Assessment of lipidomic species in hepatocyte lipid droplets from stressed mouse models

Lipid droplets are considered to be the hub for storage and metabolism of cellular lipids. In previous work we have phenotyped the lipidome of murine hepatocyte lipid droplets using liquid chromatography-mass spectrometry (UHPLC-MS) plus integrated MS/MS, followed by automatic analysis of the MS data. The organelles were isolated after intervention studies involving nutritional stress (extended feeding of a high fat diet or short term fasting), genetic stress due to knock-out of adipocyte triglyceride lipase, or by combined application of nutritional and genetic stress together (‘super stress’). Lipidomics at the level of lipid species (profiling of lipid classes) and lipid molecular species (structural analysis in parallel) has unraveled clear lipid droplet phenotypes as judged by patterns seen best in triacylglycerol (TG) lipidomes, but also in diacylglycerol and phosphatidylcholine lipidomes. The combined view of these data presented here validates the methods used and provides high quality lipidomic data for further bioinformatic inspections. Examples are given for identification of TG species subsets considered surrogates for whole TG lipidomes.

Lipid droplets are considered to be the hub for storage and metabolism of cellular lipids. In previous work we have phenotyped the lipidome of murine hepatocyte lipid droplets using liquid chromatography-mass spectrometry (UHPLC-MS) plus integrated MS/MS, followed by automatic analysis of the MS data. The organelles were isolated after intervention studies involving nutritional stress (extended feeding of a high fat diet or short term fasting), genetic stress due to knock-out of adipocyte triglyceride lipase, or by combined application of nutritional and genetic stress together ('super stress'). Lipidomics at the level of lipid species (profiling of lipid classes) and lipid molecular species (structural analysis in parallel) has unraveled clear lipid droplet phenotypes as judged by patterns seen best in triacylglycerol (TG) lipidomes, but also in diacylglycerol and phosphatidylcholine lipidomes. The combined view of these data presented here validates the methods used and provides high quality lipidomic data for further bioinformatic inspections. Examples are given for identification of TG species subsets considered surrogates for whole TG lipidomes.

