Application to Lipidomics Jonghyun Kim,† Dukjin Kang,‡ Sung Ki Lee,§ and Tae-Young Kim*,† †

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School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea ‡ Center for Bioanalysis, Division of Chemical and Medical Metrology, Korea Research Institute of Standards and Science, Daejeon 34113, Republic of Korea § Department of Obstetrics and Gynecology, College of Medicine, Konyang University, Daejeon 35365, Republic of Korea S Supporting Information *

ABSTRACT: A novel quantitative mass spectrometric method based on partial metabolic deuterium oxide (D2O) labeling, named “Deuterium Oxide Labeling for Global Omics Relative Quantification (DOLGOReQ)”, was developed for relative quantification of lipids on a global scale. To assess the precision and robustness of DOLGOReQ, labeled and unlabeled lipids from HeLa cells were mixed in various ratios based on their cell numbers. Using in-house software developed for automated high-throughput data analysis of DOLGOReQ, the number of detectable mass isotopomers and the degree of deuterium labeling were exploited to filter out low quality quantification results. Quantification of an equimolar mixture of HeLa cell lipids exhibited high reproducibility and accuracy across multiple biological and technical replicates. Two orders of magnitude of effective dynamic range for reasonable relative quantification could be established with HeLa cells mixed from 10:1 to 1:10 ratios between labeled and unlabeled samples. The quantification precision of DOLGOReQ was also illustrated with lipids commonly detected in both positive and negative ion modes. Finally, quantification performance of DOLGOReQ was demonstrated in a biological sample by measuring the relative change in the lipidome of HeLa cells under normal and hypoxia conditions.

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Chemical labeling can introduce isotopes into lipids by attaching an isotope-labeled reagent to its reactive groups. The isotope-labeled lipids can be used as an internal standard in MS. The amine and phosphate groups in the lipid headgroup have typically been targeted for chemical isotope labeling. Derivatizing reagents such as isobaric tags for relative and absolute quantitation (iTRAQ),13 4-(dimethylamino)benzoic acid (DMABA),14 and S,S′-dimethylthiobutanoyl hydroxysuccinimide (DMSNHS)15 have been utilized to label primary amino groups on glycerophosphoserine (PS) or glycerophosphoethanolamine (PE). Methylation of phosphate and carboxylic groups with trimethylsilyl diazomethane has been employed in the quantification of glycerophospholipid (GP) to improve the sensitivity and separation of liquid chromatography (LC)−MS16 and supercritical fluid chromatography (SFC)−MS.17 Smith and co-workers developed a rapid insolution derivatization method for GP, named trimethylation enhancement using diazomethane (TrEnDi), to accomplish complete methylation of primary amines, phosphate groups,

ipids are a heterogeneous group of small hydrophobic or amphiphilic compounds that facilitate diverse processes in the cell including formation of plasma membrane, energy storage, and signal transduction. 1 Many studies have demonstrated that abnormal lipid metabolism in the body is highly correlated with a variety of diseases, such as type 2 diabetes,2 rheumatoid arthritis,3 Alzheimer’s disease,4 and cancer.5 Monitoring quantitative changes in lipids between normal and disease states is a critical step in understanding the pathophysiology and uncovering biomarkers for various diseases associated with lipid metabolism. Mass spectrometry (MS)-based lipid quantification techniques can be classified largely into three categories: label-free, chemical labeling, and metabolic labeling. Label-free methods for lipid quantification exploit the loss of a functional group specific to a lipid class or subclass during different modes of tandem mass spectrometry (MS/MS) that include neutral loss scan,6−8 selected reaction monitoring,9,10 and precursor ion scan.7,11 While label-free quantification approaches are relatively simple to implement, the quality of quantification depends heavily on the choice of a normalization method of peak intensities, which is necessary to account for nonbiological signal fluctuations during the MS analysis.12 © 2019 American Chemical Society

Received: January 7, 2019 Accepted: June 19, 2019 Published: June 19, 2019 8853

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Analytical Chemistry and carboxylic acids, simultaneously.18 Methylation with deuterated diazomethane also facilitated relative quantification of GP including phosphoionsitide.19−21 Although chemical isotope labeling provides an internal standard corresponding to the target compound in mass spectra, relative quantification can be applied to only lipid species with functional groups specific to the derivatizing agent. To increase the coverage of lipid species in quantitative MS, metabolic isotope labeling can be employed to produce internal standards at the species level for various types of lipid classes. The Koellensperger group has recently reported lipidome isotope labeling of yeast (LILY)22 as a novel metabolic labeling approach to generate highly enriched (>99.5%) 13C lipid standards via yeast fermentation for MSbased lipid quantification. The Koellensperger group has also exploited 13 C LILY lipids as internal standards for quantification of lipids in human plasma and accomplished compound-specific quantification for 114 lipids from 7 classes, which account for about 30% of identified human plasma lipids.23 Despite the quantitative advantages of isotopically labeled internal standards, achieving full incorporation of heavy isotopes into biomolecules by metabolic labeling is challenging due to time and cost constraints, especially for higher organisms such as mammals. Whitelegge et al. suggested the idea of subtle modification of isotope ratio proteomics (SMIRP) utilizing partial metabolic labeling as a quantitative proteomics tool.24 They demonstrated that increasing 13C abundance in peptide ions to generate isotope distributions distinct from natural isotope abundance by a few percentage points does not hamper the protein identification performance in MS/MS experiments. The key message of SMIRP was that relative protein expression information can be extracted from the relative abundance of mass isotopomers in the isotope envelope of a mixture of intact and partially labeled peptides, when both 13 C/12C ratios are known. On the basis of this partial metabolic labeling concept, Sussman and co-workers developed a novel algorithm to automate relative protein quantification.25 With a systematic investigation of the relation between partial and full 15 N-labeling of Arabidopsis thaliana, they proved that partial metabolic labeling is comparable with full metabolic labeling in the performance of quantitative proteomics analysis. However, the partial 15N-labeling strategy cannot be applied to quantitative lipidomics, because not all lipid classes contain nitrogen in their structures. Deuterium oxide (D2O) is an attractive option for partial metabolic deuterium labeling of lipids. Deuterium (D) can be transferred from D2O into C−H bonds in the glycerol backbone of triglyceride (TG) during glyceroneogenesis or glycolysis.26 D2O also produces the enzymatic incorporation of deuterium into the growing fatty acyl chains in the fatty acid synthesis reaction.27 Moreover, other biomolecules including nucleic acids, carbohydrates, and amino acids can be deuterated in the presence of D2O through a variety of enzymatic reactions such as the pentose phosphate pathway, gluconeogenesis, and transamination reactions.28 In this study, we propose a new partial metabolic labeling strategy for quantitative MS, termed deuterium oxide labeling for global omics relative quantification (DOLGOReQ), and demonstrate its application to relative lipid quantification. We have adopted the algorithm developed by Huttlin et al. for protein quantification via partial 15N-labeling,25 and modified it to accommodate the data generated by DOLGOReQ for

