PMCID
string | Title
string | FullText
string |
|---|---|---|
PMC4606567
|
An automated shotgun lipidomics platform for high throughput, comprehensive, and quantitative analysis of blood plasma intact lipids
|
Blood plasma has gained protagonism in lipidomics studies due to its availability, uncomplicated collection and preparation, and informative readout of physiological status. At the same time, it is also technically challenging to analyze due to its complex lipid composition affected by many factors, which can hamper the throughput and/or lipidomics coverage. To tackle these issues, we developed a comprehensive, high throughput, and quantitative mass spectrometry-based shotgun lipidomics platform for blood plasma lipid analyses. The main hallmarks of this technology are (i) it is comprehensive, covering 22 quantifiable different lipid classes encompassing more than 200 lipid species; (ii) it is amenable to high-throughput, with less than 5 min acquisition time allowing the complete analysis of 200 plasma samples per day; (iii) it achieves absolute quantification, by inclusion of internal standards for every lipid class measured; (iv) it is highly reproducible, achieving an average coefficient of variation of <10% (intra-day), approx. 10% (inter-day), and approx. 15% (inter-site) for most lipid species; (v) it is easily transferable allowing the direct comparison of data acquired in different sites. Moreover, we thoroughly assessed the influence of blood stabilization with different anticoagulants and freeze-thaw cycles to exclude artifacts generated by sample preparation. Practical applications: This shotgun lipidomics platform can be implemented in different laboratories without compromising reproducibility, allowing multi-site studies and inter-laboratory comparisons. This possibility combined with the high-throughput, broad lipidomic coverage and absolute quantification are important aspects for clinical applications and biomarker research.Lipidomics is the systematic study of pathways and networks of cellular lipids by profiling and understanding the role of lipids in biological systems 1. Blood plasma is widely used in lipidomics studies mainly due to its availability. Furthermore, it carries the lipids incorporated into lipoproteins over the circulatory system from the intestine and liver to all the tissues in the body 2. Because this fluid has access to peripheral tissues, and its lipoproteins exchange lipids with them, its lipid composition should contain a detailed picture of the individual metabolic state 3,4. This makes plasma a prime target for diagnostic research but at the same time very challenging due to its compositional complexity 5. In order to establish reliable lipid diagnostic biomarkers, one needs to dissect the many factors affecting the plasma lipidome which are not necessarily disease-related, e.g., different genetic backgrounds, diet, gender, age, and life style 6–11. This creates a severe problem and often plasma lipidomics studies are either performed in a high-throughput manner but targeting only a subset of the lipidome 12 or they cover the whole lipidome for a small sample set 5. In lipidomics methodologies, like in other omics techniques, there is an inverse correlation between a method's throughput versus lipidomic structural elucidation and coverage 13. The most comprehensive plasma lipidomic coverage available, offered by the LipidMaps consortium 5, is achieved by the combination of multiple analytical set-ups running in different laboratories 14 which greatly reduces the throughput and feasibility. On the other hand, one-step chromatography-based approaches offer improved throughput at the expense of lower lipidomic coverage 15–19. Nowadays, the available shotgun lipidomics methodologies offer the highest throughput. This is achieved by direct infusion of the extract without previous chromatographic separation with the drawbacks of not providing details about lipid molecular species 20 or limited lipidomic coverage 21. In this paper, we developed a high throughput mass spectrometry (MS)-based shotgun lipidomics platform that offers quantitative and extended lipidomic coverage down to the molecular lipid species level for systematic lipidomic profiling of large populations. Our platform achieves unprecedented acquisition speed (5 min per acquisition, allowing the analysis of 200 samples per day per MS instrument including sample preparation) combined with high lipidomic coverage (22 quantifiable lipid classes encompassing more than 200 lipid species) in a single acquisition. In order to achieve this performance, we optimized the sample preparation, MS acquisition, and lipid identification approaches. All sample preparation steps were automatized for increased throughput and precision. On the instrumentation side, we took advantage of the configuration of the quadrupole-Orbitrap hardware that allows fast polarity switching, safe precursor selection for MSMS fragmentation analysis, and high mass accuracy and resolution. The combination of these features allows for obtaining multiple complementary MS scans in the same acquisition that enabled us to cover the full spectrum of intact lipids. The increase in sample number and spectral complexity (inclusion of MSMS spectra) greatly inflates the information to be processed. To handle this, we developed a new approach for individual molecular species identification and quantification. Lipid species from high resolution MS spectrum are identified and quantified followed by deconvolution of these lipid species into the different lipid molecular species using the corresponding tandem MS fragments. In addition to the high coverage and throughput, this technology is also very robust. We observed very small technical variation within the same day, between different days of acquisition and between different acquisition sites. We present, for the first time, a technology so robust that it can be implemented in other laboratories without any compromise in precision. These are important features required in clinical diagnostics screens. Moreover, we assess the impact of sample collection and storage on the plasma lipidome stability. Water, propan-2-ol, and methanol were purchased from Fischer Scientific. Methyl tert-butyl ether, chloroform, ammonium bicarbonate, and ammonium acetate were purchased from Sigma–Aldrich. All chemicals were analytical grade. Synthetic lipid standards were purchased from Avanti Polar Lipids, Larodan Fine Chemicals, and Sigma–Aldrich. For plasma isolation, blood was collected and centrifuged (2000×g, 10 min) and the supernatant was collected 22. Prior to extraction, the plasma was diluted 1:50 v/v by mixing 15 μL of it with 735 μL of 150 mM ammonium bicarbonate aqueous solution, aliquoted, and stored at −80°C. Since we use 1 μL of blood plasma per extraction, this dilution is required to minimize the error arising from handling low volumes of liquid. The whole procedure was finished in 2 h after blood collection. It was used to prepare the reference samples, where plasma from three healthy, unfasted donors was combined in equivolumetric ratios separated in batches according to the anticoagulant used. For all analysis, except the comparison of anticoagulants, the plasma derived from EDTA-stabilized blood samples was used and all samples measured were coming from the same batch of reference samples. For the anticoagulation comparison, blood was collected into S-Monovette EDTA K3, sodium citrate, and lithium–heparin (all Sarstedt) anticoagulant vacutainers (each donor donated blood to all three containers) according to the producer's manual. The data were corrected for the initial dilution of blood by citrate (blood to citrate 10:1 ratio; v/v). In the freeze and thaw assessment, the samples were prepared as described above and were thawed at 4°C for 2 h, mixed by vortexing, and frozen by placing back at −80°C for 24 h. One freeze and thaw cycle was performed on a daily basis and immediately after all cycles were completed samples were extracted and analyzed. The lipid extraction (adapted from Matyash et al. 23) was carried out in high grade polypropylene deep well plates. Fifty microliters of diluted plasma (50×) (equivalent of 1 μL of undiluted plasma) was mixed with 130 μL of ammonium bicarbonate solution and 810 μL of methyl tert-butyl ether/methanol (7:2, v/v) solution was added. Twenty-one microliters of internal standard mixture was pre-mixed with the organic solvents mixture. The internal standard mixture contained: 50 pmol of lysophasphatidylglycerol (LPG) 17:1, 50 pmol of lysophosphatic acid (LPA) 17:0, 500 pmol of phosphatidylcholine (PC) 17:0/17:0, 30 pmol of hexosylceramide (HexCer) 18:1;2/12:0, 50 pmol of phosphatidylserine (PS) 17:0/17:0, 50 pmol of phosphatidylglycerol (PG) 17:0/17:0, 50 pmol of phosphatic acid (PA) 17:0/17:0, 50 pmol of lysophposphatidylinositol (LPI 17:1), 50 pmol of lysophosphatidylserine (LPS) 17:1, 1 nmol cholesterol (Chol) D6, 100 pmol of diacylglycerol (DAG) 17:0/17:0, 50 pmol of triacylglycerol (TAG) 17:0/17:0/17:0, 50 pmol of ceramide (Cer) 18:1;2/17:0, 200 pmol of sphingomyelin (SM) 18:1;2/12:0, 50 pmol of lysophosphatidylcholine (LPC) 12:0, 30 pmol of lysophosphatidylethanolamine (LPE) 17:1, 50 pmol of phosphatidylethanolamine (PE) 17:0/17:0, 100 pmol of cholesterol ester (CE) 20:0, 50 pmol of phosphatidylinositol (PI) 16:0/16:0. The plate was then sealed with a teflon-coated lid, shaken at 4°C for 15 min, and spun down (3000 g, 5 min) to facilitate separation of the liquid phases and clean-up of the upper organic phase. Hundred microliters of the organic phase was transferred to an infusion plate and dried in a speed vacuum concentrator. Dried lipids were re-suspended in 40 μL of 7.5 mM ammonium acetate in chloroform/methanol/propanol (1:2:4, v/v/v) and the wells were sealed with an aluminum foil to avoid evaporation and contamination during infusion. All liquid handling steps were performed using Hamilton STARlet robotic platform with the Anti Droplet Control feature for organic solvents pipetting. Samples were analyzed by direct infusion in a QExactive mass spectrometer (Thermo Fisher Scientific) equipped with a TriVersa NanoMate ion source (Advion Biosciences). Five microliters were infused with gas pressure and voltage set to 1.25 psi and 0.95 kV, respectively. The delivery time was set to 4 min and 55 s with contact closure delay of 20 s to avoid initial spray instability. Polarity switch from positive to negative mode was set at 135 s after contact closure. Samples were analyzed in both polarities in a single acquisition. The MS acquisition method starts with positive ion mode by acquiring the m/z 402–412 in MS + mode at Rm/z = 200 = 140 000 to monitor the [Chol + NH4] ion for 12 s. All individual scans in every segment are the average of 2 microscans. Automatic gain control (AGC) was set to 5 × 10 and maximum ion injection time (IT) was set to 200 ms. Then we scan the m/z 550–1000 in MS + (Rm/z = 200 = 140 000) with lock mass activated at a common background (m/z = 680.48022) for 18 s. AGC was set to 10 and IT was set to 50 ms. This is followed by a MSMS + (Rm/z = 200 = 17 500) data independent analysis triggered by an inclusion list for 105 s. The inclusion list contains all the masses from 500.5 to 999.75 with 1 Da intervals. AGC was set to 10 and IT was set to 64 ms. The isolation width was set to 1.0 Da, first mass of MSMS acquisition was 250 Da and normalized collision energy was set to 20%. Both MS+ and MSMS+ data are combined to monitor SE, DAG, and TAG ions as ammonium adducts. After polarity switch to negative ion mode, a lag of 15 s before acquisition was inserted to allow spray stabilization. Then, we scan for the m/z 400–650 in FTMS − (Rm/z = 200 = 140 000) for 15 s with lock mass activated at a common background (m/z = 529.46262) to monitor LPG, LPA, LPI, LPS, and LPE as deprotonated anions and LPC and LPC O– as acetate adducts. AGC was set to 10 and IT was set to 50 ms. We then scan the m/z 520–940 in FTMS − (Rm/z = 200 = 140 000) for 15 s with lock mass activated at a common background (m/z = 529.46262). AGC was set to 10 and IT was set to 50 ms. Finally, we scan MSMS- (Rm/z= 200 = 17 500) by data independent analysis triggered by an inclusion list for 90 s. This inclusion list contains all the masses from 590.5 to 939.5 with 1 Da intervals. AGC was set to 10 and IT was set to 64 ms. Isolation width was set to 1.0 Da, first mass of MSMS acquisition was 150 Da, and normalized collision energy was set to 35%. Both MS and MSMS data were combined in order to monitor PC, PC O–, HexCer, Cer, SM as acetate adducts and PS, PG, PA, PE, PE O–, and PI as deprotonated anions. All data were analyzed with an in-house developed lipid identification software based on LipidXplorer 24,25. All Molecular Fragmentation Query Language queries used in this work are available and can be found in Supplementary Material 1. Tolerance for MS and MSMS identification was set to 2 ppm in scans where we have lock mass activated and 8 ppm when lock mass was not available. Data post-processing and normalization were performed using an in-house developed data management system. Data visualization, linear regression (linear least squares method), and correlation (two-tailed Pearson correlation) calculations were performed on Prism 6.0e software (GraphPad Software, Inc.). Automation is essential in order to handle large number of samples because it increases the speed and throughput of the method, makes it more cost-efficient, and most importantly it improves reproducibility 26 (see section 3.5). To this end, we chose the Hamilton Robotics STARlet system. It allows pipetting of wide volume ranges (1–1000 μL) at high precision with the Anti Droplet Control function limiting organic phase dripping from pipette tips. Remarkably, most lipidomics studies still use one of almost 60 years old classical extraction protocols at times with minor modifications 27,28. More recently, Matyash et al. 23 introduced a new high throughput oriented extraction procedure where chloroform is replaced by methyltert-butyl ether (MTBE). This extraction achieves high recovery of multiple lipid classes with several important advantages 23. MTBE, compared to chloroform, is non-toxic and non-carcinogenic. The organic lipid-containing phase remains on top of the aqueous phase, reducing the water-soluble contaminants during extract collection. All these factors put together greatly facilitate automated sample handling procedures. For this study, we defined the optimal time of MTBE-based lipid extraction at which maximum lipid recovery is achieved. To this end, a mixture containing standards representing the most abundant lipid classes was added post-extraction to blood plasma lipids extracted for different times. Surprisingly, we observed that in less than 2 min, equilibrium is reached for all lipid classes tracked (Fig. S1). However, the recovery was more reproducible after 5 min. For this reason, we decided that extracting for 15 min is the best compromise between extraction reproducibility and speed. The quadrupole-Orbitrap configuration allows multiple possibilities for lipid identification. Besides the consecutive acquisition in both positive and negative ion mode 20, some lipids can be detected by a top-down approach where the unambiguous identification is possible due to the high resolution of the detector, relying solely on the precursor mass accuracy 29. In order to increase the identification specificity, it can also be combined to a bottom-up approach by introducing fragmentation experiments made possible by the presence of the quadrupole. Moreover, this bottom-up approach allows additional structural elucidation which enables molecular lipid species identification 30,31. To date, molecular lipid species identification and quantification required multiple rounds of long tandem MS acquisitions in order to obtain the statistics for an accurate measurement. To bypass this and decrease the acquisition time, we propose a new approach that takes advantage of the Q-Orbitrap features. Firstly, from the FTMS high-resolution spectrum, we normalize the de-isotoped (type I and II) intensity of the monoisotopic peak of an endogenous species to the de-isotoped (type I and II) intensity of the monoisotopic peak of the standard of the same class to obtain absolute quantification at the species level: where the concentration of a given lipid species [lip spec] is given by the ratio of its de-isotoped monoisotopic intensity (I(lip spec)MS) to the added lipid standard de-isotoped monoisotopic intensity (I(lip std)MS) multiplied by the concentration of the lipid standard [lip std]. After quantification at the species level, the amounts of all x molecular phospholipid species with overlapping sum compositions, can then be deconvoluted from the n acyl anions fragmentation information contained in the tandem MS data, as follows: where the concentration of a given molecular species x, [mol spec]x, is given by the sum of the intensities of its complementary acyl anions FAx1 and FAx2, divided by the sum of the intensities of n acyl anions from all other isobaric lipid molecular species present in the same MSMS scan, which corresponds to the molar fraction of each lipid molecular species. With this approach, we circumvent the need for multiple cycles of MSMS in order to achieve accurate quantification greatly reducing the time of acquisition. Phospholipids display variable but in general efficient fragmentation that allows the elucidation of the fatty acid composition 31, but other lipid classes can be more difficult to assess. For example, because triacylglycerides contain combinations of three fatty acids, this makes the assignment of molecular lipid species without chromatographic separation and MS type of experiments virtually impossible 32. Also, the fragmentation of sphingolipids is very inefficient and not suitable for structure elucidation. The most notable case is sphingomyelin. It presents only one abundant phosphocholine fragment in positive mode that does not give any additional structural information. Conversely, in negative ion mode there is a fragment corresponding to the loss of the amide-linked fatty acid allowing the molecular lipid species elucidation but it cannot be used for identification due to its low intensity. In Table 1, we present a strategy to identify each lipid class and the level of structural detail that can be achieved. Mode of acquisition and identification (see section 2.4 for details), structural detail, optimal sample amounts and their r, dynamic range, its quantification slopes and their r, LOQ for every lipid class (see Figs. S1 and S3 for additional information) Although MSMS was used to increase the confidence of identification, we could not assign molecular species for the TAG (see text for details). Not measurable in this reference sample. One of the biggest challenges of comprehensive lipid analysis is the huge dynamic range of lipid abundances observed in biological samples like blood plasma. Chromatographic techniques minimize the ion suppression effect of highly abundant lipid species by resolving the different lipids in a temporal dimension 33. This allows for extracting a wide range of sample amounts that to some extent can be tuned for the detection of low abundant species. In a shotgun lipidomics approach, sample infusion is continuous and the acquisition is done simultaneously for all the different lipid classes, which makes it more susceptible to ion suppression 34. To minimize this, the fine tuning of the sample amount to be extracted is of paramount importance for shotgun-based experiments 21. We extracted different sample amounts together with fixed amounts of internal standards in order to determine the minimum and maximum amounts that give reliable lipid compositions. We observed that most lipid classes give a linear response over two orders of magnitude of sample amount (0.2–20 μL) (Fig. S2). Importantly, we observe a consensual sample range (1–2 μL), which provides a satisfying compromise between lipidomic coverage and sensitivity, and where reproducibility is the highest (Table1 and Fig. S1). It is important to note that this amount is optimized for healthy individuals and needs to be carefully assessed in metabolically challenged conditions where the concentration of specific lipid classes such as triglycerides in blood can increase significantly. This sample amount corresponds to a final sample dilution in the infusate of 1/320 to 1/160 which is in agreement with a previous study for the optimal sample amount for shotgun lipidomics experiments 21. For quantification, we used one internal standard per lipid class. These internal standards were chosen based on two criteria: first, its absence in plasma samples and second, its similarity to the structure and properties (e.g., extraction recovery and ionization efficiency) of the analytes. This approach is valid due to the fact that the recovery and ionization efficiency of different lipid species within a lipid class is predominantly dependent on their charged head group while their differential acyl chains only minimally alter their ionization at low lipid concentrations 35,36 (Fig. S3). Two notable exceptions are present in the SM profile. SM 42:1;2 apparent relative decrease with sample amount increase is explained by a small background peak of a similar overlapping mass observed already in the blank samples. Importantly, this effect is only significant at very low sample amounts and its impact at the optimal sample amount is minimal. For SM 34:1;2, we observed a relative increase up to 15% in its proportion across the two orders of magnitude of sample amount. Although this is a significant difference, it is not observed in other SM species. This effect is most likely related to differential matrix effects introduced by variable amounts of plasma sample extracted and its impact is irrelevant when we fix the sample amount. In conclusion, we obtain identical lipid species profiles at different sample concentrations, confirming the assumption that ionization within