Background & Summary
During the last ten years previously neglected lipid droplets (LDs) have come to the attention of researchers interested in cellular lipid metabolism 1 . This organelle has a neutral lipid core of mainly triacylglycerols (TG) and cholesterol esters, surrounded by a monolayer of mainly phospholipids (PL) and proteins [2][3][4] . LDs are not simply storage vessels for hydrophobic compounds, but are active functional units for metabolizing lipid molecules; they are involved in energy-linked metabolism, signaling, gene regulation, and autophagy [5][6][7][8] . Therefore, we have considered LDs as potential sensors for the physiological/metabolic state of the organism, a hypothesis that can be tested by a lipidomics approach 9,10 . Because liver is the central hub for lipid metabolism in health and disease [11][12][13] , we have analyzed hepatocyte LDs from mouse models that had been exposed to nutritional and/or genetic stress. Our method of choice has been ultra-high performance liquid chromatography coupled to high resolution mass spectrometry (UHPLC-MS), capable of performing full scan MS parallel to low resolution collisional activated dissociation MS/MS. The pre-separation step had enabled us to overcome the challenge of bulk TG class present in LDs that otherwise would have blunted the MS-determination of a number of lipid species from PL classes. Simultaneously, we had developed algorithms for automatic high-throughput data evaluation, called Lipid Data Analyzer 14 . In a first method paper, we presented proof of principle for all methods elaborated 15 . Next, we designed intervention studies for nutritional stress with a wild-type strain fed lab chow (control), or exposed to either long-term high fat diet or to short-term fasting ('HFD-study') 16 . A further study addressed genetic stress by using an adipose triglyceride lipase (ATGL)-deficient strain fed either lab chow or being subjected to short-term fasting; in this case controls were again wild-type animals fed or fasted as before ('ATGL-study') 17 . At the end of the intervention periods livers were removed, hepatocyte LDs isolated, and total lipids extracted and subjected to lipidomic analyses by UHPLC-MS for profiling of lipid species. Such species are defined by their numbers of carbon atoms (chain-lengths): numbers of double bonds, not counting carbons of the glycerol backbone or head-groups, e.g., TG 52:3 or PC 36:4. Further structural elucidation was attained by MS/MS methodology, furnishing lipid molecular species. Pertinent structures are written as, e.g., TG 16:0_18:1_18:2 or PI 18:0_20:4, respectively 18 . Data obtained are summarized as follows: About 98 mol% of total LD lipids belong to the TG class; remaining classes are, in quantitative order, DG, PC and other minor PL classes. Lipidomic analysis of lipid species resulted in distinct profiles, particularly for the TG class, but also for DG and PC classes. The TG lipidomes (over 100 species) ranged from TG 28:0 to 62:15, their presentation in heat maps indicated distinct patterns for each of the 3 groups in the HFD-study (nutritional stress) and 4 groups in the ATGL-study (genetic/nutritional stress). Subsequent principal component analysis (PCA) clearly separated in either case the 3 and 4 groups according to nutritional, genetic, or both stresses ('super stress'). A third approach, calculation of average numbers of carbons and double bonds in acyl residues of total TG species, resulted in significantly distinct values for each physiological/metabolic state of the animals. Still clear enough were evaluations of the 26 DG species and 24 PC species detected in respective lipidomes. In summary, MS-profiling of lipid species and their heat map presentation identified marker species for HFD treatment and fasting of animals on the one hand (Figure 1), whereas these two modes of presentation, PCA separation and calculation of average numbers allowed for clear pattern recognition on the other hand. Further structural elucidation at the lipid molecular species level, resulting in more than one molecular species present in a defined lipid species, distinguished between different stresses applied and delineated metabolic relationships between lipid molecular species of different classes 16,17 .
Based on these previous observations, we have now revisited our data for streamlining and validation of the two studies, and to assist other researchers in using these data. We offer approaches for screening TG-lipidomes for some discriminatory lipid species (written in bold in abscissas in Figure 2) that phenotype the physiological/metabolic state of the animal model. PCA and orthogonal partial least square discriminant analysis (O-PLS-DA) approaches (Figures 3 and 4), and average calculations (Table 1), enable identification of lipid species subsets as surrogates for whole lipidomes.

Methods
Descriptions are taken from our previous work [14][15][16][17]    Animals, diets and intervention studies. All animal experiments were performed in compliance with the Austrian animal protection law. The mice were housed and handled in accordance with good animal practice as defined by FELASA (www.felasa.eu/guidelines.php). The animal welfare committees of the University of Graz and the national authorities approved all animal experiments. Male wild-type (WT) and male ATGL-nullizygous C57BL/6 mice (KO) 19 were used, aged six weeks at the start of the studies.