quantitative lipidomics. The accuracy, precision, and dynamic range of DOLGOReQ were evaluated with lipids extracted from HeLa cells grown in normal and 5% D2O-enriched media for 48 h. Finally, relative quantification of HeLa lipids cultured in hypoxia and normoxia conditions was performed with DOLGOReQ as a proof-of-concept experiment.

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EXPERIMENTAL SECTION Materials. All chemicals were purchased from SigmaAldrich (St. Louis, MO) unless otherwise noted. Chloroform, methanol (MeOH), water (H2O), and acetonitrile (ACN) were purchased from Thermo Fisher Scientific (Waltham, MA). Isopropanol (IPA) is from J.T. Baker (Center Valley, PA). HeLa cell line was from Korean Collection for Type Cultures (Daejeon, Korea). Dulbecco’s modified eagle medium (DMEM) and penicillin streptomycin were obtained from Gibco (Grand Island, NY). Fetal bovine serum (FBS) was supplied by Hyclone (South Logan, UT). Trypsin-ethylenediaminetetraacetic acid (EDTA) was from Welgene (Daegu, Korea). D2O (D, 99.9%) was from Cambridge Isotope Laboratories (Andover, MA) and sterilized prior to use. Cell culture dish (100 mm) was from Corning Inc. (Canton, NY). Cell Culture and Sample Preparation. HeLa cells were grown in DMEM supplemented with 10% (v/v) FBS, and 1% penicillin/streptomycin in a cell culture dish. The D-labeled DMEM was adjusted to 5% (mol/mol) D2O enrichment by the addition of 99.9% D2O. Cultures were incubated at 37 °C in a humidified atmosphere with 5% CO2 for 48 h to reach 80−90% confluence. Hypoxia treated cells were prepared by replacing DMEM with 200 μM CoCl2 spiked media after 24 h growth. Cells were harvested by trypsinization and centrifugation, washed with PBS, and stored at −80 °C prior to lipid extraction. Before centrifugation, a small portion of cell suspension was taken for lipid normalization based on the number of HeLa cells counted with a hemocytometer (Marienfeld, Lauda-Kö nigshofen, Germany). Lipids were extracted from the pellet using Folch protocol.29 In brief, cell pellets were resuspended in 1 mL of cold chloroform/ methanol (2:1, v/v) and votexed for 20 min. After 200 μL of water was added, the mixture was centrifuged at 500g for 10 min. Next, 600 μL of the lower phase was recovered and dried in a SpeedVac (Labconco, Kansas City, MO). The dried lipids were resuspended in 100 μL of cold methanol, and 15 μL of the sample was injected for each LC−MS/MS analysis. To assess the reproducibility of DOLGOReQ, three technical replicates for each of three biological replicates were prepared. LC−MS/MS and Lipid Identification. LC−MS/MS was performed with an Agilent 1260 HPLC system coupled to an Agilent 6520 quadrupole time-of-flight (Q-TOF) mass spectrometer equipped with an electrospray ionization (ESI) source. Lipids were separated with a reversed-phase XDB-C18 column (Agilent, 4.6 mm × 50 mm, 1.8 μm, 80 Å pore size). Mobile phases A and B were 10 mM ammonium acetate in ACN/H2O (6:4, v/v) and 10 mM ammonium acetate in IPA/ ACN (9:1, v/v), respectively. The mobile phase B in a binary gradient elution was changed as follows: 0−30% at 0−1 min, 30−60% at 1−15 min, 60−100% at 15−40 min, 100% at 40− 45 min, 100−0% at 45−48 min, 0% at 48−60 min, followed by a 5 min post run. The LC flow rate was set to 0.400 mL/min. The ESI voltages for the capillary and fragmentor were set to 3.5 kV and 200 V, respectively. For nontargeted LC−MS/MS in both positive and negative ion modes, the top five abundant 8854

