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ArticleJune 20, 2016

LOBSTAHS: An Adduct-Based Lipidomics Strategy for Discovery and Identification of Oxidative Stress Biomarkers
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Analytical Chemistry

Cite this: Anal. Chem. 2016, 88, 14, 7154–7162

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https://doi.org/10.1021/acs.analchem.6b01260

Published June 20, 2016

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Abstract

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Discovery and identification of molecular biomarkers in large LC/MS data sets requires significant automation without loss of accuracy in the compound screening and annotation process. Here, we describe a lipidomics workflow and open-source software package for high-throughput annotation and putative identification of lipid, oxidized lipid, and oxylipin biomarkers in high-mass-accuracy HPLC-MS data. Lipid and oxylipin biomarker screening through adduct hierarchy sequences, or LOBSTAHS, uses orthogonal screening criteria based on adduct ion formation patterns and other properties to identify thousands of compounds while providing the user with a confidence score for each assignment. Assignments are made from one of two customizable databases; the default databases contain 14 068 unique entries. To demonstrate the software’s functionality, we screened more than 340 000 mass spectral features from an experiment in which hydrogen peroxide was used to induce oxidative stress in the marine diatom Phaeodactylum tricornutum. LOBSTAHS putatively identified 1969 unique parent compounds in 21 869 features that survived the multistage screening process. While P. tricornutum maintained more than 92% of its core lipidome under oxidative stress, patterns in biomarker distribution and abundance indicated remodeling was both subtle and pervasive. Treatment with 150 μM H₂O₂ promoted statistically significant carbon-chain elongation across lipid classes, with the strongest elongation accompanying oxidation in moieties of monogalactosyldiacylglycerol, a lipid typically localized to the chloroplast. Oxidative stress also induced a pronounced reallocation of lipidome peak area to triacylglycerols. LOBSTAHS can be used with environmental or experimental data from a variety of systems and is freely available at https://github.com/vanmooylipidomics/LOBSTAHS.

Subjects

Reactive oxygen species (ROS) represent a persistent source of stress in virtually all biological systems. (1, 2) The negative cellular effects of ROS include protein damage, mutation of DNA, and lipid peroxidation. (3) While the profound effects of oxidative stress have been documented extensively in the lipids of mammals, (4-6) ROS can induce equally significant and wide-ranging remodeling of cell lipidomes in terrestrial and marine plants. (7-9) These ROS can act through a variety of enzymatic and abiotic mechanisms to produce a broad and heterogeneous suite of lipid products whose bioactivity and diversity make them ideal as molecular biomarkers. These products include both oxidized intact polar lipids (ox-IPL; e.g., oxidized phospholipids) (8) and oxylipins, the smaller, direct derivatives of fatty acids. Lipid biomarkers (both oxidized and unoxidized) can be used to characterize the effects of ROS in humans from cancer (10) and other diseases such as atherosclerosis; (11) in the marine environment, lipids can be used to diagnose various sources of biological and abiotic stress, including those imposed by nutrient limitation (12, 13) and viral infection. (14, 15) The potency and specificity that make lipids useful as biomarkers of oxidative stress also support their function as bioactive “infochemicals”. (16-18) In the ocean, for example, oxylipins have been shown to regulate different interspecific interactions among marine microbiota (19-22) and the metabolism of sinking marine particles by heterotrophic bacteria. (23)

For several reasons, there exist few comprehensive methods to screen, identify, and annotate large numbers of these oxylipins and oxidized lipids alongside the many unoxidized lipids from which they can originate. (4, 6) Oxylipins, like their parent lipids, have a wide diversity of structures and biochemical functions. (4, 24, 25) They can be produced enzymatically (9, 26, 27) or abiotically, (28, 29) often occurring in very low abundance relative to their intact polar lipid (IPL) precursors. (6, 30) Finally, unique and tailored computational strategies are required to analyze the large volumes of data necessary for comprehensive lipidomics or metabolomics. (25, 31)

The limited number of analytical strategies developed specifically to assess the effects of oxidative stress on the lipidomes of humans (4) and mammals (32, 33) have generally focused on traditional oxylipins, such as hydroperoxy, hydroxy, epoxy, oxo, and ketol fatty acids, while ignoring most molecular precursors and intermediates. For example, direct-infusion mass spectrometry has been used to identify select oxylipins simultaneously with their unoxidized IPL and ox-IPL precursors in the model plant Arabidopsis thaliana, but these studies used manual data analysis methods to examine oxidation of compounds containing only C₁₆ and C₁₈ fatty acids. (7, 8) Ni et al. employed a shotgun approach to identify oxidized lipids in rat cardiomyocytes, but their analysis was limited only to intact carbonylated phospholipids. (34) The commercial LipidSearch software (Thermo Scientific) can identify some oxidized lipids using MS/MS fragmentation spectra, but this capability does not extend to oxylipins derived from fatty acids.

We developed a new, rules-based screening approach that can be used to identify a broad range of IPL, nonpolar lipids, ox-IPL, and oxylipins in large, high-mass-accuracy HPLC-ESI-MS data sets. Lipid and oxylipin biomarker screening through adduct hierarchy sequences, or LOBSTAHS, is implemented as an open-source package for R (35) and integrates with the existing packages xcms (36-38) and CAMERA. (39) The software is centered around a novel screening methodology that exploits the unique tendency of lipids to form adduct ions in consistent, diagnostic patterns of abundance that remain relatively consistent across sample types (e.g., for phosphatidylcholine in positive ion mode, [M + H]⁺ > [M + Na]⁺ > [M + NH₄ + ACN]⁺ > [M + 2Na – H]⁺ > [M + K]⁺). Using lipid data from cultures of a mutant strain of a marine diatom designed for studies of oxidative stress, (40) we demonstrate how LOBSTAHS can be used to resolve conflicting compound assignments, examine differential expression of compounds across experimental treatments, discover and identify potential ox-IPL and oxylipin biomarkers, and identify potential isomers and isobars.

LOBSTAHS annotates each compound assignment with a confidence score, allowing the user to define subsets for further analysis. While we apply LOBSTAHS here to identify potential biomarkers in a marine microorganism, it can be applied to any HPLC-ESI-MS data set where the user expects the relative proportions of the various adduct ions of each analyte to remain constant across samples. LOBSTAHS requires data from a mass spectrometer having both high resolving power and high mass accuracy. While we developed the software using data acquired on a Thermo Exactive Plus Orbitrap instrument, LOBSTAHS could be used to analyze data acquired via FTICR-MS or, when sufficient allowances are made for mass resolution, a Q-TOF instrument.

Theory and Design of Software and Databases

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Design and Scope of Lipid-Oxylipin Databases

LOBSTAHS draws compound assignments from customizable onboard databases that contain structural and adduct ion abundance data for various IPL, nonpolar lipids, ox-IPL, and oxylipins (Tables S1 and S3). Each database entry represents a different adduct ion of a potential analyte; because analytes present differently in positive and negative ionization modes, separate databases must be generated for compound identification in each mode. The standard package installation includes two default databases that contain entries for 14 068 unique compounds, some of them particular to marine algae (Tables S1 and S3). Alternatively, LOBSTAHS allows the user to generate his or her own databases; instructions are provided in the package documentation. The use of onboard databases distinguishes LOBSTAHS from other software packages that rely exclusively on external databases.

Database Generation

Databases are created in LOBSTAHS by pairing empirical data with an in silico simulation. To generate the default databases, we first calculated exact masses for various triacylglycerols (TAG), free fatty acids (FFA), polyunsaturated aldehydes (PUA), and molecules belonging to eight different classes of intact polar diacylglycerol (IP-DAG). Within each of these classes, we calculated the masses of a wide range of possible structures having fatty acid (FA) moieties of different acyl chain length, unsaturation, and oxidation (Table S1). While the default databases include entries for IPL and ox-IPL that contain primarily medium- and long-chain fatty acids, users may generate additional databases with entries for IPL composed of fatty acids of any length. The databases also include exact masses for several photosynthetic pigments common to the marine environment (Tables S1 and S3). TAG and IP-DAG are identified by the “sum composition” (41) of double bonds and acyl carbon atoms in each compound (e.g., PC 34:1, rather than PC 16:0–18:1).

Determination of Relative Abundances of Adduct Ions for Inclusion in Databases

During database generation, LOBSTAHS uses empirical data for the LC/MS adduct ion(s) typically formed by each compound’s parent lipid class (Table S2) to create several entries for each compound. Each of the entries represents a different adduct ion of its parent; the relative ranks of the adducts form the basis for the hierarchy-based screening of compound assignments at the core of our method. Relative adduct ion abundance data were gathered from previous work (42) and analysis of compounds commonly observed in cultures and environmental samples from marine microorganisms. Where possible, we confirmed the results using authentic standards for representative compounds. For ox-IPL, we applied adduct hierarchies observed for the corresponding, unoxidized IPL. We assumed ox-IPL would be unlikely to take charge on their oxidized functional group(s) during ionization, thus forming adducts similar to those of the corresponding unoxidized molecule. We confirmed this similarity in ionization behavior through manual inspection of several samples. Long-chain ox-IPL standards other than those containing aldehyde moieties are not available commercially.

A series of simple tables can be used to define additional analytes and/or adducts beyond those which are included in the default databases. For each new lipid or lipid class, LOBSTAHS requires (1) the elemental composition of the new lipid or parent moiety of the new lipid class, (2) a tabulation of expected adducts (defining, as necessary, any new adducts), (3) empirical adduct hierarchy data for any new adducts, and (4) if applicable, the ranges of acyl carbon atoms, double bonds, and oxidization states for which entries are to be generated. Specific instructions are contained in the online documentation for the software.

Lipidomics Workflow Based on xcms, CAMERA, and LOBSTAHS

Because high resolving power and high mass accuracy alone are often not sufficient to resolve isobaric ions or to distinguish among species that have identical monoisotopic masses but different elemental compositions, (44) we employ high-performance liquid chromatography (HPLC) in lieu of a simple direct-infusion approach. Data must first be converted to an open-source format (.mzXML) and, if necessary, from profile to centroid mode. If data are acquired using ion mode switching, LOBSTAHS requires that positive and negative ion mode scan data also be extracted into separate files. Procedural details and an R script that can be used to automate these steps for data obtained from an Orbitrap instrument are described in the Supporting Information. For purposes of the present study, we consider two ions to be isobaric when the underlying features have different exact masses but the m/z difference is less than the instrument’s demonstrated mass accuracy (in this case, 2.5 ppm). Once data files have been converted and extracted, the existing R packages xcms (36-38) and CAMERA (39) are used for feature detection, peak grouping, chromatographic alignment, identification of pseudospectra, and discovery of features representing possible secondary isotope peaks (Scheme 1, “Pre-processing & feature detection”).

Scheme 1. Preparation, Screening, and Annotation of HPLC-MS Lipid Data Using LOBSTAHSⁱ

Database Assignments and Progressive Screening in LOBSTAHS Using Orthogonal Criteria

After preprocessing, screening and annotation are performed according to the workflow in Scheme 1. First, initial compound assignments are applied to features from the database using a narrow m/z mass tolerance specified by the user in ppm. A series of optional orthogonal screening criteria can then be applied to the features and their assignments. First, users may exclude from the data set any features representing secondary isotope peaks; the presence of these features can be a significant obstacle in metabolomics. (45-47) We programmed LOBSTAHS to exclude these secondary peaks, rather than merge the elements of each feature’s isotopic envelope into a single parameter. (46)

Next, LOBSTAHS can screen the feature’s retention time against a retention time “window” defined for the accompanying assignment’s parent lipid class. LOBSTAHS includes a set of default retention time window data (Table S4) for the chromatographic conditions we describe here. Detailed instructions for application of retention time data from other chromatographic methods are included in the Supporting Information. A third filter can then be applied to exclude assignments of IPL, ox-IPL, FFA, and PUA that contain an odd total number of acyl carbon atoms. We envision that this filter would be applied to data of exclusively eukaryotic origin: Since nonacetogenic fatty acid synthesis is confined almost exclusively to bacteria and archaea, (48) FA synthesized by eukaryotes will be composed of an even total number of carbon atoms.