Rel. to total TG [‰]
WT Mice were maintained on a regular light (14 h)-dark (10 h) cycle and fed lab chow for six weeks (FED groups), or maintained on high fat diet (HFD groups), or were fasted 14 h prior to sacrifice (FAS groups). All diets were from sniff® Spezialdiäten (Soest, Germany) and are shown in Tab. f1 (Data Citation 1). Three animals per group were kept in one cage with ad libitum access to food and water. During the intervention period mice weights were monitored regularly. At the end of the six weeks trial periods, animals were euthanized at 8 AM under anesthesia. The HFD-study was run consecutively twice with always 3 mice per group (WT-HFD, WT-FED, WT-FAS) 16 . Due to excellent agreement of data they are combined and are reported as n = 6. One year later, therefore, we carried out the ATGL-study with 3 mice Isolation of hepatocytes. Mice were anesthetized subcutaneously with Narkodorm (60 μl/100 g weight) and the abdomen was surgically opened. Primary hepatocytes from livers were isolated according to Riccalton-Banks et al. with modifications 20 . Prior to liver perfusion, buffers and collagenase type II solution (20 mg collagenase type II in 100 ml Krebs-Henseleit buffer without SO 4 2 − but containing 0.1 mM CaCl 2 , 2% bovine serum albumin) were brought to 37°C. Each liver was perfused via hepatic portal vein with Krebs-Henseleit buffer without Ca 2+ and SO 4 2 − (115 mM NaCl, 25 mM NaHCO 3 , 5.9 mM KCl, 1.18 mM MgCl 2 , 1.23 mM NaH 2 PO 4 , 6 mM glucose) for 10 min, followed by perfusion with collagenase type II solution. Thereafter, each liver was carefully removed, transferred into a Petri dish, filled with 5 ml collagenase type II solution, cut into small pieces, pressed through a household sieve which was finally flushed with ice-cold Krebs-Henseleit buffer containing 1.2 mM Na 2 SO 4 and 1.25 mM CaCl 2 . The cell suspension obtained was filtered through a cell strainer (70 μm nylon filter) into a 50 ml Greiner-tube. Approximately 20 ml ice-cold Dulbecco's Modified Eagle's Medium (DMEM) was added to the filtered cell suspension, which was subsequently centrifuged at 50 g in a Beckman CS-6R rotor for 3 min at 4°C. Supernatant, containing non-parenchymal cells, was aspirated and the remaining

Sample preparation for LC-MS analysis
Lipid droplets isolated from hepatocytes were measured in individual samples per group. Lipid extraction was carried out by a methyl tert-butyl ether (MTBE) protocol as described previously 22 . In brief, each sample of the HFD-study was spiked with 40 nmol TG 17:0/17:0/17:0 and 400 pmol PC 12:0/12:0 as internal standards, the samples of the ATGL-study were spiked with 40 nmol TG 17:0/17:0/17:0, 1.2 nmol PC 12:0/12:0, 2.4 nmol PE 12:0/12:0, and 4 nmol PS 12:0/12:0 as internal standards. This set of internal standards was used to ascertain constant extraction efficiency for all samples in a batch. Briefly, for each internal standard the mean of all samples in a batch was calculated and any sample with an internal standard deviation of more than 30% was rejected. Subsequently, a volume of 3 ml methanol and then 10 ml MTBE were added to 2 ml of LD suspension and tubes were shaken for 1 h at room temperature. Upon addition of 2.5 ml deionized water and shaking, phase separation was induced. The upper organic phase was collected, the lower aqueous phase re-extracted with MTBE and upper phases were combined. The solvent was removed in vacuo (SpeedVac, Thermo Fisher Scientific, San Jose, CA, USA) and lipid extracts were re-suspended in 200 μl CHCl 3 /MeOH 1:1. Thereafter a mix of 45 LIPID MAPS internal standards (Tab. f2, Data Citation 1) was added for internal calibration and subsequent calculations.

UHPLC-MS analysis
The UHPLC system (Accela, Thermo Fisher Scientific, Bremen, Germany) was equipped with a reversedphase C18 column (100 × 1 mm i.d., 1.9 μm particle size). Mobile phase A was 10 mM ammonium acetate containing 0.1% formic acid. Mobile phase B was acetonitrile/2-propanol 5:2 (v/v) containing 10 mM ammonium acetate and 0.1% formic acid. The binary gradient started with 35 to 70% B in A for 4 min, then was raised up to 100% B in another 16 min and further held for 10 min. The flow rate was 250 μl/min, the oven temperature was 50°C and tray temperature 4°C. For analysis 5 μl sample were injected. A 7.0 Tesla LTQ-FT hybrid linear ion trap Fourier transform ion cyclotron resonance mass spectrometer (Thermo Fisher Scientific, Bremen, Germany), equipped with an electrospray ion source, was used for mass spectrometric determination. The instrument was operated in data dependent acquisition (DDA) mode on the 4 most abundant precursor ions for parallel MS/MS spectra in the linear ion trap, while running the ion cyclotron in full scan mode at 200,000 resolution (m/z 400) from m/z 350 to 1,050 in positive and from m/z 200 to 1,500 in negative ESI-mode. Helium was used as damping gas for linear ion trap collision-induced dissociation (CID) spectra. The following parameters were used for positive and negative DDA MS/MS experiments: Normalized collision energy was 35%, the repeat count was 2 and the exclusion duration 60 s. The activation Q was at 0.2 and the isolation width 2. For positive ESI spray voltage was set to 5 kV and the tube lens offset was at 120 V. For negative ESI spray voltage was -4.8 kV and the tube lens offset was -87 V. The sheath gas flow was set to 50 arbitrary