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Figure 1. Workflow for LC−MS data processing in DOLGOReQ. (A) EICs of the monoisotopic peak (M0) and mass isotopomers (Mk, k = 1,2,3,4,5) are generated for lipids identified in unlabeled samples. (B) Linear regression is performed between the EICs of M0 and Mk to determine the slope and correlation coefficient (r2). Mass isotopomers with a poor r2 value of less than 0.7 are excluded from further quantification. The slope is defined as the relative isotopic abundance (RIA), and each RIA is divided by the sum of RIAs of all mass isotopomers for a lipid ion to obtain normalized RIAs. The same procedure is repeated for D-labeled and mixed samples. (C) Using the normalized RIAs of unlabeled and D-labeled lipids, an in silico library consisting of mixed isotopic envelopes in a range of varying ratios from 1:99 to 99:1 is generated. (D) The Cartesian coordinate system is employed to determine the ratio between unlabeled and D-labeled lipids in the mixed sample. The two sets of normalized RIAs of unlabeled and D-labeled lipids are treated as two points in an N-dimensional space. (E) A total of 99 normalized RIAs in the in silico library described in (B) are also projected on the same Cartesian coordinate system. (F) The best match is determined as the mixing ratio with the minimum distance between the mixed sample and the 99 points in the library.

Relative Lipid Quantification. The relative quantification algorithm was based on the approach developed by Huttlin et al.,25 and adapted to make it amenable to D2O labeling and lipid analysis. Figure 1 demonstrates the sequence of LC−MS data-processing steps in DOLGOReQ. First, ion chromatograms were extracted for each mass isotopomer (from M0 to M5) for an identified lipid using an in-house program written in C++ script with Open MS C++ library (Figure 1A). Each extracted ion chromatogram (EIC) contained the nearest five MS data points to the observed m/z and all scans within a 1 min-chromatographic window of the MS/MS scan by which the lipid was identified. The peak intensities of mass isotopomers M1 to M5 then were plotted with respect to the monoisotopic peak (M0) across all time points within the EIC. A linear regression generated the correlation coefficient (r2) and the slope for each mass isotopomer (Figure 1B). The correlation coefficient can be used to remove low-quality EICs that interfere with coelution of other peaks or noise. Only mass isotopomers with r2 > 0.7 were admitted to the subsequent quantification steps. The slope (the peak intensity ratio of Mk to M0 in the EICs, where k is 0, 1, 2, 3, 4, or 5) obtained from the linear regression process was defined as the relative isotopic abundance (RIA) of each mass isotopomer. Each RIA was divided by the sum of RIAs for all mass isotopomers to normalize to the total amount of a lipid. Next, an in silico relative isotopic distribution library composed of the normalized RIAs for combined lipids at varying mixing ratios spanning 4 orders of magnitude (99:1, 98:2, ...2:98, 1:99) was

ions in the MS scan were selected for collision-induced dissociation in auto MS/MS scan mode with an active exclusion of 0.5 min. The isolation width of a precursor ion was 4 Da, and the collision energy was applied by the following linear formula: collision energy (V) = 0.03 × m/z + 10. The m/z ranges of the MS and MS/MS scans were 200−1600 and 50−1600, respectively. Lipid identification for unlabeled samples was performed by combining the LipidBlast30 and NIST17 MS libraries with the NIST MS search program with the following search parameters: precursor and fragment ion tolerances were 0.05 m/z and 0.1 m/z, respectively; matching factor thresholds >100; reverse dot product >800; Q-TOF scoring option. For fatty acyls that typically result in very limited fragmentation in their MS/MS spectra, the retention time and accurate mass of a fatty acid authentic standard were exploited for lipid identification. Lipid features from D-labeled and mixed samples were identified by matching their m/z and retention time to those of unlabeled samples. We adopted the shorthand notation for lipid structures proposed by Liebisch et al.31 The following abbreviations were used for lipid subclasses: triacylglycerol (TG), sphingomyelin (SM), phosphatidylserine (PS), phosphatidylinositol (PI), phosphatidylglycerol (PG), phosphatidylethanolamine (PE), phosphatidylcholine (PC), plasmalogen-PE (PE-P), plasmalogen-PC (PC-P), ether-linked phosphatidylcholine (PC-O), phosphatidic acid (PA), lysophosphatidylethanolamine (LPE), lysophosphatidylcholine (LPC), diacylglycerol (DG), and cardiolipin (CL). 8855

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Figure 2. Changes of mass isotopomer distribution of four lipid categories caused by enrichment of HeLa cells with 5% D2O for 48 h. (A) Palmitic acid, (B) PC(16:0_18:1), (C) TG(16:0_16:1_18:1), and (D) SM(d18:1/24:1) showed a decrease in the relative intensity of the monoisotopic peak (M0) and an increased number of detectable mass isotopomers after D-labeling.

prepared as an equal molar mixture of unlabeled and D-labeled HeLa lipids, and three technical replicates for each biological replicate were run under the identical mass spectrometric conditions. For the second experiment, unlabeled and Dlabeled HeLa lipidome mixtures at various ratios ranging from 1:10 to 10:1 were quantified to explore the dynamic range of DOLGOReQ. The data visualization was performed using R 3.3.3 and its packages. Relative Lipid Quantification of CoCl2 Treated HeLa Cells. A case study was conducted to assess the quantitative performance of DOLGOReQ in a biological context. HeLa cells were grown in two different culturing conditions: normoxia and hypoxia. HeLa cells under normoxia conditions were grown in 5% D2O medium for 48 h. Hypoxia conditions for HeLa cells were induced by CoCl2 solution.32,33 Hypoxia was achieved by growing HeLa cells exposed to 200 μM CoCl2 for 24 h after incubating the cells under normoxia conditions for 24 h. Three biological replicates were prepared, and equal amounts of normoxia and hypoxia cells were mixed for relative lipid quantification by DOLGOReQ. In addition, a pairwise experiment was performed to examine if hypoxic conditions affect the D-labeling efficiency and quantification results. HeLa cells under normoxia conditions were D-labeled in a forward labeling experiment, while the hypoxia samples were D-labeled in a reciprocal labeling experiment. Three biological replicates were prepared for each of the two experiments. The subsequent experimental procedures for lipid extraction, LC−MS/MS, lipid identification, and relative quantification were identical to those described above.