After applying these initial optional criteria, LOBSTAHS then screens each assignment using adduct ion hierarchy data (Scheme 1, “Application of core adduct ion hierarchy screening rules”; Table S2). This screening serves as the primary orthogonal filter to eliminate any confounding secondary isotopes and unassigned lipid-extractable features still remaining in the data set. During this process, LOBSTAHS uses a series of rules to compare the relative abundance ranks of sets of adduct ion assignments that have the same parent compound. The package makes several annotations using simple codes that indicate the degree to which the assignment complies with the hierarchy rules (Scheme 1, in bold). Assignments that fail the adduct ion hierarchy screening criteria are excised from the data set, and all remaining assignments in the data set are then pooled.

Additional rules-based screening is then performed on the pooled data to identify and annotate possible isomers and isobars (Scheme 1, “Isomer & isobar detection, annotation”). Codes can be applied to identify positional or regioisomers (code C3r), functional structural isomers (code C3f), or isobars (code C3c). LOBSTAHS can apply several of these different codes to a given assignment as long as the criterion for each is satisfied. Upon completion of screening, LOBSTAHS produces an R object containing the annotated data set. Statistical analysis can then be performed in R on the final matrix of compound assignments, or the results can be exported to a .csv file for external analysis.

Experimental Section

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Model Data Set Used To Demonstrate the Workflow

To demonstrate LOBSTAHS, we applied the workflow in Scheme 1 to examine the effect of oxidative stress on a model algal lipidome. For the analysis, we used lipid data collected from cultures of a mutant strain of the marine diatom Phaeodactylum tricornutum, which was designed for studies of oxidative stress. In the study, (40) a strain of P. tricornutum (CCMP2561; Provasoli-Guillard National Center for Marine Algae and Microbiota) was genetically modified (49) to express a reduction–oxidation sensitive green fluorescent protein (roGFP) at different locations within the cell. (50, 51) Cultures of the transformants were treated with three concentrations of H₂O₂ (0, 30, and 150 μmol L^–1) to evaluate the effects of peroxidation; culture conditions are described in van Creveld et al. (40)

Sample Collection and Extraction

In the experiment, duplicate samples for lipid analysis were collected from each treatment at 4, 8, and 24 h time points. Two procedural blanks were also collected. Sample material was collected by vacuum onto 0.7 μm pore size glass fiber filters (GF/F), which were snap frozen in liquid nitrogen and then stored at −80 °C until thawed for extraction. Extraction was performed using a modified Bligh and Dyer (52) method described in Popendorf et al.; (42) an internal standard (dinitrophenyl-phosphatidylethanolamine, DNP-PE) and a synthetic antioxidant (butylated hydroxytoluene, BHT) were added at the time of extraction. Lipid extracts were transferred to 2 mL HPLC vials, topped with argon, and stored at −80 °C prior to analysis. All chemicals used in sample extraction and chromatography were LC/MS grade or higher. Where used, water was obtained from a Milli-Q system without further treatment (EMD Millipore, Billerica, MA, USA).

HPLC-ESI-MS Analysis

Samples from the P. tricornutum data set were analyzed by HPLC-ESI-MS using a modification of the method described in Hummel et al. (53) Lipid extracts were evaporated to near dryness and reconstituted in a similar volume of 7:3 acetonitrile/isopropanol. Headspace was filled with argon to minimize further oxidation. For HPLC analysis, an Agilent 1200 system (Agilent, Santa Clara, CA, USA) comprising temperature-controlled autosampler (4 °C), binary pump, and diode array detector was coupled to a Thermo Exactive Plus Orbitrap mass spectrometer (ThermoFisher Scientific, Waltham, MA, USA). Chromatographic conditions, electrospray ionization source settings, MS acquisition settings, and procedures used for calibration of the mass spectrometer are described in the Supporting Information. Using authentic standards and two independent methods for MS feature detection, we determined the average relative mass uncertainty of the exactive was <0.2 ppm (Tables 1 and S6); evaluation of these standards is discussed below.

Table 1. Evaluation of Lipidomics Method Performance using IPL Standards

lipid class	origin of standard	moieties present in standard^a	dominant positive mode adduct ion	ion exact m/z	observed m/z^b	rel. mass uncertainty (ppm)^c	correct LOBSTAHS ID?	confidence in assignment after adduct hierarchy screening^d	structural isomers or isobars present after screening?
MGDG	natural	34:0	[M + NH₄]⁺	776.6246	776.6248	0.2	yes	high	no
	natural	36:0	[M + NH₄]⁺	804.6559	804.6561	0.3	yes	high	no
DNP-PE	synthetic	32:0	[M + NH₄]⁺	875.5505	875.5507	0.2	yes	high	no
SQDG	natural	34:3	[M + NH₄]⁺	834.5396	834.5398	0.2	yes	high	no
	natural	34:2	[M + NH₄]⁺	836.5552	836.5554	0.2	yes	high	no
PG	synthetic	32:0	[M + NH₄]⁺	740.5436	740.5438	0.3	yes	high	no
PE	synthetic	32:0	[M + H]⁺	692.5225	692.5227	0.3	yes	high	no
PC	synthetic	32:0	[M + H]⁺	734.5694	734.5696	0.2	yes	high	no
DGDG	natural	34:2	[M + NH₄]⁺	934.6462	934.6463	0.1	yes	high	yes
	natural	36:4	[M + NH₄]⁺	958.6462	958.6463	0.1	yes	high	yes
mean						0.2

Multiple moieties were present in glycolipid standards purified from natural samples; only predominant moieties are shown.

Mean observed m/z ratio in 5 independent samples.

See the following equation:

“High confidence”: Assignment fully satisfied all adduct hierarchy rules and other screening criteria.

Analysis of P. tricornutum Data Using LOBSTAHS

xcms, CAMERA, and LOBSTAHS were then used to identify and annotate lipidome components in the positive ionization mode data. The R package IPO (54) was used to optimize settings for several xcms functions, and a 2.5 ppm mass uncertainty tolerance was used to obtain database matches in LOBSTAHS. Using the annotated output we obtained from LOBSTAHS, we then calculated the relative abundances of lipidome constituents present in the 0 and 150 μM H₂O₂ treatments at 24 h. Statistical techniques were used to identify biomarkers of oxidative stress. Unless otherwise noted, we restricted our analysis to only “high confidence” assignments; these were assignments without structural isomers or isobars given codes of C1 or C2a according to the logic in Scheme 1. The specific settings used in xcms, CAMERA, and LOBSTAHS, details of statistical methods, and links to scripts we used to obtain results and figures are included in the Supporting Information text and Table S5.

Results and Discussion

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Screening and Annotation of P. tricornutum Data in LOBSTAHS

Using LOBSTAHS, we identified 21 869, or 6.4%, of the 340 991 mass spectral features initially detected in the data set using xcms. Sequential application of the various screening criteria allowed us to exclude features from the data set based on specific characteristics (Table 2). Of these initial features, 177 053, or 52%, were immediately eliminated as likely secondary isotope peaks identified by CAMERA. The 163 938 remaining features were then matched at 2.5 ppm against entries in the default positive mode database. We then used LOBSTAHS to perform screening based on feature retention time and assignment total acyl carbon number. LOBSTAHS excluded 7792 features because the retention time fell outside the range expected for the assignment’s parent lipid class. An additional 7733 features were eliminated because the compound assignment did not contain an even total number of acyl carbon atoms; this optional restriction was applied given the known eukaryotic origin of the data. Adduct ion hierarchy screening was then applied to the remaining 52 337 features. Application of this final orthogonal filter yielded a data set containing 2056 compound assignments; these assignments represented 1969 unique parent compounds (Table 2).

Table 2. Progressive Screening and Annotation of the P. tricornutum Dataset using xcms, CAMERA, and LOBSTAHS

	no. present in data set
operation(s) applied	peaks	peak groups	database assignments^a	unique parent compounds
xcms and CAMERA
initial feature detection; preprocessing	340 991	18 314
LOBSTAHS
eliminate secondary isotope peaks	163 938	12 146
apply initial compound assignments from database	67 862	5077	15 929	14 076
apply RT screening criteria	60 070	4451	13 504	11 779
exclude IP-DAG/TAG with odd total no. of acyl C atoms	52 337	3871	7458	6283
adduct ion hierarchy screening	21 869	1595	2056^b	1969

Figure reflects all assignments from database, including photosynthetic pigments.

1163, or 57%, of these had no competing assignments such as functional structural isomers or isobars; these 1163 assignments represented 990 unique parent compounds.

The identities of 1163, or 57%, of these final database assignments were unique within the scope of our database, meaning the underlying features were matched in the final data set to only one possible parent compound. 1149 of these assignments were IPL, ox-IPL, or TAG (Figure 1a); the remainder were photosynthetic pigments. We classified 1056 of these identifications as “high confidence,” indicating that the distribution of adducts present in the constituent features perfectly satisfied the adduct hierarchy rules (Figure 1a and symbols with darkest tones in Figures S2–S10); these were used in the analysis below.

Figure 1. (a) All IPL, ox-IPL, and TAG identified in the *P. tricornutum* data set with high confidence (N = 1039; figure excludes pigments). (b) Distribution by lipid class of high-confidence assignments made in the 0 and 150 μM H₂O₂ treatments at 24 h (N = 894 and N = 848, respectively). Ellipse size in (b) reflects the number of compounds identified within each class and treatment. The assignments presented in (a) and (b) fully satisfied the LOBSTAHS adduct hierarchy screening criteria (i.e., annotated “C1” or “C2a” according to the logic in Scheme 1) and had no competing assignments, such as possible structural isomers, identified in the data set. Excluded are those compounds having an odd total number of acyl carbon atoms. ^aGeneral direction of movement within m/z versus RT plot, for a given lipid class and oxidation state. The direction of movement that results from addition or removal of additional oxygen atom(s) varies by lipid class. ^bNot to scale.

Identification and Annotation of Isomers and Isobars

The remaining 893 assignments (43.4%) were characterized by some degree of ambiguity, meaning the data set contained at least one isobar or structural functional isomer of the underlying features (Table S7; symbols with lightest tones in Figures S2–S10). In 752 instances, the dominant adduct of the parent compound was a (functional) structural isomer of the dominant adduct of a different compound assigned from the database (Chart S1, first example). In 195 cases, the dominant adduct ion of the parent compound was an isobar of the primary adduct ion of a different compound (Chart S1, second example). Although these ambiguous assignments represented 43.4% of all assignments in the screened data set, they belonged to just 25% of retained features (27% of peak groups; Table S7). The difference was due to the presence of a small number of features (793) whose 54 assignments were doubly ambiguous, i.e., having both isobars and functional structural isomers (symbols with two-tone shading in Figures S2–S10; Chart S1, third example). The number of competing assignments for each identified compound varied largely by lipid class. For example, LOBSTAHS found no functional structural isomers for compounds identified in several lipid classes: digalactosyldiacylglycerol (DGDG), phosphatidylethanolamine (PE), and sulfoquinovosyldiacylglycerol (SQDG) (Figures S3, S7, and S9). Doubly ambiguous assignments were confined to only four classes: diacylglyceryl carboxyhydroxymethylcholin (DGCC), diacylglyceryl trimethylhomoserine and diacylglyceryl hydroxymethyl-trimethyl-β-alanine (DGTS and DGTA), phosphatidylcholine (PC), and phosphatidylglycerol (PG) (Figures S2, S4, S6, and S8).

Annotation of Potential Regioisomers

LOBSTAHS also identified regioisomers for 352 unique parent compounds in the P. tricornutum lipidome (Table S7; symbols with black dots in Figures S2–S10). These were instances in which the same assignment was applied to two or more features appearing at different retention times in the same sample. Many of these assignments were oxylipins and ox-IPL, indicating the presence of multiple oxidized isomers of the same parent IPL that could be used as biomarkers for oxidative stress. Without further analysis, we were unable to determine whether these isomers represented the oxidation of a fatty acid by the same mechanism at a different acyl carbon position or instead the presence of different oxidized functional groups that yielded equivalent exact masses (e.g., a dihydroxy-, hydroperoxy-, or α- or γ-ketol acid). The level of identification and annotation provided by LOBSTAHS supports a wide range of possible molecular structures for each assignment; an example from the data set is presented in Chart S1. The data were consistent with studies in both model plant (7, 8) and animal (34) systems that demonstrated the coexistence of a diversity of ox-IPL with both their parent IPL and smaller, traditional oxylipin degradation products.