Data processing
Identification and quantitation of lipid species was performed using LDA, a stand-alone Java application developed by us 14 (available from http://genome.tugraz.at/lda). Briefly, the algorithm identifies peaks in the three dimensional LC-MS data space (retention time, m/z and intensity), determines peak borders in m/z as well as in time dimension, and integrates the intensities within the borders. Furthermore, the algorithm uses the theoretical isotopic intensity distribution as peak selection criterion to improve specificity. Data were normalized to the total amount of a lipid class or whole lipidome. For class specific normalization, no standardization is required, since similar ionization efficiencies within a lipid class, e.g., TG, can be assumed. Thus, the shown values in ‰ (or %) are the signal contributions of each species in relation to the total class signal detected by MS. In contrast, for lipidome normalization, data were first normalized on multiple internal standards as described (Chapter 3.3 in ref. 14, robust standardization), in order to infer the molar amounts of lipid species. Second, data were normalized to the sum of these molar contributions to obtain comparable relative contributions of each lipid species. Quantitative data presented are expressed in ‰ (or %) relative to total amount of a lipid class, or total amount of lipidome, respectively. We favored data presentation relative to total amounts to compensate for potential differences in extraction. Shorthand notation for MS-lipid data used here was developed by us and is based on LIPID MAPS nomenclature and abbreviations 18 . LDA identifies lipids based on the number of carbon atoms and double bonds. Thus, constituent fatty acids (FA) esterified in TG species were determined in positive MS/MS spectra by respective FA neutral losses. The most abundant fragment 23 in each MS/MS spectrum is normalized to 100% intensity (base peak). Fatty acyl fragments or FA neutral loss fragments with less than 25% intensity of base peak are termed minor, all other major FA fragments. For better manageability, particularly of isobaric species, possible combinations predominantly composed of major fragments are addressed major molecular species, those predominantly composed of minor fragments minor molecular species.

Statistical analysis of MS data
Quantitative data for each lipid species are expressed in ‰ (or %) relative to total amount of the respective lipid class and pertain to HFD-study (means ± s.d. of 6 animals, Tab. f3 in Data Citation 1, Figure 2a) and ATGL-study (means of 3 animals, Tab. f4 in Data Citation 1, Figure 2b). Profiles for other lipid classes from both studies can be found in Fig. f5 (Data Citation 1). The choice of relative values enabled measuring compositional changes in respective lipidomes in a quantitative manner. Independent two-sample t-test for equal sample sizes and equal variance was used to assess statistical significance of the observed changes at levels of P o0.05, 0.01, and 0.001, respectively.