created by a weighted linear combination of the normalized RIAs for unlabeled and D-labeled lipids, which were experimentally determined by their MS scans (Figure 1C). The Cartesian coordinate system was exploited as a platform to determine the relative abundance of D-labeled to unlabeled lipids in an observed composite isotopic envelope. If the number of the normalized RIAs for a lipid ion were N, we could regard the sequence of the normalized RIAs as a point in an N-dimensional space. Therefore, the two sets of the normalized RIAs of unlabeled and D-labeled lipids were designated as two points in the multidimensional Cartesian coordinate system (Figure 1D). The Euclidean distance between the two points was defined as the “H−D distance”, which can be considered as an index of the degree of Dlabeling on a particular lipid ion: the larger the H−D distance means the more D-labeling. A total of 99 normalized RIAs in the in silico relative isotopic distribution library described above then were located between the two points corresponding to unlabeled and D-labeled lipids in the Cartesian coordinate system (Figure 1E). To assess the relative abundance in a mixture of unlabeled and D-labeled samples, the Euclidean distances between the mixed sample point and the 99 points in the in silico relative isotopic distribution library were calculated. The best match was determined as the mixing ratio that showed the closest Euclidean distance to the mixed sample point from the 99 points (Figure 1F). All linear regression, normalization, library generation, and best matching calculations were performed via a script written in python 3.6.3. Commonly detected lipids with different ion adduct forms (e.g., [M + Na]+ and [M + NH4]+) were consolidated after quantification to obtain their average values. To examine the accuracy and precision of quantitative measurement of DOLGOReQ, two experiments were designed. In the first experiment, three biological replicates were

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RESULTS AND DISCUSSION Optimization of Partial D Enrichment. Partial metabolic D2O labeling causes a slight change in the isotopic distribution of a lipid ion. For lipids with a high number of D-labeling sites, 8856

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conclude that 48 h incubation of HeLa cells with 5% D2O enrichment is appropriate for inducing metabolic partial D labeling of lipids applicable to DOLGOReQ. Empirical Parameters for Automated Data Processing. As illustrated in Figure 1, the MS data-processing method in DOLGOReQ was initiated by generating EICs for mass isotopomers of each identified lipid. Subsequently, the correlation between each generated EIC and the monoisotopic EIC was evaluated by linear regression, during which correlation coefficients were calculated and utilized as a filter (r2 > 0.7) to eliminate low-quality mass isotopomers interrupted by other coeluted lipids or a noise signal. The data-processing procedure for DOLGOReQ up to this step is identical to the relative peptide quantification method using partial metabolic 15N labeling developed by Huttlin et al.,25 except a difference in the number of mass isotopomers for extracting EICs. Another filter used by Huttlin et al. to remove low-quality quantification results was the correlation coefficient between best-matching experimental and theoretical in silico RIAs of mixed envelopes. Best-matching is the final step in the data-processing for partial metabolic labeling quantification, and a condition of r2 > 0.8 was regarded as a successful match. As compared to peptides, however, lipids typically have lower molecular masses and are more diverse in terms of chemical structure and number of D labeling sites. For instance, fatty acyls are smaller than peptides in molecular mass leading to fewer mass isotopomers. Taking into consideration the differences between peptides and lipids, two alternative experimental filters were established to judge the quality of relative lipid quantification by DOLGOReQ. The first new filter employed in DOLGOReQ is the number of detected mass isotopomers (NDMI). NDMI is defined as the number of mass isotopomers (Mk, k = 1, 2, 3, 4, 5) with a correlation coefficient larger than 0.7 in linear regression of EICs between the monoisotopic peak and other mass isotopomers. The assumption behind this filter was a lowquality isotopic distribution cannot have many mass isotopomers with good linearity. Inaccurate EICs resulting in inaccurate RIAs can be caused by factors such as coelution of other peaks overlapped with the peak of interest and a low signal-to-noise ratio due to low concentration. Thus, the NDMI could be regarded as a parameter indicating the quality of EIC, and subsequently the accuracy of RIA. However, no matter how accurately RIAs were experimentally measured, the accuracy of relative quantification of DOLGOReQ would be poor if the difference in the isotopic distributions between unlabeled and D-labeled lipids were not significant. At a minimum, the difference between the two isotopic envelops should be larger than the error range in the measurement of RIAs for the relative quantification to be valid. Our second filter for quantification quality control in DOLGOReQ was the H−D distance defined in the Experimental Section as the Euclidean distance between two points of normalized RIAs of unlabeled and D-labeled lipids in the Cartesian coordination system. The H−D distance is an indicator of the degree of D enrichment in a specific lipid, which is determined by the D2O concentration, the number of D labeling sites, and the H−D exchange kinetics. With these two experimental parameters, the NDMI and H−D distance, low-quality quantification data resulting from a low peak intensity, inefficient chromatographic separation, poor mass spectral accuracy, or a small degree of D labeling could be systematically filtered out in DOLGOReQ.

