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Lipidome

The lipidome, also known as the lipidomic profile, refers to the comprehensive analysis and characterization of the complete set of lipids within a biological system, such as a cell, tissue, or organism.
Lipids play crucial roles in various cellular processes, including energy storage, signaling, and membrane structure.
The study of the lipidome provides insights into the dynamic changes in lipid metabolism and composition, which can be associated with physiological and pathological conditions.
Leveraging cutting-edge technologies and bioinformatics tools, researchers can explore the lipidome to unravel the complex lipid networks and their implications in health and disease.
This knowledge can lead to the development of novel diagnostic biomarkers and therapeutic interventions targeting lipid-related pathways.
Exploring the lipidome offers a powerfull approach to advance our understanding of biological systems and unlock new frontiers in lipid research.

Most cited protocols related to «Lipidome»

The phagosomal lipid extractions were performed using an established
protocol.16 (link),17 (link),28 (link) Briefly, the
phagosomal preparations were washed with sterile Dulbecco’s
PBS (DPBS; three times) and transferred into a glass vial using 1
mL of DPBS. A total of 3 mL of 2:1 (v/v) chloroform (CHCl3)/methanol (MeOH) with the internal standard mix (50 pmol of each
internal standard listed in Supporting Information Table 1) was added, and the mixture was vortexed. The two phases
were separated by centrifugation at 2800g for 5 min.
The organic phase (bottom) was removed. A total of 50 μL of
formic acid was added to acidify the aqueous homogenate, and CHCl3 was added to make up a 4 mL volume. The mixture was vortexed,
and separated by centrifugation at 2800g for 5 min.
Both the organic extracts were pooled and dried under a stream of
N2. The lipidome was solubilized in 200 μL of 2:1
(v/v) CHCl3/MeOH, and 20 μL was used for the lipidomics
analysis. All the lipid species analyzed in this study were quantified
using the multiple reaction monitoring high resolution (MRM-HR) scanning
method (Supporting Information Table 1)
on a Sciex X500R QTOF mass spectrometer (MS) fitted with an Exion-LC
series UHPLC. All data were acquired and analyzed using the SciexOS
software. The LC separation was achieved using a Gemini 5U C18 column
(Phenomenex, 5 μm, 50 × 4.6 mm) coupled to a Gemini guard
column (Phenomenex, 4 × 3 mm). The LC solvents were as follows:
positive mode, buffer A, 95:5 (v/v) H2O/MeOH + 0.1% formic
acid + 10 mM ammonium formate; buffer B, 60:35:5 (v/v) isopropanol
(IPA)/MeOH/H2O + 0.1% (v/v) formic acid + 10 mM ammonium
formate; negative mode, buffer A, 95:5 (v/v) H2O/MeOH +
0.1% (v/v) NH4OH; buffer B, 60:35:5 (v/v) IPA/MeOH/H2O + 0.1% (v/v) NH4OH. All the lipid estimations
were performed using an electrospray ion (ESI) source, with following
MS parameters: turbo spray ion source, medium collision gas, curtain
gas = 20 L min–1, ion spray voltage = 4500 V (positive
mode) or −5500 V (negative mode), at 400 °C. A typical
LC-run was 55 min, with the following solvent run sequence post injection:
0.3 mL min–1 0% B for 5 min, 0.5 mL min–1 0% B for 5 min, 0.5 mL min–1 linear gradient of
B from 0–100% over 25 min, 0.5 mL min–1 of
100% B for 10 min, and re-equilibration with 0.5 mL min–1 of 0% B for 10 min. A detailed list of all the species targeted
in this MRM-HR study, describing the precursor ion mass and adduct,
the product ion targeted, and MS voltage parameters can be found in Supporting Information Table 1. All the endogenous
lipid species were quantified by measuring the area under the curve
in comparison to the respective internal standard, and then normalizing
to the total protein content of the phagosomal preparation. All the
lipidomics data are represented as mean ± SEM of four (or more)
biological replicates per group (Supporting Information Table 1).
Publication 2018
Acids Biopharmaceuticals Buffers Centrifugation Chloroform formic acid formic acid, ammonium salt Isopropyl Alcohol Lipidome Lipids Methanol Phagosomes Proteins Solvents Spindle Pole Body Sterility, Reproductive
In a benchmark test of LDA versus LipidBlast7 (link), we used data from both the first control experiment and the biological experiment, both acquired on Orbitrap Velos Pro in CID +50% and -50%, and on 4000 QTRAP +45 eV and -45 eV, respectively. For LipidBlast evaluation, we used the recommended MSPepSearchGUI (http://peptide.nist.gov/software/ms_pep_search_gui/MSPepSearch.html). The same m/z tolerances were applied in both LDA and LipidBlast. The specificity and sensitivity of LipidBlast depend on a so called matching factor22 (link), a value ranging from 0-999. Using the default setting of 450 for the matching factor, many lipid standards in control experiment 1 were not detected. Consequently, the matching factor was lowered to 10, in which case LipidBlast detected almost all of the lipid standards in negative ion mode. Further reduction did not improve the sensitivity of LipidBlast. In positive ion mode, irrespective of the matching factor setting, LipidBlast was not able to identify as many lipid molecular species as was LDA. Details about the LipidBlast parameters are given in Supplementary Note 2. In this benchmark, we used only lipid subclasses/adducts that both LDA and LipidBlast are able to detect. Correct assignment of lipid species and lipid molecular species identified in liver lipidomes was verified by manual inspection of the spectra, and by aligning them with the respective retention time data23 (link).
