Fatty acids are a class of aliphatic carboxylic acid compounds containing long-chain hydrocarbons, usually in the form of esters in various lipid components such as neutral fats, phospholipids and glycolipids. According to the carbon chain length, fatty acids can be divided into short-chain fatty acids (2-4 carbon atoms), medium-chain fatty acids (6-12 carbon atoms) and long-chain fatty acids (more than 14 carbon atoms). According to hydrocarbon chain saturation and unsaturation, fatty acids can be divided into saturated fatty acids, monounsaturated fatty acids and polyunsaturated fatty acids. Palmitic acid, stearic acid, and arachidic acid are saturated fatty acids. Myristic acid, palmitoleic acid, oleic acid, erucic acid, etc. belong to monounsaturated fatty acids. Linoleic acid, linolenic acid, arachidonic acid, eicosapentaenoic acid and docosahexaenoic acid are polyunsaturated fatty acids.
Fatty acids are associated with cardiovascular diseases, hypertension, diabetes, hyperlipidemia, obesity and other metabolic diseases. It also plays a role in anti-tumor, anti-depression, inflammation, promoting brain and retinal development, and promoting bone health.
In addition, fatty acid β oxidation is the main way of fatty acid decomposition in organisms, which provides a lot of energy for the body. Ketone bodies, the intermediate metabolites of fatty acids, including acetoacetic acid, β-hydroxybutyric acid and acetone, are also important for life activities.
Fatty acid content in human body can be regulated through dietary intake. The role of fatty acids in the body depends on the structure. We can make a detailed analysis of the composition of fatty acids contained in foods.
Creative Proteomics has established a fatty acid analysis platform, using LC-MS/MS and GC-MS/MS technologies to achieve efficient and accurate analysis of fatty acid composition and quantitative analysis of fatty acids.
Our services can be used in medicinal and oil plant composition research, metabolite kinetic analysis, metabolic pathway analysis, lipid metabolism research, disease diagnosis, food and oil standard testing, nutrition and development research, etc.
Classification and scope of analysis:
Long-Chain Saturated Fatty Acids: These are linear fatty acids characterized by full saturation, such as palmitic acid, stearic acid, and myristic acid.
Medium-Chain and Short-Chain Saturated Fatty Acids: We analyze shorter carbon chain saturated fatty acids, including lauric acid, arachidic acid, and caproic acid.
Monounsaturated Fatty Acids: This category encompasses fatty acids with a single double bond, like oleic acid, palmitoleic acid, and erucic acid.
Polyunsaturated Fatty Acids: We analyze polyunsaturated fatty acids with multiple double bonds, including linoleic acid, alpha-linolenic acid, arachidonic acid, eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA).
We specialize in the analysis of straight-chain fatty acids, which possess a linear carbon chain structure. This category covers both saturated and unsaturated straight-chain fatty acids.
Our services also include the analysis of branched-chain fatty acids, characterized by their branched structural configuration. Notable examples consist of isovaleric acid and phytanic acid.
List of detectable fatty acid at Creative Proteomics
| Alpha-Linolenic Acid | Arachidic Acid | Arachidonic Acid | Behenic Acid | Butyric Acid |
| Capric Acid | Caproic Acid | Caprylic Acid | Docosahexaenoic Acid (DHA) | Eicosapentaenoic Acid (EPA) |
| Elaidic Acid (trans C18:1) | Erucic Acid | Gadoleic Acid | Heptadecanoic Acid | Lauric Acid |
| Lignoceric Acid | Linoleic Acid | Linolelaidic Acid | Margaric Acid | Mead Acid |
| Myristic Acid | Myristoleic Acid | Nervonic Acid | Oleic Acid | Palmitic Acid |
| Palmitoleic Acid | Pentadecanoic Acid | Selacholeic Acid | Stearic Acid | Vaccenic Acid |
Technology platform for fatty acid analysis
Gas Chromatography-Mass Spectrometry (GC-MS): Our GC-MS service utilizes advanced instrumentation, including the Agilent 7890A GC coupled with Agilent 5977B MSD, for the precise analysis of fatty acids. This method is essential for lipidomics and lipid metabolism studies, providing accurate data on fatty acid composition.