Evaluation of Screening and Identification Performance Using Two Methods

As a means of validating the accuracy and reliability of our approach, we asked LOBSTAHS to identify and annotate all species present in 5 quality control (QC) samples of known composition that were interspersed randomly with samples from the P. tricornutum data set prior to analysis on the mass spectrometer (Table 1; Table S6). The samples contained a mixture of authentic IPL standards that has been used extensively in other work in our laboratory. (42, 55) Because the choice of preprocessing software can have a significant impact on feature detection, (56) we conducted parallel analyses with both xcms/CAMERA and an alternative program, MAVEN. (47, 57) In both cases, LOBSTAHS correctly identified all components of the standard mixture without ambiguity (Table 1; Table S6). Because purified standards do not exist for long-chain ox-IPL, we were unable to directly evaluate the Type 1 error rate for their identification. As a second means of validation, we compared assignments in the screened data set to two independent inventories of the P. tricornutum lipidome. (58, 59) LOBSTAHS found and identified with high confidence 13 of the 16 most abundant IPL and TAG species in one inventory (58) and nearly all those in the other. (59) While our approach also correctly identified the three remaining components in the former study (PG 32:1, PG 36, and DGTS and DGTA 40:10), functional structural isomers or isobars made unambiguous identification impossible without further manual inspection of ms² spectra.

Resilience of Core P. tricornutum Lipidome under Oxidative Stress

Evidence of the effect of oxidative stress on the lipidome of P. tricornutum was observed through comparison of compounds identified in 0 and 150 μM H₂O₂ treatments at 24 h (Figures 1 and 2a,b and Table S9). That the two treatments produced only subtle differences in molecular diversity (Figure 1b) suggests much of the core lipid inventory remained robust to the imposed oxidative stress. The vast majority of the 949 oxidized and unoxidized lipid moieties we identified in the healthy organism (879, or 92.6%) could still be identified in the lipidome of the stressed cultures (Figure 1b). On the basis of peak area, oxidized lipid moieties accounted for 5–7% of the P. tricornutum lipidome across nearly all treatments and time points. On the basis of the relatively consistent size of this oxidized lipid fraction and its persistence in even the 0 μM H₂O₂ treatment, we consider it a quantitative constraint on the baseline level of lipid peroxidation associated with metabolic processes in photosynthetic organisms. (1, 2)

Figure 2. Remodeling of the *Phaeodactylum tricornutum* lipidome after 24 h, as visualized from data analyzed with LOBSTAHS. (a) Heatmap showing relative abundances across two treatments (0 and 150 μM H₂O₂) of all IPL, ox-IPL, and TAG identified with high confidence. Each row (N = 896) represents a different compound identified from the database; Figure S11 contains an expanded version of the plot that includes labels for each individual compound. (b) Heatmap detail, showing changes in the most abundant (N = 40) moieties of monogalactosyldiacylglycerol (MGDG), a lipid typically localized to the chloroplast. (c) Fraction of total peak area identified as triacylglycerol (TAG) at three time points during the experiment. Error bars are ± SD of two replicates. In (a) and (b), shading shows the relative abundance of each compound as a fold difference of the mean peak area observed in that treatment from the mean peak area of the compound observed across all treatments. Dendrogram clustering and group definitions were determined by similarity profile analysis. (60) The numbers and identities of the components assigned to each group in (a) are given in Table S5 and Figure S11. Solid black lines in the dendrogram indicate branching that was statistically significant (P ≤ 0.01).

Differences in Degree of Remodeling between Lipid Classes and Functional Groupings

We used similarity profile analysis of the scaled LOBSTAHS data (60) to place the annotated features into 181 groups of components which clustered significantly according to their behavior (Figures 2 and S11). The components of each group are given in Table S8. We further examined the up- and downregulation of lipidome components under oxidative stress by dividing potential biomarkers into classes based on their molecular headgroups (Table S9). This allowed us to examine class-specific differences in the number of acyl carbon atoms, acyl carbon-to-carbon double bonds, and oxidation states (i.e., additional oxygen atoms) of component lipids under the two treatments. Differential expression of chemical properties within several classes (Figure 2; Table S9) suggested the P. tricornutum lipidome was remodeled in subtle but pervasive ways.

Fatty Acid Chain Elongation Is an Apparent Response to Oxidative Stress in the Chloroplast

Oxidative stress appeared to induce elongation of fatty acids throughout the P. tricornutum lipidome (Table S9). Lipid moieties upregulated by oxidative stress had longer fatty acid chains than those that were downregulated. We observed the greatest breadth of structural change in monogalactosyldiacylglycerol (MGDG), a lipid typically localized to the chloroplast (Table S9; Figure 2b). Moieties of MGDG upregulated in the 150 μM H₂O₂ treatment had significantly longer fatty acid chains and were more oxidized than those downregulated under oxidative stress; oxidation and elongation were also accompanied by a statistically significant decrease in acyl chain unsaturation (Table S9). The MGDG moieties responsible for these shifts in class structural properties were confined largely to groups 1, 2, 4, 5, 7, 9, 12, 166, 167, and 180 in our similarity profile analysis (Figures 2b and S11; Table S8). Lipid oxidation has been previously linked in the diatom Skeletonema costatum to lipolytic cleavage of MGDG and phospholipids within the chloroplast, resulting in oxylipin production from free fatty acids. (61) While intact oxidized MGDG species have not been previously observed in algae, ox-MGDG have been documented in terrestrial plants upon wounding. (8) The production of ox-IPL in Arabidopsis thaliana may be a means of binding ROS within the cell membrane to limit damage elsewhere. (62)

Significant Enrichment Observed in TAG

Whereas the impact of oxidative stress within most lipid classes was confined to relatively modest changes in structural properties, treatment with 150 μM H₂O₂ induced a very significant enrichment in the fraction of peak area we identified as triacylglycerols (TAG; Figure 2c). Enigmatically, the TAG moieties upregulated in the 150 μM treatment were significantly less oxidized than those downregulated (Table S9). We hypothesize that the growth of this chemically reduced TAG pool may be evidence of enhanced de novo production of unoxidized TAG as a response to oxidative stress. Increased TAG synthesis is a known response to nutrient starvation in virtually all algae, (63, 64) including P. tricornutum. (58, 59) While increased TAG production in algae has not been previously linked directly to oxidative stress, increased production has been observed as a response to viral infection in the haptophyte alga Emiliania huxleyi. (65)

Conclusions

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Using a model data set, LOBSTAHS allowed us to identify differences in lipid speciation between treatments that could be used as potential indicators of oxidative stress in P. tricornutum. This potential extended beyond individual oxylipins and ox-IPL to sets of highly interrelated compounds that represented different stages of degradation and oxidation within the same lipidome (Figures 2 and S11; Table S8). While we demonstrated our approach using culture data from a single marine diatom, we designed LOBSTAHS so it can be used with exact-mass HPLC/MS data from virtually any experiment or natural system.

For a minority of features in the screened data set that had isobars and/or structural isomers (Table S7; Figures S2–S10), we were unable to make unambiguous identifications using LOBSTAHS alone. Many of these could be identified more rigorously through comparison with results from alternative commercial software or by manual inspection using authentic standards or diagnostic MS fragmentation patterns. Our objective, however, was not to definitively characterize a few individual compounds with absolute certainty but instead to putatively identify a broad range of possible biomarkers for further analysis and discovery. In doing so, LOBSTAHS achieves a level of certainty between Level 3 (“Putatively characterized compound classes”) and Level 2 (“Putatively annotated compounds”), using the scheme of Sumner et al. (66) The overall results we obtained with the P. tricornutum data set demonstrate the ability of LOBSTAHS to assist in this task. The screening and annotation process allowed us to assess a range of levels of confidence on the putative assignments, producing an ample subset of high-confidence compound identifications sufficient to facilitate a detailed statistical analysis. Future integration of methods such as chiral HPLC (67) or matching of diagnostic ms² fragmentation spectra, with screening tools such as the one we present here, will assist in discovery of biomarkers in larger data sets and with even greater confidence.

Supporting Information

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The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.6b01260.

Methodological details, instructions for obtaining all software and data, supplementary discussion, supplementary data figures, supplementary data tables, and a supplementary chart showing examples of the types of isomers that can be identified with LOBSTAHS (PDF)

ac6b01260_si_001.pdf (4.64 MB)

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Author Information

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Corresponding Author
- James R. Collins - Massachusetts Institute of Technology/Woods Hole Oceanographic Institution Joint Program in Oceanography, Woods Hole, Massachusetts 02543, United States; Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543, United States; Email: [email protected]
Authors
- Bethanie R. Edwards - Massachusetts Institute of Technology/Woods Hole Oceanographic Institution Joint Program in Oceanography, Woods Hole, Massachusetts 02543, United States; Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543, United States
- Helen F. Fredricks - Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543, United States
- Benjamin A. S. Van Mooy - Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543, United States
Notes
The authors declare no competing financial interest.

Acknowledgment

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We thank Elizabeth Kujawinski, Winn Johnson, and Krista Longnecker for discussions on the analysis of metabolomics datasets and assistance with xcms; Marian Carlson and Matthew Johnson for discussions on the effects of oxidative stress in microorganisms; Eugene Melamud for answering our many questions about MAVEN; Assaf Vardi and Daniella Schatz for providing the P. tricornutum cell cultures. Liz Kujawinski provided thoughtful feedback on an earlier version of the manuscript. Finally, we thank two anonymous reviewers for critical comments that improved the manuscript significantly. This research was supported by the Gordon and Betty Moore Foundation through Grant GBMF3301 to B.A.S.V.M. This research was also funded in part by a grant to B.A.S.V.M. from the Simons Foundation and is a contribution of the Simons Collaboration on Ocean Processes and Ecology (SCOPE). J.R.C. acknowledges support from a U.S. Environmental Protection Agency (EPA) STAR Graduate Fellowship (Fellowship Assistance Agreement No. FP-91744301-0).

