Urine Metabolomics Service - Creative Proteomics Metabolomics

Q: What is the difference between untargeted and targeted urine metabolomics?

Untargeted profiles all detectable features (10,000+) for biomarker discovery. Targeted quantifies predefined metabolites by MRM for validation. Both use the same sample and normalization methods.

Urine vs Plasma Metabolomics — What Urine Captures That Blood Misses

Plasma metabolite concentrations are tightly regulated by homeostasis. Urine has no such constraint — the kidneys concentrate metabolic waste 50- to 100-fold, amplifying signals that would be subtle or undetectable in blood.

Urine captures three categories of metabolites that are underrepresented in plasma:

Renal excretion products. Organic acids, uremic toxins, and drug metabolites reflect renal function and tubular transport — information plasma cannot provide.
Gut microbial co-metabolites. Hippurate, p-cresyl sulfate, indoxyl sulfate, and TMAO are concentrated 10- to 100-fold in urine relative to plasma — the preferred matrix for microbiome-metabolome studies.
Dietary and environmental exposure biomarkers. Polyphenol metabolites, food additives, and environmental toxicants appear in urine within hours of exposure — the standard matrix for exposomics and nutritional research.

Urine Metabolomics Challenges — Normalization, Microbial Contamination, and Polarity Coverage

Urine is the most accessible biofluid — but three analytical challenges make most urine metabolomics data less reliable than it should be:

Variable dilution confounds concentration comparisons. Urine concentration varies 10-fold with hydration status. We apply multi-parameter normalization — creatinine, osmolality, and PQN — and report which method was used per study, with rationale.
Bacterial contamination alters the metabolome post-collection. Urine is not sterile. At room temperature, bacterial metabolism continues — consuming glucose and degrading urea within hours. Our protocol specifies preservatives (sodium azide 0.05% or boric acid), cold storage, and a maximum 12 h collection-to-freezing interval.
Wide polarity range requires dual-column coverage. Urine spans highly polar amino acids to non-polar steroid conjugates. A single-column method misses one end. Our platform uses HILIC for polar metabolites and C18 for non-polar — one sample, two column chemistries.

Urine Metabolomics Services — Untargeted Discovery to Targeted Validation

Service	Approach	What You Receive	Best For
Untargeted Profiling	Full-scan HRMS (HILIC + C18, pos/neg ESI) with data-dependent MS/MS	10,000+ features, differential analysis (PCA, PLS-DA, volcano plots), pathway enrichment, metabolite ID (Level 1–2)	Biomarker discovery, hypothesis generation
Targeted Quantification	Scheduled MRM on QQQ with per-analyte calibration and isotopically labeled IS	Absolute concentrations (μM or nmol/mg creatinine), QC metrics, batch-corrected values	Biomarker validation, PK studies, clinical trial support

Targeted Panels Available:

Organic acids — TCA cycle intermediates, short-chain fatty acids, and microbial metabolites
Amino acid catabolites — tryptophan, phenylalanine, tyrosine, and branched-chain amino acid pathways
Bile acids — primary, secondary, and conjugated bile acid profiling
Steroid hormones and conjugates — cortisol, androgens, estrogens, and phase II conjugates
TMAO and gut microbial metabolites — TMAO, choline, betaine, hippurate, p-cresyl sulfate, indoxyl sulfate
Eicosanoids — prostaglandins, leukotrienes, and isoprostanes
Purines and pyrimidines — uric acid, xanthine, hypoxanthine, and nucleotide catabolites
Acylcarnitines — short, medium, and long-chain acylcarnitine panels
Drug metabolites — phase I and phase II metabolites for PK and metabolism studies

Custom panels can be developed for specific metabolites or pathways. Contact us during study design to configure a panel for your research question.

Instrumentation — Dual-Column LC-MS/MS for Urine Metabolomics

Untargeted Profiling (HRMS)

MS: Thermo Q Exactive HF-X or Orbitrap Exploris 480

Ionization: ESI pos/neg; full-scan MS1 + data-dependent MS/MS

LC: Waters ACQUITY UPLC with dual-column switching (HILIC + C18)

Data Processing: XCMS/MZmine 3 → CAMERA → MetaboAnalyst / KEGG/HMDB

Coverage: 10,000+ features; 500–1,000 metabolites at Level 1–2

Targeted Quantification (MRM)

MS: SCIEX QTRAP 6500+ or Agilent 6495C QQQ

Acquisition: Scheduled MRM, 2 transitions per analyte; per-analyte IS

Normalization: Creatinine, osmolality, or specific gravity — per sample

QC: NIST SRM 3667; pooled QC every 8 injections; Westgard multi-rule

Performance: LOD 0.1–10 nM; CV ≤ 15% for targeted panels

Thermo Q Exactive HF-X (Figure from Thermo Fisher)

SCIEX Triple Quad 6500+ (Figure from Sciex)

Waters ACQUITY UPLC System (Figure from Waters)

Urine Metabolomics Workflow — From Collection to Multi-Method Normalization

Study Design and Collection Protocol

Matrix-specific guidance: first-morning void vs. 24 h collection, preservative selection, dietary/hydration control, maximum 12 h collection-to-freezing interval.

