Metabolomics Creative Proteomics

Skeletal Muscle Metabolomics Service

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Skeletal muscle, constituting a significant portion of body mass, is a dynamic tissue responsible for energy production and movement. Metabolites, the molecular intermediaries in biochemical reactions, hold critical roles in modulating muscle function and metabolic responses. Analyzing skeletal muscle metabolites facilitates a deeper understanding of energy pathways, metabolic adaptations, and physiological responses within this complex tissue.

Why Analyze Skeletal Muscle Metabolites?

1. Unraveling Energy Dynamics: Metabolite analysis offers insights into the energy fluxes and substrate preferences that sustain skeletal muscle function. By examining metabolite profiles, researchers decipher how energy substrates are utilized and interconverted during various physiological states.

2. Probing Metabolic Adaptation: Metabolite analysis enables the investigation of metabolic adaptations in response to external stimuli, such as dietary changes or physiological stressors. These insights shed light on the mechanisms that underlie muscle adaptation to diverse environmental cues.

3. Insights into Metabolic Disorders: The analysis of skeletal muscle metabolites provides a deeper understanding of metabolic disorders such as diabetes, obesity, and insulin resistance. Metabolomic signatures offer diagnostic potential and contribute to elucidating the metabolic dysregulations associated with these conditions.

4. Mechanistic Insights: By unraveling metabolic pathways and substrate interactions, metabolite analysis contributes to our understanding of fundamental biological mechanisms. This knowledge extends beyond muscle metabolism, offering insights into broader metabolic processes.

Skeletal Muscle Metabolomics Analysis Project in Creative Proteomics

Targeted Metabolite Profiling:

  • Quantitative analysis of specific metabolites involved in skeletal muscle metabolism.
  • Precise measurement of metabolites such as glucose, lactate, amino acids, and nucleotides.
  • Utilization of advanced mass spectrometry techniques for accurate quantification.

Untargeted Metabolomics:

  • Comprehensive analysis of a wide range of metabolites in skeletal muscle samples.
  • Identification of metabolite patterns associated with various physiological conditions.
  • High-resolution mass spectrometry for unbiased profiling and discovery.

Isotope Tracing Studies:

  • Investigation of metabolic fluxes in skeletal muscle using stable isotope labeling.
  • Measurement of isotopic enrichment in metabolites to understand metabolic pathways.
  • Insights into substrate utilization, glycolysis, TCA cycle, and more.

Metabolic Pathway Analysis:

  • Mapping of metabolic pathways in skeletal muscle tissue.
  • Identification of key intermediates and regulatory metabolites.
  • Integration of data to provide a holistic view of metabolic networks.

Data Analysis and Interpretation:

  • Expert data processing and statistical analysis of metabolomics data.
  • Identification of significant metabolite changes and pathway alterations.
  • Generation of comprehensive reports with actionable insights.

Skeletal Muscle Metabolites Assay Platforms

Liquid Chromatography-Mass Spectrometry (LC-MS): High-resolution LC-MS systems for accurate quantification and profiling. Identification of polar and non-polar metabolites in skeletal muscle samples.

Gas Chromatography-Mass Spectrometry (GC-MS): Analysis of volatile and semi-volatile metabolites in skeletal muscle. Quantitative measurement of metabolites involved in energy metabolism.

Stable Isotope Tracing Mass Spectrometry: Utilization of GC-MS or LC-MS to analyze isotopic labeling patterns. Determination of metabolic fluxes and substrate utilization in skeletal muscle.

Advanced Mass Spectrometry Platforms: Deployment of state-of-the-art mass spectrometers for high sensitivity and accuracy. Detection of metabolites at low concentrations and elucidation of metabolic pathways.

Data Analysis of Skeletal Muscle Metabolomics

Data Analysis StepsDetails
PreprocessingRemoval of noise, baseline correction, and alignment of metabolomics data.
Quality ControlAssessment of data quality, including retention time stability and peak intensity distribution.
Feature IdentificationMatching mass-to-charge (m/z) values and retention times with metabolite databases for identification.
QuantificationCalculation of metabolite concentrations based on peak intensities and standard curve calibration.
Statistical AnalysisDifferential analysis to identify significant changes in metabolite levels between different groups.
Pathway Analysisdentification of affected metabolic pathways and enrichment analysis to determine overrepresented pathways.
Multivariate AnalysisPrincipal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) for sample clustering and discrimination.
VisualizationCreation of heatmaps, volcano plots, and pathway diagrams to visualize the results.
InterpretationIntegration of findings to understand the metabolic implications of experimental conditions.
For Research Use Only. Not for use in diagnostic procedures.


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