Liquid Chromatography-Mass Spectrometry (LC-MS): Our LC-MS service, powered by instruments like the Thermo Scientific Q Exactive HF-X Hybrid Quadrupole-Orbitrap, allows for a comprehensive exploration of various lipids. From fatty acids to triglycerides and phospholipids, we cover the spectrum, enabling a holistic understanding of lipid profiles.
Mass Spectrometry Imaging (MSI): With cutting-edge equipment like the Waters Synapt G2-Si High Definition Mass Spectrometer, our MSI service empowers researchers to visually assess the spatial distribution of lipids. This approach is crucial for studying lipid biology and metabolism from a spatial perspective.
High-Resolution Mass Spectrometry (HRMS): Our HRMS service harnesses the capabilities of high-resolution mass spectrometry, including the Thermo Scientific Orbitrap series (e.g., Orbitrap Fusion and Orbitrap Q Exactive). This technology is pivotal in providing high sensitivity for detecting and quantifying an extensive range of lipids, offering a comprehensive view of lipid profiles in diverse biological processes.
Gas Chromatography with Flame Ionization Detection (GC-FID): Our GC-FID service is particularly tailored for the quantification of fatty acids within lipid samples. Leveraging gas chromatography with flame ionization detection, we offer a reliable and accurate method for specific fatty acid quantification, making it invaluable for researchers investigating lipid composition and metabolism.
A typical workflow of mass spectrometry-based lipidomics (Wang et al., 2019)
Sample requirements for fatty acid analysis
| Sample Type | Sample Volume | Storage Conditions | Preparation |
|---|
| Plasma/Serum | 200-500 µL | -80°C or -20°C | Protein precipitation using organic solvents. |
| Tissue (e.g., Liver) | 20-50 mg | -80°C | Homogenization in a suitable solvent (e.g., chloroform/methanol). |
| Biological Fluids (e.g., Urine) | 1-2 mL | -80°C or -20°C | Suitable protein precipitation or extraction procedure. |
| Cells (Cultured) | 1-5 x 10^6 cells | -80°C | Extraction with appropriate solvent (e.g., chloroform/methanol). |
| Food Samples | Varies depending on the matrix | -20°C or as appropriate | Extraction and purification methods specific to the sample type. |
Fatty acid analysis data analysis
| Data Analysis Component | Description |
|---|
| Data Preprocessing | - Data cleaning and quality control, including the removal of noise, artifacts, and outliers. |
| - Mass spectrometry data calibration to ensure accurate mass measurements. |
| - Conversion of raw data files into a usable format for downstream analysis. |
| Feature Detection | - Identification of individual lipid species and their associated mass spectra. |
| - Peak picking and feature extraction to determine m/z values, retention times, and peak intensities. |
| - Isotope deconvolution to resolve overlapping peaks and improve quantification accuracy. |
| Data Alignment | - Alignment of features across multiple samples to correct for retention time shifts and variations. |
| - Ensuring that corresponding peaks in different samples are accurately matched. |
| Identification and Annotation | - Annotation of detected lipid species through spectral matching with reference databases. |
| Quantification | - Relative quantification of lipid species using peak intensities or areas under the curve. |
| - Absolute quantification using internal or external standards for calibration. |
| Statistical Analysis | - Multivariate analysis (e.g., PCA, PLS-DA) to assess differences between sample groups. |
| - Univariate analysis (e.g., t-tests, ANOVA) for identification of significantly altered lipids. |
| Pathway Analysis | - Investigation of lipid metabolic pathways and their implications in biological processes. |
| Data Visualization | - Creation of graphical representations, such as heatmaps, volcano plots, and PCA plots. |
| - Visualization of lipid profiles and patterns to aid in result interpretation. |
| Reporting and Interpretation | - Preparation of comprehensive reports summarizing findings and meaningful interpretations. |
| - Discussion of results in the context of the research objectives and relevant literature. |
Creative Proteomics has many years of experience in lipidomics analysis services, and enjoys a good reputation in supporting the detection and quantification of various lipids. Based on a highly stable, reproducible and highly sensitive separation, characterization, identification and quantitative analysis system, we provide reliable, fast and cost-effective fatty acid analysis services. If you have other substances you want to test or other questions, please contact us.
