Metabolomics mainly studies small molecular metabolites with a molecular weight of less than 1500 as substrates and products of various metabolic pathways. By detecting the spectra of a series of samples, combined with chemical pattern recognition methods, qualitative or quantitative analysis of all or some metabolites in the biological system. Creative Proteomics' metabolomics services can be applied to many industries, including medicine, pharmacology, pathology, toxicology, food science, nutrition and other fields.
Creative Proteomics' Metabolomics Solutions
Untargeted Metabolomics (Metabolite Profiling) Analysis
Untargeted metabolomics involves the global analysis of metabolites in biological samples. It aims to identify and quantify a wide range of small molecules present in the sample, offering a comprehensive view of the metabolic landscape. Researchers in metabolite profiling are primarily interested in uncovering novel metabolites, identifying metabolic pathways, and understanding how metabolic profiles change under various conditions. This approach is exploratory and hypothesis-generating. Through advanced analytical techniques such as liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS), we can identify and quantify hundreds to thousands of metabolites simultaneously.
Targeted Metabolomics Analysis
Targeted metabolomics analysis is focused on the precise quantification of specific metabolites of interest. Researchers select and analyze a limited set of compounds with known importance. The focus in targeted analysis is on the accurate measurement of predefined metabolites. This approach is hypothesis-driven and often used for validation or quantitative studies.
Indoles and Indole-sulfur Compounds
Metabolic Pathway Analysis
Metabolic pathway analysis is a crucial step in metabolomics research, as it helps unravel the interconnectedness of metabolites and their involvement in biological processes. Creative Proteomics employs sophisticated bioinformatics tools and databases to map identified metabolites onto metabolic pathways and generate insightful visual representations. This analysis aids in the identification of key metabolic pathways and potential regulatory mechanisms.
Metabolomics Data Analysis and Interpretation
Creative Proteomics understands that the successful interpretation of metabolomics data is crucial for extracting meaningful insights. Our team of expert bioinformaticians and biostatisticians utilizes cutting-edge data analysis algorithms and statistical methods to process raw data, identify significant metabolites, and perform multivariate analyses. This comprehensive approach ensures the accurate interpretation of complex metabolomics datasets.
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Technical Platform for Metabolomics Services
Creative Proteomics utilizes state-of-the-art mass spectrometry techniques for metabolomics analysis, providing high sensitivity, resolution, and coverage of metabolite classes.
Thermo Q ExactiveTM series
AB Sciex 6500+
Thermo Orbitrap Fusion Lumos
Waters Xevo TQ-s
Thermo TRACE 1310-ISQ LT
Thermo TSQ 9000
Agilent 6495 Triple Quadrupole LC/MS Coupled with the Agilent 1290 Infinity II LC System
ACQUITY UPLC
Acceptable Sample Types
A variety of samples can be analyzed, such as plasma or serum, urine, saliva, cells, animal and plant tissues, fungi, bile acids, microorganisms, cerebrospinal fluid, lymphatic fluid, soil, feces and intestinal flora and other biological samples.
Absolute quantitative analysis of plant primary and secondary metabolites.
Plant metabolism profile analysis, unbiased detection of all small molecular metabolites in plant samples.
Use high-throughput detection technology to study the types and quantities of metabolites and their changes in humans and other animals to clarify the body's metabolic processes in normal life states and environmental changes.
Analysis of microbial primary and secondary metabolites. Microbial metabolic full spectrum analysis to elucidate the mechanisms of microbial-host interactions.
Hot Analytical Metabolite Categories
Applications of Metabolomics Service
Creative Proteomics's metabolomics service offers advanced solutions for exploring and analyzing metabolites, providing valuable insights into diverse areas of research.
Comprehensive profiling and focused study of metabolites in biofluids such blood, urine, and cerebrospinal fluid are made possible by metabolomics. Researchers can find potential biomarkers linked to particular diseases or physiological situations by examining the metabolite patterns.
Metabolomics, for instance, can assist in finding distinctive chemical signatures that distinguish between healthy people and cancer patients in cancer research. These biomarkers can help with early detection, prognosis evaluation, and therapy response tracking.
Metabolomics offers insights into drug metabolism, efficacy, and toxicity. Creative Proteomics's metabolomics service enables researchers to study the metabolic effects of drugs and identify potential drug targets. By analyzing the metabolomic profiles of cells, tissues, or biofluids after drug exposure, researchers can gain a deeper understanding of drug mechanisms of action and evaluate drug safety and efficacy.
Metabolomics can assist in identifying drug metabolites, elucidating metabolic pathways involved in drug metabolism, and predicting drug-drug interactions.
By making it easier to analyze the metabolite profiles of food samples, metabolomics enables researchers to determine the nutritional value, assess the quality of the food, and investigate how different food preparation methods affect metabolite profiles.
Understanding nutrition consumption and metabolism, finding bioactive substances in food, and assessing the impacts of additions or pollutants can all be done with the aid of metabolomics. Understanding the link between diet and health, creating individualized nutrition plans, and assuring the safety and quality of food products are all made easier with the use of this knowledge.