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Liq. chromatog. coupled to mass spectrometry is routinely used for metabolomics expts. In contrast to the fairly routine and automated data acquisition steps, subsequent compd. annotation and identification require extensive manual anal. and thus form a major bottleneck in data interpretation. Here the authors present CAMERA, a Bioconductor package integrating algorithms to ext. compd. spectra, annotate isotope and adduct peaks, and propose the accurate compd. mass even in highly complex data. To evaluate the algorithms, the authors compared the annotation of CAMERA against a manually defined annotation for a mixt. of known compds. spiked into a complex matrix at different concns. CAMERA successfully extd. accurate masses for 89.7% and 90.3% of the annotatable compds. in pos. and neg. ion modes, resp. Furthermore, the authors present a novel annotation approach that combines spectral information of data acquired in opposite ion modes to further improve the annotation rate. The authors demonstrate the utility of CAMERA in two different, easily adoptable plant metabolomics expts., where the application of CAMERA drastically reduced the amt. of manual anal.
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Kessner, Darren; Chambers, Matt; Burke, Robert; Agus, David; Mallick, Parag
Bioinformatics (2008), 24 (21), 2534-2536CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
Summary: The ProteoWizard software project provides a modular and extensible set of open-source, cross-platform tools and libraries. The tools perform proteomics data analyses; the libraries enable rapid tool creation by providing a robust, pluggable development framework that simplifies and unifies data file access, and performs std. proteomics and LCMS dataset computations. The library contains readers and writers of the mzML data format, which has been written using modern C++ techniques and design principles and supports a variety of platforms with native compilers. The software has been specifically released under the Apache v2 license to ensure it can be used in both academic and com. projects. In addn. to the library, we also introduce a rapidly growing set of companion tools whose implementation helps to illustrate the simplicity of developing applications on top of the ProteoWizard library.
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Kind Tobias; Fiehn Oliver
BMC bioinformatics (2006), 7 (), 234 ISSN:.
BACKGROUND: Metabolomic studies are targeted at identifying and quantifying all metabolites in a given biological context. Among the tools used for metabolomic research, mass spectrometry is one of the most powerful tools. However, metabolomics by mass spectrometry always reveals a high number of unknown compounds which complicate in depth mechanistic or biochemical understanding. In principle, mass spectrometry can be utilized within strategies of de novo structure elucidation of small molecules, starting with the computation of the elemental composition of an unknown metabolite using accurate masses with errors <5 ppm (parts per million). However even with very high mass accuracy (<1 ppm) many chemically possible formulae are obtained in higher mass regions. In automatic routines an additional orthogonal filter therefore needs to be applied in order to reduce the number of potential elemental compositions. This report demonstrates the necessity of isotope abundance information by mathematical confirmation of the concept. RESULTS: High mass accuracy (<1 ppm) alone is not enough to exclude enough candidates with complex elemental compositions (C, H, N, S, O, P, and potentially F, Cl, Br and Si). Use of isotopic abundance patterns as a single further constraint removes >95% of false candidates. This orthogonal filter can condense several thousand candidates down to only a small number of molecular formulas. Example calculations for 10, 5, 3, 1 and 0.1 ppm mass accuracy are given. Corresponding software scripts can be downloaded from http://fiehnlab.ucdavis.edu. A comparison of eight chemical databases revealed that PubChem and the Dictionary of Natural Products can be recommended for automatic queries using molecular formulae. CONCLUSION: More than 1.6 million molecular formulae in the range 0-500 Da were generated in an exhaustive manner under strict observation of mathematical and chemical rules. Assuming that ion species are fully resolved (either by chromatography or by high resolution mass spectrometry), we conclude that a mass spectrometer capable of 3 ppm mass accuracy and 2% error for isotopic abundance patterns outperforms mass spectrometers with less than 1 ppm mass accuracy or even hypothetical mass spectrometers with 0.1 ppm mass accuracy that do not include isotope information in the calculation of molecular formulae.
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Layre, Emilie; Sweet, Lindsay; Hong, Sunhee; Madigan, Cressida A.; Desjardins, Danielle; Young, David C.; Cheng, Tan-Yun; Annand, John W.; Kim, Keunpyo; Shamputa, Isdore C.; McConnell, Matthew J.; Debono, C. Anthony; Behar, Samuel M.; Minnaard, Adriaan J.; Murray, Megan; Barry, Clifton E., III; Matsunaga, Isamu; Moody, D. Branch
Chemistry & Biology (Cambridge, MA, United States) (2011), 18 (12), 1537-1549CODEN: CBOLE2; ISSN:1074-5521. (Cell Press)
The lipidic envelope of Mycobacterium tuberculosis promotes virulence in many ways, so we developed a lipidomics platform for a broad survey of cell walls. Here we report two new databases (MycoMass, MycoMap), 30 lipid fine maps, and mass spectrometry datasets that comprise a static lipidome. Further, by rapidly regenerating lipidomic datasets during biol. processes, comparative lipidomics provides statistically valid, organism-wide comparisons that broadly assess lipid changes during infection or among clin. strains of mycobacteria. Using stringent data filters, we tracked more than 5,000 mol. features in parallel with few or no false-pos. mol. discoveries. The low error rates allowed chemotaxonomic analyses of mycobacteria, which describe the extent of chem. change in each strain and identified particular strain-specific mols. for use as biomarkers.
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A rapid method of total lipide extraction and purification
Bligh, E. G.; Dyer, W. J.
Canadian Journal of Biochemistry and Physiology (1959), 37 (), 911-17CODEN: CJBPAZ; ISSN:0576-5544.
The wet tissue is homogenized with a mixt. of CHCl3 and MeOH to form a miscible system with the H2O in the tissue. Diln. with CHCl3 and H2O seps. the homogenate into 2 layers, the CHCl3 layer contg. all the lipides and the methanolic layer contg. all the non-lipides. A purified lipide ext. is obtained merely by isolating the CHCl3 layer. The method has been applied to fish muscle and may easily be adapted to use with other tissues.
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Hummel Jan; Segu Shruthi; Li Yan; Irgang Susann; Jueppner Jessica; Giavalisco Patrick
Frontiers in plant science (2011), 2 (), 54 ISSN:.
Holistic analysis of lipids is becoming increasingly popular in the life sciences. Recently, several interesting, mass spectrometry-based studies have been conducted, especially in plant biology. However, while great advancements have been made we are still far from detecting all the lipids species in an organism. In this study we developed an ultra performance liquid chromatography-based method using a high resolution, accurate mass, mass spectrometer for the comprehensive profiling of more than 260 polar and non-polar Arabidopsis thaliana leaf lipids. The method is fully compatible to the commonly used lipid extraction protocols and provides a viable alternative to the commonly used direct infusion-based shotgun lipidomics approaches. The whole process is described in detail and compared to alternative lipidomic approaches. Next to the developed method we also introduce an in-house developed database search software (GoBioSpace), which allows one to perform targeted or un-targeted lipidomic and metabolomic analysis on mass spectrometric data of every kind.
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Libiseller, G.; Dvorzak, M.; Kleb, U.; Gander, E.; Eisenberg, T.; Madeo, F.; Neumann, S.; Trausinger, G.; Sinner, F.; Pieber, T.; Magnes, C. BMC Bioinf. 2015, 16, 118 DOI: 10.1186/s12859-015-0562-8
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IPO: a tool for automated optimization of XCMS parameters
Libiseller Gunnar; Gander Edgar; Trausinger Gert; Sinner Frank; Pieber Thomas; Magnes Christoph; Dvorzak Michaela; Kleb Ulrike; Eisenberg Tobias; Madeo Frank; Madeo Frank; Neumann Steffen; Sinner Frank; Pieber Thomas
BMC bioinformatics (2015), 16 (), 118 ISSN:.
BACKGROUND: Untargeted metabolomics generates a huge amount of data. Software packages for automated data processing are crucial to successfully process these data. A variety of such software packages exist, but the outcome of data processing strongly depends on algorithm parameter settings. If they are not carefully chosen, suboptimal parameter settings can easily lead to biased results. Therefore, parameter settings also require optimization. Several parameter optimization approaches have already been proposed, but a software package for parameter optimization which is free of intricate experimental labeling steps, fast and widely applicable is still missing. RESULTS: We implemented the software package IPO ('Isotopologue Parameter Optimization') which is fast and free of labeling steps, and applicable to data from different kinds of samples and data from different methods of liquid chromatography - high resolution mass spectrometry and data from different instruments. IPO optimizes XCMS peak picking parameters by using natural, stable (13)C isotopic peaks to calculate a peak picking score. Retention time correction is optimized by minimizing relative retention time differences within peak groups. Grouping parameters are optimized by maximizing the number of peak groups that show one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiments, and the resulting scores are evaluated using response surface models. IPO was tested on three different data sets, each consisting of a training set and test set. IPO resulted in an increase of reliable groups (146% - 361%), a decrease of non-reliable groups (3% - 8%) and a decrease of the retention time deviation to one third. CONCLUSIONS: IPO was successfully applied to data derived from liquid chromatography coupled to high resolution mass spectrometry from three studies with different sample types and different chromatographic methods and devices. We were also able to show the potential of IPO to increase the reliability of metabolomics data. The source code is implemented in R, tested on Linux and Windows and it is freely available for download at https://github.com/glibiseller/IPO . The training sets and test sets can be downloaded from https://health.joanneum.at/IPO .
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Cajka, Tomas; Fiehn, Oliver
Analytical Chemistry (Washington, DC, United States) (2016), 88 (1), 524-545CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
A review. Advances in mass spectrometry (MS) and data processing led to discoveries in regulation of cellular metab. by using metabolomics and lipidomics approaches. Mass spectrometry is by far the dominating anal. platform in metabolomics and lipidomics, surpassing the use of NMR at a 5:2 ratio according to the authors' citation anal.
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The role of complex lipids in the synthesis of bioactive aldehydes of the marine diatom Skeletonema costatum
d'Ippolito, Giuliana; Tucci, Sara; Cutignano, Adele; Romano, Giovanna; Cimino, Guido; Miralto, Antonio; Fontana, Angelo
Biochimica et Biophysica Acta, Molecular and Cell Biology of Lipids (2004), 1686 (1-2), 100-107CODEN: BBMLFG; ISSN:1388-1981. (Elsevier B.V.)
Diatoms are unicellular plants broadly present in freshwater and marine ecosystems, where they play a primary role in sustaining the marine food chain. In the last 10 years, there has been accumulating evidence that diatoms may have deleterious effects on the hatching success of zooplankton crustaceans, such as copepods, thus affecting dynamics of planktonic populations and limiting secondary prodn. At the mol. level, failure to hatch is ascribed to the presence of a family of inhibitory oxylipins, which we propose to collectively name polyunsatd. short-chain aldehydes (abbreviated here as PUSCAs). Here, the authors describe the origin of PUSCAs produced by the marine diatom Skeletonema costatum via a lipoxygenase-mediated pathways involving non-esterified polyunsatd. fatty acids (PUFA). Expts. with complex lipids proved the pivotal role of chloroplast-derived glycolipids, esp. monogalactosyldiacylglycerol (MGDG), in providing hexadecatrienoic acid (C16:3 ω-4), hexadecatetraenoic acid (C16:4 ω-1) and eicosapentaenoic acid (C20:5 ω-3) to the downstream process leading to 2E,4Z-octadienal (C8:2 ω-4), 2E,4Z,7-octatrienal (C8:3 ω-1) and 2E,4Z-heptadienal (C7:2 ω-3), resp. Under physiol. conditions, the hydrolytic process is assocd. to galactolipid hydrolyzing enzyme capable of removing fatty acids from both sn positions of glycerol.
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TAG, You're it! Chlamydomonas as a reference organism for understanding algal triacylglycerol accumulation
Merchant, Sabeeha S.; Kropat, Janette; Liu, Bensheng; Shaw, Johnathan; Warakanont, Jaruswan
Current Opinion in Biotechnology (2012), 23 (3), 352-363CODEN: CUOBE3; ISSN:0958-1669. (Elsevier B.V.)
A review. Photosynthetic organisms are responsible for converting sunlight into org. matter, and they are therefore seen as a resource for the renewable fuel industry. Ethanol and esterified fatty acids (biodiesel) are the most common fuel products derived from these photosynthetic organisms. The potential of algae as producers of biodiesel precursor (or triacylglycerols (TAGs)) has yet to be realized because of the limited knowledge of the underlying biochem., cell biol. and genetics. Well-characterized pathways from fungi and land plants have been used to identify algal homologs of key enzymes in TAG synthesis, including diacylglcyerol acyltransferases, phospholipid diacylglycerol acyltransferase and phosphatidate phosphatases. Many labs. have adopted Chlamydomonas reinhardtii as a ref. organism for discovery of algal-specific adaptations of TAG metab. Stressed Chlamydomonas cells, grown either photoautotrophically or photoheterotrophically, accumulate TAG in plastid and cytoplasmic lipid bodies, reaching 46-65% of dry wt. in starch accumulation (sta) mutants. State of the art genomic technologies including expression profiling and proteomics have identified new proteins, including key components of lipid droplets, candidate regulators and lipid/TAG degrading activities. By analogy with crop plants, it is expected that advances in algal breeding and genome engineering may facilitate realizing the potential in algae.
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Hunter, J. E.; Frada, M. J.; Fredricks, H. F.; Vardi, A.; Van Mooy, B. A. S. Front. Mar. Sci. 2015, 2, 81 DOI: 10.3389/fmars.2015.00081
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Proposed minimum reporting standards for chemical analysis. Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI)
Sumner, Lloyd W.; Amberg, Alexander; Barrett, Dave; Beale, Michael H.; Beger, Richard; Daykin, Clare A.; Fan, Teresa W.-M.; Fiehn, Oliver; Goodacre, Royston; Griffin, Julian L.; Hankemeier, Thomas; Hardy, Nigel; Harnly, James; Higashi, Richard; Kopka, Joachim; Lane, Andrew N.; Lindon, John C.; Marriott, Philip; Nicholls, Andrew W.; Reily, Michael D.; Thaden, John J.; Viant, Mark R.
Metabolomics (2007), 3 (3), 211-221CODEN: METAHQ; ISSN:1573-3882. (Springer)
There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of std. metadata provides a biol. and empirical context for the data, facilitates exptl. replication, and enables the reinterrogation and comparison of data by others. Accordingly, the Metabolomics Stds. Initiative is building a general consensus concerning the min. reporting stds. for metabolomics expts. of which the Chem. Anal. Working Group (CAWG) is a member of this community effort. This article proposes the min. reporting stds. related to the chem. anal. aspects of metabolomics expts. including: sample prepn., exptl. anal., quality control, metabolite identification, and data pre-processing. These min. stds. currently focus mostly upon mass spectrometry and NMR spectroscopy due to the popularity of these techniques in metabolomics. However, addnl. input concerning other techniques is welcomed and can be provided via the CAWG online discussion forum at http://msi-workgroups.sourceforge.net/ or http://[email protected]. Further, community input related to this document can also be provided via this electronic forum.
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Analytical Chemistry