a lipid class is not lipid species specific for the sample amounts tested allowing to express the quantities in molar amounts. This observation allows us to conclude that, for the optimal sample amount, all lipid species measured within a lipid class have similar response factors enabling absolute quantification with a single standard addition per lipid class. Different lipid classes exhibit different extraction, ionization, and fragmentation efficiencies, so the limit of quantification (LOQ) and dynamic ranges are lipid class-specific. In order to determine all these parameters, we added different amounts of each lipid class standard to the fixed optimal plasma sample amount (1 μL) and we recorded how the signal acquired correlates with the standard amount added. The LOQ was defined by the minimum concentration at which the slope and the correlation coefficient (r) are not significantly compromised and the variation of the standard intensity is below 20%. The summary of the results obtained can be found in Table1 and Fig. S3. We observed that the lipid classes showing a higher LOQ (>1 µM) correspond to abundant lipid classes in plasma (e.g., Chol, SE, and TAG); therefore, a higher detection limit should not have a significant impact on their species. Interestingly, they are all acquired in positive ion mode as an ammonium adduct, so an increase in the ammonium acetate concentration in the MS-infusion mixture can potentially improve their detection sensitivity, if required. On the other hand, anionic lipids that are usually acquired in negative ion mode, exhibit LOQs down to 50 nM. We conclude that the absence or low number of lipid species identified in some classes such as HexCer, LPA, LPS, and PA is due to their low abundance in this particular sample rather than low sensitivity for these lipid classes. Reproducibility is the ability to obtain similar results by the same researcher on different days and/or by someone else working independently. In this work, we aimed at maximizing method reproducibility by automation of most of the sample preparation, extraction, data acquisition and, to some extent, lipid identification, and post-processing of the data. In order to make a thorough assessment of the reproducibility of our method, we performed a systematic series of experiments where we aliquoted the same reference sample 540 times across six independent 96-well plates. Three of these plates (270 aliquots in total) were subjected on three different days (90 aliquots per day) to extraction and analysis in one laboratory and the other three plates were processed similarly in another laboratory. Both sites used identical platforms for the sample processing and analysis. Since every sample is an aliquot of the same batch of plasma (see section 2.2), the variation observed across different acquisitions, accurately reflects the intra-plate variation (variation within the same day of acquisition), inter-day variation (variation on different days of acquisition), and inter-site variation (variation between different sites). It is important to note that in analytical methods, there is usually an inverse correlation between lipid amount present in the sample and the coefficient of variation 21. Because different lipid species can range in amounts by several orders of magnitude, one has to assess how the amount affects the reproducibility of a given molecular lipid species. To assess method variation, we sorted the lipids by abundance and divided the data in quartiles and averaged the coefficient of variation within each quartile (Fig. 1A and Table2). (A) Correlation between coefficients of variation and average lipid concentrations from 270 individual measurements performed on three different days. The dashed lines separate the quartiles according to lipid concentration (see Table2 for additional information). (B) Pearson correlation plot of averaged concentrations determined at the two sites. Every point represents the average concentration of lipid species measured from 270 independent acquisitions. Correlation coefficient (r) is given. Averaged coefficients of variation within the different lipid concentration quartiles obtained on the same day (intra-plate), on different days (inter-day), and in different sites (inter-site) (see Fig. 1 for additional information) We observed that the coefficient of variation indeed correlates inversely with the lipid amount but importantly we still obtain low CVs when the plates are processed on different days and in different laboratories (Table2 and Fig. 1B). It is important to note that, although the first and second quartiles correspond to half the number of lipid species analyzed, when taking in consideration their amounts, they correspond to 98% of the lipidome and they can be measured with CVs lower than 15% on average even when acquired at different sites. For the third and fourth quartiles, the least abundant species, we can also conclude that the higher CV can be attributed to their intrinsic low abundance and not to any irreproducibility bias. From the sample collection to spectral acquisition, there are several steps that might affect the state of the sample and as a consequence alter the final result. During the sample collection, it is necessary to add anticoagulants to the blood sample when plasma preparation is considered. However, throughout the literature, different anticoagulants have been used indiscriminately without any verification of the impact that they might have on the lipid analysis, though it is known that they can influence multiple parameters of blood 37. To assess if different anticoagulants used for blood stabilization affected the plasma lipidome, we compared plasma samples derived from blood stabilized with the three most commonly used substances: EDTA, citrate, and heparin. All samples, regardless of the anticoagulant used, yielded remarkably comparable lipidomes, with Pearson correlation coefficients exceeding 0.999 and had almost identical lipid class profiles (Fig. 2). Interestingly, we observed that citrate containing samples display systematically lower lipid amounts when compared to samples containing other anticoagulants. This is due to the fact that citrate in vacutainers is present as an aqueous solution and is mixed with the blood during drawing, therefore, diluting it. Although we corrected for the dilution to the best of our ability, we did not manage to retrieve results identical to the other anticoagulants. This has been observed previously 38, which makes us conclude that citrate should be avoided if possible due to the introduction of uncertainty in the sample volume determination. Effect of anticoagulants on the blood plasma lipidome analysis. (A) Lipid class profile comparison of plasma collected with EDTA, citrate, and heparin as anticoagulants. Averaged values are shown with error bars depicting standard deviation of 27 independent experiments for each anticoagulant. (B) Pearson correlation of the lipids species quantified. Every point represents the average concentration of lipid species calculated from 27 independent experiments for each condition. Correlation coefficients (r) versus EDTA are given. In a given study, it might be useful to re-analyze the same sample in order to obtain additional information. As it is often assumed, freeze-thawing cycles might influence sample and analyte properties: in this particular case, the lipids. We evaluated how freezing and thawing affected the plasma lipidome by performing 10 freeze-thaw cycles prior to extraction. We did not detect any significant systematic alterations in lipid levels, nor in molecular species composition of lipid classes that could be correlated to the number of freeze and thaw cycles (Fig. 3A). More importantly, we did not observe any decrease in the unsaturation level of fatty acids present in lipids, which would be a sign of oxidation of polyunsaturated fatty acids (Fig. 3B). These results corroborate previous observations 39,40. It is worth to mention that the definition of the maximum number of freeze and thaw cycles without sample damage is still controversial 41. We believe that the controversy might be related more to the specific conditions of freeze-thaw cycles in different studies than to the number of cycles performed. With this in mind, instead of claiming that one can perform 10 freeze and thaw cycles without sample degradation, we would rather emphasize that thawing the samples at 4°C as quickly as possible, should allow for at least 10 cycles without sample degradation as has been shown in this study. Taken together, these results provide guidelines for consistent sample collection, storage, and re-acquisition. However, although the effects of sample collection and freeze-thawing on the plasma lipidome were ruled out, other factors leading to degradation of lipids cannot be excluded. For instance, it is known that physical stress imposed on leukocytes during blood drawing may activate them (especially when hemolysis occurs that might take place in blood from anemic patients) and induce the arachidonic acid cascade, which in turn can influence the plasma lipidome 5. However, as these effects potentially appear during sample collection, their influence on the measured lipidome is beyond the reach of the methodology described here. Effect of the number of freeze and thaw cycles on the plasma lipidome. (A) Lipid class profile of the same sample frozen and thawed up to 10 times. (B) Double bond profile of the same sample frozen and thawed up to 10 times. Averaged values are shown with error bars depicting standard deviation of five experiments per freeze and thaw cycle. We have established a fully automated, high throughput shotgun-based lipidomics method for systematic screening of plasma samples. Currently, this is the most comprehensive MS-based lipidomics method from a single acquisition, and most importantly, it is quantitative and highly reproducible. More important than speed and reproducibility is accuracy. We sampled the literature in order to compare our data with the results obtained in studies providing quantitative data and similar lipidomic coverage to ours 5,20,21,42–44. We observed that our data fit the range of values observed in literature, with the exception of TAG and SM (Figs. 4 and S5). These discrepancies are most likely related to the increase in chylomicron content in the plasma that we used from unfasted donors. Chylomicrons are known to transport lipids from the intestine and an increase in TAG content should be expected in unfasted subjects, since they are the most TAG enriched lipoproteins particles present in plasma 45. The main lipid classes’ amount distribution described in literature 5,20,21,42–44 compared with this study. Medians of average for control samples reported in these papers are presented. Error bars denote minimal and maximal values (range). Note that not all classes were analyzed in every study. To the best of our knowledge, for the first time for an omics-type analytical technology, we showed high method precision not only on different days but also in different laboratories. The implications of these are twofold. First, it means that this technology can be easily implemented in different sites without compromising data reproducibility and second, it facilitates direct integration of data between different laboratories, allowing multi-site studies for higher throughput, if required. We believe this method paves the way for making lipidomics an accessible and indispensable tool not only in biological basic research, but also for clinical diagnostics and nutrition by offering unprecedented throughput and accuracy. The authors would like to thank to Andrej Shevchenko for feedback on the paper. This study was supported by Nestlé Institute of Health Sciences and the Klaus Tschira Foundation. Conflict of interest statement: MAS, RH, AV, CK, KS, JLS have paid employment at Lipotype GmbH and NC, DM-R, MM have paid employment at Nestlé Institute of Health Sciences S.A. This does not alter the authors’ adherence to all policies on sharing data and materials.
|
PMC10513930
|
Dynamic lipidome alterations associated with human health, disease and ageing
|
Lipids can be of endogenous or exogenous origin and affect diverse biological functions, including cell membrane maintenance, energy management and cellular signalling. Here, we report >800 lipid species, many of which are associated with health-to-disease transitions in diabetes, ageing and inflammation, as well as cytokine–lipidome networks. We performed comprehensive longitudinal lipidomic profiling and analysed >1,500 plasma samples from 112 participants followed for up to 9 years (average 3.2 years) to define the distinct physiological roles of complex lipid subclasses, including large and small triacylglycerols, ester- and ether-linked phosphatidylethanolamines, lysophosphatidylcholines, lysophosphatidylethanolamines, cholesterol esters and ceramides. Our findings reveal dynamic changes in the plasma lipidome during respiratory viral infection, insulin resistance and ageing, suggesting that lipids may have roles in immune homoeostasis and inflammation regulation. Individuals with insulin resistance exhibit disturbed immune homoeostasis, altered associations between lipids and clinical markers, and accelerated changes in specific lipid subclasses during ageing. Our dataset based on longitudinal deep lipidome profiling offers insights into personalized ageing, metabolic health and inflammation, potentially guiding future monitoring and intervention strategies.Lipids are an important and highly diverse class of molecules that have critical roles in cell structure, cell signalling and bioenergetics. Despite their critical roles in many biological processes, there is much to be learned about the diversity of lipids in humans, how their composition differs across people, and how they change over time at an individual level and during disease. Such information is expected to provide insights into biological processes such as ageing as well as the possible roles of lipids in health and disease. High-throughput omics technologies provide new avenues to understand the molecular landscape of human physiology and its dynamic changes during health and disease. To date, many studies have used next-generation sequencing owing to its accessibility and cost-effectiveness. Recently, mass spectrometry (MS) strategies have provided quantitative insights into the proteome at scale and depth. Metabolites, which can also be investigated using MS, have been studied to a lesser extent given their complex chemical diversity. Lipids comprise a major, heterogeneous family of biomolecules within the metabolome and remain challenging to characterize owing to their wide range of physicochemical properties and the relatively small number of lipidomics studies. Complex lipids can be divided into several classes and subclasses that are distinguished by lipid head groups and linkages to different aliphatic chains. Lipids such as triacylglycerols (TAGs), diacylglycerols (DAGs), phosphatidylcholines (PCs), phosphatidylethanolamines (PEs), ceramides (CERs), sphingomyelins (SMs) and cholesterol esters (CEs) each consist of a specific backbone architecture conjugated to various fatty acids (FAs). The attached FAs can vary in the number of unsaturated bonds and their positions within acyl chains; together with the backbone, FAs confer distinct physicochemical properties and physiological roles. Lipids carry out and regulate many key functions, including redox homoeostasis, energy storage, intracellular and extracellular signalling, induction and resolution of acute and chronic inflammation, and maintenance of electrochemical gradients across subcellular compartments. Abnormal lipid profiles (dyslipidaemia) have been associated with a range of diseases, including metabolic syndrome, type 2 diabetes (T2D), cancer, nephropathy and cardiovascular and neurodegenerative diseases, and may result from a combination of factors such as genetic heterogeneity, lifestyle and, as recently shown, inflammation related to coronavirus disease 2019 infection. One of the key roles of lipids in maintaining metabolic homoeostasis is to mediate the induction and attenuation of inflammatory processes (for example, leukotriene, prostanoid and endocannabinoid signalling). Because of the various roles lipids have in maintaining homoeostasis in humans, different lipid species or classes may influence perturbations that induce acute inflammation (for example, respiratory viral infections (RVIs)), as well as the resolution of inflammation, metabolic diseases (for example, T2D) and physiological processes (for example, ageing) that have been associated with changes in the regulation of chronic inflammation. In light of the diverse roles of lipids, it is important to understand their quantitative differences among individuals and their dynamics across phenotypes to characterize their potential roles in health and disease. Here, we characterize the lipidome dynamics in >100 human participants followed for up to 9 years, covering periods of health and disease, using an MS-based approach that allows a broad array of lipid types to be measured rapidly, quantitatively and rigorously. We identified distinct longitudinal lipid signatures that link lipid profiles to the microbiome, ageing and different clinical pathophysiologies, including insulin resistance (IR) and chronic and acute inflammation. Our results provide valuable insights into the associations of key lipids and lipid subclasses with distinct metabolic health states in humans, and serve as a unique resource to the scientific community. From a cohort of >100 participants with IR or insulin sensitivity (IS), we previously collected longitudinal molecular data comprising genome, transcriptome, proteome, metabolome and 16S microbiome data across different timepoints (~1,000 in total). Within this cohort, we explored various molecular signatures in health and disease and identified hundreds of molecular pathways associated with metabolic, cardiovascular and oncologic pathophysiologies. Here, we investigate the dynamics of a largely unexplored molecular layer—the ‘plasma lipidome’—and extend the longitudinal duration by 2 years to obtain a total of 1,539 samples. To investigate lipidome alterations associated with health, disease and lifestyle changes, plasma samples from 112 participants were profiled at a median of ten timepoints across 2–9 years (average 3.2 years; one participant was sampled 163 times across 9 years; Fig. 1a, Supplementary Data 1 and 2 and Supplementary Figs. 1 and 2). Samples were collected every 3 months when the participants were healthy and with an increased frequency of three to seven collections over 3 weeks during periods of illness (for example, RVI) or notable stress, as previously reported. In addition to lipid profiling, we collected 50 clinical laboratory measurements at each sampling timepoint along with medical records (Supplementary Data 2). Finally, because samples were collected during periods of stress and illness, we also profiled 62 cytokines, chemokines and growth factors in plasma at the same timepoints.Fig. 1Longitudinal lipidomics profiling.a, Profiling, using >1,500 biosamples, across 112 participants followed for up to 9 years. Dynamic changes in the lipidome were characterized in the context of health status and medication history and in comparison with the participants’ cytokine, chemokine and metabolic profiles, as well as microbiome. b, Lipid subclasses investigated in this study. Lipid species, defined by a specific combination of backbone architecture and FAs, can be grouped based on their physicochemical properties. c, We analysed 846 lipids (y axis) across multiple subclasses. d, Across all 112 participants (median estimated concentration across all participant-specific samples), lipid species (846) spanned a dynamic range of more than four orders of magnitude, with distinct estimated concentration ranges for each lipid species and subclass. e, Comparison of the CVs of QC (n = 104), intraparticipant and interparticipant samples. All boxplots report the 25% (lower hinge), 50% (centre line) and 75% (upper hinge) quantiles. Whiskers indicate observations equal to or outside the hinge ± 1.5× the interquartile range (IQR). Outliers (beyond 1.5× the IQR) are not plotted.Source data a, Profiling, using >1,500 biosamples, across 112 participants followed for up to 9 years. Dynamic changes in the lipidome were characterized in the context of health status and medication history and in comparison with the participants’ cytokine, chemokine and metabolic profiles, as well as microbiome. b, Lipid subclasses investigated in this study. Lipid species, defined by a specific combination of backbone architecture and FAs, can be grouped based on their physicochemical properties. c, We analysed 846 lipids (y axis) across multiple subclasses. d, Across all 112 participants (median estimated concentration across all participant-specific samples), lipid species (846) spanned a dynamic range of more than four orders of magnitude, with distinct estimated concentration ranges for each lipid species and subclass. e, Comparison of the CVs of QC (n = 104), intraparticipant and interparticipant samples. All boxplots report the 25% (lower hinge), 50% (centre line) and 75% (upper hinge) quantiles. Whiskers indicate observations equal to or outside the hinge ± 1.5× the interquartile range (IQR). Outliers (beyond 1.5× the IQR) are not plotted. Source data The human lipidome was characterized using a high-throughput quantitative lipidomics pipeline (Lipidyzer) consisting of a triple-quadrupole mass spectrometer (Sciex QTRAP 5500) in conjunction with a differential mobility separation (DMS) device. This setup allows the identification and robust quantification (estimated concentrations) of >1,000 lipid species across 16 subclasses (free FA (FFA), TAG, DAG, CE, PC, lysophosphatidylcholine (LPC), PE, alkyl ether substituent containing PE (PE-O), alkenyl ether (Plasmalogen) substituent containing PE (PE-P), lysophosphatidylethanolamine (LPE), SM, PI, CER, hexosylceramide (HCER), lactosylceramide (LCER) and dihydroceramide (DCER); Fig. 1b). In addition, we observed the differential behaviour of smaller and larger TAGs, which comprise ≤48 and ≥49 carbons across all FAs, respectively, and evaluated these separately in most analyses. For accurate quantification and to control for variance introduced during lipid extraction, we included a mix of 54 deuterated spike-in standards for nine lipid subclasses at known concentrations. Lipid species that were not present as labelled spike-in standards were normalized against the spike-in standards based on structural similarity and signal correlation (described in Methods). We randomized the samples separately for lipid extraction and MS data acquisition. After filtering (described in Methods), we quantified, on average, 778 lipids in each sample and 846 lipid species across >1,600 samples (including quality control (QC) samples). We found the highest number (373) of lipid species in the large TAG subclass and the smallest number (4) in the DCER subclass (Fig. 1c). Lipids comprise chemically heterogeneous molecules that exert a broad spectrum of biological functions ranging from bioenergetics to cellular signalling. This is partially visible in lipid subclass-specific abundance distributions. Figure 1d shows the abundance distributions across more than four orders of magnitude and for each lipid subclass, and depicts two distinct properties: (1) the median abundance of that subclass and (2) the abundance range across all interrogated plasma samples (including healthy and disease timepoints). SMs and FFAs were observed, on average, as the most abundant subclasses, but they spanned a relatively small dynamic range. Other lipid subclasses, including LPCs, CEs and TAGs, had a lower median abundance but a much wider dynamic range. Our study demonstrated high technical reproducibility. As anticipated, the 104 QC samples clustered distinctly (Extended Data Fig. 1); the median coefficient of variation (CV) for the QC samples was low, with values between 6.5% (small TAGs) and 20.7% (DAGs). In contrast, CVs calculated across participants and sampling timepoints ranged from 19.9% (SMs) to 91.4% (small TAGs), indicating sufficient assay reproducibility to discern biological differences. To ensure the highest robustness in our analysis, we focused on 736 lipid species for which (1) QC CVs were <20% and (2) CVs in biosamples were larger than CVs in QC samples. Except for FFAs, intraparticipant variance was consistently lower than interparticipant variance, suggesting that individual lipid signatures are distinct and stable over time (Fig. 1e). Interestingly, both small and large TAGs and ester- and ether-linked PEs (PE versus PE-O and PE-P) exhibited significant differences within their respective subclasses in terms of variance (Fig. 1e) and abundance distribution (Fig. 1d). This implies the existence of unique physiological and participant-specific differences, which may provide new insights into biological processes. We first sought to investigate lipid abundance differences across individuals by characterizing the lipidome in ‘healthy’ baseline samples, defined as samples from participants in the absence of any self-reported acute disease. This does not preclude latent, asymptomatic chronic conditions such as prediabetes or potential undiagnosed conditions. Overall, we analysed 802 healthy baseline samples derived from 96 participants from whom we collected samples at two or more timepoints. The number of baseline samples per participant is shown in Supplementary Fig. 3; most participants had approximately ten healthy visits, except one outlier with 52 healthy baseline samples. In comparison with the transcriptome, proteome and general metabolome, lipid signatures can be highly personalized when assessed longitudinally. To investigate the participant specificity of lipid profiles for healthy sampling timepoints at timescales of months to years, we examined which lipid subclasses show the largest interindividual differences and quantified how much of the variance observed for each lipid species can be attributed to interparticipant differences (Fig. 2a). Many lipids, in particular among TAGs, SMs, HCERs and CEs, showed a high degree of participant-specific variance, in some cases >50%. In contrast, FFAs were found to have relatively low participant-specific variation. To further illustrate participant specificity, we performed t-distributed stochastic neighbour embedding (t-SNE) on data from participants with >12 healthy visits, based on 100 lipids that we determined to be the most personalized (Fig. 2b). Most, but not all, samples clustered by individual participants (Fig. 2b,c), showing that some lipids can comprise personalized signatures even across years.Fig. 2Interindividual differences in healthy baseline.a, Top, bar plot showing the number of lipid species per class ordered by the variance explained by the participant factor; bottom, boxplot showing the variance explained by participants in each lipid class (left y axis) and line graph showing the mean log10(estimated concentration) (red line, right y axis) of each lipid class. Variance decomposition analysis was conducted using n = 802 healthy samples. b, t-SNE clustering of 11 participants who contributed ≥12 healthy samples (n = 191), based on the 100 most personalized lipids. c, Intraparticipant distance, which refers to the Euclidean distance between each pair of samples belonging to the same participant, and interparticipant distance, which refers to the distance between the centroids from each pair of participants, for the t-SNE results. Boxplots report the 25% (lower hinge), 50% (centre line) and 75% (upper hinge) quantiles. Whiskers indicate observations equal to or outside the hinge ± 1.5× the IQR. Outliers (beyond 1.5× the IQR) are not plotted. The intraparticipant and interparticipant distances were compared using a two-sided t test. d, WGCNA modules and their correlation (BH-adjusted FDR cut-off of 5%) with clinical measures. Dot size depicts the BH-adjusted −log10(FDR). The colour scale indicates the degree and direction of the correlation. TGL, total triglyceride; CHOL, total cholesterol; NHDL, non-HDL; CHOLHDL, cholesterol to HDL ratio; LDLHDL, LDL to HDL ratio; GLU, glucose; INSF, fasting insulin; HSCRP, high-sensitivity CRP; WBC, white blood cell count; NEUT, neutrophil percent; NEUTAB, neutrophil absolute count; LYM, lymphocyte percent; LYMAB, lymphocyte absolute count; MONO, monocyte percent; MONOAB, monocyte absolute count; EOS, eosinophil percent; EOSAB, eosinophil absolute count; BASO, basophil percent; BASOAB, basophil absolute count; IGM, immunoglobulin M; RBC, red blood cell count; HGB, haemoglobin; HCT, haematocrit; MCV, mean corpuscular volume; MCH, mean corpuscular haemoglobin; MCHC, mean corpuscular haemoglobin concentration; RDW, red cell distribution width; PLT, platelet; AG, albumin to globulin ratio; CR, creatinine; BUN, blood urea nitrogen; EGFR, estimated glomerular filtration rate; UALB, urine albumin; ALCRU, aluminium to creatinine ratio, urine; UALBCR, urine albumin to creatinine ratio; TP, total protein; ALB, albumin; TBIL, total bilirubin; ALKP, alkaline phosphatase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GLOB, globulin. e, Module composition for the WGCNA analysis shown in c, coloured by lipid subclass. f, Enrichment analysis results based on Fisher’s exact test, depicting the BH-adjusted −log10(FDR) for enriched subclasses for each WGCNA module.Source data a, Top, bar plot showing the number of lipid species per class ordered by the variance explained by the participant factor; bottom, boxplot showing the variance explained by participants in each lipid class (left y axis) and line graph showing the mean log10(estimated concentration) (red line, right y axis) of each lipid class. Variance decomposition analysis was conducted using n = 802 healthy samples. b, t-SNE clustering of 11 participants who contributed ≥12 healthy samples (n = 191), based on the 100 most personalized lipids. c, Intraparticipant distance, which refers to the Euclidean distance between each pair of samples belonging to the same participant, and interparticipant distance, which refers to the distance between the centroids from each pair of participants, for the t-SNE results. Boxplots report the 25% (lower hinge), 50% (centre line) and 75% (upper hinge) quantiles. Whiskers indicate observations equal to or outside the hinge ± 1.5× the IQR. Outliers (beyond 1.5× the IQR) are not plotted. The intraparticipant and interparticipant distances were compared using a two-sided t test. d, WGCNA modules and their correlation (BH-adjusted FDR cut-off of 5%) with clinical measures. Dot size depicts the BH-adjusted −log10(FDR). The colour scale indicates the degree and direction of the correlation. TGL, total triglyceride; CHOL, total cholesterol; NHDL, non-HDL; CHOLHDL, cholesterol to HDL ratio; LDLHDL, LDL to HDL ratio; GLU, glucose; INSF, fasting insulin; HSCRP, high-sensitivity CRP; WBC, white blood cell count; NEUT, neutrophil percent; NEUTAB, neutrophil absolute count; LYM, lymphocyte percent; LYMAB, lymphocyte absolute count; MONO, monocyte percent; MONOAB, monocyte absolute count; EOS, eosinophil percent; EOSAB, eosinophil absolute count; BASO, basophil percent; BASOAB, basophil absolute count; IGM, immunoglobulin M; RBC, red blood cell count; HGB, haemoglobin; HCT, haematocrit; MCV, mean corpuscular volume; MCH, mean corpuscular haemoglobin; MCHC, mean corpuscular haemoglobin concentration; RDW, red cell distribution width; PLT, platelet; AG, albumin to globulin ratio; CR, creatinine; BUN, blood urea nitrogen; EGFR, estimated glomerular filtration rate; UALB, urine albumin; ALCRU, aluminium to creatinine ratio, urine; UALBCR, urine albumin to creatinine ratio; TP, total protein; ALB, albumin; TBIL, total bilirubin; ALKP, alkaline phosphatase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GLOB, globulin. e, Module composition for the WGCNA analysis shown in c, coloured by lipid subclass. f, Enrichment analysis results based on Fisher’s exact test, depicting the BH-adjusted −log10(FDR) for enriched subclasses for each WGCNA module. Source data Given the high variation in specific lipid classes among individuals, we next investigated the degree to which global lipidome profiles from healthy baseline samples are associated with clinical measures. We first grouped lipids into seven modules by applying weighted gene correlation network analysis (WGCNA) based on the similarity of lipid profiles and then associated these seven modules with 50 clinical measures while controlling for the covariates sex, age, ethnicity and body mass index (BMI; Fig. 2d–f and Supplementary Fig. 4). Controlling for these covariates allows investigation of the direct associations between the lipid modules and clinical measures by ruling out potentially confounding effects from sex, age and BMI. Modules M1 and M5, which were enriched for CER and PE, as well as small TAG (mainly M1) and large TAG (mainly M5), showed the strongest positive association with T2D measures, including glycated haemoglobin (A1C), fasting blood glucose and fasting insulin. Moreover, they showed a positive association with inflammatory markers, including high-sensitivity C-reactive protein (CRP) level and white blood cell count, and a negative association with high-density lipoprotein (HDL; ‘good cholesterol’) levels. Hence, lipids in M1 and M2 have negative health associations based on conventional clinical measures. In contrast, M7, which contained some FFAs and LPCs, correlated with lower CRP and A1C levels. M3, which was enriched for PE-P and PE-O, showed an association with higher levels of HDL and lower levels of fasting insulin, and, compared with the dominant T2D patterns in M1 and M5, demonstrated a signature that is generally considered healthier. In addition, we investigated lipid–microbiome associations and observed mostly negatively correlated lipids, including several TAG species for the bacterial family Oscillospiraceae and (L)PE, PC and CE for Clostridiaceae (Supplementary Fig. 5 and Supplementary Note 1). These microorganisms are known to be abundant in the gut of participants with IS in this cohort, suggesting a potentially beneficial role of Clostridia in host lipid metabolism. Finally, an outlier analysis identified participants with abnormally high or low lipid signatures, some of which we could correlate with underlying medical conditions such as hepatic steatosis (Supplementary Fig. 6 and Supplementary Note 2). Overall, this global analysis suggests that many lipid subclasses are associated with and potentially have a role (for example, proinflammatory, anti-inflammatory or metabolic role) in clinical conditions, or may serve as biomarkers to stratify health states. As many clinical measures were associated with specific lipid subclasses, we next determined how the lipidome is influenced by the chronic metabolic disorder IR. IR commonly occurs in T2D and is a condition in which cells, mainly muscle cells and adipocytes, are unresponsive to insulin, leading to high glucose levels in the blood. IR is often associated with chronic inflammation as well as metabolic syndrome, including dyslipidaemia, and can lead to non-alcoholic fatty liver disease. Elucidating how the lipid network is perturbed in individuals with IR is important to better understand the molecular mechanisms and prognosis of metabolic disorders. IR can be diagnosed by measuring the steady-state plasma glucose (SSPG) level after endogenous insulin secretion is suppressed and insulin and glucose are infused at fixed concentrations. IR or IS (IR/IS) status was measured using SSPG assays in 69 participants, of whom 36 and 33 were classified as having IR (SSPG >150 mg dl) and IS (SSPG ≤150 mg dl), respectively. At the global level, we observed some capacity of lipid signatures to distinguish IR and IS (Fig. 3a). Using regression analyses that controlled for age, sex, ethnicity and baseline BMI, we resolved comprehensive differences between IR and IS across most lipid subclasses (Fig. 3b–d), such that more than half of the lipids (424) were significantly associated with SSPG levels. Lipids and lipid subclasses that had a significant positive correlation with SSPG included TAGs and DAGs, which is consistent with our observations (Fig. 2d) and previous reports of higher levels of these lipids in individuals with dyslipidaemia and metabolic syndrome. We also observed subsets of CERs to have increased abundance, contributing to the development of obesity-induced IR in mice and humans (Fig. 3b,c), and making possible the lipid-based differentiation of IR and IS (Fig. 3a and Supplementary Fig. 7).Fig. 3IR- and IS-associated lipid signatures.a, Principal component analysis comparing IR and IS. The density plot on the right indicates the distribution of eigenvectors for each data point along the second principal component (PC2). Eigenvector comparison between IR and IS was conducted using a two-sided t test. b, Regression analysis (n = 69): 424 of 736 lipids had significant correlations with SSPG (BH FDR < 5%; corrected for age, sex, ethnicity and baseline BMI). c, Boxplot depicting regression coefficients for the respective lipid classes by using 69 samples for which the SSPG level was measured at the visit. Larger coefficients indicate stronger associations with higher SSPG levels. Colour indicates distributions for which the 25th or 75th percentile is positive or negative. Boxplots report the 25% (lower hinge), 50% (centre line) and 75% (upper hinge) quantiles. Whiskers indicate observations equal to or outside the hinge ± 1.5× the IQR. Outliers (beyond 1.5× the IQR) are not plotted. d, Proportional differences between IR and IS detected in participants. Centre numbers indicate the total number of lipids in each class. Enzyme names are shown in red. CDP-Cho, cytidine diphosphocholine; CDP-Eth, cytidine diphosphoethanolamine; CPT, choline phosphotransferase; EPT, ethanolamine phosphotransferase; GPAT, glycerol-3-phosphate acyltransferase; LPAAT, lysophosphatidic acid acyltransferases; PAP, phosphatidate phosphatase; DGAT, DAG acyltransferase; G-3-P, glyceraldehyde-3-phosphate; CDS, CDP-diacylglycerol synthase; PSD, PS decarboxylase; PSS, PS synthase; PGS, PG synthase; PIS, PI synthase; SPT, serine palmitoyl transferase; CerS, ceramide synthase; SMase, sphingomyelinase; DES, dihydroceramide desaturase; Acetyl-CoA, acetyl coenzyme A; TCA, tricarboxylic acid. e, Enrichment analysis (Fisher’s exact test) performed on the coefficients from SSPG regression. Enriched annotations were calculated for positive coefficients with BH FDR < 10% (positive log2(odds)) and negative coefficients with BH FDR < 10% (negative log2(odds)). For enriched annotations, a BH FDR cut-off of 5% was applied. f, Correlations between clinical measures and lipid profiles for IR and IS. Correlations are shown when the correlations in IR and IS were significantly different and the absolute Δ correlations in IR and IS were >0.2. In addition, the overall correlations between lipids and clinical measures across IR and IS are depicted when the aforementioned two criteria were met.Source data a, Principal component analysis comparing IR and IS. The density plot on the right indicates the distribution of eigenvectors for each data point along the second principal component (PC2). Eigenvector comparison between IR and IS was conducted using a two-sided t test. b, Regression analysis (n = 69): 424 of 736 lipids had significant correlations with SSPG (BH FDR < 5%; corrected for age, sex, ethnicity and baseline BMI). c, Boxplot depicting regression coefficients for the respective lipid classes by using 69 samples for which the SSPG level was measured at the visit. Larger coefficients indicate stronger associations with higher SSPG levels. Colour indicates distributions for which the 25th or 75th percentile is positive or negative. Boxplots report the 25% (lower hinge), 50% (centre line) and 75% (upper hinge) quantiles. Whiskers indicate observations equal to or outside the hinge ± 1.5× the IQR. Outliers (beyond 1.5× the IQR) are not plotted. d, Proportional differences between IR and IS detected in participants. Centre numbers indicate the total number of lipids in each class. Enzyme names are shown in red. CDP-Cho, cytidine diphosphocholine; CDP-Eth, cytidine diphosphoethanolamine; CPT, choline phosphotransferase; EPT, ethanolamine phosphotransferase; GPAT, glycerol-3-phosphate acyltransferase; LPAAT, lysophosphatidic acid acyltransferases; PAP, phosphatidate phosphatase; DGAT, DAG acyltransferase; G-3-P, glyceraldehyde-3-phosphate; CDS, CDP-diacylglycerol synthase; PSD, PS decarboxylase; PSS, PS synthase; PGS, PG synthase; PIS, PI synthase; SPT, serine palmitoyl transferase; CerS, ceramide synthase; SMase, sphingomyelinase; DES, dihydroceramide desaturase; Acetyl-CoA, acetyl coenzyme A; TCA, tricarboxylic acid. e, Enrichment analysis (Fisher’s exact test) performed on the coefficients from SSPG regression. Enriched annotations were calculated for positive coefficients with BH FDR < 10% (positive log2(odds)) and negative coefficients with BH FDR < 10% (negative log2(odds)). For enriched annotations, a BH FDR cut-off of 5% was applied. f, Correlations between clinical measures and lipid profiles for IR and IS. Correlations are shown when the correlations in IR and IS were significantly different and the absolute Δ correlations in IR and IS were >0.2. In addition, the overall correlations between lipids and clinical measures across IR and IS are depicted when the aforementioned two criteria were met. Source data To investigate over- and under-representations of specific subgroups of lipids, we performed an enrichment analysis on positive and negative model coefficients. As TAGs comprise the largest subclass of lipids in our data and could dominate the results, we performed enrichment analyses separately for each lipid subclass and across all lipids (Fig. 3e). Enrichments were evaluated at the subclass level (Fig. 1b) and for FA composition (global saturation level and specific FAs). Importantly, to our knowledge, new associations were found, including an association of ether-linked PE (PE-P)—in contrast to PE in general—with lower SSPG levels. Ether-linked PEs are involved in cell signalling and can act as antioxidants. Together with increased levels of TAGs with higher SSPG levels, reduced PE-P levels suggest IR-associated inflammation and may indicate a PE-mediated link between oxidative stress, inflammation and IR. In Fig. 2d, we demonstrated lipid modules that correlate with a variety of clinical measures. As it is well documented that IR affects both lipid regulation (dyslipidaemia) and clinical phenotypes, we investigated whether associations between lipids and clinical measures are affected by the IR/IS status, which would have important health implications for these participants (Fig. 3f and Supplementary Fig. 8). Intriguingly, we found many significant differences in both the effect sizes and the direction of the correlation of lipid signatures and clinical measures between participants with IR and those with IS. For instance, in IR unlike in IS, the low-density lipoprotein (LDL) to HDL ratio was positively associated with the ether-linked PE-P and PE-O, and negatively associated with LPE (Fig. 3f). Moreover, in participants with IR and IS, we observed opposite correlations of immune and blood cell measurements with lipid subclasses, including A1C–SM, SSPG–CER and SSPG–PI, as well as immunoglobulin M–PE-P/PE-O, monocyte–PE-P/PE-O, eosinophil–TAG and white blood cell–PI (Fig. 3f). Overall, these data indicate that, depending on the IR/IS status, lipid–clinical measure associations can vary significantly and the key lipids involved in energy regulation, cell signalling and immune homoeostasis exhibit broad dysregulation in IR. In addition to their role in chronic inflammatory and metabolic conditions such as IR, complex lipids are key mediators of acute inflammatory