Metabolomics enables the analysis of metabolomic profiles in organisms exposed to environmental stressors or pollutants. By studying the metabolic responses of organisms to environmental changes, researchers can assess the impact on their metabolism and identify potential biomarkers of exposure or effect.
Metabolomics can help in understanding the biochemical pathways involved in the detoxification and metabolism of environmental contaminants. It aids in predicting ecological risks, assessing the effects of pollutants on human health, and contributing to environmental protection efforts.
Plant metabolite profiles may be analyzed more easily due to metabolomics, which reveals details about how plants use their nutrients and respond to biotic and abiotic challenges.
Understanding the biochemical pathways involved in crop development, finding metabolites linked to desired features, and optimizing agricultural techniques for better yield and quality are all possible with the help of metabolomics. This knowledge aids in the development of sustainable agricultural methods, the improvement of crop resistance to diseases, and the breeding of improved crop types.
Systems biology approaches are supported by metabolomics by providing the essential analytical tools and knowledge. Researchers can obtain a comprehensive understanding of biological processes and their regulation by integrating metabolomics data with other "omics" technologies including genomes, transcriptomics, and proteomics. Discovering new metabolic pathways, locating important regulatory nodes, and figuring out how biological phenomena are caused are all made easier by metabolomics. This holistic strategy can be used to model diseases, comprehend cellular metabolism, and find possible treatment targets.
Case: Metabolic Dysfunction in Young Adulthood Predicts Long-Term Cardiovascular Outcomes: Insights from a Multi-Cohort Study
Background
Understanding the antecedents of cardiovascular risk in young adults is crucial for effective prevention. Traditional risk assessment methods may not capture early metabolic changes associated with cardiovascular diseases (CVD). This study aims to identify specific metabolic features in young adulthood that predict an adverse cardiovascular phenome over two decades, utilizing advanced analytical techniques.
Sample
The study includes 2330 individuals from the CARDIA cohort (mean age 32.1±3.6 years; 45% women; 45% Black). Additionally, validation is performed on 1898 participants from the FHS Offspring Cohort (mean age at metabolite measurement 54.9±9.7 years).
Methods
Statistical Methods:
- Graphical examination of distributions of continuous subclinical endpoints and covariates.
- Transformation of variables, including logarithmic and hyperbolic arcsine transformations.
- Pooled cohort equation risk calculation and standardization of continuous endpoints and covariates.
- Metabolite imputation and log transformation, followed by correlation analysis and PCA.
Single Metabolite Regressions:
- Linear or logistic regression for each subclinical CVD outcome against each metabolite.
- Three sets of models: unadjusted, adjusted for demographic factors, and adjusted for clinical risk factors.
- Application of Benjamini-Hochberg false discovery rate control.
Elastic Net and PCA:
- Two-phase regression approach using elastic net regression and subsequent PCA to identify metabolites linked to subclinical CVD endpoints.
- Generation of vascular and myocardial scores based on metabolite weighting and concentration.
- Survival analysis using Cox regression with adjustment for clinical factors.
FHS Validation:
- Validation of identified metabolites and scores in the FHS cohort using linear regressions.
- Examination of the association with incident CVD and death.
Pathway Analysis:
- Detailed pathway analysis to explore the underlying mechanisms of metabolic dysfunction.
Study scheme.
Results
Clinical Characteristics:
- Description of demographic and clinical characteristics of the study populations.
- Comparison between the CARDIA and FHS cohorts.
Metabolic Dysfunction Associations:
- Associations between metabolites in young adulthood and subsequent development of adverse cardiovascular phenome.
- Network in silico approach revealing pathways implicated in myocardial and vascular remodeling.
Metabolic Scores and Cardiovascular Risk:
- Generation of vascular and myocardial health scores associated with specific metabolites.
- Scores demonstrated associations with future CVD, and the association was stronger in young adulthood.
Validation and Age-Dependent Association:
- Replication of findings in the FHS cohort.
- Strong interaction between age and metabolic scores in predicting CVD, with the highest hazard in young adulthood.
Race Differences and Clinical Relevance:
- Identification of race differences in myocardial health scores.
- Emphasis on the clinical relevance of precise metabolic measures in early adulthood for predicting long-term cardiovascular outcomes.
Metabolites in early adulthood are associated with the cardiovascular phenome and identify pathways central to cardiovascular disease development.
Reference
- Murthy, Venkatesh L., et al. "Comprehensive metabolic phenotyping refines cardiovascular risk in young adults." Circulation 142.22 (2020): 2110-2127.
Q: What are external standards and internal standards in metabolomics? If an internal standard is added during detection, does that constitute internal standard method detection?
A: External standards refer to standard substances used in metabolite detection that are consistent with the analyte. Since the concentration and amount of the standard substance are known, by using standard substances of different concentrations to construct a standard curve and obtain peak areas, the concentration of the analyte can be calculated based on the peak areas of the standard curve, the concentration of the standard curve, and the peak area of the analyte, thereby achieving absolute quantification and qualitative analysis of the analyte.
