What sample types can be analyzed?

We accept a wide variety of biological samples including serum, plasma, urine, cerebrospinal fluid, tissue lysates, cell pellets, plant extracts, and microbial cultures. Specific sample volume and storage requirements are confirmed during project scoping.

What is the typical turnaround time?

Turnaround time depends on sample number, analytical platforms selected, and depth of data analysis. An estimated range is provided in the project proposal after sample specification; please consult our team for project-specific timelines.

What level of metabolite identification can I expect?

We assign putative identifications using high-resolution MS/MS matching against curated libraries (HMDB, METLIN, mzCloud). Annotation confidence is reported per the Metabolomics Standards Initiative (MSI) levels, typically Level 2 (putatively annotated) for most features. Level 1 (confirmed with authentic standard) can be included when standards are available.

Can I integrate metabolomics data with other omics datasets?

Yes. Our pipeline includes pathway enrichment and correlation analysis that can be extended to joint analysis with transcriptomics, proteomics, or lipidomics data. Integration scope is defined during consultation.

How is batch effect controlled in large cohorts?

We incorporate pooled quality control samples at regular intervals throughout the analytical run. Data normalization methods such as median centering and ComBat-style batch correction are applied when multiple batches are required.

Do you provide raw data and full methods for publication?

Yes. All raw data files (e.g.,.raw,.mzML), processed feature tables, statistical outputs, and a detailed methods report are provided to support manuscript preparation and data repository submissions.

Services

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Untargeted Metabolomics Profiling

Metabolomics & Lipidomics

Capture the full picture of metabolic changes to discover novel biomarkers and disease pathways without predefined targets.

Global profiling of hundreds to thousands of metabolites
Hypothesis-free discovery for biomarker and pathway identification
High-resolution LC-MS and GC-MS platforms for broad coverage
Integrated statistical analysis and pathway mapping

Start Your Discovery

Why Choose Untargeted Metabolomics?

Gain an unbiased snapshot of the metabolome to reveal unexpected biochemical shifts and generate new research directions.

Comprehensive metabolite coverage from polar to lipid species
Advanced mass spectrometry for high sensitivity and mass accuracy
Robust multivariate statistics for differential feature prioritization
Customizable experiments from pilot to large-cohort studies

Comprehensive

Unbiased

Discovery-driven

Our Approach

End-to-End Untargeted Metabolomics Pipeline

From sample preparation to biological interpretation, our validated workflow ensures reliable, publication-ready results.

Core Discovery Pipeline

We combine high-resolution LC-MS and GC-MS with rigorous data processing to detect and annotate metabolites across diverse sample types.

Optimized extraction protocols for biofluids, tissues, and cells
Dual-polarity ionization and orthogonal chromatographic strategies
Feature extraction, alignment, and missing value imputation

Multi-Omics Integration

Connect metabolomic data with transcriptomics or proteomics to build systems-level models of disease mechanisms.

Correlation networks linking metabolites to genes and proteins
Pathway enrichment across KEGG, Reactome, and MetaCyc
Custom integration pipelines scoped per project

Quality Control

Pooled QCs, internal standards, and batch correction ensure reproducibility across runs and cohorts.

Statistical Rigor

Unsupervised (PCA) and supervised (PLS-DA, OPLS-DA) methods, plus univariate tests with multiple testing correction.

Biological Interpretation

Annotation at MSI confidence levels, enriched pathway maps, and network analysis to contextualize findings.

Deliverables

What You Receive

Every project delivers a comprehensive data package designed for straightforward publication and downstream analysis.

Data Deliverables

Processed feature matrix with relative intensities across all samples
Annotated metabolite list (putative IDs, MSI levels, adducts)
Differential statistics: fold-change, p-values, VIP scores
Multivariate plots: PCA scores, volcano plot, heatmaps

Reporting & Support

Detailed experimental and QC summary report
Pathway enrichment maps (KEGG, Reactome)
Raw data files and processing logs for traceability
Optional consultation for multi-omics integration

Service Scope

Configurable Parameters

Project scope is defined case by case through consultation. Typical capabilities are listed below.

Parameter	Typical Project Scope
Sample Type	Serum, plasma, urine, CSF, tissue lysates, cell extracts, plant material, microbial cultures
Analytical Platform	LC-MS (Orbitrap, Q-TOF), GC-MS (single quadrupole, TOF), or combined platform
Separation Strategy	C18 reversed-phase, HILIC, amide; or GC capillary column (polar/non-polar)
Data Processing	Feature extraction, retention time alignment, noise filtering, 80% rule missing value imputation
Statistical Analysis	PCA, PLS-DA, OPLS-DA, t-test, ANOVA, BH correction, VIP > 1.0, p < 0.05
Identification Level	MSI Level 1–4 with library matching (HMDB, METLIN, mzCloud, NIST)
Pathway Mapping	KEGG, Reactome, MetaCyc enrichment; network correlation with other omics
Sample Throughput	Pilot (<20) to="" large="" cohort="">200) – confirmation during consultation

Workflow

How We Work

A systematic five-step process that converts biological samples into actionable metabolic insights.