Publication 2017
Biopharmaceuticals Hypersensitivity Immune Tolerance Lipidome Lipids Liver Peptides Retention (Psychology)
All the BLCA specimens were procured by a prior written informed consent under institute review board (IRB) approved protocols. Metabolites were extracted from BLCA tissues, and mouse liver pool was used as a quality control and followed the extraction procedure described8 (link),12 (link)–14 . Briefly, 10mg of tissue was used for the metabolic extraction. The extraction step starts with addition of 750μL ice-cold methanol: water (4:1) containing 20μL spiked internal standards (ISTDs). After homogenization, ice-cold chloroform and water were added in a 3:1 ratio for a final proportion of 4:3:2 methanol:chloroform:water. The organic and aqueous layers were collected, dried, and resuspended in methanol: water (1:1). The extract was deproteinized using a 3kDa molecular filter and the filtrate was dried under vacuum. The dried extracts were re-suspended in 100μL of injection solvent composed of 1:1 methanol: water and subjected to LC-MS.
Lipids were extracted using a modified Bligh-Dyer method15 (link). The extraction was carried out using 2:2:2 ratio of water: methanol: dichloromethane at room temparature after ISTDs into tissues and quality control pool12 (link). After homogenization of the samples, the organic layer was collected and completely dried under vacuum. Before MS analysis, the dried extract was resuspended in 100μL of buffer containing 10mM NH4Ac and subjected to LC/MS. The lipidome was separated using reverse-phase (RP) chromatography. To monitor the lipid extraction process, we used a standard pool of tissue samples from aliquots of the same samples.
Publication 2019
Buffers Chloroform Chromatography, Reverse-Phase Cold Temperature Lipidome Lipids Liver Methanol Methylene Chloride Mice, Laboratory Solvents Tissues Vacuum
Metabolomic and lipidomic profiling of liver samples was conducted using a combined targeted and untargeted workflow for the lipidome, metabolome, and exposome analysis (LIMeX) [42 (link),43 (link)]. Extraction was carried out using a biphasic solvent system of cold methanol, MTBE, and 10% methanol. Four different LC-MS platforms were used for metabolomic and lipidomic profiling: (i) lipidomics of complex lipids in positive ion mode, (ii) lipidomics of complex lipids in negative ion mode, (iii) metabolomics of polar metabolites in positive ion mode, and (iv) metabolomics of polar metabolites in negative ion mode. Details of sample preparation, LC-MS conditions, raw data processing and curation, and list of annotated complex lipids and polar metabolites are in Supplementary Materials.
Publication 2021
Cold Temperature Exposome Lipidome Lipids Liver Metabolome Methanol methyl tert-butyl ether NADH Dehydrogenase Complex 1 Solvents
Raw data were collected in .d format, and MS/MS data were analyzed using Agilent MassHunter Qualitative Analysis and LipidAnnotator for lipid identification (19 (link)). Lipids identified in LipidAnnotator were exported to PCDL format to create individual comprehensive libraries for each tissue. Identifications for select lipids from different class were checked for accuracy through retention time correlations with lipids of the same class and fragmentation pattern assessment. Data to compare the lipidome of cold versus room temperature mice were collected in MS1 and imported into Agilent Profinder for lipid identification and peak integration using the tissue-specific libraries. Data were exported to .csv files, and in-house R scripts were used for normalization to ISs and starting tissue amount (R Core Team, R, version 4.0.2).
Lipid annotations were further screened for redundancies through criteria based on analysis of raw chromatograms. The reasons for these redundancies included multiple adducts of the same lipid, slight retention time differences corresponding to the same lipid peak, and in-source fragmentation. As these redundant annotations were also present for lipid standards, we developed additional filtering criteria to obtain high-confidence unique lipid identifications. For positive ionization, individual identifications for the same lipid were filtered by dropping any duplicates if retention time difference for identication <0.10 min or if the lower intensity annotation was <25% of the more abundant identification. Cutoffs were determined by evaluating duplicate annotations for ISs. To determine total unique identifications, positive and negative mode data were compared for identical lipids annotated in both modes, and the lower abundance identification was dropped. All raw data files have been deposited to MetaboLights (#), and R code is available on Github (URL). Any other files are available upon request.
Publication 2022
Cold Temperature Lipidome Lipids Mice, House Retention (Psychology) Tandem Mass Spectrometry Tissues Tissue Specificity

