Animal Metabolomics Service — LC-MS & GC-MS Targeted & Untargeted Metabolite Profiling

What Is Animal Metabolomics and Why It Matters for Preclinical & Translational Research

Animal metabolomics is the comprehensive analysis of metabolites — small molecules below 1,500 Da — in biological samples from laboratory and veterinary animal models. Unlike human studies, animal experiments provide controlled genetic backgrounds, defined environmental conditions, access to tissues that cannot be sampled in humans (brain, liver, heart, kidney), and the ability to collect longitudinal samples from the same individual — making animal metabolomics the essential bridge between in vitro mechanistic studies and human translational research.

Our service covers the full analytical pipeline across three complementary modalities — all combinable from the same sample set. Fecal metabolomics captures gut microbial activity, tissue metabolomics reveals organ-level metabolic reprogramming, and biofluid profiling provides systemic readouts of metabolic state. The result is the most complete metabolic picture available for your animal model — from hypothesis generation through publication-ready data.

Animal Metabolomics Service Portfolio — Three Complementary Modalities for Complete Metabolic Coverage

Why Choose Our Animal Metabolomics Services

• 15+ Years of Expertise, 500+ Animal Projects Completed
  Samples processed from every major laboratory species across oncology, neuroscience, immunology, metabolic disease, cardiovascular, and toxicology models — translating to species- and matrix-specific protocols refined through thousands of experiments.
• Multi-Species, Multi-Matrix, Multi-Platform — One Integrated Service
  From a single mouse brain region to a longitudinal NHP study with 500+ samples — standardized protocols per species (9), per matrix (15+), and per platform (LC-MS, GC-MS, NMR) ensure consistent data quality across your entire project portfolio.
• Transparent QC Metrics Published With Every Report
  Pooled QC RSD per metabolite class, internal standard recovery per sample, blank carryover, and batch-effect documentation — every QC metric that matters for manuscript reviewers is plotted, tabulated, and delivered.
• End-to-End Service — Experimental Design Through Publication
  We engage at study design: species selection, sample size estimation, sampling timepoint optimization, confounder identification (anesthesia, circadian rhythm, diet). Bioinformatics team delivers complete analysis, pathway interpretation, and publication-ready figures.
• Global Sample Logistics & 24-Hour Integrity Verification
  Dry ice shipping with temperature loggers worldwide. Sample integrity confirmed within 1 business day. Collection kit protocols provided before your experiment begins.
• Reproducibility by Design
  Raw instrument files, complete processing parameters, annotated R Markdown/Jupyter notebooks, and methods documentation delivered alongside processed results — every step traceable and repository-ready (MetaboLights, Metabolomics Workbench).

Animal Metabolomics Workflow — From Experimental Design to Biological Insight

1

Study Design & Pre-Experimental Consultation

Define species, strain, group size (n greater than or equal to 5 per group recommended), sampling timepoints, and statistical power. Review metabolic confounders — circadian rhythm, diet, fasting status, anesthesia method, and euthanasia technique — each affects the metabolome independently of treatment. Randomization and blocking strategies to minimize batch effects.

2

Sample Collection & Metabolic Quenching

Species- and matrix-specific collection protocols with warm ischemia time strictly controlled (less than 30 s for metabolic tissues). All samples flash-frozen in liquid N2, stored at -80 degree C. Metadata recorded: collection time, fasting duration, anesthetic used, tissue collection order, freeze-thaw history.

3

Metabolite Extraction & Sample Preparation

Tissue homogenization in ice-cold extraction solvent with isotopically labeled internal standards spiked at homogenization. Derivatization (MSTFA/MBTFA) for GC-MS; reconstitution in HILIC or RP-appropriate solvent for LC-MS. Matrix-matched QC and calibration samples prepared in parallel.

4

LC-MS / GC-MS / NMR Data Acquisition

Analysis sequence: solvent blank, 8-level calibrators, pooled QC, randomized study samples with QC every 8-10 injections, blank. Platform selection per metabolite class — HILIC-LC-MS for polar metabolites, RP-LC-MS for lipids, GC-MS for volatiles, NMR for biofluid fingerprinting.

5

Data Processing, QC & Statistical Analysis

Peak detection, alignment (OBI-Warp/LOESS), missing value imputation (kNN, BPCA), normalization (quantile, LOESS, IS-based), batch-effect correction (ComBat, QC-RLSC). Feature filtration (RSD threshold). Univariate analysis with Benjamini-Hochberg FDR. Multivariate: PCA, PLS-DA/OPLS-DA with permutation testing (n above or equal to 1,000).

6

Biological Interpretation & Report Delivery

KEGG/Reactome pathway enrichment (ORA + MSEA) with integrated pathway maps. Multi-omics integration via DIABLO, MOFA+, or O2PLS if transcriptomics/proteomics data provided. Final package: QC report, processed data tables, metabolite identification (MSI Level 1-4), statistical report, publication-ready figures (300 DPI TIFF + vector PDF), methods documentation, and reproducible analysis code.