Experimental Design & Sample Preparation

Define groups, replicates, and power. Quench metabolism, extract metabolites using protein precipitation or liquid-liquid extraction, include pooled QCs for monitoring system stability.

Metabolite Detection

Acquire full-scan MS1 data with high-resolution mass spectrometry (Orbitrap/TOF) using C18 and/or HILIC separation, dual-polarity ionization, and MS/MS fragmentation for structural information.

Data Preprocessing & Feature Extraction

Remove noise and background via deconvolution, baseline correction, retention time alignment, and feature extraction. Impute missing values and normalize for batch or ion suppression effects.

Statistical Screening

Apply unsupervised PCA for clustering and QC assessment, then supervised PLS-DA/OPLS-DA and univariate tests (t-test, ANOVA) to identify significant features (VIP > 1.0, p < 0.05).

Identification & Biological Interpretation

Match features against curated databases (HMDB, METLIN, mzCloud) to assign putative identities at MSI confidence levels. Map metabolites to KEGG/Reactome pathways and, when specified, integrate with other omics data.

Applications

Powering Discovery Across Research Areas

Untargeted metabolomics is used to address a wide range of biological and translational questions.

Identify Diagnostic and Prognostic Signatures

Detect differentially abundant metabolites between disease and control groups to uncover candidate biomarkers for early diagnosis or patient stratification.

Case-control and longitudinal study designs
Serum, plasma, or urine metabolome for non-invasive screening
Validation via subsequent targeted quantification

Mechanistic Insights into Disease Biology

Map dysregulated metabolic pathways to understand disease mechanisms at the molecular level.

KEGG and Reactome enrichment analysis
Time-course profiling of metabolic perturbations
Integration with transcriptomics or proteomics for systems biology

Monitor Metabolic Responses to Treatment

Assess pharmacodynamic effects and off-target metabolic alterations following drug treatment.

Preclinical and clinical sample analysis
Unbiased profiling before/after intervention
Identification of metabolite signatures of efficacy or toxicity

FAQ

Frequently Asked Questions

Common questions about untargeted metabolomics profiling and our service.

We accept a wide variety of biological samples including serum, plasma, urine, cerebrospinal fluid, tissue lysates, cell pellets, plant extracts, and microbial cultures. Specific sample volume and storage requirements are confirmed during project scoping.
Turnaround time depends on sample number, analytical platforms selected, and depth of data analysis. An estimated range is provided in the project proposal after sample specification; please consult our team for project-specific timelines.
We assign putative identifications using high-resolution MS/MS matching against curated libraries (HMDB, METLIN, mzCloud). Annotation confidence is reported per the Metabolomics Standards Initiative (MSI) levels, typically Level 2 (putatively annotated) for most features. Level 1 (confirmed with authentic standard) can be included when standards are available.
Yes. Our pipeline includes pathway enrichment and correlation analysis that can be extended to joint analysis with transcriptomics, proteomics, or lipidomics data. Integration scope is defined during consultation.
We incorporate pooled quality control samples at regular intervals throughout the analytical run. Data normalization methods such as median centering and ComBat-style batch correction are applied when multiple batches are required.
Yes. All raw data files (e.g.,.raw,.mzML), processed feature tables, statistical outputs, and a detailed methods report are provided to support manuscript preparation and data repository submissions.

Scientific Literature

References

Zeki ÖC, Eylem CC, Reçber T, et al. Integration of GC-MS and LC-MS for untargeted metabolomics profiling. Journal of pharmaceutical and biomedical analysis. 2020;190:113509. View on PubMed
Chalikiopoulou C, Gómez-Tamayo JC, Katsila T. Untargeted Metabolomics for Disease-Specific Signatures. Methods in molecular biology (Clifton, N.J.). 2023;2571:71-81. View on PubMed
Souza AL, Patti GJ. A Protocol for Untargeted Metabolomic Analysis: From Sample Preparation to Data Processing. Methods in molecular biology (Clifton, N.J.). 2021;2276:357-382. View on PubMed
Morville T, Sahl RE, Moritz T, et al. Plasma Metabolome Profiling of Resistance Exercise and Endurance Exercise in Humans. Cell reports. 2020;33(13):108554. View on PubMed
Jacobs JP, Spencer EA, Helmus DS, et al. Age-related patterns of microbial dysbiosis in multiplex inflammatory bowel disease families. Gut. 2024;73(12):1953-1964. View on PubMed
Zhang Y, Spitzer BW, Zhang Y, et al. Untargeted metabolome atlas for sleep-related phenotypes in the Hispanic community health study/study of Latinos. EBioMedicine. 2025;111:105507. View on PubMed

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