Statistical analysis by PCA
Lipid species data from the ATGL-study in ‰ relative to total amount of a lipid class or whole lipidome (Tab. f4), respectively, are further analyzed by PCA. This is performed in R 24 (version 3.1.0) using the function prcomp with default parameters. The loadings of the TG species calculated in PCA, stored in Tab. f6 (contains data for DG and PC classes as well, Data Citation 1), led to clear separation of TG species between sample groups of mice under the various stresses applied to the animals (Figure 2a in ref. 17 and in Fig. f7 of Data Citation 1). Based on these results, we can now detect lipid species whose abundance levels are significant for a stressed state. Since PCA analysis is in fact a coordinate transformation, where each species measurement contributes additively, the contribution of each species is measurable. Consequently, a transformed principal component (PC) coordinate ( = factor score) can be illustrated in scatter plots (e.g., Figure 4) and is calculated as follows: m i corresponds to the measured abundance of lipid species i, x is the index of the principal component (e.g., PC1, PC2, etc.), l x,i represents the loading for lipid species i in principal component x and n denotes the number of lipid species. In order to detect species contributing the most to the discriminatory effect between sample groups, we calculated the mean of each lipid species for each sample group, subtracted the means from one another, and multiplied it by the loading, e.g.: The mean of sample group with the lower PC1 value was subtracted from the one with the higher one. Consequently, for PC1, positive values strengthen the evidence of difference between sample groups, negative values argue against. The same was done for PC2, but sample groups with a more positive value in PC1 may be more negative in PC2, in such case the negative values may be the ones that strengthen the evidence of a difference. In order to avoid confusion, we place species supporting separation at the top and contradicting and minor contributions at the bottom of 'Contribution tables' stored in Tab. f8 and Tab. f9, both in Data Citation 1. This shows clearly that some species contribute to the separation of the individual groups of stressed animals to a much higher extent. Consequently, we presume that a low number of lipid species is sufficient to phenotype the physiological/metabolic state of the animal. The major contributors of TG class were TG 52:2, 52:3, 52:4, 52:5, 54:4, 54:5, 54:6, 56:6, 56:7, and 56:8 (10 species).

Statistical analysis by O-PLS-DA
This multivariate data analysis 25 method aims to transform the data into a new coordinate system, where the variance between groups is maximized in the first transformed dimension. Lipid species data from the ATGL-study in ‰ relative to total amount of a lipid class or whole lipidome (Tab. f4), respectively, were analyzed in R 24 (version 3.1.0) using the DEVIUM platform for multivariate analysis (http://github.com/ dgrapov/devium). Lipid species contributing the most to the discrimination between sample groups were identified based on their correlation to the scores and their weighted loadings. This relationship can be visualized in form of an S-plot (Figure 3a). The following TG species were putative candidates for discriminating WT-FED from WT-FAS sample groups: TG 42:1, 42:2, 44:4, 44:5, 46:5, and 46:6 showing a positive correlation, and TG 48:3, 52:6, 54:6, 56:11 and 58:10 with a negative correlation, respectively (Figure 3b,c). We analyzed both subsets and their union by PCA (Fig. f10, Data Citation 1). All three subsets separated the WT-FED and the WT-FAS data well, but the absolute distance between the groups in PC1 was much higher by the negative subset, and the loadings of the union subset were dominated by elements of the negative subset. Consequently, we used as subsets of the O-PLS-DA results only the species showing negative correlation (5 species) and the union of positive and negative correlation (11 species) for subsequent analysis.

Verification of discriminatory species identified by PCA and O-PLS-DA
To verify the capability of separating phenotypes by subsets of lipid species, selected by statistical methods, we tested the classification of sample groups from the independent HFD-dataset 16 based on PCA loadings derived from our TG subsets of the ATGL-dataset (training data) 17 . Classification was performed using the R function predict with default parameters. Since the HFD-study is based on the wild-type genotype only, the classification is based on sample groups WT-FED and WT-FAS of the ATGL-study.
We tested the predictive ability for phenotyping the animals' physiological/metabolic states on the whole TG lipidome (100 species, Figure 4a), a set of species identified by PCA (10 species, Figure 4b), and two subsets identified by O-PLS-DA (11 and 5 species, Figure 4c,d, respectively). All approaches show a clear separation of the independent validation data, and all samples are projected in close distance to the correct group of the ATGL training set. Surprisingly, the smallest subset, the negative O-PLS-DA where 100% of the discriminative power is in PC1, grouped the independent validation data closest to the training data ( Figure 4) and performs as such much better than a classification based on the whole TG lipidome.