the intensity of the monoisotopic peak (M0) is no longer the highest, and the mass isotopomer M1 becomes the most intense peak in the isotopic distribution of a D-labeled lipid ion. If M1 is selected as a precursor ion in MS/MS, the corresponding tandem mass spectrum will result in no or incorrect identification in the database search, leading to a decrease in the number of identified lipids. Indeed, our datadependent LC−MS/MS experiments identified fewer lipids in D-labeled samples as compared to unlabeled samples (Table S1). To alleviate this limitation, we identified D-labeled and mixed lipid samples by matching their m/z and retention time to those of unlabeled samples. Thus, the number of identified unlabeled lipids set the maximum number of identifications for labeled and mixed lipids in our experimental design. On average, 315, 302, and 291 lipids were commonly identified in three technical replicates for each of three biological replicates for unlabeled, mixed, and labeled samples, respectively (Table S2). More than 12 different lipid subclasses were identified, and TG was the most frequently identified lipid subclass, followed by PE (Figure S1). Interestingly, the identified PCs were mostly detected as the sodium adduct when we employed the LipidBlast library search alone for lipid identification. In addition, the identification numbers of PC, a main structural component of eukaryotic cell membranes, were relatively low as compared to a previous report on HeLa lipidome by Yu et al.34 This result can be attributed to a mismatch between experimental and in silico MS/MS spectra for PCs. For a protonated form of PC, a fragment ion corresponding to a choline headgroup was detected as the base peak in our experimental MS/MS spectra, whereas product ions generated by neutral loss of the sn-1 and sn-2 chains were major peaks in the LipidBlast library (Figure S2A). Because of the discrepancy in the two MS/MS spectra, most of the protonated PCs were removed during the identification step based on the LipidBlast library only. These missed protonated PCs could be identified using real tandem mass spectra from the NIST17 MS library (Figure S2B), which resulted in the identification of additional 20−35 PCs for each technical replicate. Figure 2 displays the mass isotopomer distributions of four lipids each representing a lipid category. The lipids were extracted from HeLa cells cultured in normal and 5% D2O media for 48 h. Typical isotopic distributions contributed by natural 13C abundance were observed for unlabeled lipids. For D-labeled lipids, the relative abundances of mass isotopomers were significantly changed. Compared against the other three lipids, palmitic acid, corresponding to the fatty acyl category, showed a small shift in the mass isotopomer distribution after D2O enrichment (Figure 2A), mainly because palmitic acid has only a single fatty acyl chain on which H attached to the carbon backbone can be replaced by D.27 For palmitic acid, the relative intensity of the second mass isotopomer peak (M1) to that of the monoisotopic peak (M0) increased from 20% to 50% over the labeling time, but M0 was still the most abundant among the mass isotopomers after 48 h labeling. For PC(16:0_18:1), TG(16:0_16:1_18:1), and SM(d18:1/24:1), on the contrary, M1 became the most intense peak, and high mass isotopomers such as M3 and M4 appeared with significant peak intensities for D-labeled lipids. The relatively higher D enrichment in glycerophospholipid, glycerolipid, and sphingolipid categories can be ascribed to multiple acyl chains and glycerol moiety where enzymatic D labeling can occur on C− H bonds.26 On the basis of observed changes in the mass isotopomer distributions for D-labeled lipids, we could 8857

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Figure 3. Scatter plots of log2-fold changes as a function of the H−D distance in 9 (3 biological × 3 technical) replicates mixed in a 1:1 ratio between D-labeled and unlabeled HeLa lipids. The plots were drawn with the following conditions: (A) NDMI > 0 and H−D distance > 0, (B) NDMI > 1 and H−D distance > 0, (C) NDMI > 2 and H−D distance > 0, and (D) NDMI > 2 and H−D distance > 0.1. The colors of yellow, green, light red, red, and dark red dots represent the minimum NDMIs of 1, 2, 3, 4, and 5, respectively.

Relative Quantification of Lipids Mixed in an Equimolar Ratio. The quantification performance of DOLGOReQ was examined with mixtures of unlabeled and D-labeled HeLa lipids in a 1:1 ratio. Figure 3 shows scatter plots of log2-fold changes as a function of the NDMI and H− D distance for quantification results from 9 (technical triplicates for each of 3 biological replicates) 1:1 ratio mixtures of unlabeled and labeled HeLa lipids. Without applying any filtering process, 4110 pairs of lipids were quantified. The fold changes were widely scattered and not consistent with the expected 1:1 ratio, particularly for lipids with NDMI = 1 (Figure 3A). When the quantification results of lipids with NDMI = 1 were filtered out, the dispersion of the fold changes for 2298 lipids became significantly reduced (Figure 3B). In the case of NDMI > 2, the fold changes for 1321 lipids were mostly centered on the anticipated equimolar ratio, except for lipids with small H−D distances (Figure 3C). Inaccurate quantification results for lipids with low NDMI values can be attributed to an error in the normalized RIAs. Normalized RIAs were calculated by dividing each RIA by the sum of RIAs for all mass isotopomers. For a lipid with NDMI = 1, the fraction of RIAs for M0 and M1 mass isotopomers can be considerably less than the total sum of RIAs for all mass isotopomers, leading to overestimation of the normalized RIAs for M0 and M1 mass isotopomers. An error in the normalized RIAs caused by a small NDMI will be even worse for lipid categories, such as glycerolipids, for which the mass isotopomer distribution can be extended to M4, M5, or higher for D-labeled lipids (Figure S3). Finally, the H−D distance filter was applied to remove bad quantification results due to insufficient D-labeling for lipids. With a threshold of NDMI >

