Energy Metabolism Analysis by Targeted and Untargeted LC-MS Service

Overview of Our Energy Metabolism Metabolomics Service

Energy metabolism is central to how cells grow, adapt, and respond to stress. It links:

• Carbon flux through glycolysis and the TCA cycle
• Mitochondrial function and oxidative phosphorylation
• Amino acid utilization, lipid oxidation, and ketone body production

Our energy metabolism metabolomics service provides targeted LC–MS/MS panels together with broader LC–MS-based metabolomics profiling focused on these pathways. It is designed to help you:

• Demonstrate how a gene, drug, diet, training program, or stress condition reshapes energy metabolism
• Connect phenotypes, transcriptomics, and proteomics with metabolite-level changes
• Build a clear mechanistic understanding of how energy pathways are regulated in your model

Metabolites and Pathways Covered

We focus on key nodes that define cellular and whole-body energy status. Panels can be customized to your model and research question.

Key Pathways and Example Metabolites

*Measured and reported where technically feasible for a given matrix and project design.

Targeted, Semi-Quantitative, and Exploratory Options

Targeted quantification

• Predefined panel of energy-related metabolites
• Internal standards for improved accuracy
• Best for hypothesis-driven projects

Semi-quantitative profiling

• Expanded list of energy-associated metabolites
• Relative comparison across experimental groups
• Useful for pathway-level interpretation and screening

Untargeted profiling (optional)

• LC–MS-based untargeted metabolomics
• Global metabolite coverage beyond predefined energy panels
• Helps identify additional metabolic processes influenced by your intervention

Research Applications of Energy Metabolism Metabolomics

Cancer Metabolism and Immunometabolism

Typical goals

• Characterize metabolic reprogramming in tumor and immune cells
• Compare tumor vs. adjacent normal tissue energy profiles
• Assess how targeted therapies or checkpoint inhibitors affect glycolysis, TCA cycle, and lactate production

Common samples

• Tumor tissue and xenografts
• Cancer and immune cell lines
• Plasma or serum from tumor-bearing animals

Metabolic Disease, Obesity, and Fatty Liver

Typical goals

• Evaluate the impact of high-fat or high-sugar diets on systemic energy metabolism
• Test candidate drugs, natural products, or dietary interventions in metabolic disease models
• Investigate energy handling in white and brown adipose tissue

Common samples

• Rodent plasma or serum
• Liver, skeletal muscle, white/brown adipose tissue
• Feces or portal blood where host–microbiome interactions are of interest

Exercise Physiology, Performance, and Fatigue

Typical goals

• Profile acute and chronic responses to endurance or resistance training
• Track time-resolved changes in lactate, ketone bodies, acylcarnitines, and amino acids
• Study exercise-induced fatigue and recovery dynamics

Common samples

• Human or animal plasma/serum
• Urine and, in some cases, saliva
• Skeletal muscle biopsies or animal muscle tissue

Aging, Organ Function, and Mitochondrial Biology

Typical goals

• Map age-related shifts in tissue energy metabolism
• Evaluate mitochondrial dysfunction in genetic or pharmacological models
• Study organ-specific energy profiles in heart, brain, kidney, and other high-demand tissues

Common samples

• Heart, brain, skeletal muscle, adipose tissue
• Plasma or serum
• Isolated mitochondria (by arrangement)

Plant and Crop Stress, Yield, and Quality

Typical goals

• Analyze central carbon metabolism under drought, salinity, temperature, or nutrient stress
• Link energy metabolism to growth, yield, and product quality traits
• Connect primary energy pathways with secondary metabolites such as pigments and defense compounds

Common samples

• Leaves, roots, stems
• Seeds, grains, fruits
• Culture media or exudates

Microbial, Fermentation, and Synthetic Biology Systems

Typical goals

• Monitor central carbon and energy metabolism in microbial strains
• Identify metabolic bottlenecks that limit productivity in fermentation processes
• Evaluate metabolic burden and pathway performance in engineered strains

Common samples

• Microbial pellets and biofilms
• Culture supernatants and fermentation broths

Why Choose Our Energy Metabolism Analysis Services?