Statistical analysis by averaging numbers of carbons and double bonds of main lipid species
A rather simple approach to gauge phenotypes of lipid species profiles is averaging chain-lengths of constituent fatty acids ( = the numbers of carbon atoms) and the numbers of double bonds contained therein. On the one hand, due to the relationship between chain-elongation and desaturation during fatty acid biosynthesis or degradation the number of double bonds always increases or decreases with the number of carbon atoms. On the other hand, the lipid profile is based on abundances of each lipid species within the lipidome, consequently the most abundant species will contribute more to average values than less abundant ones. We have shown these relationships earlier (Figure 3 in ref. 17) for the whole lipidomes of TG, DG, and PC classes, respectively. Here we average TG species only. Since four physiological/metabolic states are investigated in the ATGL-study, the mean of the areas of all four sample groups are taken and the reference is the peak for the lipid species of highest abundance per class (base peak = 100%). When the selection level was set to 25% and above relative to base peak, six TG species were singled out and used for averaging. The values obtained and presented in Tab. 1 are lower than those based on whole TG lipidome, due to overall less abundance. Importantly, significant differences characteristic for the phenotypes are as clear as seen for the whole TG lipidome. When the selection value was set to e.g., 10%, 11 TG species were identified and subsequent averaging furnished similar results.  File 4: Tab. f4-Lipidomes from ATGL-study: Quantitative lipid species data were obtained by LC-MS analysis. The values correspond to the relative contributions of each lipid species to the whole lipidome, TG-, DG-, and PC lipidomes, respectively. Mean values shown here were used for PCA analysis (e.g., Fig. f7).

Data Records
File 5: Fig. f5-Selected PL and DG species from HFD-and ATGL-studies: Profiles of main species determined for DG, PC, PE, PS, and PI classes by UHPLC-MS in ‰ relative to total amount of respective class.
File 6: Tab. f6-PCA loadings of the four lipidomes from ATGL-study: The columns indicate the principal component affected and whether the contribution is positive or negative (e.g., PC1-contributes to a more negative outcome). The rows correspond to individual lipid species that are sorted by the absolute value of their loadings.
File 7: Fig. f7-PCA scatter plot for whole lipid droplet lipidome from ATGL-study: The plot demonstrates clear separation of the 4 sample groups.
File 8: Tab. f8-PCA contributions of whole lipid droplet lipidome from ATGL-study: The columns indicate the affected principal component. The rows contain the lipid species sorted by their discriminative contribution to the separation to the sample groups. Species supporting separation are at the top; contradicting and minor contributions are at the bottom of the tables. In this analysis, we investigated each species for its discriminatory effect between the sample groups. The same applies to file 9.

Data record 2
Lipidomic raw data from MS. These data are deposited at the MetaboLights database of the European Bioinformatics Institute (EBI), (http://www.ebi.ac.uk/metabolights), the identifier for the HFD-study is MTBLS26 (Data Citation 2), for the ATGL-study MTBLS81 (Data Citation 3).