Reference:
- Wang, Jianing, Chunyan Wang, and Xianlin Han. "Tutorial on lipidomics." Analytica chimica acta 1061 (2019): 28-41.
Case: Targeted Metabolomic Profiling Reveals Associations Between Total Fatty Acids and Autoimmune Diseases
Background
This study aimed to investigate the potential of total fatty acids (TFAs) as biomarkers for autoimmune diseases (ADs) such as thyroid autoimmune disease, rheumatoid arthritis, multiple sclerosis, vitiligo, psoriasis, and inflammatory bowel disease. Metabolomics, combined with advanced statistical tools, was used to assess the metabolic alterations in AD patients.
Sample
A total of 403 participants were included in the study, consisting of 240 patients with ADs (the case group) and 163 individuals in the control group. The majority of AD patients had autoimmune thyroid disease, and a substantial portion had more than one autoimmune condition.
Technical Methods
Metabolomic Profiling: Targeted metabolomic profiling was conducted using gas chromatography-mass spectrometry (GC-MS) to analyze 23 TFAs in serum samples from the study participants.
Statistical Analysis: Data analysis included univariate tests (chi-square, Mann-Whitney, Wilcoxon Sign Rank) to compare the case and control groups. Principal Component Analysis (PCA) was employed to reduce the dimensionality of the dataset. Binary logistic regression models were used to assess the relationship between the presence of ADs and variables such as gender, age, exercise, alcohol consumption, body mass index (BMI), and TFAs.
Artificial Neural Networks (ANN): An ANN analysis was conducted, initially using two layers of architecture, with 11 variables in the input layer. The dataset was divided into training, test, and holdout sets for ANN modeling.
Results
Metabolite Alterations: AD patients exhibited significant alterations in specific TFAs when compared to the control group. Notably, they had increased levels of certain TFAs, including C14:1, C15:1, C16:1n7, C20:1n9, C22:1n9, C18:3n3, C18:3n6, and the total omega-6/total omega-3 ratio, while they had lower levels of total omega-3 fatty acids, C12:0, C17:0, C18:0.
Correlation Analysis: Strong correlations between TFAs were observed in the case group of AD patients. Notable correlations included lauric acid (C12:0), pentadecanoic acid (C15:0), stearic acid (C18:0), myristoleic acid (C14:1), cis-10 pentadecanoic acid (C15:1), and arachidonic acid (C20:4n6).
Predictive Models: Three predictive models were developed, with the ANN model revealing that specific TFAs, lifestyle factors like exercise and alcohol consumption, and demographic variables were significant predictors for ADs. The accuracy of these models ranged from 74.7% to 78.9%.
Impact of Lifestyle Factors: The study emphasized the impact of exercise and alcohol consumption on ADs, highlighting their potential roles in disease prevention and management.
Role of Insulin Resistance: The study suggested a link between insulin resistance and ADs, indicating the potential of TFAs, especially saturated and monounsaturated fatty acids, as biomarkers related to insulin resistance.
A scatter plot correlation matrix of the main variables used in the model.
Principal component analysis on total fatty acids of patients with autoimmune diseases compared to control group
ROC curve for the straightforward binary logistic Model.
Reference
- Tsoukalas, Dimitris, et al. "Targeted metabolomic analysis of serum fatty acids for the prediction of autoimmune diseases." Frontiers in Molecular Biosciences 6 (2019): 120.