2 and H−D distance > 0.1, 1280 lipids were quantified with reasonable accuracy (Figure 3D). The threshold for the two empirical parameters can vary depending on the characteristics of molecules to be quantified and experimental conditions. For instance, the threshold for the NDMI and H−D distance could be set at higher values for large molecules labeled with a high concentration of D2O. To interpret the threshold values of the NDMI and H−D distance employed in DOLGOReQ as the measures of the quantification reliability, the number of quantification results of 1:1 mixtures of unlabeled and D-labeled HeLa lipids were counted as functions of the NDMI and H−D distance within three different fold-change ranges of 1.3, 1.5, and 2, which are generally employed thresholds in quantitative studies (Table S3). Without the NDMI filter, only 54.4%, 66.6%, and 78.3% of quantification results were found to have less than 1.3-, 1.5-, and 2-fold-changes, respectively. Upon applying the threshold of NDMI > 2, 86.2%, 93.6%, and 96.8% of quantification results were found in the 3-fold-change ranges, respectively. With the NDMI > 2 and the H−D distance > 0.1, 87.6%, 95.1%, and 98.2% of quantification results belong to 1.3-, 1.5-, and 2-fold-change ranges, respectively. Although this translation of the NDMI and H−D distance filters into a reliable quantification range was validated with HeLa cells labeled with 5% D2O, it would be a good reference to set up a new threshold for reliable quantification with DOLGOReQ. To examine the quantification accuracy and precision of DOLGOReQ, the distributions of log2-fold changes for lipids that passed our empirical thresholds (NDMI > 2 and H−D distance > 0.1) were represented as box-whisker plots with respect to 9 experimental replicates (Figure 4A). While the 8858

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(RSD) of the median fold changes among 3 biological replicates was 24.1%. In an attempt to find the reason for a large variation in the median fold changes among biological replicates, we compared the degree of D-labeling of lipids from batch to batch. The D-labeled lipids quantified from three biological replicates showed similar distributions of the H−D distance, of which the average ± standard deviations for biological replicates 1, 2, and 3 were 0.328 ± 0.109, 0.338 ± 0.102, and 0.324 ± 0.113, respectively (Figure S4). These results imply that a large deviation in the median fold changes among biological replicates was not caused by batch-to-batch variability in the efficiency of metabolic D-labeling of lipids. The main difference between biological and technical replicates was the sample preparation process. Each biological replicate of an equimolar mixture was prepared on the basis of cell counts of every unlabeled and D-labeled HeLa cell with a hemocytometer. It was then split into three aliquots to generate three technical replicates. A large RSD of median fold changes among biological replicates could be due to inaccuracy of manual cell counting with a hemocytometer, which is known to be prone to measurement error.35 Using a more accurate sample normalization method for lipids would reduce the variance of the median fold changes among biological replicates. To compensate for a systematic error originated from cell counting, each quantification result was centered by dividing the result by the median fold change within each technical replicate as shown in Figure 4B. With the application of the centering process, 95% and 98% of the quantification results for equimolar lipid mixtures were within 1.5-fold and 2fold ranges, respectively (Figure S5). A full list of the quantification results for 1:1 mixtures of unlabeled and Dlabeled lipids is provided in Table S6. Evaluation of Relative Quantification of Lipids Mixed in Various Ratios. The range and precision of lipid quantification by DOLGOReQ were investigated with HeLa cells in various mixing ratios of unlabeled and D-labeled samples. To control a systematic error by cell counting, unlabeled lipids from a single pooled cell suspension were mixed with D-labeled lipids from another single pooled cell suspension in different ratios, and all quantification results were divided by the median fold change of the sample mixed in a 1:1 ratio. As a result, the median fold change for the 1:1 mixture was centered to unity.

Figure 4. Box-whisker plots of log2-fold changes for 9 (3 biological × 3 technical) replicates mixed in a 1:1 ratio between D-labeled and unlabeled HeLa lipids (A) before and (B) after applying the following centering process: each quantification result was divided by the median fold change within each replicate. The median fold change and the interquartile range can be regarded as measures of the accuracy and precision of quantification, respectively. The combined first and second numbers in the bottom of each column in abscissa denote arbitrary indices of biological and technical replicates, respectively. The numbers of lipids quantified in each replicate are indicated in parentheses.

median fold changes among 3 technical replicates for each biological replicate were identical, relative standard deviation

Figure 5. (A) Box-whisker plots of log2-fold changes for lipids obtained by mixing D-labeled and unlabeled HeLa cells in various ratios ranging from 10:1 to 1:10 and (B) table of expected and measured median fold changes according to the mixing ratios of D-labeled to unlabeled HeLa lipids. 8859

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Figure 6. Bar graphs for comparison of relative quantification for (A) PE-P(16:0_22:4), (B) PE(18:0_18:1), and (C) PE(18:0_22:4) lipids detected in both positive and negative ion modes across variable mixing ratios between D-labeled and unlabeled HeLa cells. The quantification results between the two ion modes generally agree with each other, although the consistency tends to be worse as the mixing ratio goes to the two extremes.