Technical Validation
Validity of the LC-MS/MS method Technical validation of the LC-MS/MS method was attained by non-endogenous lipid standards spiked at various concentrations into an LD sample. This was carried out before we started sample measurement and was described in more detail earlier 15 . The reference standards used in this procedure are TG 17:0/17:0/17:0, PC 12:0/12:0, PE 12:0/12:0 and PS 12:0/12:0. In a nutshell, reference standards are quantified by our LDA algorithm as described in the data processing section by a one point calibration. The reference point here is the median of all internal standards of a lipid class distributed over the whole retention time range of respective lipid class. The number of internal standards varies from 10 to 4 per lipid class (Tab. f2). LC-MS/MS method validation shows accuracy values from 85.8 to 116.1% at medium concentration range of the calibration curve. Method precision for five technical replicates including sample preparation is between 4 and 9.6%. This is well in accordance with Food and Drug Administration (FDA) guidelines for targeted LC-MS methods which propose a maximum of 15% CV. Although PC 12:0/12:0 and PS 12:0/12:0 are slightly above the proposed 15% deviation for concentration accuracy, one has to consider that our method is not a targeted single reaction monitoring (SRM) assay usually used for quantitation. Rather it is a non-targeted omics approach where higher deviations are to be expected due to the nature of the LC-MS set-up. The dynamic range of the method covers more than 4 orders of magnitude and is between 40 amol and 656 fmol on column. Linear fits for calibration curves in this concentration range yield correlation coefficients between 0.952 and 0.981, which again is acceptable for a non-targeted omics method. Furthermore, comparability of internal standards across the whole retention time range by equimolar mixtures of 8 internal TG standards at 5 different concentration points is shown in Fig. f11 (Data Citation 1). Quantitative precision of these 8 compounds is 9%, which is well within the expected and tolerable range and allows reliable quantitation. Additionally, the relationship of the different concentration points of internal standards is highly linear, thus allowing comparison of measurements with different internal standard concentrations.

Overall validity of the assay
Here we checked the validity of the data in a comparison of the two separately derived data sets from the two trials within the HFD-study. Of course, this kind of comparison also factors in the biological variance, not only the technical variance. It is thus considered to be an appropriate tool for the validity of our overall results, in a sense the whole pipeline from mouse to data analysis. Normalization of lipid species is given by using their ‰ values relative to total molar amount for each lipid class in respective samples, as evidenced above by using such data in the verification process across HFD-and ATGL-studies. The comparison of the lump sums of all lipid species from all lipid classes investigated resulted in an average deviation of per mille values to 25.9% between the HFD-trials. Application of the same procedure to comparison of the overall HFD-study with the ATGL-study, the deviation rose to 36.6%. If only main lipid species, which make up 90% of the LD-lipidome are taken into calculation, the deviation between HFD-and ATGL-studies decreased impressively to 19.0%. This reflects the fact that lipid species with higher abundance have much higher signal intensities and thus an increased quantitative confidence compared to minor species detected close to the limit of detection (Fig. f5).

Usage Notes
The lipidomic data acquired by the Thermo LTQ-FT instrument are publicly available in '.raw' file format at European Bioinformatics Institute's MetaboLights repository (http://www.ebi.ac.uk/metabolights). After download the data are available for analysis by different software packages. In fact, this repository contains raw data of the whole lipidomes determined in lipid droplets. They enable application of bioinformatic processing, as shown in this paper for TG-lipidomes, to all other lipid classes. The data reported here already demonstrate the potential to diagnose hepatocyte lipid droplets obtained from dietor genetically induced steatotic livers. Beyond a diagnostic value in general, such data provide insights into metabolic paths and how to interfere in them. Further files illustrate in detail the data necessary to execute PCA and O-PLS-DA as general methods for phenotyping. Finally, targeted and quantitative data obtained by LC-MS technology can be automatically processed and rapid quantitative evaluation of data can now easily be carried out by LDA (http://genome.tugraz.at/lda). Another software option for quantitative analysis is Lipid Search (Thermo Scientific, San Jose, CA, USA). Non-targeted differential lipidomic profiles featuring global differences between the sample groups can be obtained by Sieve (Thermo Scientific, San Jose, CA, USA).
The data sets documented here will allow singling out small subsets of lipid species indicative for a physiological/metabolic state. Such classification abilities can be validated on an independent data set. In fact, subsets of lipid species are sufficient to describe the state of an animal, and as such they can be regarded as true surrogates for extended lipidomes and even as potential candidates for biomarkers.