LC-MS Metabolomics Service

LC-MS metabolomics is an advanced analytical method that combines liquid chromatography (LC) with mass spectrometry (MS) to analyze and quantify metabolites in complex biological samples. Based on different research objectives, LC-MS metabolomics can be classified into targeted and untargeted metabolomics. Targeted metabolomics focuses on the precise quantification of known metabolites, providing accurate concentration data, while untargeted metabolomics aims to comprehensively explore unknown metabolite profiles in samples, enabling the discovery of new biomarkers and metabolic pathways. Leveraging the high sensitivity and specificity of LC-MS, this method can identify a wide range of metabolites, including those present in trace amounts. LC-MS metabolomics has been widely applied in biomedical, pharmaceutical, and environmental research, offering valuable insights into disease-related metabolic changes, drug responses, and the effects of environmental exposures.

 

LC-MS metabolomics offers unique advantages in understanding biochemical pathways and mechanisms under physiological and pathological conditions. Whether it is the quantitative analysis of targeted metabolomics or the comprehensive screening of untargeted metabolomics, LC-MS metabolomics aims at solving complex biological problems, such as identifying metabolic biomarkers for early disease diagnosis, exploring potential therapeutic targets, and investigating the effects of genetic or environmental factors on metabolism. Additionally, LC-MS metabolomics can monitor metabolic responses to interventions, providing crucial insights for drug development, nutritional studies, and personalized medicine. By integrating advanced technologies with robust bioinformatics tools, LC-MS metabolomics helps researchers decode complex metabolic networks and advance precision healthcare.

 



Sarkar, J. et al. Proteomics Clin Appl. 2024.

Figure 1. General Workflow for Untargeted Metabolomics and Targeted Metabolomics

 

Service at MtoZ Biolabs

MtoZ Biolabs, an integrated Chromatography and Mass Spectrometry (MS) Services Provider, provides advanced proteomics, metabolomics, and biopharmaceutical analysis services to researchers in biochemistry, biotechnology, and biopharmaceutical fields. Our ultimate aim is to provide more rapid, high-throughput, and cost-effective analysis, with exceptional data quality and minimal sample consumption. MtoZ Biolabs' LC-MS metabolomics service utilizes highly sensitive liquid chromatography-mass spectrometry technology to offer both targeted and untargeted metabolomics solutions. This service comprehensively covers a wide range of metabolites, from precise quantification of known compounds to the global exploration of unknown metabolites, providing robust support for analyzing metabolic changes and their underlying mechanisms in biological samples. It is widely applied in disease research, biomarker discovery, drug development, and precision medicine. If you are interested in our service, please contact us freely.

 

Service Advantages

1. High Sensitivity  

MtoZ Biolabs optimizes the liquid chromatography-mass spectrometry (LC-MS) system to achieve high sensitivity in metabolite detection. This enables the accurate identification of even trace metabolites in complex samples, providing reliable support for disease research and metabolic pathway analysis.  

 

2. Wide Coverage  

LC-MS metabolomics service platform at MtoZ Biolabs is designed to detect metabolites with diverse chemical properties, including polar, non-polar, and neutral metabolites. This ensures comprehensive metabolite profiling and supports in-depth exploration of metabolic networks.  

 

3. Optimized Workflow  

MtoZ Biolabs employs a meticulously optimized workflow, including sample preparation, separation conditions, and mass spectrometry parameters. Combined with customized data processing and biological interpretation services, this ensures precise and meaningful results in metabolomics analysis.

 

Case Study

1.  Effects of Tang Luo Ning on Diabetic Peripheral Neuropathy in Rats Revealed by LC-MS Metabolomics Approach

Diabetic peripheral neuropathy (DPN) is one of the most common complications of diabetes, yet treatment options remain limited. Traditional Chinese medicine compound Tangluoning (TLN) has been clinically and experimentally proven effective in treating DPN. However, the potential metabolic mechanisms underlying TLN's therapeutic effects on DPN remain unclear. A study investigated the therapeutic effects of TLN on DPN using HPLC-IT-TOF/MS technology to explore metabolic changes associated with DPN and the mechanisms of TLN in treating high-glucose-induced DPN. Further analysis of metabolic pathways was conducted to examine the metabolic changes caused by DPN and TLN. The results demonstrated that TLN improved peripheral nerve function in DPN rats and alleviated sciatic nerve demyelination. LC-MS metabolomics identified 14 potential biomarkers associated with DPN and TLN treatment (including citric acid, creatine, fumaric acid, glyceric acid, glycine, and succinic acid). Pathway analysis revealed that these metabolic changes were primarily related to the tricarboxylic acid (TCA) cycle, glycine, serine, and threonine metabolism, as well as glyoxylate and dicarboxylate metabolism.