Cite this: Anal. Chem. 2016, 88, 14, 7154–7162

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https://doi.org/10.1021/acs.analchem.6b01260

Published June 20, 2016

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Abstract
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Scheme 1
Scheme 1. Preparation, Screening, and Annotation of HPLC-MS Lipid Data Using LOBSTAHSⁱ
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Scheme aWe automate several functions of the ProteoWizard msConvert tool. (43)
Scheme b,cxcms (36-38) was chosen for its command-line features and because it permits follow-on use of the R package CAMERA (39) to identify isotopes.
Scheme dIPO (54) can be used to optimize the values of parameters for some xcms and CAMERA functions.
Scheme eMultiple assignments will likely exist for many peakgroups in a typical data set.
Scheme fThis criterion may be useful when the subject data set contains lipids of exclusively eukaryotic origin.
Scheme gIn the case of C2a, the adduct ion hierarchy for the parent compound is completely satisfied; i.e., the pseudospectrum contains peakgroups representing every adduct ion of the compound of greater theoretical abundance than the least abundant adduct ion present. In the case of C2b, the adduct ion of greatest theoretical abundance and some lesser adduct ion is present, but adduct ions of intermediate abundance are not observed.
Scheme hBoth outcomes may apply simultaneously at this decision point if the data set contains isobars and isomers of the assignment.
Scheme iAnnotation codes (in bold) may be applied as indicated; these are designed to assist the user in evaluating assignment confidence during subsequent data analysis.
Figure 1
Figure 1. (a) All IPL, ox-IPL, and TAG identified in the P. tricornutum data set with high confidence (N = 1039; figure excludes pigments). (b) Distribution by lipid class of high-confidence assignments made in the 0 and 150 μM H₂O₂ treatments at 24 h (N = 894 and N = 848, respectively). Ellipse size in (b) reflects the number of compounds identified within each class and treatment. The assignments presented in (a) and (b) fully satisfied the LOBSTAHS adduct hierarchy screening criteria (i.e., annotated “C1” or “C2a” according to the logic in Scheme 1) and had no competing assignments, such as possible structural isomers, identified in the data set. Excluded are those compounds having an odd total number of acyl carbon atoms. ^aGeneral direction of movement within m/z versus RT plot, for a given lipid class and oxidation state. The direction of movement that results from addition or removal of additional oxygen atom(s) varies by lipid class. ^bNot to scale.
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Figure 2
Figure 2. Remodeling of the Phaeodactylum tricornutum lipidome after 24 h, as visualized from data analyzed with LOBSTAHS. (a) Heatmap showing relative abundances across two treatments (0 and 150 μM H₂O₂) of all IPL, ox-IPL, and TAG identified with high confidence. Each row (N = 896) represents a different compound identified from the database; Figure S11 contains an expanded version of the plot that includes labels for each individual compound. (b) Heatmap detail, showing changes in the most abundant (N = 40) moieties of monogalactosyldiacylglycerol (MGDG), a lipid typically localized to the chloroplast. (c) Fraction of total peak area identified as triacylglycerol (TAG) at three time points during the experiment. Error bars are ± SD of two replicates. In (a) and (b), shading shows the relative abundance of each compound as a fold difference of the mean peak area observed in that treatment from the mean peak area of the compound observed across all treatments. Dendrogram clustering and group definitions were determined by similarity profile analysis. (60) The numbers and identities of the components assigned to each group in (a) are given in Table S5 and Figure S11. Solid black lines in the dendrogram indicate branching that was statistically significant (P ≤ 0.01).
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  There is no corresponding record for this reference.
13. 13
  Van Mooy, B. A. S.; Fredricks, H. F.; Pedler, B. E.; Dyhrman, S. T.; Karl, D. M.; Koblizek, M.; Lomas, M. W.; Mincer, T. J.; Moore, L. R.; Moutin, T.; Rappe, M. S.; Webb, E. A. Nature 2009, 458, 69– 72 DOI: 10.1038/nature07659
  There is no corresponding record for this reference.
14. 14
  Fulton, J. M.; Fredricks, H. F.; Bidle, K. D.; Vardi, A.; Kendrick, B. J.; DiTullio, G. R.; Van Mooy, B. A. S. Environ. Microbiol. 2014, 16, 1137– 1149 DOI: 10.1111/1462-2920.12358
  There is no corresponding record for this reference.
15. 15
  Vardi, A.; Van Mooy, B. A. S.; Fredricks, H. F.; Popendorf, K. J.; Ossolinski, J. E.; Haramaty, L.; Bidle, K. D. Science 2009, 326, 861– 865 DOI: 10.1126/science.1177322
  There is no corresponding record for this reference.
16. 16
  Ianora, A.; Miralto, A. Ecotoxicology 2010, 19, 493– 511 DOI: 10.1007/s10646-009-0434-y
  There is no corresponding record for this reference.
17. 17
  Vardi, A. Commun. Integr. Biol. 2008, 1, 134– 136 DOI: 10.4161/cib.1.2.6867
  17
  Cell signaling in marine diatoms
  Vardi Assaf
  Communicative & integrative biology (2008), 1 (2), 134-6 ISSN:.
  Marine photosynthetic microorganisms (phytoplankton) are the basis of marine foodwebs and are responsible for nearly 50% of the global annual carbon-based primary production.1 Phytoplankton can grow rapidly and form massive blooms that can be regulated by environmental factors such as nutrients and light availability and biotic interaction with grazers and viruses.2,3 Their crucial role in drawing down atmospheric CO(2) and their potential use for future biofuel production4 raises the critical need for better understanding of fundamental features of their biology.5 Although traditionally phytoplankton were considered passive drifters with the currents (from Greek-"Planktos"), our recent reports demonstrate how cells employ a complex mechanism to sense changes in environmental cues and activate chemical-based defense strategies.
18. 18
  Pohnert, G. In Algal Chemical Ecology; Amsler, C. D., Ed.; Springer-Verlag: Berlin, 2008; pp 195– 202.
  There is no corresponding record for this reference.
19. 19
  Casotti, R.; Mazza, S.; Brunet, C.; Vantrepotte, V.; Ianora, A.; Miralto, A. J. Phycol. 2005, 41, 7– 20 DOI: 10.1111/j.1529-8817.2005.04052.x
  There is no corresponding record for this reference.
20. 20
  Miralto, A.; Barone, G.; Romano, G.; Poulet, S. A.; Ianora, A.; Russo, G. L.; Buttino, I.; Mazzarella, G.; Laabir, M.; Cabrini, M.; Giacobbe, M. G. Nature 1999, 402, 173– 176 DOI: 10.1038/46023
  There is no corresponding record for this reference.
21. 21
  Balestra, C.; Alonso-Saez, L.; Gasol, J. M.; Casotti, R. Aquat. Microb. Ecol. 2011, 63, 123– 131 DOI: 10.3354/ame01486
  There is no corresponding record for this reference.
22. 22
  Ribalet, F.; Intertaglia, L.; Lebaron, P.; Casotti, R. Aquat. Toxicol. 2008, 86, 249– 255 DOI: 10.1016/j.aquatox.2007.11.005
  22
  Differential effect of three polyunsaturated aldehydes on marine bacterial isolates
  Ribalet, Francois; Intertaglia, Laurent; Lebaron, Philippe; Casotti, Raffaella
  Aquatic Toxicology (2008), 86 (2), 249-255CODEN: AQTODG; ISSN:0166-445X. (Elsevier B.V.)
  Bioactive polyunsatd. aldehydes (PUAs) are produced by several marine phytoplankton (mainly diatoms) and have been shown to have a detrimental effect on a wide variety of organisms, including phytoplankton and invertebrates. However, their potential impact on marine bacteria has been largely neglected. We assess here the effect of three PUAs produced by marine diatoms: 2E,4E-decadienal, 2E,4E-octadienal and 2E,4E-heptadienal, on the growth of 33 marine bacterial strains, including 16 strains isolated during a bloom of the PUA-producing diatom Skeletonema marinoi in the Northern Adriatic Sea. A concn.-dependent growth redn. was obsd. for 19 bacterial strains at concns. ranging from 3 to 145 μmol L-1. Surprisingly, Eudora adriatica strain MOLA358 (Flavobacteriaceae) and Alteromonas hispanica strain MOLA151 (Alteromonadaceae) showed growth stimulation upon exposure to PUAs at concns. between 13 and 18 μmol L-1. The remaining 12 strains were unaffected by even very high PUA concns. Strains isolated during the diatom bloom showed remarkable resistance to PUA exposures, with only two out of 16 strains showing growth inhibition at PUA concns. below 106, 130, and 145 μmol L-1 for 2E,4E-decadienal, 2E,4E-octadienal and 2E,4E-heptadienal, resp. No correlation between taxonomical position and sensitivity to PUA was obsd. Considering that many bacteria thrive in close vicinity of diatom cells, it is likely that these compds. may shape the structure of assocd. bacterial communities by representing a selection force. This is even more relevant during the final stages of blooms, when senescence and nutrient limitation increase the potential prodn. and release of aldehydes.
23. 23
  Edwards, B. R.; Bidle, K. D.; Van Mooy, B. A. S. Proc. Natl. Acad. Sci. U. S. A. 2015, 112, 5909– 5914 DOI: 10.1073/pnas.1422664112
  There is no corresponding record for this reference.
24. 24
  Brügger, B. Annu. Rev. Biochem. 2014, 83, 79– 98 DOI: 10.1146/annurev-biochem-060713-035324
  24
  Lipidomics: analysis of the lipid composition of cells and subcellular organelles by electrospray ionization mass spectrometry
  Bruegger, Britta
  Annual Review of Biochemistry (2014), 83 (), 79-98CODEN: ARBOAW; ISSN:0066-4154. (Annual Reviews)
  A review. Lipidomics aims to quant. define lipid classes, including their mol. species, in biol. systems. Lipidomics has experienced rapid progress, mainly because of continuous tech. advances in instrumentation that are now enabling quant. lipid analyses with an unprecedented level of sensitivity and precision. The still-growing category of lipids includes a broad diversity of chem. structures with a wide range of physicochem. properties. Reflecting this diversity, different methods and strategies are being applied to the quantification of lipids. Here, I review state-of-the-art electrospray ionization tandem mass spectrometric approaches and direct infusion to quant. assess lipid compns. of cells and subcellular fractions. Finally, I discuss a few examples of the power of mass spectrometry-based lipidomics in addressing cell biol. questions.
25. 25
  Holčapek, M. Anal. Bioanal. Chem. 2015, 407, 4971– 4972 DOI: 10.1007/s00216-015-8740-0
  25
  Lipidomics
  Holcapek, Michal
  Analytical and Bioanalytical Chemistry (2015), 407 (17), 4971-4972CODEN: ABCNBP; ISSN:1618-2642. (Springer)
  There is no expanded citation for this reference.
26. 26
  Andreou, A.; Feussner, I. Phytochemistry 2009, 70, 1504– 1510 DOI: 10.1016/j.phytochem.2009.05.008
  There is no corresponding record for this reference.
27. 27
  Lamari, N.; Ruggiero, M. V.; d’Ippolito, G.; Kooistra, W. H. C. F.; Fontana, A.; Montresor, M. PLoS One 2013, 8, e73281 DOI: 10.1371/journal.pone.0073281
  There is no corresponding record for this reference.
28. 28
  Girotti, A. W. J. Lipid Res. 1998, 39, 1529– 1542
  There is no corresponding record for this reference.
29. 29
  Triantaphylides, C.; Krischke, M.; Hoeberichts, F. A.; Ksas, B.; Gresser, G.; Havaux, M.; Van Breusegem, F.; Mueller, M. J. Plant Physiol. 2008, 148, 960– 968 DOI: 10.1104/pp.108.125690
  There is no corresponding record for this reference.
30. 30
  Sparvero, L. J.; Amoscato, A. A.; Kochanek, P. M.; Pitt, B. R.; Kagan, V. E.; Bayır, H. J. Neurochem. 2010, 115, 1322– 1336 DOI: 10.1111/j.1471-4159.2010.07055.x
  30
  Mass-spectrometry based oxidative lipidomics and lipid imaging: applications in traumatic brain injury
  Sparvero, Louis J.; Amoscato, Andrew A.; Kochanek, Patrick M.; Pitt, Bruce R.; Kagan, Valerian E.; Bayir, Hulya
  Journal of Neurochemistry (2010), 115 (6), 1322-1336CODEN: JONRA9; ISSN:0022-3042. (Wiley-Blackwell)
  A review. Lipids, particularly phospholipids, are fundamental to CNS tissue architecture and function. Endogenous polyunsatd. fatty acid chains of phospholipids possess cis-double bonds each sepd. by one methylene group. These phospholipids are very susceptible to free-radical attack and oxidative modifications. A combination of anal. methods including different versions of chromatog. and mass spectrometry allows detailed information to be obtained on the content and distribution of lipids and their oxidn. products thus constituting the newly emerging field of oxidative lipidomics. It is becoming evident that specific oxidative modifications of lipids are crit. to a no. of cellular functions, disease states and responses to oxidative stresses. Oxidative lipidomics is beginning to provide new mechanistic insights into traumatic brain injury which may have significant translational potential for development of therapies in acute CNS insults. In particular, selective oxidn. of a mitochondria-specific phospholipid, cardiolipin, was assocd. with the initiation and progression of apoptosis in injured neurons thus indicating new drug discovery targets. Furthermore, imaging mass-spectrometry represents an exciting new opportunity for correlating maps of lipid profiles and their oxidn. products with structure and neuropathol. This review is focused on these most recent advancements in the field of lipidomics and oxidative lipidomics based on the applicaitons of mass spectrometry and imaging mass spectrometry as they relate to studies of phospholipids in traumatic brain injury.
31. 31
  Spickett, C. M.; Pitt, A. R. Antioxid. Redox Signaling 2015, 22, 1646– 1666 DOI: 10.1089/ars.2014.6098
  31
  Oxidative Lipidomics Coming of Age: Advances in Analysis of Oxidized Phospholipids in Physiology and Pathology
  Spickett, Corinne M.; Pitt, Andrew R.
  Antioxidants & Redox Signaling (2015), 22 (18), 1646-1666CODEN: ARSIF2; ISSN:1523-0864. (Mary Ann Liebert, Inc.)
  Significance: Oxidized phospholipids are now well recognized as markers of biol. oxidative stress and bioactive mols. with both pro-inflammatory and anti-inflammatory effects. While anal. methods continue to be developed for studies of generic lipid oxidn., mass spectrometry (MS) has underpinned the advances in knowledge of specific oxidized phospholipids by allowing their identification and characterization, and it is responsible for the expansion of oxidative lipidomics. Recent Advances: Studies of oxidized phospholipids in biol. samples, from both animal models and clin. samples, have been facilitated by the recent improvements in MS, esp. targeted routines that depend on the fragmentation pattern of the parent mol. ion and improved resoln. and mass accuracy. MS can be used to identify selectively individual compds. or groups of compds. with common features, which greatly improves the sensitivity and specificity of detection. Application of these methods has enabled important advances in understanding the mechanisms of inflammatory diseases such as atherosclerosis, steatohepatitis, leprosy, and cystic fibrosis, and it offers potential for developing biomarkers of mol. aspects of the diseases. Crit. Issues and Future Directions: The future in this field will depend on development of improved MS technologies, such as ion mobility, novel enrichment methods and databases, and software for data anal., owing to the very large amt. of data generated in these expts. Imaging of oxidized phospholipids in tissue MS is an addnl. exciting direction emerging that can be expected to advance understanding of physiol. and disease. Antioxid. Redox Signal. 22, 1646-1666.