Most recents protocols related to «Lipidome»

Data were analyzed in R (4.0.3) implemented in RStudio (2021.09.2 Build 382). Metabolite concentrations were analyzed with linear mixed models (lmer in the lme4 package) with oxygen status (2 levels) and BCP (3 levels) as fixed effects and Experiment as a random effect. Analyte data were log-transformed for analysis. Statistical differences were assessed by ANOVA (Anova in the car package) using Type II Wald F tests with Kenward-Roger degrees of freedom. Differences among the three BCP groups was determined by Tukey HSD tests (glht in the multcomp package). Oxygen consumption data were analyzed by two-way ANOVA with Tukey HSD tests for significance among groups.
Nonmetric multidimensional scale (NMDS) analysis is a powerful tool to investigate relational patterns in transcriptome and lipidome profiles. NMDS allows the representation of high-dimensional data in low-dimensional space based while maintaining the similarities between data points, which has been useful for consolidating both transcriptomic and metabolomic data. NMDS was used here to assess global patterns of hypoxia and BCP treatments on UFH-001 cells. NMDS was performed on standardized lipodomic (25 analytes) and transcriptomic (20,209 gene rows) data using Bray-Curtis dissimilaries with the metaMDS function in the vegan package in R (35). The number of output dimensions was constrained to 2. The maximum number of random starts was set to 50 and the lipid and gene expression solutions were each achieved in 20 iterations. The ordellipse function (vegan) was used to generate 95% confidence ranges in the 2-dimensional NMDS plotting space wherein non-overlapping ellipses indicate statistical separation at α = 0.05.
The statistical outputs for all data (organized by Figure number) are presented in Excel as a Supplemental file (S1 Table).
Publication 2023
Cells Gene Expression Gene Expression Profiling Genes Hypoxia Lipidome Lipids neuro-oncological ventral antigen 2, human Oxygen Oxygen Consumption Transcriptome Vegan
Genetic QTL mapping was performed using the R/qtl2 (v0.24) package78 (link) which fit a linear mixed effect model that included accounting for overall genetic relationship with a random effect, that is, kinship effect. The leave one chromosome out (LOCO) method was used, which accounts for population structure without reducing QTL mapping power. For each gut microbiome trait and caecal lipidome traits, sex, days on diet and mouse cohort (wave) were used as additive covariates as described previously13 (link). For gut microbiome traits and caecal lipidome traits, normalized abundance/coverage was transformed to normal quantiles. The mapping statistic reported was the log10 likelihood ratio (LOD score). The QTL support interval was defined using the 95% Bayesian confidence interval78 (link). Significance thresholds for QTL were determined by permutation analysis (n = 1,000). We included 2,803 gut microbiome function traits, 197 gut microbiome taxon traits and 3,384 caecal lipid feature traits for our QTL mapping. The reported genome-wide P values were not adjusted for the multiple phenotypes to avoid overly declaring QTL in the initial analysis. We used genome-wide P < 0.05 for significant QTL and used genome-wide P < 0.2 to find concordant QTL mapping and hotspots.
Publication 2023
Cecum Chromosomes Diet Gastrointestinal Microbiome Genome Lipidome Lipids Mice, House Phenotype
A blocked experimental design with one replicate of each FFA in the library, together with multiple BSA controls per 96-well plate, was chosen (n=3). Raw lipidomic profiles received from the Metabolomics Platform at the Broad Institute were filtered for samples with strongly deviating sample medians (manual cutoff, 7 out of 280 or 3% of the samples were discarded). Lipid metabolites with more than 30% of missing data points were removed, otherwise missing values were substituted with 50% of the minimum value of the respective metabolite’s intensity. To account for variations in total amount of captured metabolites, samples were scaled towards the global sample median. Only annotated lipid metabolites were used for further differential abundance analysis. We sought to understand the relationship between structural features of externally added FFAs and changes in the triglyceride fraction of the cells (Fig. 1C). For each externally added FFA, triglyceride intensity deviations from the BSA control were summed based on the structural feature of interest (number of C-atoms, number of double bonds). Then, triglyceride profiles of externally added FFAs were summarized based on the structural feature of interest of the FFAs (number of C-atoms, number of double bonds) and normalized to the number of FFAs making up each group. For assessment of the global lipidome in response to erucic and palmitic acid, lipid metabolites were filtered as described and subsequently imported into lipidR (Mohamed, Molendijk, and Hill 2020 (link)). Differential analysis of lipid abundance was calculated using the empirical Bayes procedure. Fold change in lipid abundance (EA vs. PA) was then normalized based on noted structural features (number of C-atoms, number of double bonds) and visualized in R. Network analysis of the biochemical relationship between differentially abundant lipid species was performed using the Lipid Network Explorer with default settings (Köhler et al. 2021 (link)).
