Metabolomics Creative Proteomics

Untargeted Metabolomics Service

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Untargeted Metabolomics in Creative Proteomics

Untargeted metabolomics services are mainly for comprehensive biochemical information of metabolism in organisms. Based on liquid chromatography-mass spectrometry technology (LC-MS), qualitative and quantitative analysis of almost all metabolites under certain physiological conditions or under specific conditions are carried out to find and identify different metabolites. Our untargeted metabolome analysis platform can accurately and rapidly analyze the small molecule metabolites covering more than 95% pathway in various biological samples (blood, urine, tissue, saliva, amniotic fluid, cells and cell fluid, etc.) and plant samples. Untargeted metabolomics, to the greatest extent, reflects the multiple dynamic responses of living organisms to external stimuli, pathophysiological changes and their own gene mutations in metabolite levels in vivo, providing a new perspective for disease diagnosis, pathological research, new drug development, drug toxicology and other studies.

Our technology platform can serve multiple industries, including biomedicine, pharmaceuticals, agriculture, animal husbandry, nutrition and other fields.

What we can provide:

  • Metabolite Identification and Quantitative Analysis: Comprehensive identification and quantitative analysis of metabolites in the sample, including organic acids, amino acids, nucleotides, lipids, and more. Precise determination of metabolite concentrations to understand the metabolic status and changes in the sample.
  • Metabolite Composition Analysis: Analysis of the overall composition of metabolites in the sample, revealing the interconnected metabolic network in complex biological systems. Discovery of potential new biomarkers and bioactive molecules through unbiased analysis.
  • Biomarker Identification: Identification of potential biomarkers associated with specific physiological states, diseases, or treatments. Provision of critical information for biomedical research and clinical diagnosis.
  • Metabolic Pathway Analysis: Analysis of key metabolites in metabolic pathways, revealing metabolic focal points in the sample. Understanding the response mechanisms of organisms to external stimuli through comprehensive analysis of metabolic pathways.

Technology Platform for Untargeted Metabolomics

Creative Proteomics uses an advanced LC-MS/MS system for untargeted metabolomics analysis services.

  • UHPLC-QTOF-MS (Agilent 1290)

Workflow for untargeted metabolomics serviceWorkflow for untargeted metabolomics service

Sample Preparation for Untargeted Metabolomics Service

  • Animal tissues: ≥ 200 mg
  • Cell samples: Collect cell pellets, rinse with PBS 3 times to remove residual serum. The sample volume is 1×107 or the cell pellet is not less than 50 μl.
  • Cellular supernatant: ≥ 1mL
  • Plasma/serum sample: fresh blood is taken out, anticoagulated or coagulated, centrifuged at 4°C, and the supernatant is taken to obtain plasma/serum. During the preparation process, ensure that hemolysis is not allowed, and the recommended sample volume should not be less than 250 μl.
  • Urine: ≥ 1ml
  • Saliva: ≥ 0.5ml
  • Follicular fluid, cerebrospinal fluid, lymphatic fluid, etc.: ≥ 200 μL
  • Viruses, bacteria or fungi: wet precipitate weight ≥ 200 mg
  • Stool and intestinal contents ≥ 200 mg
  • Plant tissues: ≥200 mg
  • Plant seeds: ≥100 mg

Please contact us for other special samples.

Data Analysis of Untargeted Metabolomics

  • Quality control: QC sample results including TIC and PCA results
  • Quantification and characterization results of differential metabolites
  • Univariate analysis: T test and/or ANOVA analysis of all identified metabolites
  • Multivariate analysis: PCA, PLS-DA, OPLS-DA of all identified metabolites
  • Functional analysis: Differential metabolite cluster analysis; Differential metabolite KEGG and KEGG enrichment analysis; Metabolite difference correlation analysis
  • Biomarker ROC analysis

Creative Proteomics offers several approaches to provide untargeted metabolomics service and deliver precise and detailed data and analysis report. We can also customize methods or establish new methods together with our collaborators to meet the specific needs of any project. If you have any questions, you can tell us through the inquiry form, and our technicians will communicate with you.

Case: Metabolomic Profiling Reveals Distinct Pathway Abnormalities and Potential Therapeutic Targets in Amyotrophic Lateral Sclerosis (ALS)


Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disorder, and understanding its metabolic profile can offer insights into disease mechanisms and identify novel therapeutic opportunities. Metabolomic analyses provide a comprehensive view of endogenous and exogenous influences on ALS, allowing the identification of biomarkers and potential drug targets.


The study included 125 ALS and 71 control participants, matched for key demographics. ALS cases represented a typical population with median diagnostic age, symptom onset-to-diagnosis interval, and distribution across onset segments. Metabolite profiling involved the identification of 1051 metabolites, with 144 excluded due to high missingness. A total of 899 metabolites were used for downstream analysis.

Technical Methods

Metabolite Profiling: 1051 metabolites were identified, and 144 with >60% missingness were excluded.

Drug Metabolite Exclusion: Eight drug metabolites were removed from analysis due to weak correlations and high missingness.

Differential Analysis: Wilcoxon rank-sum tests identified 303 significant metabolites. Logistic regression models, adjusted for sex, age, and BMI, further identified 300 metabolites. Partial Least Squares Discriminant Analysis (PLS-DA) and Group Lasso methods were also employed.

Pathway Enrichment Analysis: Identified significantly over-represented sub-pathways among differential metabolites.

Machine Learning Classification: Seven algorithms were applied to predict ALS cases using 259 Group Lasso-selected metabolites.


Differential Metabolites: 303 metabolites were identified as differentially expressed in ALS, with overlaps across various analytical methods.