32. 32
  Balvers, M. G.; Verhoeckx, K. C.; Bijlsma, S.; Rubingh, C. M.; Meijerink, J.; Wortelboer, H. M.; Witkamp, R. F. Metabolomics 2012, 8, 1130– 1147 DOI: 10.1007/s11306-012-0421-9
  There is no corresponding record for this reference.
33. 33
  Bruins, M. J.; Dane, A. D.; Strassburg, K.; Vreeken, R. J.; Newman, J. W.; Salem, N., Jr.; Tyburczy, C.; Brenna, J. T. J. Lipid Res. 2013, 54, 1598– 1607 DOI: 10.1194/jlr.M034918
  There is no corresponding record for this reference.
34. 34
  Ni, Z.; Milic, I.; Fedorova, M. Anal. Bioanal. Chem. 2015, 407, 5161– 5173 DOI: 10.1007/s00216-015-8536-2
  34
  Identification of carbonylated lipids from different phospholipid classes by shotgun and LC-MS lipidomics
  Ni, Zhixu; Milic, Ivana; Fedorova, Maria
  Analytical and Bioanalytical Chemistry (2015), 407 (17), 5161-5173CODEN: ABCNBP; ISSN:1618-2642. (Springer)
  Oxidized lipids play a significant role in the pathogenesis of numerous oxidative stress-related human disorders, such as atherosclerosis, obesity, inflammation, and autoimmune diseases. Lipid peroxidn., induced by reactive oxygen and nitrogen species, yields a high variety of modified lipids. Among them, carbonylated lipid peroxidn. products (oxoLPP), formed by oxidn. of the fatty acid moiety yielding aldehydes or ketones (carbonyl groups), are electrophilic compds. that are able to modify nucleophilic substrates like proteins, nucleic acid, and aminophospholipids. Some carbonylated phosphatidylcholines possess pro-inflammatory activities. However, little is known about oxoLPP derived from other phospholipid (PL) classes. Here, the authors present a new anal. strategy based on the mass spectrometry (MS) of PL-oxoLPP derivatized with 7-(diethylamino)coumarin-3-carbohydrazide (CHH). Shotgun MS revealed many oxoLPP derived from in vitro oxidized glycerophosphatidylglycerols (PG, 31), glycerophosphatidylcholine (PC, 23), glycerophosphatidylethanolamine (PE, 34), glycerophosphatidylserines (PS, 7), glycerophosphatidic acids (PA, 17), and phosphatidylinositiolphosphates (PIP, 6) vesicles. This data were used to optimize LipidXplorer-assisted identification, and a python-based post-processing script was developed to increase both throughput and accuracy. When applied to full lipid exts. from rat primary cardiomyocytes treated with peroxynitrite donor SIN-1, ten PL-bound oxoLPP were unambiguously identified by LC-MS, including two PC-, two PE-, one PG-, two PS-, and three PA-derived species. Some of the known carbonylated PC were detected, while most PL-oxoLPP were shown for the first time. [Figure not available: see fulltext.].
35. 35
  R Core Team. R Foundation for Statistical Computing: Vienna, Austria, 2015.
  There is no corresponding record for this reference.
36. 36
  Smith, C. A.; Want, E. J.; O’Maille, G.; Abagyan, R.; Siuzdak, G. Anal. Chem. 2006, 78, 779– 787 DOI: 10.1021/ac051437y
  36
  XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification
  Smith, Colin A.; Want, Elizabeth J.; O'Maille, Grace; Abagyan, Ruben; Siuzdak, Gary
  Analytical Chemistry (2006), 78 (3), 779-787CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
  Metabolite profiling in biomarker discovery, enzyme substrate assignment, drug activity/specificity detn., and basic metabolic research requires new data preprocessing approaches to correlate specific metabolites to their biol. origin. Here we introduce an LC/MS-based data anal. approach, XCMS, which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, and peak matching. Without using internal stds., the method dynamically identifies hundreds of endogenous metabolites for use as stds., calcg. a nonlinear retention time correction profile for each sample. Following retention time correction, the relative metabolite ion intensities are directly compared to identify changes in specific endogenous metabolites, such as potential biomarkers. The software is demonstrated using data sets from a previously reported enzyme knockout study and a large-scale study of plasma samples. XCMS is freely available under an open-source license at http://metlin.scripps.edu/download/.
37. 37
  Tautenhahn, R.; Boettcher, C.; Neumann, S. BMC Bioinf. 2008, 9, 504 DOI: 10.1186/1471-2105-9-504
  37
  Highly sensitive feature detection for high resolution LC/MS
  Tautenhahn Ralf; Bottcher Christoph; Neumann Steffen
  BMC bioinformatics (2008), 9 (), 504 ISSN:.
  BACKGROUND: Liquid chromatography coupled to mass spectrometry (LC/MS) is an important analytical technology for e.g. metabolomics experiments. Determining the boundaries, centres and intensities of the two-dimensional signals in the LC/MS raw data is called feature detection. For the subsequent analysis of complex samples such as plant extracts, which may contain hundreds of compounds, corresponding to thousands of features -- a reliable feature detection is mandatory. RESULTS: We developed a new feature detection algorithm centWave for high-resolution LC/MS data sets, which collects regions of interest (partial mass traces) in the raw-data, and applies continuous wavelet transformation and optionally Gauss-fitting in the chromatographic domain. We evaluated our feature detection algorithm on dilution series and mixtures of seed and leaf extracts, and estimated recall, precision and F-score of seed and leaf specific features in two experiments of different complexity. CONCLUSION: The new feature detection algorithm meets the requirements of current metabolomics experiments. centWave can detect close-by and partially overlapping features and has the highest overall recall and precision values compared to the other algorithms, matchedFilter (the original algorithm of XCMS) and the centroidPicker from MZmine. The centWave algorithm was integrated into the Bioconductor R-package XCMS and is available from (http://www.bioconductor.org/).
38. 38
  Benton, H. P.; Want, E. J.; Ebbels, T. M. D. Bioinformatics 2010, 26, 2488– 2489 DOI: 10.1093/bioinformatics/btq441
  There is no corresponding record for this reference.
39. 39
  Kuhl, C.; Tautenhahn, R.; Bottcher, C.; Larson, T. R.; Neumann, S. Anal. Chem. 2012, 84, 283– 289 DOI: 10.1021/ac202450g
  39
  CAMERA: An Integrated Strategy for Compound Spectra Extraction and Annotation of Liquid Chromatography/Mass Spectrometry Data Sets
  Kuhl, Carsten; Tautenhahn, Ralf; Boettcher, Christoph; Larson, Tony R.; Neumann, Steffen
  Analytical Chemistry (Washington, DC, United States) (2012), 84 (1), 283-289CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
  Liq. chromatog. coupled to mass spectrometry is routinely used for metabolomics expts. In contrast to the fairly routine and automated data acquisition steps, subsequent compd. annotation and identification require extensive manual anal. and thus form a major bottleneck in data interpretation. Here the authors present CAMERA, a Bioconductor package integrating algorithms to ext. compd. spectra, annotate isotope and adduct peaks, and propose the accurate compd. mass even in highly complex data. To evaluate the algorithms, the authors compared the annotation of CAMERA against a manually defined annotation for a mixt. of known compds. spiked into a complex matrix at different concns. CAMERA successfully extd. accurate masses for 89.7% and 90.3% of the annotatable compds. in pos. and neg. ion modes, resp. Furthermore, the authors present a novel annotation approach that combines spectral information of data acquired in opposite ion modes to further improve the annotation rate. The authors demonstrate the utility of CAMERA in two different, easily adoptable plant metabolomics expts., where the application of CAMERA drastically reduced the amt. of manual anal.
40. 40
  van Creveld, S. G.; Rosenwasser, S.; Schatz, D.; Koren, I.; Vardi, A. ISME J. 2015, 9, 385– 395 DOI: 10.1038/ismej.2014.136
  There is no corresponding record for this reference.
41. 41
  Husen, P.; Tarasov, K.; Katafiasz, M.; Sokol, E.; Vogt, J.; Baumgart, J.; Nitsch, R.; Ekroos, K.; Ejsing, C. S. PLoS One 2013, 8, e79736 DOI: 10.1371/journal.pone.0079736
  There is no corresponding record for this reference.
42. 42
  Popendorf, K. J.; Fredricks, H. F.; Van Mooy, B. A. S. Lipids 2013, 48, 185– 195 DOI: 10.1007/s11745-012-3748-0
  There is no corresponding record for this reference.
43. 43
  Kessner, D.; Chambers, M.; Burke, R.; Agus, D.; Mallick, P. Bioinformatics 2008, 24, 2534– 2536 DOI: 10.1093/bioinformatics/btn323
  43
  ProteoWizard: open source software for rapid proteomics tools development
  Kessner, Darren; Chambers, Matt; Burke, Robert; Agus, David; Mallick, Parag
  Bioinformatics (2008), 24 (21), 2534-2536CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
  Summary: The ProteoWizard software project provides a modular and extensible set of open-source, cross-platform tools and libraries. The tools perform proteomics data analyses; the libraries enable rapid tool creation by providing a robust, pluggable development framework that simplifies and unifies data file access, and performs std. proteomics and LCMS dataset computations. The library contains readers and writers of the mzML data format, which has been written using modern C++ techniques and design principles and supports a variety of platforms with native compilers. The software has been specifically released under the Apache v2 license to ensure it can be used in both academic and com. projects. In addn. to the library, we also introduce a rapidly growing set of companion tools whose implementation helps to illustrate the simplicity of developing applications on top of the ProteoWizard library.
44. 44
  Kind, T.; Fiehn, O. BMC Bioinf. 2006, 7, 234 DOI: 10.1186/1471-2105-7-234
  44
  Metabolomic database annotations via query of elemental compositions: mass accuracy is insufficient even at less than 1 ppm
  Kind Tobias; Fiehn Oliver
  BMC bioinformatics (2006), 7 (), 234 ISSN:.
  BACKGROUND: Metabolomic studies are targeted at identifying and quantifying all metabolites in a given biological context. Among the tools used for metabolomic research, mass spectrometry is one of the most powerful tools. However, metabolomics by mass spectrometry always reveals a high number of unknown compounds which complicate in depth mechanistic or biochemical understanding. In principle, mass spectrometry can be utilized within strategies of de novo structure elucidation of small molecules, starting with the computation of the elemental composition of an unknown metabolite using accurate masses with errors <5 ppm (parts per million). However even with very high mass accuracy (<1 ppm) many chemically possible formulae are obtained in higher mass regions. In automatic routines an additional orthogonal filter therefore needs to be applied in order to reduce the number of potential elemental compositions. This report demonstrates the necessity of isotope abundance information by mathematical confirmation of the concept. RESULTS: High mass accuracy (<1 ppm) alone is not enough to exclude enough candidates with complex elemental compositions (C, H, N, S, O, P, and potentially F, Cl, Br and Si). Use of isotopic abundance patterns as a single further constraint removes >95% of false candidates. This orthogonal filter can condense several thousand candidates down to only a small number of molecular formulas. Example calculations for 10, 5, 3, 1 and 0.1 ppm mass accuracy are given. Corresponding software scripts can be downloaded from http://fiehnlab.ucdavis.edu. A comparison of eight chemical databases revealed that PubChem and the Dictionary of Natural Products can be recommended for automatic queries using molecular formulae. CONCLUSION: More than 1.6 million molecular formulae in the range 0-500 Da were generated in an exhaustive manner under strict observation of mathematical and chemical rules. Assuming that ion species are fully resolved (either by chromatography or by high resolution mass spectrometry), we conclude that a mass spectrometer capable of 3 ppm mass accuracy and 2% error for isotopic abundance patterns outperforms mass spectrometers with less than 1 ppm mass accuracy or even hypothetical mass spectrometers with 0.1 ppm mass accuracy that do not include isotope information in the calculation of molecular formulae.
45. 45
  Ejsing, C. S.; Duchoslav, E.; Sampaio, J.; Simons, K.; Bonner, R.; Thiele, C.; Ekroos, K.; Shevchenko, A. Anal. Chem. 2006, 78, 6202– 6214 DOI: 10.1021/ac060545x
  There is no corresponding record for this reference.
46. 46
  Layre, E.; Sweet, L.; Hong, S.; Madigan, C. A.; Desjardins, D.; Young, D. C.; Cheng, T. Y.; Annand, J. W.; Kim, K.; Shamputa, I. C.; McConnell, M. J.; Debono, C. A.; Behar, S. M.; Minnaard, A. J.; Murray, M.; Barry, C. E., 3rd; Matsunaga, I.; Moody, D. B. Chem. Biol. 2011, 18, 1537– 1549 DOI: 10.1016/j.chembiol.2011.10.013
  46
  A Comparative Lipidomics Platform for Chemotaxonomic Analysis of Mycobacterium tuberculosis
  Layre, Emilie; Sweet, Lindsay; Hong, Sunhee; Madigan, Cressida A.; Desjardins, Danielle; Young, David C.; Cheng, Tan-Yun; Annand, John W.; Kim, Keunpyo; Shamputa, Isdore C.; McConnell, Matthew J.; Debono, C. Anthony; Behar, Samuel M.; Minnaard, Adriaan J.; Murray, Megan; Barry, Clifton E., III; Matsunaga, Isamu; Moody, D. Branch
  Chemistry & Biology (Cambridge, MA, United States) (2011), 18 (12), 1537-1549CODEN: CBOLE2; ISSN:1074-5521. (Cell Press)
  The lipidic envelope of Mycobacterium tuberculosis promotes virulence in many ways, so we developed a lipidomics platform for a broad survey of cell walls. Here we report two new databases (MycoMass, MycoMap), 30 lipid fine maps, and mass spectrometry datasets that comprise a static lipidome. Further, by rapidly regenerating lipidomic datasets during biol. processes, comparative lipidomics provides statistically valid, organism-wide comparisons that broadly assess lipid changes during infection or among clin. strains of mycobacteria. Using stringent data filters, we tracked more than 5,000 mol. features in parallel with few or no false-pos. mol. discoveries. The low error rates allowed chemotaxonomic analyses of mycobacteria, which describe the extent of chem. change in each strain and identified particular strain-specific mols. for use as biomarkers.