Publication Preprint 2023
Cells DNA Library DNA Replication Lipidome Lipids Nonesterified Fatty Acids Palmitic Acid Triglycerides
Mass spectrometric analysis of the lipidome was conducted at the Kansas Lipidomics Research Center using a Xevo TQ-S mass spectrometer (Waters Co., Milford, MA). Individual lipids were identified by direct infusion in positive ion mode with precursor and neutral loss scans (Xiao et al., 2010 (link); Peters et al., 2010 (link); Li et al., 2014 (link)), using the scans shown in Supplemental Table 4. Response factor corrections were applied to the MGDG and DGDG analyses to correct for differences in the response of the mass spectrometer to unsaturated galactolipid species as compared to the saturated internal standards. Phospholipid data did not require such response factor corrections, as the biological phospholipids and the internal standard have similar response factors.
The metabolomics data from this study are available at the NIH Common Fund’s National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench (https://www.metabolomicsworkbench.org) (Sud et al., 2016 (link)), where it has been assigned Study ID ST002252. The data can be accessed directly via its Project DOI: http://dx.doi.org/10.21228/M8KQ6P.
Publication 2023
Biopharmaceuticals Galactolipids Lipidome Lipids Mass Spectrometry Phospholipids Radionuclide Imaging
The analysis of lipids was performed by direct flow injection analysis (FIA) using a high-resolution Fourier Transform (FT) hybrid quadrupole-Orbitrap mass spectrometer (FIA-FTMS) [53 (link)]. TG, diglycerides (DG) and cholesteryl esters (CE) were recorded in positive ion mode as [M + NH4]+ in m/z range 500–1000 and a target resolution of 140,000 (at m/z 200). CE species were corrected for their species-specific response [54 (link)]. Ceramides (Cer), phosphatidylcholines (PC), ether PC (PC O), phosphatidylethanolamines (PE), ether PE (PE O), phosphatidylglycerols (PG), phosphatidylinositols (PI), and sphingomyelins (SM) were analyzed in negative ion mode in m/z range 520–960; lysophosphatidylcholines (LPC) and lysophosphatidylethanolamine (LPE) in m/z range 400–650. Multiplexed acquisition (MSX) was applied for free cholesterol (FC) and the internal standard FC[D7] [54 (link)]. Lipid annotation is based on the latest update of the shorthand notation [55 (link)].
The datasets from liver and plasma lipidomes were subjected to principal component analysis (PCA) using the MetaboAnalystR 3.2 package for R version 4.2.1. For the PCA, the relative metabolite composition of individual lipid species within the different lipid classes were used. Prior to the PCA, variables with missing values were either excluded from the analyzes if more than 50% of the samples were missing or the missing values were replaced by the limit of detection (1/5 of the minimum positive value of each variable). After normalization by log transformation and autoscaling the remaining values were used for the PCA.
Publication 2023
Ceramides Cholesterol Cholesterol Esters Diglycerides Ethyl Ether Flow Injection Analysis Hybrids Lipidome Lipids Liver Lysophosphatidylcholines lysophosphatidylethanolamine M-200 Phosphatidylcholines Phosphatidylethanolamines Phosphatidylglycerols Phosphatidylinositols Plasma Sphingomyelins

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More about "Lipidome"

The lipidome, also known as the lipidomic profile, refers to the comprehensive analysis and characterization of the complete set of lipids within a biological system, such as a cell, tissue, or organism.
Lipids play crucial roles in various cellular processes, including energy storage, signaling, and membrane structure.
The study of the lipidome provides insights into the dynamic changes in lipid metabolism and composition, which can be associated with physiological and pathological conditions.
Leveraging cutting-edge technologies like the TriVersa NanoMate, Acquity UPLC, and software tools such as SIMCA-P and GraphPad Prism 5, researchers can explore the lipidome to unravel the complex lipid networks and their implications in health and disease.
This knowledge can lead to the development of novel diagnostic biomarkers and therapeutic interventions targeting lipid-related pathways.
Explore the lipidome of genetically modified mouse models like the B6;C3-Tg(Prnp-SNCA*A53T)83Vle/J, and utilize tools like AMIX 3.9.14 to analyze your data.
Dive into the role of lipids in signaling pathways and the impact of compounds like Fingolimod and Lithium heparin on the lipidome.
Leverage the Lipid Droplet Isolation Kit to study the dynamic changes in lipid storage and metabolism.
Discover the power of PubCompare.ai's AI-driven platform for optimizing your lipidome research.
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