• Energy-focused, not generic – Panels and interpretation are built specifically around glycolysis, TCA, fatty acid oxidation, ketone bodies, and amino acid–linked energy pathways.
• High information per run – Up to 100+ energy-related metabolites in a single targeted LC–MS/MS assay, covering central carbon metabolism in one dataset.
• Reproducible, study-ready data – Optimized methods and QC typically keep key markers within low single-digit %CV where feasible, supporting robust group and time-course comparisons.
• Clear, mechanism-oriented outputs – Results are delivered as pathway-level changes (what shifts, how much, and in which direction), making it faster to move from data to experimental decisions.

Analytical Setup and Study Design for Energy Metabolism Profiling

Core LC–MS/MS Platform

• Triple-quadrupole or equivalent LC–MS/MS systems
• Reversed-phase and/or HILIC separation for polar and semi-polar metabolites
• Positive and negative ion modes in scheduled MRM/SRM
• Internal standards and pooled QCs in every batch for robust quantification

Ideal for: focused energy metabolism panels in animal, cell, plant, or microbial studies.

High-Resolution LC–MS for Discovery

• Orbitrap/TOF-class high-resolution LC–MS
• Full-scan acquisition with MS/MS (DDA or DIA)
• Feature detection and annotation for pathway-level exploration

Ideal for: discovery projects and multi-omics integration where you want to see beyond the predefined energy panel.

Stable Isotope Tracing (Optional)

• ¹³C-glucose, ¹³C-glutamine, or other labeled substrates
• Isotopologue-resolved LC–MS/MS methods
• Readouts include labeling patterns in glycolysis, TCA cycle, and related routes

Best suited for: flux-oriented studies that aim to quantify how carbon flows through energy pathways, not just how much metabolite is present.

Key Technical Parameters

Agilent 1260 Infinity II HPLC (Figure from Agilent)

Agilent 6495C Triple quadrupole (Figure from Agilent)

Thermo Fisher Q Exactive (Figure from Thermo Fisher)

Sample Types and Biological Matrices for Energy Metabolism Studies

The platform supports a wide range of matrices. Typical requirements (exact amounts depend on panel and matrix) are shared during project setup.

*Feasibility depends on sample volume, stability, and expected concentration range.

Collection and Storage Guidance

• Biofluids: clear instructions for fasting, anticoagulants, processing time, and storage
• Tissues & cells: standardized quenching and extraction procedures to preserve labile metabolites
• Storage: typically at −80 °C with minimal freeze–thaw cycles

Data, Reports, and Bioinformatics Deliverables

At project completion, you typically receive:

• Raw data files (e.g., MS data, where applicable)
• Processed data matrix (sample × metabolite, with annotations)
• Statistical analysis results (p-values, fold changes, FDR/adjusted p where applied)
• Pathway analysis outputs focused on energy metabolism
• Graphical figures suitable as a starting point for publication/slide decks
• Summary report explaining methods, major findings, and interpretation

Hierarchical clustered heatmap of energy metabolism metabolites across experimental groups, showing distinct patterns in glycolysis, TCA cycle, fatty acid oxidation, ketone bodies, and amino acid–linked pathways.

PCA/PLS-DA scores plot and corresponding loadings plot illustrating clear separation between control, model, and treatment groups, and identifying the top energy metabolism metabolites driving group discrimination.

Simplified energy metabolism pathway map with key metabolites overlaid as up- or down-regulated, highlighting coordinated changes in glycolysis, TCA cycle, fatty acid oxidation, ketone bodies, and amino acid anaplerosis.

Time-course profiles of representative energy metabolites (e.g., lactate, β-hydroxybutyrate, citrate, creatinine) across pre, post, and recovery time points, demonstrating dynamic responses to intervention with error bars and significance markers.