deviations were observed as the mixing ratio approaches two ends (Figure 6). The evaluation of relative quantification of HeLa lipids mixed in equimolar and various ratios demonstrated that DOLGOReQ can provide consistent quantification performance with reasonable accuracy over 2 orders of magnitude in mixing ratio. A complete list of the quantification results for various mixing ratios between unlabeled and Dlabeled lipids is provided in Table S7. Application of DOLGOReQ to Comparison of Hypoxia versus Normoxia HeLa Cells. As a practical application of DOLGOReQ, quantitative lipidomics was performed on hypoxia versus normoxia HeLa cells. Hypoxia is a status of low oxygen in a tissue and is involved in many physiological and pathological responses to changes in cellular microenvironment.36,37 Hypoxia-inducible factor-1 (HIF-1) is an oxygen-sensitive transcriptional activator mediating the reactions to hypoxia and has two subunits: HIF-1α and HIF-1β.38 Under well-oxygenated conditions (normoxia), HIF-1α is hydroxylated by prolyl hydroxylase domain protein that utilizes oxygen as a substrate. Subsequently, hydroxylated HIF-1α undergoes ubiquitination by von Hippel−Lindau (VHL) E3 ubiquitin ligase, leading to proteasomal degradation. Under hypoxic conditions, deficient oxygen prevents HIF-1α from being hydroxylated, which results in accumulation of HIF-1α due to the absence of the degradation mechanism of HIF-1α ubiquitination by VHL. Cobalt can mimic hypoxia by inhibiting the hydroxylation of HIF-1α.39 To quantify the relative difference in the amount of individual lipids under hypoxia and normoxia conditions, HeLa cells were grown under two culture-media conditions: one with CoCl2 for hypoxia and another enriched with 5% D2O for normoxia as described in the Experimental Section. With a threshold of NDMI > 2 and H−D distance > 0.1, 116, 115, and 112 lipids were quantified in each of three biological replicates. A full list of the quantification results for the hypoxia versus normoxia experiment can be found in Table S8. Figure 7 displays a volcano plot for identification of differential lipids under the two conditions among quantified lipids commonly found in three biological replicates. Thirteen lipids (8 TGs, 3 PIs, 1 PG, and 1 PE) showed a significant fold-change (p < 0.05) between D-labeled normoxia and unlabeled hypoxia HeLa cells. Among them, three TGs (TG(16:0_18:0_20:1), TG(16:0_16:0_18:0), and TG(14:0_16:0_16:0)) were in-

Figure 5 shows box-whisker plots of log2-fold changes for lipids mixed in the range from 10:1 to 1:10 (D-labeled:unlabeled) and their measured mean fold changes with the expected values. The interquartile range in the boxplot was the smallest at 1:1 and became larger at two extreme mixing ratios. The measured median fold changes approximately matched the estimated mixing ratios, although the quantification accuracy had a tendency to be worse toward extreme mixing ratios. On the basis of these results, we could conclude that DOLGOReQ can provide reasonable quantification results for lipids within the mixing ratios between 10:1 and 1:10. At more extreme mixing ratios outside this range, quantification by DOLGOReQ would offer more qualitative than quantitative information, because it would be much harder to discern the contributions of unlabeled and labeled samples to the shape of composite isotopic distribution when one form (unlabeled or labeled) is dominant in the mixture. The effective dynamic range of lipid quantification in DOLGOReQ over 2 orders of magnitude is comparable to that of protein quantification via partial 15N-labeling described by Huttlin et al.25 To evaluate if DOLGOReQ displays a bias for a particular lipid subclass in terms of quantification performance, correlation coefficients between the predicted and measured fold-changes of lipids mixed at various ratios were calculated for lipids including TG, PI, PE-P, PE, PC-P, PC, PA, LPE, LPC, DG, and CL subclasses. All lipid subclasses except CL revealed high average correlation coefficients of 0.942 or higher (Table S4). A relatively low average correlation coefficient (R = 0.843) for CL can be attributed to its unique structure. A large number of D-labeling sites in four fatty acyl chains of CL results in a wide isotopic envelope with low intensities, which is disadvantageous for accurate quantification by DOLGOReQ. Thus, DOLGOReQ showed no significant difference in quantification performance with respect to lipid subclasses. Precision of lipid quantification by DOLGOReQ was examined with lipids commonly detected in both positive and negative ion modes. PE can be observed in both ion modes because of positively charged amine and negatively charged phosphate groups. The relative quantification for PEP(16:0_22:4), PE(18:0_18:1), and PE(18:0_22:4) showed good consistency between the two ion modes over 2 orders of magnitude of dynamic range, although relatively larger 8860

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(0.163) in the reciprocal experiment. Despite a relatively large difference in the H−D distance, it showed comparable foldchanges between the forward (0.706) and reciprocal (0.840) experiments, indicating that the quantification accuracy in DOLGOReQ could be somewhat insensitive to the efficiency of D2O labeling. This seemingly contradicting result can be interpreted in relation to the DOLGOReQ algorithm. In DOLGOReQ, the relative abundance of D-labeled to unlabeled lipids in an observed composite isotopic envelope is extracted from the isotopic distributions of unlabeled and Dlabeled samples that were independently measured. Thus, even if the D-labeling efficiency is varied from batch to batch, it is still possible to obtain fairly consistent quantification results among different replicates as long as the H−D distances are large enough for reliable quantification.

Figure 7. Volcano plot of statistical significance against lipid foldchange between D-labeled normoxia and unlabeled hypoxia HeLa cells, which is displayed by averaging the quantification results of three biological replicates. Three TGs denoted by red dots show a significant fold change (p < 0.05) greater than 2 under hypoxic conditions.