Sample Preparation and Normalization

Urine centrifuged, diluted, split for HILIC and C18. Creatinine and osmolality measured per sample. For targeted: SPE or direct dilution with isotopically labeled IS.

Dual-Column LC-MS/MS Acquisition

Untargeted: full-scan HRMS with data-dependent MS/MS, pos/neg ESI, HILIC + C18. Targeted: scheduled MRM on QQQ. Pooled QC every 8 injections.

Data Processing and Multi-Method Normalization

XCMS/MZmine feature detection. Statistical analysis with creatinine/osmolality/PQN normalization. Pooled QC-based LOESS batch correction.

Data Delivery

Quantitative tables with raw and normalized values, QC metrics, differential analysis, metabolite identification, pathway enrichment, methods appendix.

Why Choose Our Urine Metabolomics Service?

Multi-Method Normalization — Not Just Creatinine
We apply creatinine correction, osmolality adjustment, and PQN, reporting the rationale for each. Raw urinary peak areas are uninterpretable without dilution correction. Our data package includes both raw and normalized values per sample.
Dual-Column Coverage — No Polarity Gaps
HILIC for polar metabolites and C18 for non-polar metabolites from the same urine sample. A single-column method misses one end of the polarity spectrum.
Urine-Optimized Collection and Preservation
Detailed guidance on timing, preservatives, and collection-to-freezing interval — specific to urine, not generic metabolomics guidelines.
Discovery-to-Validation on One Platform
Untargeted profiling to identify candidates, targeted MRM to validate them — same instrument, same lab, same sample cohort.

Urine Sample Collection and Preparation Guidelines

Parameter	Recommendation
Collection timing	First-morning void preferred. 24 h collection for quantitative excretion studies. Mid-stream to reduce epithelial contamination.
Preservative	Sodium azide 0.05% or boric acid 0.1% to inhibit bacterial growth.
Collection-to-freezing	Maximum 12 h at 4°C for untargeted. Longer intervals risk bacterial overgrowth.
Minimum volume	≥ 2 mL untargeted; ≥ 500 μL targeted; ≥ 5 mL combined. Plus 50 μL for creatinine.
Storage	−80°C immediately; ship on dry ice. Avoid freeze-thaw cycles.
Normalization	Creatinine (enzymatic), osmolality, and specific gravity per sample. Method reported with rationale.

Critical Notes:

Hydration status confounds concentration. Never compare raw urinary concentrations between subjects. Always normalize to creatinine or osmolality.
Diet affects the urine metabolome within hours. Standardize diet for 24 h before collection. Record dietary intake as a covariate.
Menstrual blood contamination. Avoid collection during menstruation for untargeted studies — blood proteins confound the urinary profile.

Urine Metabolomics Data Analysis and Deliverables — Raw Data, Normalized Values, and Pathway Mapping

Untargeted Results
Feature table with m/z, RT, peak area, annotation. PCA/PLS-DA plots. Volcano plots and heatmaps. Differentially abundant features with statistics.

Targeted Results
Absolute concentration table — raw (μM) and creatinine-normalized. QC metrics, LOD/LOQ, per-sample normalization parameters.

Pathway and Network Analysis
KEGG pathway enrichment. Metabolite set enrichment analysis. Correlation networks.

Raw Data and Methods Appendix
Vendor-native and .mzML files. Extraction protocol, LC gradient, MRM transitions, normalization documentation. QC reports.

PCA score plot of urinary metabolome from healthy controls and CKD patients, showing clear group separation driven by differential renal clearance of organic acids and amino acid catabolites.

Volcano plot of urinary metabolites from CKD vs. control. Red: significantly increased (FDR < 0.05, FC> 2). Blue: decreased. Key uremic toxins among top-ranked features.

Applications of Urine Metabolomics

Biomarker Discovery and Validation

Untargeted discovery of urinary biomarkers for kidney disease, cancer, and metabolic disorders; targeted MRM validation in independent cohorts

Drug Metabolism and Pharmacokinetics

Quantify drug metabolites from urine — the standard matrix for excretion studies, with creatinine-normalized time-course data

Population Health and Exposomics

Large-scale urinary metabolomics for cohort studies; dietary biomarkers, environmental toxicants, and gut microbial co-metabolites

Case Study: Urine Metabolomics Enables Multi-Cancer Screening from a Single Sample

Development of a Urine-Based Metabolomics Approach for Multi-Cancer Screening and Tumor Origin Prediction

Xu, X., Zeng, C., Qing, B., He, Y., Li, Y., Chen, Y., and Xia, Z. | Frontiers in Immunology, 2024, 15, 1449103

DOI: 10.3389/fimmu.2024.1449103

Background

Multi-cancer early detection from a non-invasive biospecimen is a major goal in oncology. Blood-based approaches dominate, but urine offers unique advantages: it collects systemic metabolic waste, requires no venipuncture, and can be self-collected repeatedly — ideal for population screening. The question: can the urinary metabolome discriminate multiple cancer types from healthy controls and predict tumor origin?