responses, for example, by releasing arachidonic acid (FA(20:4)). Hence, complex lipids may be modified, released and activated during RVIs and possibly vaccinations while also having important roles in these processes in an IR-dependent manner. Participants in this cohort were densely sampled during periods of RVI (72 distinct RVI episodes in 36 participants for a total of 390 samples) and vaccination (44 episodes in 24 participants for a total of 275 visits; Supplementary Fig. 9). For both RVI and vaccination, we classified longitudinally collected samples as early-phase (days 1–6), later-phase (days 7–14) and recovery-phase (weeks 3–5) samples (Fig. 4a). Using linear models, we identified 210 lipids that were significantly changed during RVI (false discovery rate (FDR) < 10%) across most subclasses (Fig. 4b), some of which have previously been implicated in acute inflammation. For instance, PEs have been reported to have a critical role in apoptotic cell clearance and the aetiology of various viruses. Another example is PIs, which bind to the respiratory syncytial virus with high affinity, preventing virus attachment to epithelial cells. LPCs, which we observed in increased abundance during inflammation, have been demonstrated to have therapeutic effects (after intraperitoneal administration in mice) in severe infections through immune cell recruitment and modulation.Fig. 4RVIs and vaccination.a, Longitudinal sampling at five timepoints during RVIs: before infection (healthy), early event, late event, recovery and after infection (healthy). b, Lipid class breakdown for all detected lipids. Dark green depicts 210 significantly changed lipids throughout RVI. Enriched subclass. Fisher’s exact test was used for the lipid class enrichment analysis of the significant lipids (BH FDR for each lipid subclass: CE, 3.35 × 10; CER, 0.95; DCER, 0.49; HCER, 0.87; LCER, 1; DAG, 1; FFA, 0.56; LPC, 6.32 × 10; LPE, 8.40 × 10; PC, 3.01 × 10; PE, 0.27; PE-O, 1.01 × 10; PE-P, 1.00 × 10; PI, 7.65 × 10; SM, 1; large TAG, 1; small TAG, 3.66 × 10). c, Lipid enrichment analysis for significantly changed lipids during RVI, across (left column) and within classes. d, Trajectory analysis of the 210 significantly changed lipids following RVI and their corresponding profiles in each cluster. e, Associations of lipid profiles in RVI and clinical measures. Depicted are correlations between the identified lipid clusters (d) and 50 clinical laboratory measures (BH FDR cut-off of 5%). Dot size depicts −log10(FDR); colour scale represents the correlation direction and degree. f, Differential profile of lipids that were significantly changed during RVI, comparing IR and IS. For each lipid feature, the shaded blocks demonstrate the time intervals during which the corresponding lipid was significantly different between IR and IS. The orange shaded blocks representing the lipid profiles at this time interval are dominant (with higher lipid levels) in IR, and the blue shaded blocks representing the lipid profiles at this time interval are dominant in IS.Source data a, Longitudinal sampling at five timepoints during RVIs: before infection (healthy), early event, late event, recovery and after infection (healthy). b, Lipid class breakdown for all detected lipids. Dark green depicts 210 significantly changed lipids throughout RVI. Enriched subclass. Fisher’s exact test was used for the lipid class enrichment analysis of the significant lipids (BH FDR for each lipid subclass: CE, 3.35 × 10; CER, 0.95; DCER, 0.49; HCER, 0.87; LCER, 1; DAG, 1; FFA, 0.56; LPC, 6.32 × 10; LPE, 8.40 × 10; PC, 3.01 × 10; PE, 0.27; PE-O, 1.01 × 10; PE-P, 1.00 × 10; PI, 7.65 × 10; SM, 1; large TAG, 1; small TAG, 3.66 × 10). c, Lipid enrichment analysis for significantly changed lipids during RVI, across (left column) and within classes. d, Trajectory analysis of the 210 significantly changed lipids following RVI and their corresponding profiles in each cluster. e, Associations of lipid profiles in RVI and clinical measures. Depicted are correlations between the identified lipid clusters (d) and 50 clinical laboratory measures (BH FDR cut-off of 5%). Dot size depicts −log10(FDR); colour scale represents the correlation direction and degree. f, Differential profile of lipids that were significantly changed during RVI, comparing IR and IS. For each lipid feature, the shaded blocks demonstrate the time intervals during which the corresponding lipid was significantly different between IR and IS. The orange shaded blocks representing the lipid profiles at this time interval are dominant (with higher lipid levels) in IR, and the blue shaded blocks representing the lipid profiles at this time interval are dominant in IS. Source data To further investigate the lipid-associated processes that are involved in acute infection, we examined enriched lipids during infection (Fig. 4c). We observed significant changes in specific lipid subclasses, including ether-linked PEs and TAGs containing saturated FAs (SFAs) such as dodecanoic acid (FA(12:0)), following RVI. Dodecanoic acid and palmitic acid (FA(16:0)) are proinflammatory compounds that upregulate cyclooxygenase 2 (ref. ) and have key roles in the activation of inflammatory responses. Overall, this suggests that different key lipid subclasses may be important for various aspects of viral biology as well as the immune response, and undergo significant changes during RVI. To explore the choreography of lipid dynamics over time, we examined their trajectory during the different phases of RVI. The 210 significantly changed lipids were mapped to four major clusters, using a hierarchical clustering approach based on the Euclidean distance between lipid species as the similarity measure (Fig. 4d), and these main clusters were linked with clinical measures (Fig. 4e). Except for the green cluster, which was significantly enriched for PC, all profiles showed decreased levels during infection. The blue cluster was significantly enriched for small TAG and showed sharply decreased lipid levels in early RVI, with a rapid recovery that correlated with clinical measures of total lipids, including cholesterol and LDL. This indicates a metabolic shift in early infection, potentially to support increased energy metabolism. The orange cluster, enriched for LPC, large TAG and ether-linked PE, showed a similar profile to the blue cluster but a delayed recovery to baseline levels. Lipids in this cluster were positively correlated with the clinical lipid panel and blood glucose levels but negatively correlated with CRP level and neutrophils. This suggests that early changes in energy metabolism (reduction in lipid and blood glucose levels) are coupled with increased inflammation (reduction in LPC and ether-linked PE levels, as well as an increase in the CRP level and neutrophils) followed by a slow attenuation of inflammation at later stages of RVI. The purple cluster, which was enriched for FFA, represents slowly decreasing lipid levels and reached the lowest point during the RVI recovery phase before reverting to the baseline levels. In particular, the late-stage correlation with immune-related parameters (CRP level, lymphocyte count) suggests that reduction in the levels of some lipids in this cluster may relate to a temporary strong immunosuppression to attenuate early- to mid-phase inflammation and promote a return to homoeostasis. Overall, our data suggest links between differential responses of lipids and specific biological roles, with rapid shifts in energy metabolism to support inflammation early in infection and possible attenuation in later stages. Reflecting important global shifts in cell signalling, metabolism and inflammation during RVI, these lipids may allow the assessment of disease severity and prognosis or offer an opportunity for therapeutic intervention. We next investigated whether individuals with IR and IS respond differently to infections and vaccination (Fig. 4f and Supplementary Fig. 10). Through a longitudinal differential analysis, we found distinct longitudinal profiles for IR and IS. We observed a higher abundance of several FFAs during the early stage of RVI and greater elevation of PC levels in the middle to late stage in participants with IR than in participants with IS. In contrast, TAGs and some PEs were differentially elevated in IS compared with IR throughout the middle to later stages of infection. The IR/IS-specific FFA and TAG responses may reflect the altered energy metabolism in IR, whereas differences in PCs and other lipid classes may indicate changes in immune-associated signalling pathways. Importantly, we found that the patterns after vaccination were distinct from those during infection (Supplementary Fig. 10). For example, fewer TAG species showed elevated levels in IS, whereas a distinct population of LCERs were upregulated in IR after vaccination. As individuals with T2D associated with IR often exert a more compromised immune response to RVI, such changes may be biologically significant. Ageing increases the risk of cardiovascular diseases and is accompanied by a variety of diseases including T2D and chronic inflammation. In our study, the participants spanned an age range of 20–79 years (healthy timepoints, median 57 years) and were longitudinally sampled on average over 3 years (Fig. 5a). Across the cohort, we observed an increase in BMI with higher age (Fig. 5b). We previously identified age-associated molecular signatures in a subset of this cohort, including inflammation (acute-phase proteins), blood glucose and lipid metabolism (A1C, apolipoprotein A-IV protein), but had not investigated age-associated lipidome changes. To identify lipids and pathways that change with ageing and may be associated with the development of age-related pathologies, such as chronic low-grade inflammation, we investigated longitudinal changes in the lipidome. In cross-sectional studies, lipid content can differ across participants with different ages, owing to biological ageing or the period during which the cohort aged, or other cohort effects. Periods and cohorts are social contexts affecting individuals and are inherently and mathematically confounded by the individuals’ age. These comprise environmental factors differently affecting young and old participants, due to them being born in different generations, and include generation-dependent exposures that may also affect lipidome compositions (for example, diet, lifestyle and/or diseases) rather than actual age. However, we note that the longitudinal nature of our data better enabled us to eliminate some biases and focus on the same individual across time. Furthermore, we previously did not observe major dietary changes in the cohort. To identify lipid changes that occur with ageing in our longitudinal cohort, we used a linear model that estimates relative lipid changes as a function of the change in age (Δage model) while also controlling for sample storage length and BMI. With this model, we determined the ‘ageing’ effect (β coefficient) for each lipid subclass (Fig. 5c) and across lipid species (Fig. 5d).Fig. 5Age-associated changes in the lipidome.a, Median ages, age range (horizontal lines) and number of visits (y axis) of 90 healthy participants. Violin plot shows the distribution of age within the cohort. Inner boxplot reports the 25% (left hinge), 50% (centre line) and 75% (right hinge) quantiles. Whiskers indicate observations equal to or outside the hinge ± 1.5× the IQR. Outliers (beyond 1.5× the IQR) are not plotted. b, Correlation of median BMI and median age across healthy participants. Vertical lines depict the BMI range for each participant across all collected healthy samples. Regression line (red) from a linear model is shown with the 95% confidence band (grey). c, Significantly (BH FDR < 5%) changed lipid subclasses (percentage change for the summed untransformed concentration of respective lipid species) with ageing across 5 years based on the Δage model controlling for BMI and sample storage length. d, Fisher’s exact test enrichment analysis comparing physicochemical properties associated with higher age (positive log2(odds), red, determined for all positive Δage model coefficients at the lipid species level with a BH FDR of <10%) and those associated with lower age (negative log2(odds), blue, determined for all negative Δage model coefficients at the lipid species level with a BH FDR of <10%). Enrichments were calculated independently within lipid subclasses, as well as across all lipid species (‘all’). log2(odds) values are depicted for significant associations with lower or higher age (BH FDR < 5%). Infinite log2(odds) values are imputed with 0.5× the mean value of positive/negative log2(odds) determined across all data. MUFA, monounsaturated FA. e, Δage coefficients (ageing–sex) of individual lipid subclasses for male and female participants, controlling for sample storage length and BMI. f, Δage coefficients (ageing–IR/IS) of individual lipid subclasses for IR and IS, controlling for storage length, BMI and sex. For e and f, data are presented as the mean of estimated coefficients ± s.d., determined using an ordinary least-squares regression test.Source data a, Median ages, age range (horizontal lines) and number of visits (y axis) of 90 healthy participants. Violin plot shows the distribution of age within the cohort. Inner boxplot reports the 25% (left hinge), 50% (centre line) and 75% (right hinge) quantiles. Whiskers indicate observations equal to or outside the hinge ± 1.5× the IQR. Outliers (beyond 1.5× the IQR) are not plotted. b, Correlation of median BMI and median age across healthy participants. Vertical lines depict the BMI range for each participant across all collected healthy samples. Regression line (red) from a linear model is shown with the 95% confidence band (grey). c, Significantly (BH FDR < 5%) changed lipid subclasses (percentage change for the summed untransformed concentration of respective lipid species) with ageing across 5 years based on the Δage model controlling for BMI and sample storage length. d, Fisher’s exact test enrichment analysis comparing physicochemical properties associated with higher age (positive log2(odds), red, determined for all positive Δage model coefficients at the lipid species level with a BH FDR of <10%) and those associated with lower age (negative log2(odds), blue, determined for all negative Δage model coefficients at the lipid species level with a BH FDR of <10%). Enrichments were calculated independently within lipid subclasses, as well as across all lipid species (‘all’). log2(odds) values are depicted for significant associations with lower or higher age (BH FDR < 5%). Infinite log2(odds) values are imputed with 0.5× the mean value of positive/negative log2(odds) determined across all data. MUFA, monounsaturated FA. e, Δage coefficients (ageing–sex) of individual lipid subclasses for male and female participants, controlling for sample storage length and BMI. f, Δage coefficients (ageing–IR/IS) of individual lipid subclasses for IR and IS, controlling for storage length, BMI and sex. For e and f, data are presented as the mean of estimated coefficients ± s.d., determined using an ordinary least-squares regression test. Source data We found that the levels of most lipid subclasses increased with ageing, most prominently CERs (LCER, HCER, DCER), SMs, LPCs and CEs, with some of the observed variance suggesting more complex lipid–ageing dependencies (Fig. 5c). A general increase in the levels of multiple lipid species and subclasses is consistent with previous observations. Intriguingly, the levels of TAGs generally increased over time (Supplementary Fig. 11), but this trend disappeared when controlling for BMI. We performed an enrichment analysis on the Δage model coefficients at the species level and observed a shift in the physicochemical properties of lipids associated with ageing, including increased levels of SFAs and monounsaturated FAs, whereas the levels of polyunsaturated FAs (PUFAs) were reduced (Fig. 5d). This pattern has been previously associated with dyslipidaemia and inflammation, underlining progressive deterioration of metabolic health during ageing. We also observed depleted levels of (beneficial) omega-3 FAs. In particular, the levels of docosahexaenoic acid (FA(22:6), TAG) and eicosapentaenoic acid (FA(20:5), PE) decreased with ageing. These omega-3 FAs have been indicated to have beneficial health effects by lowering plasma cholesterol levels and serving as precursors for mediators that resolve inflammation, such as resolvins, protectins and maresins. In addition, decreased levels of linoleic acid (FA(18:2)) have been reported in aged skin; our data show that this is also a significant ageing biomarker in blood plasma, suggesting a more systemic decrease. Through desaturation and elongation, linoleic acid is metabolized to arachidonic acid (FA(20:4)), which we found to increase in abundance with increasing age when we applied less stringent filtering (Supplementary Fig. 12), further substantiating a general shift towards inflammation with ageing. Furthermore, large and small TAGs showed distinct patterns, underlining the different functional roles along the TAG spectrum. Interestingly, the levels of LPCs, which have been implicated in cardiovascular diseases and neurodegeneration and some of which are anti-correlated with CRP (Fig. 2), increased with ageing, further underlining their pleiotropic role in human health. We also observed a strong sex dimorphism for multiple subclasses (Fig. 5e). Beyene et al. reported sex-associated differences in lyso- and ether-phospholipid metabolism, which we confirmed in our study. In addition, we observed sex-associated differences for small TAGs as a prominent signature in ageing, with higher levels in men and lower levels in women. We next investigated the extent to which IR alters molecular ageing signatures and observed that participants with IR had larger coefficients for multiple subclasses, including HCER, LCER, SM and CE, than participants with IS. Larger coefficients indicate that ageing-related changes may be accelerated in IR versus IS (controlling for storage length, sex and BMI; Fig. 5f). In contrast to previous reports that did not distinguish IR status, our study identified a negative association between DAGs and ageing in participants with IR. Intriguingly, higher DAG levels are commonly linked to dyslipidaemia and IR; however, similar to TAGs (see above), DAGs may have a stronger association with BMI, which was controlled for in the model. Moreover, PI and PE showed opposite ageing effects in participants with IR and IS, which suggests IR-specific changes in phospholipid metabolism with ageing. In sum, the composition of many lipid subclasses (that is, degree of unsaturation, omega-3 FAs, large TAGs, ether-linked PEs) changes significantly with ageing, a process that—for some lipid subclasses—differs between the sexes and is distinctly accelerated in the presence of IR. Given the importance of cytokines, chemokines and growth factors in diverse biological processes, we characterized their relationship to lipids across homoeostasis and various pathophysiological disease processes in our longitudinal cohort. We investigated the degree to which the abundance of a particular lipid predicts the level of cytokines, chemokines or growth factors, controlling for BMI, sex, ethnicity and multiple measurements per participant as random effects across all samples and timepoints for which both measures were available (1,180 samples). Overall, we found 1,245 significant (FDR < 5%) positive and negative associations between a majority of lipids (580) and 40 cytokines, chemokines and growth factors (Fig. 6a).Fig. 6Lipid–cytokine associations.a–e, Network of 1,245 significant (BH FDR < 5%) lipid–cytokine associations, indicating positive (red) and negative (blue) associations calculated across 1,180 samples, across all lipids (a) and for PCs (b), PEs (c), LPCs (d) and LPEs (e). Networks were pruned based on a BH FDR of 5% for coefficients determined in linear mixed-effects models. Colour indicates lipid class; edge width represents coefficients; and node size represents node connectivity (popularity). The network was assembled using the ‘graphopt’ layout algorithm. f, Fisher’s exact test enrichment analysis comparing the physicochemical properties of lipids (y axis), at the subclass, global FA and individual FA level, that are associated with a particular cytokine (x axis). The analysis was performed for TAGs only (i), for all non-TAG lipids (ii) and across all lipids (iii). Enrichments (log2(odds)) among lipids with positive β coefficients (BH FDR < 10%) are indicated in red; enrichments (log2(odds)) among lipids with negative β coefficients (BH FDR < 10%) are indicated in blue; black denotes cases for which a certain property was enriched in both populations (positive and negative associations). log2(odds) values are depicted when the respective annotation was significantly associated with a BH FDR of <5%. Infinite log2(odds) values are imputed with 0.5× the positive/negative log2(odds) values determined across all data. IL-1Ra, IL-1 receptor antagonist; ICAM1, intercellular adhesion molecule 1; SDF1⍺, stromal cell-derived factor 1⍺; RANTES, regulated on activation, normal T cell expressed and secreted; PDGF-BB, platelet-derived growth factor-BB; GRO⍺, growth-regulated ⍺ protein; FasL, Fas ligand; TRAIL, tumour necrosis factor-related apoptosis-inducing ligand.Source data a–e, Network of 1,245 significant (BH FDR < 5%) lipid–cytokine associations, indicating positive (red) and negative (blue) associations calculated across 1,180 samples, across all lipids (a) and for PCs (b), PEs (c), LPCs (d) and LPEs (e). Networks were pruned based on a BH FDR of 5% for coefficients determined in linear mixed-effects models. Colour indicates lipid class; edge width represents coefficients; and node size represents node connectivity (popularity). The network was assembled using the ‘graphopt’ layout algorithm. f, Fisher’s exact test enrichment analysis comparing the physicochemical