Pathway Enrichment: Shared sub-pathways, including 'sphingomyelins,' 'ceramides,' 'benzoate metabolism,' and 'fatty acid metabolism,' were enriched across different models. Group Lasso uniquely identified 'diacylglycerol,' 'chemical,' and other sub-pathways.

Metabolite Correlations: Interconnections between significant metabolites and their sub-pathways were visualized, revealing associations in sphingolipid metabolism, polyamine metabolism, and more.

Diagnostic Potential: Machine learning models using 259 Group Lasso-selected metabolites demonstrated high diagnostic potential, with Receiver Operating Characteristic (ROC) analysis showing an AUC of 0.98.

Partial least squares-discriminant analysis (PLS-DA) analysis of amyotrophic lateral sclerosis (ALS) cases versus controls. (A) PLS-DA score plot of ALS cases (red) versus controls (blue); each dot represents an individual subject. (B) The variable importance in projection (VIP) score plot of the top 30 PLS-DA metabolites, which most significantly separate cases from controls.Partial least squares-discriminant analysis (PLS-DA) analysis of amyotrophic lateral sclerosis (ALS) cases versus controls. (A) PLS-DA score plot of ALS cases (red) versus controls (blue); each dot represents an individual subject. (B) The variable importance in projection (VIP) score plot of the top 30 PLS-DA metabolites, which most significantly separate cases from controls.

PPathway enrichment of adjusted logistic regression-selected, partial least squares-discriminant analysis (PLS-DA)-selected and group lassoselected metabolitesPathway enrichment of adjusted logistic regression-selected, partial least squares-discriminant analysis (PLS-DA)-selected and group lassoselected metabolites

Metabolite correlation analysis of group lasso-selected metabolitesMetabolite correlation analysis of group lasso-selected metabolites


  1. Goutman, Stephen A., et al. "Untargeted metabolomics yields insight into ALS disease mechanisms." Journal of Neurology, Neurosurgery & Psychiatry 91.12 (2020): 1329-1338.

Q: Is internal standard added in untargeted metabolomics LC-MS? What is its specific role?

A: Internal standard (2-chloro-L-phenylalanine) is indeed added in untargeted metabolomics, but it does not participate in any data analysis. It is solely used by the laboratory for internal assessment of instrument and experimental stability.

Q: For blood samples undergoing untargeted metabolomics analysis, which is better, serum or plasma samples?

A: Both serum and plasma are samples obtained after processing blood, and existing literature reports differences in the types and abundance of metabolites between serum and plasma. However, for research purposes, there is no clear indication that one sample type is superior to the other. Therefore, when choosing between serum or plasma, it is only necessary to ensure uniformity at the time of collection, and blood samples are preferably collected with EDTA or heparin anticoagulants. During collection, hemolysis should be avoided, and samples should be stored at -80°C to prevent repeated freeze-thaw cycles.

Q: Does repeated freeze-thaw cycles significantly affect metabolite detection?

A: Studies have shown that freeze-thaw cycles can cause changes in metabolites, and analysis of these substances reveals no intersection between differential substances. Therefore, differential substances selected using frozen-thawed samples likely include differences caused by freezing and thawing. In other words, freeze-thaw cycles can generate new differential substances, resulting in inaccurate representation of the true metabolic levels of the samples.

Q: How many substances can be detected in untargeted metabolomics?

A: Nanomix Metabolomics' comprehensive targeted metabolomics database contains over 5000 metabolites. All detected metabolites through LC-MS/MS analysis platform must undergo primary and secondary matching with metabolites in the standard library. This standard library data includes amino acids and derivatives, nucleotides and derivatives, flavonoids, terpenes, phenylamines, fatty acids, etc.

Q: Why can't some common metabolites be detected in untargeted metabolomics?

A: Firstly, untargeted metabolomics detection is not targeted towards specific metabolite types of interest, so detected metabolites may not be the desired results. Secondly, the untargeted metabolomics detection technology may interfere with metabolites with low signal intensity, leading to signal masking and difficulty in identifying the substances of interest. Additionally, the detection range should be considered; Nanomix Metabolomics' untargeted metabolomics mass spectrometry scanning range is 100-1000 m/z, and substances whose mass-to-charge ratio falls outside this range cannot be detected. If there is a specific research direction, it is recommended that customers use targeted metabolomics. Targeted metabolomics focuses on the detection of specific metabolites of interest, and the detection results are more ideal.

Q: What do "pos" and "neg" mean in untargeted metabolomics results? How are these two types of metabolites treated during analysis?

A: "Pos" and "neg" represent positive ion and negative ion modes during data acquisition. Positive ion and negative ion modes are two sets of mode data generated during data acquisition, so primary analysis (MS) results provide lists of detection results for both pos and neg modes. However, in secondary analysis (MS/MS) results, we merge the substances identified in positive and negative ion modes, so secondary analysis is done based on metabolites and does not distinguish between positive and negative ion modes.

Q: Why are there thousands of feature peaks detected in untargeted metabolomics results, but very few compounds are finally identified?

A: Currently, metabolomics typically identifies around 300-400 metabolites. Data analysis uses strict standard databases for comparison, with low false positives. Some metabolites may not be in the standard database, so they cannot be detected; public databases match based solely on molecular weight, resulting in many candidates but high false positives. Additionally, a metabolite may be detected multiple times in different forms of ions (charged), such as protonation, deprotonation, adduct ions, isotopic peaks, dimers, trimers, and unique ion forms, so there are many detected ion peaks, but many can only be qualitatively identified as one metabolite.

For Research Use Only. Not for use in diagnostic procedures.


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