Challenge: Develop a urine-based LC-MS metabolomics approach for screening lung, gastric, and colorectal cancers simultaneously, with independent cohort validation.

Key Findings

Metric	Finding
Study design	Untargeted LC-MS of urine from 312 cancer patients (lung, gastric, colorectal) and 158 controls; independent validation (n = 186)
Multi-cancer classification	AUC 0.96 in validation cohort across all three cancer types
Tumor origin prediction	Metabolite panels correctly classified tumor origin with >85% accuracy
Key metabolites	Altered amino acid catabolism, purine/pyrimidine metabolism, and gut microbial co-metabolites

What This Means for Your Research

Urine is a viable matrix for clinical-grade biomarker development. AUC 0.96 in independent validation demonstrates the urinary metabolome carries sufficient systemic signal for high-performance classification. Our untargeted-to-targeted workflow is built for this pipeline.
Creatinine normalization and standardized collection were essential. First-morning void samples with creatinine normalization — the same protocols we specify. Without these controls, inter-subject variability overwhelms the disease signal.
Gut microbial co-metabolites were among the top-ranked discriminatory features. Altered purine/pyrimidine metabolism and microbial metabolites consistently distinguished cancer patients from controls — consistent with urine's unique role as the matrix where systemic and microbial metabolism intersect. Our targeted panels include these metabolites with per-analyte IS.

Conclusion

This study validates urine metabolomics for multi-cancer screening and demonstrates that matrix-specific protocols are essential for clinical-grade biomarker development. Our platform provides the same capability — untargeted discovery with multi-method normalization, followed by targeted validation.

Read the full paper: Xu et al., Frontiers in Immunology, 2024

What is the difference between untargeted and targeted urine metabolomics?

Untargeted profiles all detectable features (10,000+) using full-scan HRMS — ideal for biomarker discovery. Targeted quantifies a predefined list using MRM on a QQQ — ideal for validation. Both use the same sample and normalization methods. Many projects start with untargeted discovery and transition to targeted validation on the same platform.

Why do you normalize urinary metabolites to creatinine?

Urine concentration varies 10-fold with hydration. Creatinine is produced at a constant rate, making it a reliable dilution correction. We also offer osmolality and specific gravity normalization, reporting the rationale per study. Raw urinary concentrations should never be compared without normalization.

How should I collect and store urine for metabolomics?

First-morning void preferred. Add sodium azide (0.05%) or boric acid (0.1%). Centrifuge to remove cells. Freeze at −80°C within 12 hours. Avoid freeze-thaw cycles. Detailed protocols provided per study.

What is the minimum urine volume required?

≥ 2 mL for untargeted, ≥ 500 μL for targeted, ≥ 5 mL for combined. Plus 50 μL for creatinine. Protocol adjustments available for limited volumes.

How do you cover the wide polarity range of urinary metabolites?

No single LC column covers urine's full polarity range. We split each sample: HILIC for polar metabolites and C18 for non-polar — same MS platform, no coverage gap.

Can you quantify gut microbial metabolites in urine?

Yes — urine is the preferred matrix. Hippurate, p-cresyl sulfate, indoxyl sulfate, and TMAO are concentrated 10- to 100-fold in urine. Our targeted panels include these with per-analyte IS and creatinine normalization.

How many biological replicates do I need?

Animal: ≥ 6 per group. Human: ≥ 10 for discovery, ≥ 30 for validation. Urine has higher intra-individual variability than plasma due to diet and hydration — larger sample sizes capture signal above this background.

What is the typical project timeline?

Project-dependent, based on sample number and service scope. Contact us during study design for scheduling and phased delivery options.

Development of a Urine-Based Metabolomics Approach for Multi-Cancer Screening and Tumor Origin Prediction

Xu, X., Zeng, C., et al.

Journal: Frontiers in Immunology, 2024, 15, 1449103

Urine LC-MS metabolomics for multi-cancer screening. AUC 0.96 in independent validation. Demonstrates creatinine-normalized urinary metabolomics for clinical biomarker development.

Metabolomics in Chronic Kidney Disease: From Bench to Bedside

Rhee, E. P.

Journal: Nature Reviews Nephrology, 2016, 12(12), 723–735

Comprehensive review of metabolomics in kidney disease. Establishes urine as a matrix for uremic toxin quantification and early CKD biomarker discovery.