properties of lipids (y axis), at the subclass, global FA and individual FA level, that are associated with a particular cytokine (x axis). The analysis was performed for TAGs only (i), for all non-TAG lipids (ii) and across all lipids (iii). Enrichments (log2(odds)) among lipids with positive β coefficients (BH FDR < 10%) are indicated in red; enrichments (log2(odds)) among lipids with negative β coefficients (BH FDR < 10%) are indicated in blue; black denotes cases for which a certain property was enriched in both populations (positive and negative associations). log2(odds) values are depicted when the respective annotation was significantly associated with a BH FDR of <5%. Infinite log2(odds) values are imputed with 0.5× the positive/negative log2(odds) values determined across all data. IL-1Ra, IL-1 receptor antagonist; ICAM1, intercellular adhesion molecule 1; SDF1⍺, stromal cell-derived factor 1⍺; RANTES, regulated on activation, normal T cell expressed and secreted; PDGF-BB, platelet-derived growth factor-BB; GRO⍺, growth-regulated ⍺ protein; FasL, Fas ligand; TRAIL, tumour necrosis factor-related apoptosis-inducing ligand. Source data The largest numbers of positive associations were between granulocyte–macrophage colony-stimulating factor (GM-CSF) and TAGs and between leptin and TAGs (Fig. 6a and Supplementary Fig. 13). The adipokine leptin regulates caloric intake and is commonly present in elevated levels in obesity, contributing to the associated inflammatory state. Its amount in the blood correlates with the amount of adipose tissue. Its receptor is expressed in the hypothalamus, hippocampus and many immune cells; thus, it also acts as a neuroregulator and an immunoregulator. The cytokine GM-CSF, originally defined as a haemopoietic growth factor, has other biological roles, including exerting proinflammatory effects. These signatures are consistent with the inflammatory effect of the high TAG levels that we observed and are also found as a consequence of a high-fat diet, obesity and hepatic adiposity. The pleiotropic cytokine interleukin-6 (IL-6), whose inflammatory and anti-inflammatory effects are context dependent, together with the anti-inflammatory cytokine IL-10 (ref. ), showed negative associations with some TAGs and clustered distinctly from the positive TAG–leptin and TAG–GM-CSF associations, suggesting functional differences among different TAG species in immunoregulatory networks (Fig. 6a). TAGs showed the overall highest number of associations with leptin and GM-CSF, whereas lipids from other subclasses, such as PE, PC and DAG, were also positively associated (Fig. 6b,c and Supplementary Fig. 13). In contrast, lyso species of PE and PC (Fig. 6d,e) showed fewer associations with and less central roles for GM-CSF and leptin. Overall, these results suggest regulatory commonalities across lipid classes (for example, positive associations of TAGs, DAGs, PCs and PEs with leptin) and differences within subclasses for proinflammatory and immunoregulatory pathways. To elucidate the extent to which specific subsets of lipid species are associated with cytokines and chemokines, we performed an enrichment analysis (Fig. 6f). Overall, we observed strong associations of FAs with distinct cytokines. For instance, positive leptin–TAG associations were significantly enriched for SFA, the polyunsaturated FA(18:3) and small TAGs. In contrast, large TAGs were negatively associated with IL-6 and IL-10. Moreover, we observed a hub of negative associations between TAGs containing FA(22:5) and multiple cytokines, including the anti-inflammatory IL-10 and the proinflammatory IL-23, as well as IL-6. Enrichment of TAG subclasses for positive and negative associations within both proinflammatory and immunoregulatory cytokines suggests that TAG subclasses (in terms of both the length and saturation of the acyl chain) have distinct roles in immunoregulation and signalling. In Fig. 2, we found that some LPCs were associated with anti-inflammatory, hence healthier, signatures. Here, LPCs were positively associated with several growth factors, such as epidermal growth factor (EGF), vascular endothelial growth factor (VEGF) and brain-derived neurotrophic factor (BDNF), and resistin. VEGF is involved in promoting angiogenesis, whereas BDNF and EGF promote cell proliferation, with BDNF having a cardinal role in neurogenesis and plasticity. In addition, LPCs were positively associated with the soluble CD40 ligand (sCD40L), which is secreted by activated T cells and platelets during inflammation, as well as with the inflammatory cytokine IL-1⍺ and the adipose tissue-specific secretory factor resistin, which induces other cytokines and has been suggested to contribute to a chronic proinflammatory cascade in T2D. Together, LPCs demonstrated contrasting associations, including some anti-inflammatory and tissue repair as well as proinflammatory signatures. These associations may highlight a difference between chronic inflammation (for example, mediated by factors such as resistin during T2D) and acute inflammation (for example, during an infection), which is strongly associated with high CRP levels. It may also reflect that both inflammatory and anti-inflammatory mediators are present in amounts that regulate a response so that it is effective but not excessive. Moreover, PCs containing linoleic acid (FA(18:2)) were negatively associated with the chemokines CXC motif ligand 9 (CXCL9; also known as MIG (monokine induced by interferon-γ (IFNγ))) and CXCL10 (also known as IP-10 (IFNγ-induced protein 10 kDa); Fig. 6b,f). CXCL9 and CXCL10 are induced by IFNγ to recruit cells to sites of inflammation; they bind to the same chemokine receptor, CXCR3. This association suggests that these lipids may affect immune cell migration during inflammation, in addition to their immune modulation role that we observed during RVI (Fig. 4). Overall, our multiomics data outline complex associations between cytokines and lipid subclasses as well as differential associations of lipids with specific FA compositions, suggesting distinct roles ranging from immune activation to immunosuppression. Until recently, most omics studies have focused on transcriptomics, proteomics and, more recently, metabolomics, which is more closely associated with many phenotypes. However, lipidomics remains largely underexplored despite lipids’ important roles in cell signalling, cell structure and energy management. The lipidome has been difficult to study owing to its complexity and the fact that it is derived from both endogenous and exogenous factors, such as the microbiome and lifestyle (diet and physical activity). Investigating the dynamic range of lipid changes, including which lipids change with acute or chronic conditions over what period, may reveal markers of early disease onset and progression as well as mechanistic insights that can be used to develop better and personalized treatments. By following participants for up to 9 years, we identified highly participant-specific lipids and lipid subclasses (Figs. 1e and 2a–c), functional modules of lipids that map to clinical measures at baseline and throughout diseases (Figs. 2d,f and 3f), and lipid outlier signatures that may be predictive of diseases such as hepatic steatosis (Supplementary Fig. 6). Across perturbations such as IR (Fig. 3), viral infections (Fig. 4), ageing (Fig. 5) and cytokine–lipid associations (Fig. 6), we observed distinct behaviours among many lipid subclasses, such as ether- and ester-linked PEs, small and large TAGs, and lipids with specific FA configurations (for example, omega-3/omega-6 FAs, PUFAs and SFAs). Overall, our results point to the distinct biological roles of lipid subclasses and demonstrate that conventional clinical lipid profiles (that is, overall TAG levels) do not resolve many changes relevant to metabolic health. Throughout our analyses, we consistently observed distinct behaviours between ester-linked and ether-linked PEs (PE versus PE-O/PE-P), as well as between two functionally distinct subgroups of TAGs (small TAGs (≤48 carbons across all FAs) and large TAGs (≥49 carbons across all FAs)). Ether-linked PEs have been implicated in cell signalling and as antioxidants, and we found them to be significantly associated with healthy phenotypes including low SSPG levels and high HDL levels. In addition, ether-linked PEs are depleted early during infection, putatively increasing the inflammatory state, and/or depleted by scavenging radical oxygen species resulting from inflammation. In addition, PE, PE-P and PE-O show sex- and IR/IS-specific signatures during ageing (see below). Together, these observations suggest that ether-linked PEs are associated with health and chronically low levels of these PEs may have detrimental effects in humans. In this context, it will also be interesting to investigate other ether-linked lipid subclasses known to be detectable in blood plasma, such as PCs with alkyl ether substituent (PC-O) and PCs with alkenyl ether (Plasmalogen) substituent (PC-P), in future studies. Recently, we reported the differential regulation of small and large TAGs within a 60-min recovery phase after exercise. Here, we confirm and expand our previous observations suggesting new clinically relevant physiological roles of these TAGs within a much larger longitudinal cohort. Our data demonstrate that (1) many biological variations are captured in TAGs’ abundance profiles (Figs. 1e and 2a), which (2) are distinct for TAG subgroups including large and small TAGs, and those containing specific FAs (Figs. 5d and 6f). For instance, small TAGs show distinct associations with certain cytokines and chemokines and are rapidly depleted during early RVI, followed by a rapid recovery to baseline levels. Depletion of small TAGs during infection suggests an important role in energy metabolism and signalling to support early inflammation. In addition, during ageing, large and small TAGs differ markedly, suggesting distinct roles in ageing-related energy metabolism and lipid-mediated signalling. TAGs also showed the highest technical reproducibility in this study, making them an ideal target for new biomarkers at the subclass and species levels. We therefore propose that small and large TAGs as well as ether-linked PEs could be further explored as health biomarkers. Moreover, dietary supplements affecting plasma levels may provide a therapeutic avenue to reduce chronic inflammation and the detrimental effects of ageing and other conditions. In addition to identifying lipids that decrease in abundance with ageing, such as PUFAs, omega-3 FAs, FA(18:3) and FA(18:2), we identified many lipid subclasses and properties that are enhanced with ageing, including the proinflammatory CEs, CERs, SMs, arachidonic acid and SFAs, as well as LPCs whose role could be more ambiguous. Intriguingly, some of these effects were stronger (for example, HCERs, CEs, SMs) or directionally different (for example, PEs, DAGs) in participants with IR, which can be interpreted as accelerated or differential ageing. IR is associated with chronic inflammation and can lead to dyslipidaemia and metabolic syndrome, which, in turn, increases the risk of age-related morbidities such as diabetes or cardiovascular diseases. Together with a distinct regulation between IR and IS, these observations indicate a large-scale realignment of chronic inflammatory processes during ageing, which may be accelerated in individuals with IR. Moreover, we observed strong sex-specific ageing signatures for lipid subclasses including CE. As cholesterol is a precursor of steroid hormones, many of these sex differences in CE levels may relate to or even cause differences in sex-specific hormone levels. In addition to ageing-related differences, we found significant differences in both the effect sizes and the direction of the correlation of lipid subclasses and clinical measures between participants with IR and those with IS. Importantly, our models controlled for—among other variables—BMI, which is commonly associated with IR. Our analysis therefore highlights significant IR/IS differences separate from BMI effects. Key IR/IS-related differences include clinical markers related to T2D and the immune response; haematological, hepatic and renal measures; as well as electrolytes. For instance, PE-P/PE-O, LPE and LPC showed distinct directions of associations with multiple clinical measures, such as the LDL to HDL ratio and various cell populations, for IR and IS, expanding the previously suggested interplay between clinical measures and complex lipids. As many lipids have both bioenergetic and signalling functions, our observation indicates important differences in cellular signalling in participants with IR. Overall, these results underscore that the holistic assessment of metabolic health state is highly useful, and in some cases necessary, to improve the interpretation of conventional clinical measures such as the LDL to HDL ratio. We identified distinct lipid subclasses and species that change in various disease states such as acute (RVI) and chronic (diabetes, ageing) inflammatory conditions. The role of lipids in immunity is only partially understood given the complexity of both the immune system and lipid metabolism. Our analysis covered myriad positive and negative cytokine–lipid associations, providing a valuable reference and resource for future studies. An important finding in this study is the strong positive associations of GM-CSF, leptin and the chemokine eotaxin (CCL11) with small TAG species (Fig. 6). As GM-CSF and leptin are involved in regulating and promoting inflammation, their strong associations with small TAGs underscore the central and distinct role these lipids may have in immunoregulation in comparison with groups of large TAGs. Moreover, our data provide strong evidence for the pleiotropic role of LPCs, which are categorized as proinflammatory. We found a strong positive association with proinflammatory signalling molecules, including IL-1⍺ and sCD40L, in addition to positive associations with growth factors such as BDNF and negative correlations with the proinflammatory markers CRP and SSPG. There may be feedback loops such that a response is balanced, leading to the production of both inflammatory and anti-inflammatory cytokines with different kinetics. Inflammatory responses must be proportionate to the stimulus so that they are effective but not excessive (leading to damage, for example, a cytokine storm). After the initial acute inflammation, the response needs to diminish and anti-inflammatory signals need to increase. This process needs to be tuned in both magnitude and kinetics, and associated lipids may have a role in these responses. Although this study was not designed to mechanistically resolve the role of LPCs and other lipid subclasses that we observed to be longitudinally associated with, for instance, viral infections, our data provide a resource for future studies and highlight putatively competing roles that may depend on physiological and pathological contexts, including comorbidities, age, BMI or IR/IS status. Although the study cohort is ethnically diverse and sex balanced similar to the US population, there are some limitations. (1) There is a bias towards middle-aged and highly educated participants with a higher proportion of individuals living in northern California. (2) Some of the insights were generated based on small sample numbers (for example, outlier analysis; Supplementary Fig. 6). Therefore, not all observations and findings may be generalizable to a wider population that is subject to different lifestyles. Although we identified many signatures that have been observed in previous studies, supporting the validity of our findings, lifestyle differences can have an effect and future studies should be designed to confirm and extend our observations. (3) Our lipidomics pipeline targets >1,100 lipid species but does not always resolve the exact molecule identity (for example, position of a double bond in FAs). To expand the set of lipids investigated in this study, we included phosphatidylinositol (PI) as a new lipid class in multiple-reaction-monitoring transitions (MRMs) and observed multiple PI species to be significantly altered, for example, during RVIs. For this lipid class, however, no internal spike-in standards were included in the sample preparation, which limits the accuracy of the abundance information for all PIs. Moreover, not all putatively detectable blood plasma lipids, including PC-O and PC-P, were monitored. (4) The biphasic extraction method used here is an established and efficient procedure that is compatible with high-quality non-glass laboratory equipment. However, we have observed that FFAs have higher baseline levels when extracted with non-glass material, which can lead to an overestimation of the levels of certain FFAs. This ratio compression can reduce the sensitivity for detecting subtle changes between participants. (5) We used descriptive models (that is, we did not evaluate the predictive power by using cross-validation and hold-out data). Although we confirmed many previous findings and observed new lipid–phenotype associations, correlations are not proof of causation. In addition, our models controlled for multiple covariates (for example, sex and BMI), allowing for separation of effects, such as lipid–ageing versus lipid–BMI associations. Ageing is a complex process, and increasing BMI may be a default process for industrialized societies and lifestyles. Moreover, high BMI has been linked to inflammation. Thus, controlling for BMI may not always be desirable for understanding age- and inflammation-related changes in these cohorts. (6) The roles of complex lipids are diverse, and the physiological impacts of lipids with specific properties (for example, the fraction of complex lipids containing arachidonic acid) may not be due to direct effects. Our study provides a longitudinal, in-depth analysis of intricate lipid–health relationships during acute and chronic inflammation, metabolic diseases and ageing. The multitude of lipid–phenotype connections we have revealed here serve as a valuable resource for biomarker discovery, a starting point to investigate disease mechanisms, and a basis to conceive therapeutic and preventive strategies. For instance, although many lipids undergo intestinal lipolysis driven by endogenous enzymes and microbial processes, some dietary lipids are absorbed directly. Our research suggests a variety of potential dietary interventions that could improve human health. Supplements such as ether-linked PEs may alleviate inflammation, providing effective treatment for chronic inflammation and facilitating recovery after RVI by minimizing damage related to reactive oxidative agents. Alongside adjusting the small TAG to large TAG ratio, these lipids could also be instrumental in addressing ageing- and IR-associated dyslipidaemia. Moreover, some lipid species and subclasses, including FFAs, ether-linked PEs, CEs or CERs, demonstrate sex-specific and ageing-related patterns. This prominent sex dimorphism suggests that sex- and age-specific interventions should be considered and might enhance therapeutic effects. It would also be worthwhile to assess the impact of genetic polymorphisms, particularly in relation to lipid species and subclasses exhibiting high variance, such as certain TAG, DAG and PE species, which may be relevant for personalized interventions. Together, future studies should explore how altering the exogenous lipid intake (for example, through diet) or targeting lipid conversion enzymes can affect both plasma lipid signatures and clinical phenotypes such as IR, acute and chronic inflammation, and molecular ageing. Participants were enrolled as ‘healthy volunteers’ in the framework of the National Institutes of Health integrated Human Microbiome Project 2 (ref. ). Inclusion and exclusion criteria were previously described in detail. Participants provided informed written consent for the study under research study protocol 23602 approved by the Stanford University Institutional Review Board. Plasma samples were prepared and analysed in a randomized order. Plasma was thawed on ice, and lipids were extracted using a biphasic separation technique (ice-cold methanol, methyl tert-butyl ether and water). A 260-μl volume of methanol and 40 μl of a spike-in standard (cat. no. 5040156, Sciex) were added to 40 μl of plasma, and the mixture was vortexed for 20 s. Lipids were extracted by adding 1,000 μl of methyl tert-butyl ether and incubating the samples under agitation for 30 min at 4 °C. Phase separation was induced by adding 250 μl of ice-cold water, followed by vortexing for 1 min and centrifugation at 14,000g for 15 min at 4 °C. The upper phase containing the lipids was collected, dried down under nitrogen and stored at −20 °C in 200 μl of methanol. On the day of MS acquisition, lipids were dried down under nitrogen and reconstituted with 300 μl of 10 mM ammonium acetate in a 9:1 mixture of methanol and toluene. The QTRAP 5500 system (Sciex) equipped with a DMS device (Lipidyzer) was operated with a Shimadzu SIL30AC autosampler for targeted lipidomics, with a modified strategy to include additional lipid species in the acquisition. To ensure robustness of results, the Lipidyzer was cleaned and tuned after each batch (every 48 h; Supplementary Fig. 14). The tuning solution contained 40 μl of the SPLASH internal standard mix, 100 μl of the Sciex tuning mix, 100 μl of Lyso-tune mix (1 mg ml 17:1 lysophosphatidylglycerol (LPG), 1 mg ml 17:1 lysophosphatidylserine (LPS), 0.1 mg ml 17:1 lysophosphatidylinositol (LPI), 10 μg ml lysophosphatidic acid (LPA)) and 760 μl toluene–methanol (1:9) with 10 mM ammonium acetate. For lipid extracts from 40 μl of plasma, three acquisition methods were used. The injection volumes were 42, 50 and 39 μl for methods 1, 2 and 3, respectively. The source temperature was set to 150 °C for all methods. Methods 1 and 3 were operated with DMS enabled and at a separation voltage of 3,700 V. Lipid classes were monitored as follows: method 1—PC (140), PE (119), PE-O (36), PE-P (61), LPC (26), LPE (26); method 2—CE (26), CER (12), DCER (12), HCER (12), LCER (12), FFA (26), TAG (519), DAG (59); method 3—SM (12), phosphatidic acid (PA; 77), LPA (12), phosphatidylglycerol (PG; 78), LPG (16), PI (77), LPI (16), phosphatidylserine (PS; 78), LPS (16). Each transition was acquired 20 times (see Supplementary Data 2 for the compensation voltage, Q1 and Q3 masses, and dwell times). Method 1, method 2 and the positive mode of method 3 (SM) contained transitions of the Lipidyzer original setup. Method 3 negative mode targets additional lipids. Data acquisition was performed similarly to the processing of the Lipidyzer Workflow Manager. First, *.wiff files were converted to *.mzML files with MSConvert (v.3.0), setting ‘write index’ and ‘TPP compatibility’ to true. For each raw file, data extraction was performed in R. In brief, *.mzML files were imported with openMSfile(FileAndPath, backend = “pwiz”)chromatograms(‘openMSfile_output) using mzR (v.2.6.2). Next, all transitions with more than two zero intensities throughout the 20 repeated measurements were excluded (reported as ‘not available’). For all remaining transitions, the mean intensity was calculated (excluding zero-intensity recordings). Lipid species identities were matched based on the Q1 and Q3 masses and the corresponding scan index (the order in which MRMs were scheduled) in methods 1 (negative mode: PC, PE, LPC, LPE), 2 (negative mode: FFA; positive mode: TAG, CE, DAG, CER, DCER, HCER, LCER) and 3 (negative mode: LPG, PG, LPI, PI, LPS, PS, LPA, PA; positive mode: SM). Note that the transitions PG, PS, PA and their respective lyso forms were not considered for analysis. For lipids monitored in methods 1 and 2, as well as SM (method 3), internal standards from the Sciex Lipidyzer internal spike (LPISTDKIT-102b) were matched according to the Sciex Lipidyzer protocol. Individual concentrations were estimated based on the known abundance of the corresponding spike-ins. In brief, concentrations (‘actual concentration’) of all spike-in standards were retrieved from the ‘certificate of analysis’ of the internal spikes and converted to ‘nmol ml’. Lipidyzer assumes that, in plasma, nmol g = nmol ml. Internal spike stocks of individual lipid classes were mixed, dried down and resuspended in a volume to adjust their respective stock concentration to the expected plasma levels (using the Lipidyzer Workflow Manager as a reference). The internal spike area measured by MS was compared with that of the respective endogenous lipids to approximate the absolute concentrations of the endogenous lipids. For complex lipids with two identical FAs, it was assumed that the measured signal from the fragment ions was at 2× intensity. Lipids belonging to the additional classes in method 3 had no corresponding standards and were normalized based on one of the other spiked-in lipids, as detailed in the next section. The Lipidyzer resolves an individual FA as part of a TAG within each transition, a layer of information that we use to evaluate changes in FA compositions. For analyses that do not rely on the specific FA composition depicted in Fig. 3c, we aggregated TAGs to groups defined by summed FA carbons and the number of unsaturations, summing the untransformed concentrations of the corresponding TAGs. Spike-in standards (internal spikes) were not available at the time of lipid extraction for the lipids monitored here. Although this allows a relative comparison across samples given a reproducible workflow, we desired to leverage the information of the other internal spikes to further normalize for variances introduced across samples. To that end, we performed a correlation analysis by using QC samples derived from the same stocks that were measured across all batches. The lipid intensities in these samples are expected to be the same, and the ratios of internal lipids compared with internal spikes will be the same if the variation introduced by lipid extraction or MS analysis affects them in a similar manner. This setup allowed us to identify spike-in standards to normalize the additional lipids monitored in method 3 (PA, LPA, PG, LPG, PS, LPS, PI, LPI). For this normalization, we only considered internal spikes of the classes PC, PE, LPC and LPE, as those were also acquired in positive mode with DMS enabled. We calculated the Pearson correlation coefficients for log10(intensities) comparing internal spikes and the new lipid species, and selected pairs according to the following hierarchy: (1) the highest correlating internal spike with at least 50% complete observations across QC samples was selected; (2) if a match could not be determined for a lipid species–internal spike pair, we selected the internal spike that showed the highest correlation with any other lipid of the same class; and (3) if both (1) and (2) did not select an internal spike, the highest correlating spike-in standard across all additional lipid classes was selected. For all original lipid classes, internal spikes of known concentrations allow the approximation of absolute abundances. As described above, the missing internal spikes for new lipids in method 3 do not allow direct inference of absolute abundances. Using a linear regression model based on all the known concentrations of lipids in the samples, we predicted the concentrations of the new lipids. As the normalized abundances for these lipids (method 3) are not based on labelled spike-in of the same molecular class and thus do not account for ionization efficiency differences, they provide an estimate of the absolute abundance range of the new classes. Importantly, this normalization does not affect the comparison of the relative abundance of the same lipid species across samples. To ensure the accuracy and reliability of our analysis, we implemented several data filtering criteria. First, we excluded from the analysis biosamples with >25% missing data. Additionally, lipids with <10% valid values, as determined by the Lipidyzer reporting requirements, were also excluded. To further ensure high-quality quantitative results for the results presented in Figs. 2–6, we removed any lipid with a CV of >20% in QC samples and the few lipids for which the CV in QC samples was higher than the CV across the remaining biosamples. Furthermore, owing to limitations in separation by DMS, we did not include PAs in our analysis. We also excluded PS/LPS and PG/LPG from the analysis as they showed a significant number of missing values. PI(16:0/18:3) was removed from the dataset owing to its association with incorrect masses. Finally, QC_73 from batch 21 was removed owing to separate clustering compared with all other QC samples. Four internal spikes were not consistently quantified across the samples (missingness rate >5%) and were substituted with similar deuterated (d) standards belonging to the same class: dDAG(16:0/18:3) missing in 21% of the samples was substituted with dDAG(16:0/18:2); dDAG(16:0/20:5) missing in 12% was substituted with dDAG(16:0/20:4); dPE(18:0/22:5) missing in 44% was substituted with dPE(18:0/20:4); and dPE(18:0/20:5) missing in 8% was substituted with dPE(18:0/20:4). Lipids were normalized based on the internal spike-in standards (see above), similar to the standard Lipidyzer workflow that has been validated previously. Within an expanded method published by Su et al., additional MRMs can be used for isotope correction. Here, we did not acquire all of the MRMs needed for isotope correction. Although the extent of correction depends on the abundance of interfering species and can significantly mask the signal of a targeted lipid species, Su et al. reported that no species was corrected by >6% and, outside of TAGs, no species was corrected by >3% (ref. ). Cytokines were obtained in three separate batches. Data were log2 transformed and corrected for the effect of the batches using the ‘dbnorm’ (v.0.2.2) package. The ComBat model (sva (v.3.38.0)) showed the best performance and thus was considered in this study. Missing values were imputed using a K-nearest-neighbour strategy that accounts for a truncated distribution (Extended Data Fig. 2). This approach involves drawing from intensities at the detection limit defined for each lipid class separately. This was a reasonable yet conservative assumption that allowed for the imputation of missing values without inflating fold changes by considering the sensitivity of MS. Missing weight measures in the ageing analysis were imputed by taking the mean between the two closest adjacent timepoints (Supplementary Data 2). Missing levels of several cytokines in batch 1 (that is, hepatocyte growth factor (HGF), basic fibroblast growth factor (FGFb), IL-8, IL-9, MIP-1⍺, stem cell factor (SCF) and tumour necrosis factor-β (TNFβ)) and batch 2 (that is, IFN⍺2 and FGFb) were imputed using the K-nearest-neighbour method with the number of neighbours being 10. CV was calculated for non-imputed, untransformed data. If, for a participant, multiple samples were collected on the same day, those were excluded. Only participants with at least three sampling timepoints were considered. Lipids with fewer than three quantifications were excluded. Note that, although the average intraparticipant CV was larger than the average QC CV, a small subset of low-abundance lipid species for a subset of participants showed lower CVs. These CVs emerged from the low signal that results in discrete quantifications from the mass analyser. t-SNE scatterplots were generated after log2 transformation and z-score scaling of the data using the R package ‘Rtsne’ (v.0.15) with the following parameters: perplexity = 5, θ = 0.5. Network analysis using self-reported healthy samples (Fig. 2d) was performed using the WGCNA R package (v.1.70-3). The soft-threshold power was optimized to achieve approximate scale-free topology (R > 0.8). Networks were constructed using the ‘blockwiseModules’ function. The network dendrogram was created using average linkage hierarchical clustering of the topological overlap dissimilarity matrix (1 − TOM). Modules were defined as branches of the dendrogram by using the hybrid dynamic tree-cutting method, selecting a minimum module size of 5. Modules were presented by their first principal component (module eigengene) of the standardized expression profiles. Modules with eigengene correlations of >0.8 were merged together, generating seven lipid modules. Next, the Pearson correlation coefficients between the module eigengene and clinical measures were calculated using the ‘cor.test’ function in R (stats (v.3.6.2)), and all the obtained P values for the correlations were corrected for multiple hypotheses through the Benjamini–Hochberg (BH) procedure (stats (v.3.6.2)). To determine over- and under-representation of functional subgroups of lipids, we classified all lipid species based on their physicochemical properties (Supplementary Data 2) as reported in the LION database. Under- or over-representation was evaluated using a hypergeometric test (Fisher’s exact test) or one-dimensional annotation enrichment. Our dataset is dominated by TAG species, which could bias the enrichment analysis results. For that reason, we performed the enrichment analysis across all lipids and within subclasses. For each figure, the following statistics were used to calculate enrichments for categories: ‘OddEven_All’, ‘Omega_All’, ‘Saturation_All’, ‘Lipid_Class_Detailed_Special’ and ‘FA_All’, applying a BH FDR set to 0.1 by using the ‘p.adjust()’ function. Figure 3e shows the Fisher’s exact test comparing positive SSPG coefficients with negative SSPG coefficients. Figure 4c shows the Fisher’s exact test determining whether the significantly changed lipids during infection (RVI) were enriched. Figure 5f shows the Fisher’s exact test comparing positive Δage coefficients with negative Δage coefficients. Figure 6f shows the Fisher’s exact test calculating enrichments in positive coefficients as well as negative coefficients. Negative infinite log2(odds) and positive infinite log2(odds) were imputed with 0.5× the minimum log2(odds) and 0.5× the maximum log2(odds), respectively. If a category was enriched among negative and positive coefficients for a lipid (that is, an over-representation was observed in the FDR-significant positive and negative coefficients), the enrichment was set to 0 and highlighted in black in the heat map. Correlations between lipid levels and clinical measures were calculated using the ‘cor.test’ function in R. Lipidomics data and laboratory measures were both standardized before correlation calculation. To investigate differences between the correlations in IR and IS, we calculated the correlations by using only the healthy samples from participants with IR and IS, and we highlight only the correlation contrasts that were significantly different between IR and IS. The correlations between lipid levels and clinical measures from using all healthy samples from participants with IR and IS were also calculated and are presented as reference values. K-means clustering was performed to investigate lipid similarities following infection by using the lipidomics data of infection events after log2 transformation and z-score scaling. We calculated the minimum centroid distance for a range of cluster numbers, and the optimal number was chosen using the ‘elbow’ method. The median values of the lipid profiles belonging to each cluster were correlated with the clinical measures to indicate medical implications. The correlations were calculated using the ‘cor.test’ function in R, and all the obtained P values for the correlations were corrected for multiple hypotheses using the BH procedure. To detect the time intervals of differentially abundant lipids between IR and IS during infection events, we used a longitudinal analysis method, OmicsLonDA. For each lipid in each group (IR or IS), we used a generalized additive mixed model for modelling nonlinear time-series abundance during the inflammation episodes. OmicsLonDA is an extension of MetaLonDA to account for correlated data, repeated measurements and multiple covariates. We accounted for sex, age, ethnicity and BMI as covariates, whereas participant identifiers were used as random effects. The P values for each lipid at each time interval (the time interval unit was set to 1 day) were obtained and then adjusted for multiple testing by using the BH procedure. We implemented this process on both infection and immunization events, identified the significantly different time intervals between IR and IS in both kinds of events, and compared these significant time intervals. To identify lipids that were associated with SSPG, linear mixed models were applied using log-transformed lipid measurements, controlling for participants, sex, ethnicity, age and BMI (Fig. 3). The R package ‘lme4’ (v.1.1-27.1) was used to construct the linear mixed models, as well as output estimates and nominal P values. The obtained raw P values were corrected for multiple hypotheses through the BH procedure by using the ‘p.adjust’ function in R. To identify lipids that were significantly changed during infection episodes, linear mixed models were applied using log-transformed lipid measurements, controlling for participants, sex, ethnicity, age and BMI (Fig. 4). The R package ‘lme4’ (v.1.1-27.1) was used to construct the linear mixed models, as well as output estimates and nominal P values. The obtained raw P values were corrected for multiple hypotheses through the BH procedure by using the ‘p.adjust’ function in R. For each individual, ageing-associated lipid changes were calculated by subtracting measurements obtained at each visit from the baseline values (Fig. 5). Accordingly, the number of years since onset was calculated as the number of years from the first recorded measurement. To estimate the fractional changes in lipid measurements, we used a linear regression model with log-transformed lipid measurements, controlling for BMI and storage length (and sex if indicated in the figure). To control for potential biases related to the number of samples per individual, we excluded measurements from one participant with a uniquely large number of samples. To control for potential biases related to a few participants with measurements spread across a longer enrolment time, we excluded a few samples collected >5 years since onset. All coefficients and s.d. values were estimated using the ordinary least-squares implementation of the linear regression method in the ‘statsmodels’ package with the default parameters in Python (v.3.7). Linear models were run either at the lipid species level (Fig. 5d) or the lipid class level (sum of raw concentrations) for all participants (Fig. 5c), as well as for sex and IR/IS (Fig. 5e,f). We used linear mixed-effects models (lmer ) controlling for BMI, sex, ethnicity and participants (random effects) to estimate cytokine levels as a function of estimated lipid concentrations (Fig. 6). Both cytokine levels and lipid signals were scaled and centred (scale()). Restricted maximum likelihood was set to false, and P values were estimated using summ and corrected for multiple-hypothesis testing with p.adjust() applying a BH FDR of <5% for network generation. The cytokine–lipid network was constructed based on model coefficients by using the ‘graphopt’ layout algorithm in graphlayout (v.2.1.0), igraph (v.1.5.0) and tidygraph (v.1.2). The network was pruned to exclude all coefficients with a BH FDR of >0.05. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
|
PMC6691112
|
Alterations in lipid metabolism of spinal cord linked to amyotrophic lateral sclerosis
|
Amyotrophic lateral sclerosis (ALS) is characterized by progressive loss of upper and lower motor neurons leading to muscle paralysis and death. While a link between dysregulated lipid metabolism and ALS has been proposed, lipidome alterations involved in disease progression are still understudied. Using a rodent model of ALS overexpressing mutant human Cu/Zn-superoxide dismutase gene (SOD1-G93A), we performed a comparative lipidomic analysis in motor cortex and spinal cord tissues of SOD1-G93A and WT rats at asymptomatic (~70 days) and symptomatic stages (~120 days). Interestingly, lipidome alterations in motor cortex were mostly related to age than ALS. In contrast, drastic changes were observed in spinal cord of SOD1-G93A 120d group, including decreased levels of cardiolipin and a 6-fold increase in several cholesteryl esters linked to polyunsaturated fatty acids. Consistent with previous studies, our findings suggest abnormal mitochondria in motor neurons and lipid droplets accumulation in aberrant astrocytes. Although the mechanism leading to cholesteryl esters accumulation remains to be established, we postulate a hypothetical model based on neuroprotection of polyunsaturated fatty acids into lipid droplets in response to increased oxidative stress. Implicated in the pathology of other neurodegenerative diseases, cholesteryl esters appear as attractive targets for further investigations.The central nervous system (CNS) is characterized by the presence of high amounts and a wide variety of lipids. According to their molecular characteristics and cellular localization, lipids play a critical role in the CNS controlling membrane fluidity, improving transmissions of electrical signals and serving as precursors for various second messengers. Although molecular alterations in CNS at different stages of life are part of the physiological development, aging can modulate alterations in the lipidome, especially in response to increased reactive oxygen species (ROS). As a consequence, alterations in lipid metabolism could contribute to the onset of neurodegenerative disorders. This paper is focused on ALS, a neurodegenerative disorder characterized by loss of motor neurons in CNS. Symptomatic stages of ALS lead to muscular atrophy, paralysis and death within 5 years after the onset of symptoms. Among familial cases of ALS, G93A mutation in the gene that encodes for the antioxidant enzyme Cu/Zn-superoxide dismutase (SOD1-G93A) is one of the most studied models. The putative mechanisms involved in ALS progression consist in protein aggregation of mutant SOD1, abnormal production of ROS and alterations in mitochondrial functions. Lipid alterations in ALS have been mainly investigated through targeted analysis of some specific lipid species. Historically, a pioneer study by Cutler et al. (2002) revealed increased levels of sphingolipids and cholesteryl esters based on targeted lipid analysis of the spinal cord from an ALS mouse model. In addition, these authors suggested a link between sphingolipid metabolism and synthesis of cholesteryl esters in the CNS. Alterations in sphingolipid metabolism were also emphasized in skeletal muscle and spinal cord from SOD1 mice. Increased sphingolipid content, specially ceramides and glucosylceramides, as well as accumulation of phosphatidylcholine (PC 36:4) were reported as the main lipid alterations in cerebrospinal fluid of ALS patients. Collectively, these studies and others have found evidence for significant lipid metabolism alterations in advanced stages of ALS. Here, we sought to capture global lipidome alterations linked to ALS progression by performing an untargeted mass spectrometry-based lipidomic analysis of motor cortex and spinal cord tissues from ALS asymptomatic (SOD1-G93A 70 days old) and symptomatic (SOD1-G93A 120 days old) rats in comparison to their wild types as controls. Our analysis revealed drastic changes in lipid metabolism in the CNS both according to aging and disease progression. To investigate how lipids are affected in ALS, we performed a global lipidome analysis of motor cortex and spinal cord from asymptomatic (SOD1-G93A 70 days) and symptomatic (SOD1-G93A 120 days) ALS rats, and their respective age-matched wild type (WT 70 days and WT 120 days) as control groups. Results from these analyses are presented separately below. Using untargeted lipidomics, we identified and quantified 285 lipid molecular species in the motor cortex, which were sorted into 26 lipid subclasses (Fig. 1A). Glycerophospholipids (n = 120) followed by sphingolipids (n = 78), glycerolipids (n = 69) and free fatty acids (n = 14) showed the highest diversity of individual lipid molecular species. In terms of relative abundance, plasmenyl and diacyl phosphatidylethanolamine (pPE and PE) encompassed together about 70% in mass of the total identified lipids in motor cortex of both SOD1-G93A and WT rats (Fig. 1B). The remaining 30% of lipids were mainly composed of phosphatidylserine (PS, ~7%), diacylglycerol (DAG, ~5%), free fatty acids (FFA, ~2%), among others.Figure 1Lipidome profile in motor cortex of SOD1-G93A and WT groups at 70 and 120 days old. (A) Number of the identified lipid molecular species per lipid subclass. (B) Relative abundance of summed concentrations of each lipid subclass. Abbreviations: FFA, free fatty acids; CL, cardiolipin; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PG, phosphatidylglycerol; PI, phosphatidylinositol; PS, phosphatidylserine; Cer, ceramide; GalC, galactosylceramide; 1G-aC, monoglycosylated acylceramide; SM, sphingomyelin; 1G-AEG, monoglucosyl-acyl-ether-glycerol; DAG, diacylglycerol; TAG, triacylglycerol; Ch, cholesterol; CE, cholesteryl ester; UbQ, ubiquinone; p- or o- before phospholipids (PC, PE, PG and PS) indicate plasmenyl or plasmanyl, respectively. Lipidome profile in motor cortex of SOD1-G93A and WT groups at 70 and 120 days old. (A) Number of the identified lipid molecular species per lipid subclass. (B) Relative abundance of summed concentrations of each lipid subclass. Abbreviations: FFA, free fatty acids; CL, cardiolipin; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PG, phosphatidylglycerol; PI, phosphatidylinositol; PS, phosphatidylserine; Cer, ceramide; GalC, galactosylceramide; 1G-aC, monoglycosylated acylceramide; SM, sphingomyelin; 1G-AEG, monoglucosyl-acyl-ether-glycerol; DAG, diacylglycerol; TAG, triacylglycerol; Ch, cholesterol; CE, cholesteryl ester; UbQ, ubiquinone; p- or o- before phospholipids (PC, PE, PG and PS) indicate plasmenyl or plasmanyl, respectively. Multivariate analysis was performed by sparse partial least squares analysis (sPLS-DA) in the motor cortex lipidome. The score plot of the sPLS-DA showed a relatively clear segregation of experimental groups according to age (component 1 = 18.1%, Fig. 2A). This trend was related to high levels of several sphingolipids observed in older compared to younger animals as revealed by the top 20 loadings 1 values (Fig. 2B). Univariate analysis performed by one-way ANOVA yielded 61 altered lipid molecular species when comparing all groups. These altered lipids are displayed as clusters in the heatmap plot (Fig. 2C). Of note, most samples from both SOD1-G93A 120d and WT 120d clustered together displaying increased levels of galactosyl ceramides (GalC; 14 species), sulfatides (16 species) and monoglycosylated acylceramides (1G-aC; 8 species). The clustering of samples in the heatmap plot and sPLS-DA suggest that age, rather than the disease per se, modulates major lipidome alterations in motor cortex.Figure 2Multivariate analysis and heatmap plot of motor cortex lipidomes from SOD1-G93A and WT groups. Log transformation of lipid concentrations was applied to lipid concentrations prior to statistical analysis in Metaboanalyst. (A) Score plot of the sparse partial least squares analysis (sPLS-DA); (B) Top 20 lipid species according to loadings 1 values of the sPLS-DA (C) Heatmap plot displaying clusters of samples and the 61 significantly altered lipid molecular species according to one-way ANOVA followed by Tukey’s post-test (p < 0.05; FDR-adjusted). Individual lipids are shown in rows and samples displayed in columns, according to cluster analysis (clustering distance was calculated by Pearson and clustering algorithm estimated by Ward). Each colored cell on the heatmap plot corresponds to values above (red) or below (blue) the mean normalized concentrations for a given lipid. Abbreviations of lipid subclasses are described in Fig. 1. Nomenclature: “p” before a given fatty acyl chain indicates sn-1 plasmenyl followed by a sn-2 linked fatty acid (as in pPE (p18:0/18:1); “d” refers to the sphingoid base followed by a n-acyl chain, for example, Sulfatide (d18:1/24:1). “OH” refers to the hydroxyl group in the n-acyl chain, for example, Cer (18:0/24:1-OH). Please note that fatty acid composition of phospholipids, TAG and DAG does not reflect their specific position in the glycerol backbone. GL, glycerolipids; GP, glycerophospholipids; SP, sphingolipids and TG, triglycerides. Multivariate analysis and heatmap plot of motor cortex lipidomes from SOD1-G93A and WT groups. Log transformation of lipid concentrations was applied to lipid concentrations prior to statistical analysis in Metaboanalyst. (A) Score plot of the sparse partial least squares analysis (sPLS-DA); (B) Top 20 lipid species according to loadings 1 values of the sPLS-DA (C) Heatmap plot displaying clusters of samples and the 61 significantly altered lipid molecular species according to one-way ANOVA followed by Tukey’s post-test (p < 0.05; FDR-adjusted). Individual lipids are shown in rows and samples displayed in columns, according to cluster analysis (clustering distance was calculated by Pearson and clustering algorithm estimated by Ward). Each colored cell on the heatmap plot corresponds to values above (red) or below (blue) the mean normalized concentrations for a given lipid. Abbreviations of lipid subclasses are described in Fig. 1. Nomenclature: “p” before a given fatty acyl chain indicates sn-1 plasmenyl followed by a sn-2 linked fatty acid (as in pPE (p18:0/18:1); “d” refers to the sphingoid base followed by a n-acyl chain, for example, Sulfatide (d18:1/24:1). “OH” refers to the hydroxyl group in the n-acyl chain, for example, Cer (18:0/24:1-OH). Please note that fatty acid composition of phospholipids, TAG and DAG does not reflect their specific position in the glycerol backbone. GL, glycerolipids; GP, glycerophospholipids; SP, sphingolipids and TG, triglycerides. Alterations in sphingolipids concentration evidenced by both multivariate and univariate analyses occurred in several individual molecular species and included variations in the composition of sphingosine base, hydroxylation and length of n-acyl chains (Fig. S1). Taken together, our findings suggest that age plays a critical role modulating sphingolipids metabolism in motor cortex. In line with previous studies, our data indeed revealed that some sphingolipids, particularly the summed concentrations of GalC and 1G-aC (Fig. S2), were significantly increased in the SOD1-G93A 120d group relative to the other groups. This finding suggests a link between ALS progression and lipidome alterations in motor cortex, and we thus decided to examine specific differences between SOD1-G93A 120d and WT 120d groups. For this purpose, we performed a comparison between these groups through an OPLS-DA and volcano plot (Fig. 3). As shown in Fig. 3A,B, the discrimination between these groups in multivariate analysis is mainly linked to alterations in the levels of TAG, DAG and one specimen of pPE (top 20 lipid species ranked according to loading 1 values of the OPLS-DA). Differences in lipidome alterations evidenced by multivariate analysis need to be interpreted with caution since univariate analysis by volcano plot yielded a single altered specimen (i.e., pPE (p18:0/16:0)). These results reinforce the idea that lipid alterations in motor cortex are clearly more linked to age than disease progression per se.Figure 3Pairwise comparison by orthogonal partial least squares–discriminate analysis (OPLS-DA) and volcano plot of motor cortex lipidomes of SOD1-G93A 120d and WT 120d groups. Data used for this comparison have been Log-transformed prior to statistical analysis in Metaboanalyst. (A) Score plot of the OPLS-DA revealing a clear segregation of groups; (B) Loadings plot of the OPLS-DA. Colored features representing lipid subclasses displaying the top 20 lipid species according to highest p values; (C) Volcano plot of the pairwise comparison SOD1-G93A 120d versus WT 120d groups represented by the log2 (fold change) plotted against the –log10 (p value). This analysis revealed only one molecular specimen altered between groups. Statistical significance was evaluated by t-test (p < 0.05; FDR-adjusted) and the fold change was set to >1.5. Pairwise comparison by orthogonal partial least squares–discriminate analysis (OPLS-DA) and volcano plot of motor cortex lipidomes of SOD1-G93A 120d and WT 120d groups. Data used for this comparison have been Log-transformed prior to statistical analysis in Metaboanalyst. (A) Score plot of the OPLS-DA revealing a clear segregation of groups; (B) Loadings plot of the OPLS-DA. Colored features representing lipid subclasses displaying the top 20 lipid species according to highest p values; (C) Volcano plot of the pairwise comparison SOD1-G93A 120d versus WT 120d groups represented by the log2 (fold change) plotted against the –log10 (p value). This analysis revealed only one molecular specimen altered between groups. Statistical significance was evaluated by t-test (p < 0.05; FDR-adjusted) and the fold change was set to >1.5. The lipidomic analysis of spinal cord revealed 406 lipid molecular species sorted into 33 lipid subclasses. Glycerophospholipids (n = 177) followed by sphingolipids (n = 101) and glycerolipids (n = 103) showed the highest diversity of individual lipid molecular species in spinal cord tissues (Fig. 4A). The most abundant lipid subclasses were PE and PS which represented together approximately 50% in mass of total lipid molecular species (Fig. 4B). Sphingolipids representing ~13% were the third most abundant lipid subclass, which showed sphingomyelins (SM) and sulfatides as the most abundant subclasses. In comparison to motor cortex lipidomes (Fig. 1), higher percentages of storage lipids (represented by triacylglycerols and cholesteryl esters) and cardiolipin (CL) were found in spinal cord tissues.Figure 4Lipidome profile identified in spinal cord of SOD1-G93A and WT groups at 70 and 120 days old. (A) Number of lipid species identified in each lipid subclass. (B) Relative abundance of summed concentrations of lipid subclasses. Abbreviations: FFA, free fatty acids; CL, cardiolipin; PA, phosphatidic acid; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PG, phosphatidylglycerol; PI, phosphatidylinositol; PS, phosphatidylserine; Cer, ceramide; GalC, galactosylceramide; 1G-aC, monoglycosylated acylceramide; SM, sphingomyelin; 1G-AEG, monoglucosyl-acyl-ether-glycerol; DAG, diacylglycerol; TAG, triacylglycerol; Ch, cholesterol; CE, cholesteryl ester; UbQ, ubiquinone; p- or o- before lipid subclass mean plasmenyl or plasmanyl, respectively. Lipidome profile identified in spinal cord of SOD1-G93A and WT groups at 70 and 120 days old. (A) Number of lipid species identified in each lipid subclass. (B) Relative abundance of summed concentrations of lipid subclasses. Abbreviations: FFA, free fatty acids; CL, cardiolipin; PA, phosphatidic acid; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PG, phosphatidylglycerol; PI, phosphatidylinositol; PS, phosphatidylserine; Cer, ceramide; GalC, galactosylceramide; 1G-aC, monoglycosylated acylceramide; SM, sphingomyelin; 1G-AEG, monoglucosyl-acyl-ether-glycerol; DAG, diacylglycerol; TAG, triacylglycerol; Ch, cholesterol; CE, cholesteryl ester; UbQ, ubiquinone; p- or o- before lipid subclass mean plasmenyl or plasmanyl, respectively. As shown in Fig. 5A, the spinal cord lipidome of SOD1-G93A 120d group is spatially segregated from the others as explained by component 1 of the sPLS-DA (10.3%). Analysis of the top 20 lipid species ranked according to values of loadings 1 of the sPLS-DA reveals enrichment of ceramides and cholesteryl esters in SOD1-G93A 120d group and reduced amounts of cardiolipins relative to the other groups (Fig. 5B). A clear separation of the SOD1-G93A 120d group from others is also observed in the heatmap plot (Fig. 5C), displaying the top 12 significantly altered lipid species from the spinal cord (one-way ANOVA). Univariate analysis revealed modulation of 4 sphingolipids (ceramides and 1G-aC) and all cholesteryl esters species identified by lipidomic analysis. Importantly, 2 ceramides and all cholesteryl esters were increased exclusively in the symptomatic group (Fig. 5C), linking these lipids to ALS progression.Figure 5Multivariate analysis and heatmap plot of spinal cord lipidomes of SOD1-G93A and WT groups. (A) Score plot of the sPLS-DA; (B) Top 20 lipid species according to loadings 1 values of the sPLS-DA; (C) Heatmap plot displaying clusters of 12 significantly altered lipid molecular species (according to one-way ANOVA) and samples. For statistical parameters and graphic details see Fig. 2 caption. Abbreviations of lipid subclasses are described in Fig. 4. Nomenclature: “d” refers to the sphingoid base followed by a n-acyl chain, for example, Cer (d18:0/22:0). “OH” refers to the hydroxyl group in the n-acyl chain, for example, Cer (18:0/24:1-OH). Multivariate analysis and heatmap plot of spinal cord lipidomes of SOD1-G93A and WT groups. (A) Score plot of the sPLS-DA; (B) Top 20 lipid species according to loadings 1 values of the sPLS-DA; (C) Heatmap plot displaying clusters of 12 significantly altered lipid molecular species (according to one-way ANOVA) and samples. For statistical parameters and graphic details see Fig. 2 caption. Abbreviations of lipid subclasses are described in Fig. 4. Nomenclature: “d” refers to the sphingoid base followed by a n-acyl chain, for example, Cer (d18:0/22:0). “OH” refers to the hydroxyl group in the n-acyl chain, for example, Cer (18:0/24:1-OH). To examine detailed differences linked to symptomatic stage of ALS, we performed a pairwise comparison between SOD1-G93A 120d and WT 120d groups through OPLS-DA and volcano plot (Fig. 6). Together, multivariate and univariate analyses confirmed significant alterations in ceramides and cholesteryl esters, which were both found markedly elevated in SOD1-G93A 120d group. In addition, several cardiolipins (CL) species were found reduced in the SOD1-G93A 120d group.Figure 6Pairwise comparison by OPLS-DA and volcano plot of spinal cord lipidomes of SOD1-G93A 120d and WT 120d groups. (A) Multivariate statistical analysis using orthogonal partial least squares–discriminate analysis (OPLS-DA); (B) Feature importance of the top 20 lipid species altered according to the OPLS-DA analysis; (C) Volcano plot is represented by the log2 (fold change) of SOD1-G93A 120d/WT 120d groups plotted against the –log10 (p). Statistical significance was evaluated by t-test (p < 0.05; FDR-adjusted) and the fold change was set to >1.5. The significantly altered lipid molecular species are colored according to lipid subclasses. List of altered cardiolipins is sorted by log2 (fold change). Pairwise comparison by OPLS-DA and volcano plot of spinal cord lipidomes of SOD1-G93A 120d and WT 120d groups. (A) Multivariate statistical analysis using orthogonal partial least squares–discriminate analysis (OPLS-DA); (B) Feature importance of the top 20 lipid species altered according to the OPLS-DA analysis; (C) Volcano plot is represented by the log2 (fold change) of SOD1-G93A 120d/WT 120d groups plotted against the –log10 (p). Statistical significance was evaluated by t-test (p < 0.05; FDR-adjusted) and the fold change was set to >1.5. The significantly altered lipid molecular species are colored according to lipid subclasses. List of altered cardiolipins is sorted by log2 (fold change). Among ceramide species, remarkable changes were observed in Cer (d18:0/24:0-OH) and Cer (d18:0/24:1-OH). These hydroxylated ceramides were drastically elevated in the SOD1-G93A 120d group compared to the age-matched WT group (Fig. S3). In addition, changes in ceramides levels were examined by calculating the ratios of hydroxylated/non-hydroxylated (OH/n-OH) and very long chain/long chain (VLC/LC) species. Both ratios were found significantly increased in the SOD1-G93A 120d group, whereas the ratio ceramide/dihydroceramide (Cer/dh-Cer) was reduced (Fig. S4B). Therefore, these data confirm previous findings indicating an association between ceramide metabolism/remodeling and ALS disease development. The most conspicuous changes in the lipidome of spinal cord in the SOD1-G93A 120d group were related to alterations in cholesteryl esters and cardiolipin levels (Fig. 7). Of note, the SOD1-G93A 120d group showed a 6-fold increase in total concentration of cholesteryl esters (Fig. 7A). Elevated cholesteryl esters levels were mainly linked to those molecular species esterified to polyunsaturated fatty acids (PUFAs), with arachidonic (20:4), eicosapentaenoic (20:5) and adrenic (22:4) acids (Fig. 7B). Contrasting with the increase in cholesteryl esters, 10 out of 32 cardiolipin species were found reduced in the SOD1-G93A 120d group relative to the age-matched WT (Fig. S5). This change was reflected not only in total concentration of cardiolipins, which was lower in SOD1-G93A 120d group (Fig. 7C), but also in the major fatty acids esterified to cardiolipins (Fig. 7D).Figure 7Altered concentrations of cholesteryl esters (CE) and cardiolipins (CL) identified in the spinal cord of SOD1-G93A 120d and WT 120d groups. (A) Concentration of total CE. (B) Fatty acid composition of CE. (C) Concentration of total CL. (D) Fatty acid composition of CL. Data are shown as mean ± standard error of mean (SEM). Statistical significance was evaluated by t-test (p < 0.05; FDR-adjusted) using Metaboanalyst. (*) Statistically different when compared to the WT 120d group. Altered concentrations of cholesteryl esters (CE) and cardiolipins (CL) identified in the spinal cord of SOD1-G93A 120d and WT 120d groups. (A) Concentration of total CE. (B) Fatty acid composition of CE. (C) Concentration of total CL. (D) Fatty acid composition of CL. Data are shown as mean ± standard error of mean (SEM). Statistical significance was evaluated by t-test (p < 0.05; FDR-adjusted) using Metaboanalyst. (*) Statistically different when compared to the WT 120d group. Guided by findings that astrocytes isolated from the spinal cord of an identical ALS rat model has aberrant features and accumulates lipid droplets, we performed immunohistochemistry of the astrocyte-specific glial fibrillary acidic protein (GFAP). This analysis was conducted to confirm astrogliosis, a hallmark of ALS, and it was restricted to the spinal cords of SOD1-G93A 120d group and its age-matched control (WT 120d). As depicted in the Fig. S6, the spinal cord of the SOD1-G93A 120d group exhibited severe astrogliosis (stellate appearance) in the grey matter of the spinal cord ventral horn, as reported in previous studies. Lipids play a critical role in structuring the CNS through membrane fluidity control, transmission of electrical signals and stabilization of synapses. Alterations of lipid metabolism in neurons and glial cells (astrocytes, oligodendrocytes and microglia) modulate processes linked to aging and neurodegenerative diseases. To address lipidome alterations in the CNS, we investigated the motor cortex and spinal cord of SOD1-G93A transgenic rats as model for ALS and compared them to wild type as control. In this study, an untargeted lipidomic analysis using high resolution UHPLC-Q-TOF-MS was performed. Manual identification of the most abundant molecular ions based exclusively on their MS/MS profile allowed us to unambiguously annotate a wide range of lipid molecular species occurring in motor cortex (285 species, Fig. 1) and spinal cord (406 species, Fig. 4) tissues. Major lipidome changes in the motor cortex were linked to altered sphingolipid metabolism and to animal age (Figs 2 and 3). In contrast, lipidome alterations in the spinal cord were strongly associated with ALS at symptomatic stage (SOD1-G93A 120d group) (Figs 5 and 6). Alterations in the lipidome of motor cortex were mostly related to age, except for a single lipid (pPE (p18:0/16:0)) that was significantly decreased in SOD1-G93A 120d relative to age-matched WT (Fig. 3C). Among sphingolipids, several GalC, sulfatide and 1G-aC were increased in both WT and SOD1-G93A groups at 120d compared to 70d (Figs 2 and S1). Our findings are in line with active production of myelin by adults, either from newly formed oligodendrocytes or synthesis of new membranes by existing cells, reflecting the constant increase in sphingolipids content with age. Interestingly, increased content of 1G-aC was observed in motor cortex of older animals (Fig. S2). To our knowledge, 1G-aC have never been reported in the CNS and their modulation with age together with increased GalC and sulfatide concentrations suggest a possible link of these sphingolipids to myelination and brain development. In contrast to motor cortex, drastic differences in lipidome profiles were observed in the spinal cord of SOD1-G93A 120d rats, particularly when compared to its age-matched control (WT 120d) (Fig. 6). Major alterations were linked to cholesteryl esters and cardiolipin levels and to a minor extent in the abundance of ceramides (Figs 7 and S3). Differences in ceramide metabolism have been repeatedly reported in spinal cord tissues of ALS patients and rodent models. Here, we detected increased concentrations of two hydroxylated ceramides, the Cer (d18:0/24:0-OH) and Cer (d18:0/24:1-OH). Hydroxylated ceramides are essential for myelin stability and maturation, as demonstrated by knockdown and mutations in fatty acid 2-hydroxylase (FA2H, the enzyme responsible for hydroxylation of n-acyl chains) that results in neural impairments in mice and humans. Ceramides have also been implicated in cellular signaling, and in ALS they are thought to activate the oxysterol-binding protein (OSBP). According to these authors, OSBP binds and delivers oxysterols to the endoplasmic reticulum thereby activating acylcoenzyme A:cholesterol acyltransferase (ACAT) yielding cholesteryl esters as products. Accumulation of either hydroxylated ceramides or dihydroceramides could also trigger cell survival events such as autophagy. Since one of the hallmarks of ALS is the