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CONCLUSION In this study, we developed a novel global scale relative quantification method based on partial metabolic D2O labeling, termed DOLGOReQ. Application of DOLGOReQ to HeLa lipidome combined with high-resolution LC−MS/MS demonstrated effective D-labeling and quantification of lipids including fatty acyl, glycerolipids, glycerophospholipids, and sphingolipids. DOLGOReQ provided an effective quantification dynamic range for mixing ratios from 10:1 to 1:10 (labeled:unlabeled) with reasonable accuracy and precision. DOLGOReQ also enabled the quantitative analysis of changes in HeLa lipids exposed to CoCl2-treated hypoxia conditions at the molecular lipid species level. With respect to sensitivity and selectivity, quantification methods utilizing MS/MS transitions would be better than MS1-based quantification schemes such as DOLGOReQ. Partial labeling in DOLGOReQ can cause a significant decrease of the monoisotopic peak, which would be detrimental for the identification of lipid species using MS/ MS spectra in D-labeled and mixed samples. To overcome this limitation, we identified lipids from D-labeled and mixed samples by matching their m/z and retention time to those of unlabeled samples. Therefore, highly reproducible chromatographic separation is critical for successful quantification by DOLGOReQ. As compared to full metabolic labeling strategies, partial labeling is more cost-efficient for isotope enrichment in the target molecules. Furthermore, the ease of administration and low cost of D2O make it suitable for a longterm quantitative analysis in a wider range of biological systems including plants and mammals. On the basis of the characteristic of D2O that enables D-labeling in various biomolecules such as protein and carbohydrate, DOLGOReQ can be applicable to other quantitative omics studies. The quantification potential of DOLGOReQ in proteomics will be demonstrated in follow-up studies.

creased more than 2-fold under hypoxic conditions. To validate our quantification results by DOLGOReQ, a labelfree quantification was performed on the 13 lipids that exhibited statistically significant fold-change (p < 0.05) by summing the peak height of each mass isotopomer corresponding to a specific lipid ion. The fold-changes obtained from the two quantification methods were consistent with a Pearson’s correlation coefficient of 0.981 (Table S5). Interestingly, the p-values indicative of the reproducibility of the label-free quantification were slightly higher than those of DOLGOReQ, which implies that DOLGOReQ is relatively free from intersample variation of ionization efficiency that is a common issue in a conventional label-free quantification.40 The increase of TGs in hypoxic HeLa cells is consistent with the result reported by the Simos group.41 They observed TG accumulation in human hepatoblastoma and cervical adenocarcinoma cells exposed to hypoxia and demonstrated that HIF-1 activated under low oxygen conditions stimulates the expression of lipin 1 that catalyzes the second-to-last step in TG biosynthesis. Interestingly, the fatty acid composition of three TGs that showed an increase under hypoxic conditions was mostly saturated fatty acids such as palmitic acid and stearic acid. This result is also coincident with the recent study by Ackerman and co-workers in which hypoxia induced increased fatty acid saturation in TG to counter saturated fatty acid-induced lipotoxicity.42 Application of DOLGOReQ to a comparative lipidomics study could provide detailed quantitative and qualitative information on lipid metabolism during hypoxic stress. Finally, a pairwise analysis consisting of forward and reciprocal labeling experiments was conducted to examine labeling bias under hypoxic conditions. HeLa cells under normoxia and hypoxia conditions were grown with D2O in the forward and reciprocal labeling experiments, respectively. (The H−D distances and fold-changes determined from each experiment were summarized in Table S9.) For 41 lipid species that were commonly quantified among three biological replicates, the H−D distances displayed a good correlation between the forward and reciprocal experiments (Pearson’s correlation coefficient = 0.904) (Figure S6). In the case of TG(18:1/18:1/18:1), however, the H−D distance in the forward labeling experiment (0.323) was almost twice that

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.9b00086. Bar graphs for the number of lipids identified from unlabeled and D-labeled HeLa cells (Figure S1); an MS/ MS spectrum matching result of the protonated form of PC(16:0_18:1) with two MS libraries (Figure S2); comparison of the degree of D-labeling between fatty 8861

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acyl and TG from HeLa cells (Figure S3); distributions of the H−D distance of the D-labeled lipids quantified from biological replicates (Figure S4); distribution of fold change for all lipids quantified in nine replicates of a 1:1 mixture of unlabeled and D-labeled HeLa cells (Figure S5); a scatter plot of the H−D distance relationship between the forward and reciprocal labeling experiments (Figure S6); the number of lipid in unlabeled and D-labeled HeLa cells for each technical replicate (Table S1); the number of lipid identified in unlabeled, mixed, and D-labeled HeLa cells for each biological replicate (Table S2); the ratio of the number of quantification results as functions of the NDMI and H−D distance (Table S3); number of quantification and average Pearson’s correlation coefficient between the expected and measured fold-changes for lipids mixed at various ratios (Table S4); and comparison of the relative quantification results between label-free and DOLGOReQ methods for lipids displaying a significant fold change under hypoxic conditions (Table S5) (PDF) Individual quantification results for lipids in equimolar mixtures (Table S6); individual quantification results for lipids in various mixing ratios (Table S7); individual quantification results under hypoxic conditions (Table S8); and individual quantification results in the pairwise analysis (Table S9) (ZIP)

AUTHOR INFORMATION

Corresponding Author

*Tel.: +82-62-715-3647. E-mail: [email protected]. ORCID

Tae-Young Kim: 0000-0002-8846-3338 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We acknowledge support from the National Research Foundation (NRF-2014R1A1A1003643) and a grant (HI17C1238) from the Korea Health Technology R&D project through the Korea Health Industry Development Institute (KHIDI) of the Republic of Korea.

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