 



Li, Y. et al. Biomed Chromatogr. 2022.

Figure 2. Identified Potential Biomarkers Associated with DPN and TLN Treatment

 

2. Exploration of Circulating Metabolic Signature of Erythrodermic Psoriasis Based on LC-MS Metabolomics

Erythrodermic psoriasis (EP) is a rare and life-threatening disease with largely unknown pathogenesis. Metabolomics analysis provides comprehensive insights into the disease's pathophysiology, potential biomarkers, and intervention strategies. To better understand the mechanisms of EP and explore its serum metabolic profile, we conducted untargeted metabolomics analysis on 20 EP patients and 20 healthy controls. Additionally, targeted metabolomics analysis focusing on key metabolites was performed on serum samples from 30 EP patients and 30 patients with psoriasis vulgaris (PsV). In the untargeted analysis, a total of 2,992 molecular features were extracted from each sample, and the peak intensities of each feature were recorded. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) revealed significant intergroup differences. Screening identified 98 metabolites significantly dysregulated in EP, with 67 downregulated and 31 upregulated. EP patients exhibited lower levels of L-tryptophan, L-isoleucine, retinol, and lysophosphatidylcholine (LPC), and higher levels of betaine and uric acid. KEGG pathway analysis indicated that the differential metabolites were enriched in amino acid metabolism and glycerophospholipid metabolism. Targeted metabolomics revealed significantly lower L-tryptophan levels in EP compared to PsV, with a negative correlation between L-tryptophan levels and PASI scores. These findings uncovered the serum metabolic characteristics of EP, highlighting dysregulated amino acid and glycerophospholipid metabolism. The identified metabolic differences provide clues to the pathogenesis of EP and potential insights for therapeutic interventions.

 



Guo, L. et al. Exp Dermatol. 2024.

Figure 3. KEGG Pathway Analysis of the Differential Metabolites in Erythrodermic Psoriasis and Healthy Controls

 

FAQ

Q1: Plasma or Serum for LC-MS Metabolomics?  

Answer: Both plasma and serum are commonly used samples in LC-MS metabolomics, each with unique advantages and application scenarios. The choice between them depends on your specific research needs and objectives. Below are some suitable application scenarios for each:  

 

1. Scenarios for Plasma  

1.1 Dynamic Process Studies  

Plasma is obtained by centrifuging blood with added anticoagulants, retaining almost all metabolites, including those related to coagulation processes. Therefore, plasma is particularly suitable for studying metabolic changes involved in dynamic processes such as coagulation and platelet activation.

 

1.2 Monitoring Acute Physiological Changes  

Plasma can reflect rapid metabolic changes in the body, making it highly sensitive for monitoring acute physiological changes, such as inflammatory responses and stress from trauma.

 

1.3 Drug Metabolism Research

Plasma is commonly used in pharmacokinetics studies to monitor changes in drug and metabolite concentrations, helping to understand how a drug is absorbed, distributed, metabolized, and excreted.

 

2. Scenarios for Serum  

2.1 Steady-State Metabolism Studies  

Serum is obtained by centrifuging blood after natural clotting, which removes fibrinogen and better reflects the metabolic state under steady conditions. It is suitable for studying chronic diseases or long-term exposure to environmental pollutants.  

 

2.2 Disease Diagnosis and Prognosis Assessment  

Changes in the levels of certain metabolites in serum are closely associated with the onset, progression, and prognosis of specific diseases. Serum metabolomics research can be used to establish diagnostic models and prognostic assessment systems based on metabolic changes.  

 

2.3 Nutritional Studies  

Serum metabolomics is valuable for evaluating the impact of dietary intake on body metabolism, providing insights for nutritional research and healthy dietary guidance.  

 

3. Recommendations for Selection  

3.1 If your research focuses on exploring metabolic pathways related to the coagulation system or assessing the metabolic state before coagulation, plasma may be the better choice.

  

3.2 If your study focuses on specific hormones or other biomarkers unaffected by coagulation factors, serum may be more appropriate.  

 

3.2 Consider consistency in experimental design and standardization in comparative studies when selecting the sample type.  

 

Deliverables

1. Comprehensive Experimental Details

2. Materials, Instruments, and Methods

3. Relevant Liquid Chromatography and Mass Spectrometry Parameters

4. The Detailed Information of Metabolomics

5. Mass Spectrometry Image

6. Raw Data