Internal standards refer to isotopes of the analyte used in metabolite detection. Isotopes are detected together with the analyte. Since the concentration and amount of isotopes are known, the content of the analyte can be inferred based on the peak areas of the isotopes and the analyte, thereby achieving relative quantification and qualitative analysis of the analyte. Adding an internal standard does not necessarily constitute internal standard method detection. External standards are used for absolute quantification and qualitative analysis of the analyte, while internal standards are used to correct quantitative differences caused by variations in sample amounts and eliminate the influence of matrices on quantification.
Q: What are the requirements for biological replicates in metabolomics?
A: Compared to other omics studies, metabolomics, being downstream, requires a larger number of samples to avoid analytical errors due to individual differences in metabolic profiles. It is generally recommended to have at least 8 parallel samples for plant, microbial, and cellular samples, around 10 parallel samples for model animals, and at least 30 parallel samples for clinical samples. If the number of biological replicates is too small, the subsequent model construction will be poor, and there will be very few differentially expressed metabolites that meet the conditions.
Q: If the sample quality is below the collection standard, can metabolomics analysis still be performed?
A: Because the quality of samples in metabolomics analysis is often positively correlated with the number of metabolites detected, the requirement of 100mg in the collection standard is based on project experience, balancing the customized sample requirements and the quantity of detected substances. It does not mean that samples below 100mg cannot be tested; it just indicates that fewer substances might be detected compared to standard samples of the same type.
Q: For clinical samples with a long collection period, can they be batched for testing and combined analysis?
A: It is not recommended to batch test and combine analysis as different instrument states at different times can affect the results and even change the number of detected differential molecules. It is advisable to test all samples together after collection. If the sample size is large and it's not possible to test all samples in one batch, we have strategies for batch testing large sample queues to minimize batch effects.
Q: Can untargeted metabolomics analyze different tissue types together (e.g., one being animal tissue and the other intestinal contents)?
A: They can be analyzed together on the same machine and analyzed together using shared quality controls (QCs). If you don't want to mix a QC, and want to use two untargeted results for combined analysis, theoretically, we do not recommend it because different tissue types contain different types of substances, and some substances specific to certain tissue types might be filtered out during data filtering.
Q: What concentration of extracted metabolites is required for machine analysis?
A: Extracted samples can undergo untargeted metabolomics machine analysis; the approximate concentration of the extracted material needs to be noted. Additionally, certain extraction agents (e.g., DMSO) may crystallize at low temperatures, so special notes are necessary. If the extraction concentration is too high, dilution might be needed. The laboratory typically uses methanol for dilution, and the maximum concentration for machine analysis is 20ppm.
Q: Does the laboratory perform desalination for high-salt samples?
A: The laboratory directly performs metabolite extraction without desalination steps. High salt content can interfere with metabolite detection, and customers are informed of this risk beforehand. The laboratory will rely on actual test results, and any anomalies during the experiment will be promptly reported.
Q: Why aren't untargeted results validated by targeted methods?
A: Non-targeted and targeted metabolite detection are two completely different principles. Non-targeted metabolomics is broad-spectrum, aiming to collect as many metabolite ion peaks as possible through different chromatographic columns and ionization methods, identifying differential metabolites through database comparisons to find biomarkers or compare differences between different groups. Targeted metabolomics, on the other hand, quantitatively analyzes and compares target metabolites using Multiple Reaction Monitoring (MRM) methods, matching parent ion/daughter ion pairs. Thus, the two methods have different purposes, identification methods, and result presentations. Additionally, the extraction methods differ: untargeted extraction methods obtain most metabolites from samples without specificity, while targeted methods are specific to certain types of substances.
Q: Can untargeted metabolomics data determine the presence or absence of substances?
A: After each sample is run separately, all mass spectrometry files of the samples are processed together in software like XCMS for deconvolution, peak alignment, retention time correction, and null value filling. After merging and searching the library for qualitative analysis, differences between different samples are eliminated. Subsequently, based on the principle of >50%, metabolites are selected, with substances appearing less than 50% being removed, and the remaining substances are filled with null values. Therefore, there is no concept of presence or absence in metabolomics results.
Q: What is the difference between metabolic ions and metabolites?
A: Metabolic ions detected in mass spectrometers are annotated during data analysis, resulting in metabolites. Results annotated using mass-to-charge ratio are primary metabolites, while those annotated using peak spectrum are secondary metabolites. Therefore, the results of secondary metabolites are more accurate than those of primary metabolites and are preferred for analysis.
Q: What do "pos" and "neg" mean?
A: "Pos" and "neg" represent two scanning modes. After ionization, some metabolites tend to carry a positive charge, while others tend to carry a negative charge. To detect more metabolite ions, we perform scanning in both positive ion mode and negative ion mode. For subsequent data filtering, it is recommended to analyze the results of both modes to obtain a more complete result.
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