47. 47
  Clasquin, M. F.; Melamud, E.; Rabinowitz, J. D. Curr. Protoc Bioinformatics 2012, 37, 14.11.1– 14.11.23 DOI: 10.1002/0471250953.bi1411s37
  There is no corresponding record for this reference.
48. 48
  Pearson, A. In Treatise on Geochemistry; Holland, H. D.; Turekian, K. K., Eds.; Elsevier: Oxford, 2014; pp 291– 336.
  There is no corresponding record for this reference.
49. 49
  Rosenwasser, S.; Graff van Creveld, S.; Schatz, D.; Malitsky, S.; Tzfadia, O.; Aharoni, A.; Levin, Y.; Gabashvili, A.; Feldmesser, E.; Vardi, A. Proc. Natl. Acad. Sci. U. S. A. 2014, 111, 2740– 2745 DOI: 10.1073/pnas.1319773111
  There is no corresponding record for this reference.
50. 50
  Dooley, C. T.; Dore, T. M.; Hanson, G. T.; Jackson, W. C.; Remington, S. J.; Tsien, R. Y. J. Biol. Chem. 2004, 279, 22284– 22293 DOI: 10.1074/jbc.M312847200
  There is no corresponding record for this reference.
51. 51
  Hanson, G. T.; Aggeler, R.; Oglesbee, D.; Cannon, M.; Capaldi, R. A.; Tsien, R. Y.; Remington, S. J. J. Biol. Chem. 2004, 279, 13044– 13053 DOI: 10.1074/jbc.M312846200
  There is no corresponding record for this reference.
52. 52
  Bligh, E. G.; Dyer, W. J. Can. J. Biochem. Physiol. 1959, 37, 911– 917 DOI: 10.1139/o59-099
  52
  A rapid method of total lipide extraction and purification
  Bligh, E. G.; Dyer, W. J.
  Canadian Journal of Biochemistry and Physiology (1959), 37 (), 911-17CODEN: CJBPAZ; ISSN:0576-5544.
  The wet tissue is homogenized with a mixt. of CHCl3 and MeOH to form a miscible system with the H2O in the tissue. Diln. with CHCl3 and H2O seps. the homogenate into 2 layers, the CHCl3 layer contg. all the lipides and the methanolic layer contg. all the non-lipides. A purified lipide ext. is obtained merely by isolating the CHCl3 layer. The method has been applied to fish muscle and may easily be adapted to use with other tissues.
53. 53
  Hummel, J.; Segu, S.; Li, Y.; Irgang, S.; Jueppner, J.; Giavalisco, P. Front. Plant Sci. 2011, 2, 54 DOI: 10.3389/fpls.2011.00054
  53
  Ultra performance liquid chromatography and high resolution mass spectrometry for the analysis of plant lipids
  Hummel Jan; Segu Shruthi; Li Yan; Irgang Susann; Jueppner Jessica; Giavalisco Patrick
  Frontiers in plant science (2011), 2 (), 54 ISSN:.
  Holistic analysis of lipids is becoming increasingly popular in the life sciences. Recently, several interesting, mass spectrometry-based studies have been conducted, especially in plant biology. However, while great advancements have been made we are still far from detecting all the lipids species in an organism. In this study we developed an ultra performance liquid chromatography-based method using a high resolution, accurate mass, mass spectrometer for the comprehensive profiling of more than 260 polar and non-polar Arabidopsis thaliana leaf lipids. The method is fully compatible to the commonly used lipid extraction protocols and provides a viable alternative to the commonly used direct infusion-based shotgun lipidomics approaches. The whole process is described in detail and compared to alternative lipidomic approaches. Next to the developed method we also introduce an in-house developed database search software (GoBioSpace), which allows one to perform targeted or un-targeted lipidomic and metabolomic analysis on mass spectrometric data of every kind.
54. 54
  Libiseller, G.; Dvorzak, M.; Kleb, U.; Gander, E.; Eisenberg, T.; Madeo, F.; Neumann, S.; Trausinger, G.; Sinner, F.; Pieber, T.; Magnes, C. BMC Bioinf. 2015, 16, 118 DOI: 10.1186/s12859-015-0562-8
  54
  IPO: a tool for automated optimization of XCMS parameters
  Libiseller Gunnar; Gander Edgar; Trausinger Gert; Sinner Frank; Pieber Thomas; Magnes Christoph; Dvorzak Michaela; Kleb Ulrike; Eisenberg Tobias; Madeo Frank; Madeo Frank; Neumann Steffen; Sinner Frank; Pieber Thomas
  BMC bioinformatics (2015), 16 (), 118 ISSN:.
  BACKGROUND: Untargeted metabolomics generates a huge amount of data. Software packages for automated data processing are crucial to successfully process these data. A variety of such software packages exist, but the outcome of data processing strongly depends on algorithm parameter settings. If they are not carefully chosen, suboptimal parameter settings can easily lead to biased results. Therefore, parameter settings also require optimization. Several parameter optimization approaches have already been proposed, but a software package for parameter optimization which is free of intricate experimental labeling steps, fast and widely applicable is still missing. RESULTS: We implemented the software package IPO ('Isotopologue Parameter Optimization') which is fast and free of labeling steps, and applicable to data from different kinds of samples and data from different methods of liquid chromatography - high resolution mass spectrometry and data from different instruments. IPO optimizes XCMS peak picking parameters by using natural, stable (13)C isotopic peaks to calculate a peak picking score. Retention time correction is optimized by minimizing relative retention time differences within peak groups. Grouping parameters are optimized by maximizing the number of peak groups that show one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiments, and the resulting scores are evaluated using response surface models. IPO was tested on three different data sets, each consisting of a training set and test set. IPO resulted in an increase of reliable groups (146% - 361%), a decrease of non-reliable groups (3% - 8%) and a decrease of the retention time deviation to one third. CONCLUSIONS: IPO was successfully applied to data derived from liquid chromatography coupled to high resolution mass spectrometry from three studies with different sample types and different chromatographic methods and devices. We were also able to show the potential of IPO to increase the reliability of metabolomics data. The source code is implemented in R, tested on Linux and Windows and it is freely available for download at https://github.com/glibiseller/IPO . The training sets and test sets can be downloaded from https://health.joanneum.at/IPO .
55. 55
  Van Mooy, B. A. S.; Fredricks, H. F. Geochim. Cosmochim. Acta 2010, 74, 6499– 6516 DOI: 10.1016/j.gca.2010.08.026
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56. 56
  Cajka, T.; Fiehn, O. Anal. Chem. 2016, 88, 524– 545 DOI: 10.1021/acs.analchem.5b04491
  56
  Toward Merging Untargeted and Targeted Methods in Mass Spectrometry-Based Metabolomics and Lipidomics
  Cajka, Tomas; Fiehn, Oliver
  Analytical Chemistry (Washington, DC, United States) (2016), 88 (1), 524-545CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
  A review. Advances in mass spectrometry (MS) and data processing led to discoveries in regulation of cellular metab. by using metabolomics and lipidomics approaches. Mass spectrometry is by far the dominating anal. platform in metabolomics and lipidomics, surpassing the use of NMR at a 5:2 ratio according to the authors' citation anal.
57. 57
  Melamud, E.; Vastag, L.; Rabinowitz, J. D. Anal. Chem. 2010, 82, 9818– 9826 DOI: 10.1021/ac1021166
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58. 58
  Abida, H.; Dolch, L. J.; Mei, C.; Villanova, V.; Conte, M.; Block, M. A.; Finazzi, G.; Bastien, O.; Tirichine, L.; Bowler, C.; Rebeille, F.; Petroutsos, D.; Jouhet, J.; Marechal, E. Plant Physiol. 2015, 167, 118– 136 DOI: 10.1104/pp.114.252395
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  Levitan, O.; Dinamarca, J.; Zelzion, E.; Lun, D. S.; Guerra, L. T.; Kim, M. K.; Kim, J.; Van Mooy, B. A. S.; Bhattacharya, D.; Falkowski, P. G. Proc. Natl. Acad. Sci. U. S. A. 2015, 112, 412– 417 DOI: 10.1073/pnas.1419818112
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60. 60
  Clarke, K. R.; Somerfield, P. J.; Gorley, R. N. J. Exp. Mar. Biol. Ecol. 2008, 366, 56– 69 DOI: 10.1016/j.jembe.2008.07.009
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  d’Ippolito, G.; Tucci, S.; Cutignano, A.; Romano, G.; Cimino, G.; Miralto, A.; Fontana, A. Biochim. Biophys. Acta, Mol. Cell Biol. Lipids 2004, 1686, 100– 107 DOI: 10.1016/j.bbalip.2004.09.002
  61
  The role of complex lipids in the synthesis of bioactive aldehydes of the marine diatom Skeletonema costatum
  d'Ippolito, Giuliana; Tucci, Sara; Cutignano, Adele; Romano, Giovanna; Cimino, Guido; Miralto, Antonio; Fontana, Angelo
  Biochimica et Biophysica Acta, Molecular and Cell Biology of Lipids (2004), 1686 (1-2), 100-107CODEN: BBMLFG; ISSN:1388-1981. (Elsevier B.V.)
  Diatoms are unicellular plants broadly present in freshwater and marine ecosystems, where they play a primary role in sustaining the marine food chain. In the last 10 years, there has been accumulating evidence that diatoms may have deleterious effects on the hatching success of zooplankton crustaceans, such as copepods, thus affecting dynamics of planktonic populations and limiting secondary prodn. At the mol. level, failure to hatch is ascribed to the presence of a family of inhibitory oxylipins, which we propose to collectively name polyunsatd. short-chain aldehydes (abbreviated here as PUSCAs). Here, the authors describe the origin of PUSCAs produced by the marine diatom Skeletonema costatum via a lipoxygenase-mediated pathways involving non-esterified polyunsatd. fatty acids (PUFA). Expts. with complex lipids proved the pivotal role of chloroplast-derived glycolipids, esp. monogalactosyldiacylglycerol (MGDG), in providing hexadecatrienoic acid (C16:3 ω-4), hexadecatetraenoic acid (C16:4 ω-1) and eicosapentaenoic acid (C20:5 ω-3) to the downstream process leading to 2E,4Z-octadienal (C8:2 ω-4), 2E,4Z,7-octatrienal (C8:3 ω-1) and 2E,4Z-heptadienal (C7:2 ω-3), resp. Under physiol. conditions, the hydrolytic process is assocd. to galactolipid hydrolyzing enzyme capable of removing fatty acids from both sn positions of glycerol.
62. 62
  Mene-Saffrane, L.; Dubugnon, L.; Chetelat, A.; Stolz, S.; Gouhier-Darimont, C.; Farmer, E. E. J. Biol. Chem. 2009, 284, 1702– 1708 DOI: 10.1074/jbc.M807114200
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  Goncalves, E. C.; Wilkie, A. C.; Kirst, M.; Rathinasabapathi, B. Plant Biotechnol. J. 2015, DOI: 10.1111/pbi.12523
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  Merchant, S. S.; Kropat, J.; Liu, B.; Shaw, J.; Warakanont, J. Curr. Opin. Biotechnol. 2012, 23, 352– 363 DOI: 10.1016/j.copbio.2011.12.001
  64
  TAG, You're it! Chlamydomonas as a reference organism for understanding algal triacylglycerol accumulation
  Merchant, Sabeeha S.; Kropat, Janette; Liu, Bensheng; Shaw, Johnathan; Warakanont, Jaruswan
  Current Opinion in Biotechnology (2012), 23 (3), 352-363CODEN: CUOBE3; ISSN:0958-1669. (Elsevier B.V.)
  A review. Photosynthetic organisms are responsible for converting sunlight into org. matter, and they are therefore seen as a resource for the renewable fuel industry. Ethanol and esterified fatty acids (biodiesel) are the most common fuel products derived from these photosynthetic organisms. The potential of algae as producers of biodiesel precursor (or triacylglycerols (TAGs)) has yet to be realized because of the limited knowledge of the underlying biochem., cell biol. and genetics. Well-characterized pathways from fungi and land plants have been used to identify algal homologs of key enzymes in TAG synthesis, including diacylglcyerol acyltransferases, phospholipid diacylglycerol acyltransferase and phosphatidate phosphatases. Many labs. have adopted Chlamydomonas reinhardtii as a ref. organism for discovery of algal-specific adaptations of TAG metab. Stressed Chlamydomonas cells, grown either photoautotrophically or photoheterotrophically, accumulate TAG in plastid and cytoplasmic lipid bodies, reaching 46-65% of dry wt. in starch accumulation (sta) mutants. State of the art genomic technologies including expression profiling and proteomics have identified new proteins, including key components of lipid droplets, candidate regulators and lipid/TAG degrading activities. By analogy with crop plants, it is expected that advances in algal breeding and genome engineering may facilitate realizing the potential in algae.
65. 65
  Hunter, J. E.; Frada, M. J.; Fredricks, H. F.; Vardi, A.; Van Mooy, B. A. S. Front. Mar. Sci. 2015, 2, 81 DOI: 10.3389/fmars.2015.00081
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  66
  Proposed minimum reporting standards for chemical analysis. Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI)
  Sumner, Lloyd W.; Amberg, Alexander; Barrett, Dave; Beale, Michael H.; Beger, Richard; Daykin, Clare A.; Fan, Teresa W.-M.; Fiehn, Oliver; Goodacre, Royston; Griffin, Julian L.; Hankemeier, Thomas; Hardy, Nigel; Harnly, James; Higashi, Richard; Kopka, Joachim; Lane, Andrew N.; Lindon, John C.; Marriott, Philip; Nicholls, Andrew W.; Reily, Michael D.; Thaden, John J.; Viant, Mark R.
  Metabolomics (2007), 3 (3), 211-221CODEN: METAHQ; ISSN:1573-3882. (Springer)
  There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of std. metadata provides a biol. and empirical context for the data, facilitates exptl. replication, and enables the reinterrogation and comparison of data by others. Accordingly, the Metabolomics Stds. Initiative is building a general consensus concerning the min. reporting stds. for metabolomics expts. of which the Chem. Anal. Working Group (CAWG) is a member of this community effort. This article proposes the min. reporting stds. related to the chem. anal. aspects of metabolomics expts. including: sample prepn., exptl. anal., quality control, metabolite identification, and data pre-processing. These min. stds. currently focus mostly upon mass spectrometry and NMR spectroscopy due to the popularity of these techniques in metabolomics. However, addnl. input concerning other techniques is welcomed and can be provided via the CAWG online discussion forum at http://msi-workgroups.sourceforge.net/ or http://[email protected]. Further, community input related to this document can also be provided via this electronic forum.
67. 67
  Senger, T.; Wichard, T.; Kunze, S.; Gobel, C.; Lerchl, J.; Pohnert, G.; Feussner, I. J. Biol. Chem. 2005, 280, 7588– 7596 DOI: 10.1074/jbc.M411738200
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Supporting Information
Supporting Information
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.6b01260.
- Methodological details, instructions for obtaining all software and data, supplementary discussion, supplementary data figures, supplementary data tables, and a supplementary chart showing examples of the types of isomers that can be identified with LOBSTAHS (PDF)
- ac6b01260_si_001.pdf (4.64 MB)
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Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