presence of cytoplasmic inclusions or protein aggregates in affected motor neurons, the stimulation of autophagic process by these ceramides could be a potential pathway for elimination of protein aggregates and damaged organelles. As highlighted by our data, ALS symptomatic rats displayed conspicuously elevated levels of cholesteryl esters and decreased concentrations of cardiolipin species in the spinal cord as major lipid signatures of the disease (Fig. 6). Changes in cholesteryl ester metabolism were not reflected in alterations in the pools of free cholesterol (Fig. S7) or 24-hydroxycholesterol, the latter only observed in trace amounts in spinal cord tissues (data not shown). That is, it appears that cholesterol synthesis is upregulated but does not result in accumulation of free cholesterol nor 24-hydroxycholesterol, which is generally more soluble and can cross the brain-blood-barrier at a much faster rate than cholesterol. A growing body of evidence implicates impaired energy metabolism in ALS patients and models. Of note, ROS generation, protein aggregation and mitochondrial dysfunction in motor neurons represent a clinical hallmark of ALS. Whether elevated ROS is caused by SOD1 aggregation and glutamate accumulation leading to imbalanced calcium homeostasis in neurons remains unanswered. Nonetheless, elevated ROS is associated with mitochondrial dysfunction, leading to changes that range from decreased energy metabolism to major morphological modifications. Abnormal mitochondrial morphology has been repeatedly observed in motor neurons of diverse familial cases of ALS models. These alterations consist of mitochondria clusters, swollen mitochondria, loss of cristae and vacuoles derived from degenerating mitochondria. Reflecting dysfunctional mitochondria, our data reveal a significant decrease in cardiolipin levels in spinal cord of SOD1-G93A 120d group when compared to the WT 120d group (Fig. 6). Cardiolipin is a major phospholipid of mitochondria specifically located at mitochondrial inner membranes. The functions of cardiolipin have been primarily related to ATP production, curvature stress control and mitochondria cristae morphology. Given its function in structuring mitochondrial function and morphology, we suggest that a decrease in cardiolipin levels may partially reflect the loss of mitochondrial cristae and thus dysfunctional mitochondria in the spinal cord of ALS symptomatic rats. Neurons are known to use lactate from glia to fuel the glycolytic pathway. By inducing ROS formation in fruit flies neurons, Liu et al. (2017) described lactate being converted to pyruvate and acetyl-CoA, with the latter driving synthesis of fatty acids that are stored as triacylglycerols in lipid droplets. Importantly, these lipid droplets are transported to and accumulate in glial cells for neuroprotection. A similar coordination of neurons and astrocytes in the metabolism of fatty acids was recently demonstrated in primary hippocampal neurons and astrocytes from rats. To our knowledge, a detailed lipidomic analysis was not performed by these studies. According to our findings, neutral or storage lipid accumulation was evidenced almost exclusively as a significant increase in cholesteryl esters concentration in spinal cords of ALS symptomatic rats, with no major alterations in the pool of triacylglycerols (Fig. S4C,D). A pertinent question is therefore why cholesteryl esters and not triacylglycerols are stored as neutral lipids in droplets? We hypothesize that the answer relies in part in the efficiency by which lipid droplets can be shuttled from neurons to astrocytes by apolipoproteins (ApoE/D) for neuroprotection as demonstrated by Liu et al.. Accumulation of storage lipids in spinal cords of ALS SOD1-G93A rats was already evidenced by isolation of aberrant astrocytes bearing abundant lipid droplets. Not surprisingly and similar to previous studies with ALS models, immunohistochemistry of the astrocyte-specific glial fibrillary acidic protein (GFAP) revealed extensive reactive astrogliosis in the spinal cord ventral horn of the SOD1-G93A 120d group (Fig. S6). Nevertheless, the studies evidencing either astrogliosis or aberrant astrocytes in ALS models have not conducted lipidomic analysis. It is thus tempting to suggest that lipids accumulating in aberrant astrocytes are in fact lipid droplets composed mainly of cholesteryl esters. Of note, the 6-fold increase of cholesteryl esters in spinal cords of SOD1-G93A 120d group relative to WT 120d group was mostly related to accumulation of molecular species esterified to PUFAs such as arachidonic (20:4), eicosapentaenoic (20:5) and adrenic (22:4) acids. Since mammals cannot synthesize these fatty acids, we speculate that this accumulation likely reflects a protective mechanism against oxidative stress. That is, under elevated ROS, neuronal and glial cells store their highly susceptible membrane-bound PUFAs as cholesteryl esters or triacylglycerols, thereby avoiding membrane lipid peroxidation. It is becoming apparent that sequestering toxic fatty acids into lipid droplets is a widespread cellular strategy to avoid lipotoxicity. Once protected in lipid droplets, these neutral lipids may be shuttled to glial cells and undergo fatty acid beta-oxidation. Whether fatty acids can be quantitatively used as fuel for energy metabolism in glial cells remains debatable. The controversy is that fatty acid beta-oxidation requires more oxygen than glucose, and may deplete the oxygen demand by neurons. Furthermore, beta-oxidation also generates superoxide anion that may enhance oxidative stress, and for this reason neurons likely rely on glial cells for storing excess lipids. We suggest that a delicate balance between neuron-glia lipid transport and energy metabolism (i.e. significant contribution of beta-oxidation) is likely relevant to neuroprotection. In retrospect, any perturbation of this feedback loop may result in augmented neurodegeneration. Here, we attempted to formulate a model shown in Fig. 8 based on: 1) elevated ROS and dysfunctional neuronal mitochondria in ALS (supported by the data showing decreased cardiolipin content in spinal cords of SOD1-G93A 120d group); 2) upregulation of cholesterol synthesis due to altered lactate metabolism in neuronal mitochondria; 3) accumulation of cholesteryl esters composed of PUFAs as a protective mechanism against lipid peroxidation. This hypothetical model is adapted from previously published models, with the exception that in our study we performed lipidomics analysis, which revealed significant changes regarding cardiolipin and cholesteryl esters levels related to ALS progression in spinal cord.Figure 8Proposed model for the accumulation of lipid droplets in astrocytes. Under normal conditions, circulating glucose is taken up by astrocytes, converted to lactate and shuttled to neurons for ATP production. Increasing evidences indicate that elevated ROS and mitochondrial defects in neurons trigger lipid droplet formation in astrocytes. In this scenario, our lipidomic data demonstrate that lipid accumulating in the spinal cord are mainly consisted of cholesteryl ester species. Scheme adapted from Liu et al., 2017 and Ioannou et al.. Proposed model for the accumulation of lipid droplets in astrocytes. Under normal conditions, circulating glucose is taken up by astrocytes, converted to lactate and shuttled to neurons for ATP production. Increasing evidences indicate that elevated ROS and mitochondrial defects in neurons trigger lipid droplet formation in astrocytes. In this scenario, our lipidomic data demonstrate that lipid accumulating in the spinal cord are mainly consisted of cholesteryl ester species. Scheme adapted from Liu et al., 2017 and Ioannou et al.. Our findings reiterate that alterations in cholesterol metabolism of CNS are associated with ALS. In other neurodegenerative diseases such as Alzheimer’s disease (AD), inhibition of cholesteryl ester synthesis by deletion of ACAT1 prevented Tau phosphorylation and restored cognitive deficits. More recently, the mechanism of cholesteryl esters accumulation avoiding phosphorylated Tau degradation was attributed to a negative regulation of ubiquitin-proteasome system (UPS). The UPS activity is also down-regulated in ALS, leading to accumulation of cytotoxic protein aggregates and ER stress, a hallmark of ALS. In addition, emerging evidence suggests that lipid droplets play a fundamental role in protein aggregation and clearance. Thus, metabolic pathways linked to synthesis and degradation of cholesteryl esters represent potential targets for reduction of ALS disease progression, similar to those reported for AD. Overall, our study shows the spinal cord is more susceptible to lipid alterations occurring during ALS disease progression than the motor cortex. In addition, our results provide evidence for lipids accumulating as cholesteryl esters in the spinal cords of SOD1-G93A 120d rats, which are under apparent astrogliosis. We proposed a model based on the neuroprotective mechanism against ROS that supports lipid droplets accumulation as cholesteryl esters in astrocytes from the spinal cords of ALS rats. Since fatty acid beta-oxidation is likely limited in the CNS, cholesteryl esters might represent a suitable vehicle for lipid transport within and perhaps across the CNS. Thus, cellular processes involving cholesteryl esters metabolism and transport may represent potential targets for treatment of neurodegenerative diseases. The precise analysis of molecular species of lipids as performed in our study may help to further explore the role of oxidative stress regulating lipid metabolism in neurodegenerative diseases and aging. The lipids used as internal standards were 1,2-diheptadecanoyl-sn-glycero-3-phosphocholine (PC 17:0/17:0), 1,2-diheptadecanoyl-sn-glycero-3-phosphoethanolamine (PE 17:0/17:0), 1-heptadecanoyl-2-hydroxy-sn-glycero-3-phosphocoline (LPC 17:0), 1,2-diheptadecanoyl-sn-glycero-3-phosphoserine (PS 17:0/17:0), 1,2-diheptadecanoyl-sn-glycero-3-phospho-(1′-rac-glycerol) (PG 17:0/17:0), 1′, 3′-bis[1,2-dimyristoyl-sn-glycero-3-phospho]-glycerol (CL 14:0 × 4), N-decanoyl-D-erythro-sphingosine (Ceramide d18:1/10:0), 3-O-sulfo-D-galactosyl-ß1-1′-N-heptadecanoyl-D-erythro-sphingosine (Sulfatide d18:1/17:0) and N-heptadecanoyl-D-erytro-sphingosylphosphorylcholine (SM d18:1/17:0), all purchased from Avanti Polar Lipids Inc. (Alabaster, AL. USA). In our analysis, we observed co-elution of internal standards PC (17:0/17:0) and PE (17:0/17:0) with isomers contained in the samples that were composed of 16:0/18:0 fatty acid chain combination. In addition, several other lipid subclasses lacked sufficient amounts or were unavailable (e.g. ubiquinone) in our laboratory to be added as internal standards or were not available for purchase (e.g. 1G-aC, Semino, 1G-AEG). In cases of co-elution and lack of internal standards mentioned above, we used external calibration curves to calculate lipid concentrations. The lipids used for construction of external calibration curves were 1,2-diheptadecanoyl-sn-glycero-3-phosphoinositol (PI 17:0/14:1), free fatty acid (FFA 17:0), (PC 17:0/17:0), (PE 17:0/17:0), d5-1, 3-diheptadecanoyl-glycerol (d5-DAG 17:0 × 2), triacylglycerol (TAG 17:0 × 3), 27-hydroxy-cholesterol and cholesteryl ester (CE 15:0), all obtained from Avanti Polar Lipids Inc (Alabaster, AL. USA). These lipids were all corrected to LPC (17:0) for normalization (see details below). Ammonium formate and ammonium acetate were obtained from Sigma-Aldrich (St Louis, MO, USA) as well as all HPLC grade organic solvents used in this study. Animal study was conducted in accordance with the ethical principles for animal experimentation and conducted according to the guidelines of the National Council for Animal Experimentation Control (Conselho Nacional de Controle de Experimentação Animal – CONCEA, Ministry of Science, Technology, Innovation and Communications, Brazil). Male Sprague Dawley rats overexpressing human SOD1-G93A were obtained from Taconic and bred with wild-type Sprague-Dawley females to establish a colony. Genotyping by PCR to detect exogenous hSOD1 transgene was performed by amplification of ear DNA at 20 days of age. Rats were housed under controlled laboratory conditions, including room temperature, a 12 hours light/12 hours dark cycle with food and water ad libitum. Asymptomatic ALS rats (SOD1-G93A 70d; n = 7) and their wild type controls (WT 70d; n = 7) were sacrificed at 73 ± 4 days of age, while symptomatic ALS rats (SOD1-G93A 120d; n = 13) and their control (WT 120d; n = 15) were sacrificed at 122 ± 6 days of age. The criterion for euthanasia of symptomatic SOD1-G93A rats was loss of 15% of their maximum body weight. Rats were fasted for 6 h and anesthetized by isoflurane inhalation at dose of 4% for induction and 2% for maintenance. Motor cortex and spinal cord were collected and stored at −80 °C until further processing. All animal procedures were approved by University of Sao Paulo - Chemistry Institute’s Animal Care and Use Committee under the protocol number 14/2013. Lipid extraction was performed according to the method established by Yoshida et al.. Motor cortex or spinal cord tissues (200 mg) were homogenized in ice by a tissue grinder in 1 mL of 10 mM phosphate buffer (pH 7.4) containing deferoxamine mesylate 100 μM. Briefly, 100 μL of motor cortex or spinal cord homogenate were mixed with 400 μL of phosphate buffer, 400 μL of ice-cold methanol and 100 μL of internal standards (Supplementary Table 1). Next, 1.5 mL of chloroform/ethyl acetate (4:1) was added to the mixture, which was thoroughly vortexed for 30 s. After centrifugation at 1500 g for 2 min at 4 °C, the lower phase containing the total lipid extract (TLE) was transferred to a new tube and dried under N2 gas. Simultaneously, yeast samples (10 mg) were extracted and used as quality controls for reproducibility analysis. Dried TLE were redissolved in 100 μL of isopropanol and the injection volume was set at 1 µL. Blanks and quality controls were injected every 5 and 10 samples, respectively. TLEs were analyzed by ESI-Q-TOFMS (Triple TOF 6600, Sciex, Concord, US) interfaced with an ultra-high performance liquid chromatography (UHPLC Nexera, Shimadzu, Kyoto, Japan). The samples were loaded into a CORTECS (UPLC C18 column, 1.6 µm, 2.1 mm i.d. × 100 mm) with a flow rate of 0.2 mL min and the oven temperature maintained at 35 °C. For reverse-phase LC, mobile phase A consisted of water/acetonitrile (60:40), while mobile phase B composed of isopropanol/acetonitrile/water (88:10:2). Mobile phases A and B contained ammonium acetate or ammonium formate (at a final concentration of 10 mM) for experiments performed in negative or positive ionization mode, respectively. Lipids were separated by a 20 min linear gradient as follows: from 40 to 100% B over the first 10 min., hold at 100% B from 10–12 min., decreased from 100 to 40% B during 12–13 min., and hold at 40% B from 13–20 min. The MS was operated in both positive and negative ionization modes, and the scan range set at a mass-to-charge ratio of 200–2000 Da. Data for lipid molecular species identification and quantification was obtained by Information Dependent Acquisition (IDA). Data acquisition using Analyst® 1.7.1 was performed with a cycle time period of 1.05 s with 100 ms acquisition time for MS1 scan and 25 ms acquisition time to obtain the top 36 precursor ions. An ion spray voltage of −4.5 kV and 5.5 kV (for negative and positive modes, respectively) and the cone voltage at +/−80 V were set to analysis. Additional parameters included curtain gas set at 25 psi, nebulizer and heater gases at 45 psi and interface heater of 450 °C. The LC-MS/MS data were analyzed with PeakView. Lipid molecular species were manually identified based on their exact masses, specific fragments and/or neutral losses and with the help of an in-house manufactured Excel-based macro. Also, a maximum error of 5 mDa was defined for the attribution of the precursor ion. After identification, the area of lipid species was obtained by MS data using MultiQuant. Each peak integration was carefully inspected for correct peak detection and accurate area determination. For quantification, the area ratio of each lipid was calculated by dividing the peak area of the lipid by the corresponding internal standard or using external calibration (Supplementary Table 1). The concentration of lipid species was calculated by either multiplying the area ratio by the concentration of the corresponding internal standard or by external calibration curves relative to LPC (17:0). The external calibration curves used for FFA, PC, PI, PE, cholesterol, CE, DAG and TAG to determine subclass-specific response factors are presented in Supplementary Tables 2 and 3. The total amount of lipids was expressed in µg/g of tissue. Data are presented as mean ± standard error of mean (SEM) (calculated by summing up individual lipid species within each subclass). Note that standards of some lipid subclasses (GalC, 1G-aC, Semino, 1G-AEG and UbQ) were unavailable in the laboratory or commercially and thus lack absolute concentrations. Thus, their concentrations can be compared among samples, but not with other compounds. Data reproducibility analysis was performed by quality controls in both negative and positive ion mode (Supplementary Tables 4 and 5). The peak area and retention time of selected lipids in quality controls (extracted from a yeast sample) were measured at the beginning, after every 10 samples and at the end of the LC-MS/MS experiments. Tissue preparation for histological analysis was performed as previously described with minor modifications. Animals were deeply anesthetized via an intraperitoneal injection of a mixture of ketamine hydrochloride and xylazine and then rapidly perfused transcardially with 0.9% saline, followed by 4% formaldehyde in phosphate-saline buffer, at 4 °C. Spinal cords were removed and postfixed for several days, and then placed in a solution containing 20% sucrose diluted in buffered 4% formaldehyde. The frozen spinal cords (lombar) were mounted on a freezing microtome and cut into 30 μm coronal sections. The slices were collected in cold cryoprotectant solution (0.05 M sodium phosphate buffer, pH 7.3, 30% ethylene glycol, and 20% glycerol) and stored at −20 °C. The sections were mounted onto adhesive microscope slides and subjected to antigen retrieval by incubation with proteinase K 10 µg/ml at 37 °C for 30 minutes. Following three washes with KPBS, conventional immunofluorescence in fixed tissue was performed as described elsewhere by incubating washed sections in blocking solution (KPBS containing 1% BSA and 0.4% Triton™ X-100) for 30 min. Using the same buffer solution composition, the sections were incubated overnight at 4 °C with primary antibody anti-glial fibrillary acidic protein (GFAP; Millipore #MAB360). After incubation with the primary antibody, the sections were rinsed in KPBS and incubated with Alexa Fluor® -488 anti-mouse secondary antibodies for 120 min. Sections were then rinsed with KPBS, counterstained with DAPI for nuclear labeling, and coverslip with PVA/DABCO mounting media. Epifluorescence photomicrographs (Nikon microscope) were processed to enhance contrast and image quality using GIMP (http://www.gimp.org/) and were assembled using Inkscape (http://inkscape.org). Pictures representing different groups received equivalent image treatment. All statistical analyses were performed with Metaboanalyst (website: www.metaboanalyst.ca). In brief, data were log transformed prior to statistical analyses. All groups were compared by multivariate analysis (sPLS-DA) and one-way ANOVA followed by Tukey’s post-hoc (p < 0.05; FDR-adjusted). We also performed heatmap plots using clusters of lipids that were statistically altered in the different groups (one-way ANOVA p < 0.05; FDR-adjusted) and clusters of samples. For pairwise comparisons of SOD1-G93A 120d group with WT 120d group, we performed multivariate analysis (OPLS-DA) and volcano plots consisting of p-values of t-test (assuming unequal variances; Welch’s test; p < 0.05; FDR-adjusted) and fold change set to >1.5. Graphics were generated using R 3.5.0 base packages (website: www.r-project.org), ggplot2, Metaboanalyst, GraphPad Prism 6 and Excel. Throughout our analysis using Metaboanalyst, we followed the protocols by Xia and others.
|
PMC9352691
| "Comprehensive lipid and lipid-related gene investigations of host immune responses to characterize (...TRUNCATED)
| "Despite remarkable success in the prevention and treatment of tuberculosis (TB), it remains one of (...TRUNCATED)
|
PMC11127996
|
Lipidome atlas of the adult human brain
| "Lipids are the most abundant but poorly explored components of the human brain. Here, we present a (...TRUNCATED)
|
PMC6097988
| "Phospholipidome of endothelial cells shows a different adaptation response upon oxidative, glycativ(...TRUNCATED)
| "Endothelial dysfunction has been widely associated with oxidative stress, glucotoxicity and lipotox(...TRUNCATED)
|
PMC5050431
| "LC-MS Based Sphingolipidomic Study on A2780 Human Ovarian Cancer Cell Line and its Taxol-resistant (...TRUNCATED)
| "Drug resistance elicited by cancer cells continue to cause huge problems world-wide, for example, t(...TRUNCATED)
|
PMC9011034
| "Quantitative Lipidomics and Spatial MS-Imaging Uncovered Neurological and Systemic Lipid Metabolic (...TRUNCATED)
| "Sinocyclocheilus represents a rare, freshwater teleost genus endemic to China that comprises the ri(...TRUNCATED)
|
PMC8342504
| "Evaluation of two highly effective lipid-lowering therapies in subjects with acute myocardial infar(...TRUNCATED)
| "For cardiovascular disease prevention, statins alone or combined with ezetimibe have been recommend(...TRUNCATED)
|
PMC9170690
| "Comprehensive genetic analysis of the human lipidome identifies loci associated with lipid homeosta(...TRUNCATED)
| "We integrated lipidomics and genomics to unravel the genetic architecture of lipid metabolism and i(...TRUNCATED)
|
End of preview. Expand
in Data Studio
README.md exists but content is empty.
- Downloads last month
- 23