Analytical Platform & Instrumentation for Animal Metabolomics Profiling

Platform Selection Guide — LC-MS vs GC-MS vs NMR

Bioinformatics & Statistical Analysis for Animal Metabolomics Data

• Exploratory & Multivariate Analysis
  PCA for unsupervised pattern recognition and QC clustering. PLS-DA and OPLS-DA with VIP scoring and permutation testing (n above or equal to 1,000) for robust supervised classification with R2 and Q2 metrics reported against permuted null distributions.
• Univariate Differential Analysis
  Parametric (t-test, ANOVA with Tukey/Dunnett) and non-parametric (Mann-Whitney, Kruskal-Wallis with Dunn) tests with Benjamini-Hochberg FDR correction. Volcano plots with log2(FC) vs -log10(FDR). Box and violin plots for top differential metabolites.
• Clustering & Pattern Discovery
  Hierarchical clustering (Euclidean/Pearson, Ward/complete linkage) with annotated heatmaps. K-means and WGCNA for co-expression network analysis linking metabolite modules to phenotypic traits in time-series and multi-group designs.
• Machine Learning-Based Biomarker Discovery
  Random forest, SVM, and XGBoost with recursive feature elimination and LASSO regularization. k-fold cross-validation (k=5 or 10) with independent test set. ROC analysis for single and combined biomarker performance.
• Pathway Enrichment & Network Analysis
  ORA (hypergeometric test) and MSEA (quantitative) against KEGG, Reactome, HMDB, and SMPDB. Integrated KEGG pathway maps with metabolite nodes colored by fold-change. Metabolite-metabolite correlation and metabolite-phenotype association networks.
• Multi-Omics Integration
  DIABLO for supervised multi-omics with feature selection. MOFA+ for unsupervised factor analysis. O2PLS for bidirectional metabolomics-transcriptomics modeling. Circos plots, correlation heatmaps, and integrated pathway visualizations. Learn more about multi-omics integration.
• Time-Series & Longitudinal Analysis
  Repeated-measures ANOVA and linear mixed-effects models for longitudinal designs. Trend clustering (Mfuzz/STEM) for metabolite trajectory grouping. Paired statistical tests for pre- vs. post-intervention within-animal comparisons.

Species Coverage & Model Systems for Animal Metabolomics Studies

Each laboratory animal species has unique metabolic characteristics — from the high metabolic rate of mice to the ruminant physiology of cattle. Our protocols are optimized per species for sample collection, metabolite extraction, and data normalization. For detailed sample amounts and collection protocols per matrix, see the Sample Matrix Coverage table below.

Sample Matrix Coverage — Tissues, Biofluids & Biological Specimens

Our laboratory processes 15+ distinct biological matrices from animal models. Each matrix has validated collection, quenching, extraction, and normalization protocols. All samples shipped on dry ice with temperature monitoring.

Applications of Animal Metabolomics in Biomedical, Veterinary & Agricultural Research

Animal Metabolomics Deliverables — Complete Data Package for Publication & Repository Deposition

Multivariate analysis: PCA scores plot showing group separation and QC sample clustering (left), and OPLS-DA scores plot with permutation test validation (right, n=1,000 permutations), confirming model reliability with Q2 intercept below 0.05.

Differential metabolite analysis: volcano plot displaying log2(fold-change) vs -log10(FDR) with significance thresholds (left), and hierarchical clustering heatmap of top 50 differential metabolites across animal experimental groups with metabolite class annotations (right).

KEGG pathway enrichment: bubble chart of top 20 enriched metabolic pathways with enrichment ratio, FDR-corrected P-value, and metabolite hit count per pathway (left). Integrated metabolic network diagram showing differential metabolites mapped to key biochemical pathways (right).

Multi-omics integration: DIABLO/MOFA+ correlation Circos plot linking metabolite modules to gene expression and phenotypic traits across animal groups (left). Metabolite-transcript correlation network highlighting key metabolic hub genes and their associated metabolites (right).

Case Study — Multi-Tissue Metabolomics Uncovers Microbiota-Driven Gut-Brain Metabolic Communication in Mice

High-coverage metabolomics uncovers microbiota-driven biochemical landscape of interorgan transport and gut-brain communication in mice

Lai, Y.J., Liu, Y.L., Tsai, M.L., et al. | Nature Communications, 2021 | IF: 14.7

DOI: 10.1038/s41467-021-26508-2

The Research Question

How does the gut microbiota shape the biochemical landscape across distant host organs? The gut-brain axis is well-established, but the specific metabolites mediating inter-organ communication — and how microbial presence alters their tissue-level distribution — remained poorly characterized. This required a systematic multi-tissue metabolomics comparison between germ-free (GF) and specific-pathogen-free (SPF) mice: quantifying hundreds of metabolites across brain, plasma, liver, and intestine simultaneously, then tracing which metabolites showed microbiota-dependent tissue enrichment.

Key Findings Enabled by Comprehensive Animal Metabolomics

Analytical Approach — How Our Service Replicates This Rigor

This study demonstrates the analytical framework that defines rigorous animal metabolomics: (1) multi-tissue design comparing profiles across anatomically distinct compartments rather than a single matrix; (2) dual-chromatography untargeted LC-MS/MS (HILIC + RP C18) for maximum metabolome coverage exceeding 1,200 features; (3) clean binary variable (GF vs. SPF) isolating microbiota effects; (4) inter-organ correlation network analysis identifying specific metabolite transport axes. Our service provides the same analytical depth — multi-tissue, multi-platform profiling with the bioinformatics infrastructure to map inter-organ metabolic communication in your experimental model, using the identical pipeline to this landmark study.

Reference

1. Lai, Y.J., Liu, Y.L., Tsai, M.L., et al. High-coverage metabolomics uncovers microbiota-driven biochemical landscape of interorgan transport and gut-brain communication in mice. Nature Communications 12, 6000 (2021).

Frequently Asked Questions About Animal Metabolomics Analysis

Selected Publications in Animal Metabolomics Research