LOBSTAHS: An Adduct-Based Lipidomics Strategy for Discovery and Identification of Oxidative Stress BiomarkersClick to copy article linkArticle link copied!

Analytical Chemistry

Abstract

Theory and Design of Software and Databases

Design and Scope of Lipid-Oxylipin Databases

Database Generation

Determination of Relative Abundances of Adduct Ions for Inclusion in Databases

Lipidomics Workflow Based on xcms, CAMERA, and LOBSTAHS

Scheme 1

Database Assignments and Progressive Screening in LOBSTAHS Using Orthogonal Criteria

Experimental Section

Model Data Set Used To Demonstrate the Workflow

Sample Collection and Extraction

HPLC-ESI-MS Analysis

Analysis of P. tricornutum Data Using LOBSTAHS

Results and Discussion

Screening and Annotation of P. tricornutum Data in LOBSTAHS

Figure 1

Identification and Annotation of Isomers and Isobars

Annotation of Potential Regioisomers

Evaluation of Screening and Identification Performance Using Two Methods

Resilience of Core P. tricornutum Lipidome under Oxidative Stress

Figure 2

Differences in Degree of Remodeling between Lipid Classes and Functional Groupings

Fatty Acid Chain Elongation Is an Apparent Response to Oxidative Stress in the Chloroplast

Significant Enrichment Observed in TAG

Conclusions

Supporting Information

Terms & Conditions

Author Information

Acknowledgment

References

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Abstract

Scheme 1

Figure 1

Figure 2

References

Supporting Information

Supporting Information

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LOBSTAHS: An Adduct-Based Lipidomics Strategy for Discovery and Identification of Oxidative Stress Biomarkers
Click to copy article linkArticle link copied!