Metabolomics · Orbitrap LC-MS · Untargeted & Targeted

The molecular layer closest to phenotype.

The metabolome is the closest molecular layer to phenotype. Orbitrap LC-MS/MS delivers the mass accuracy, resolution, and sensitivity to profile hundreds of metabolites across central carbon metabolism, lipids, amino acids, and xenobiotics — untargeted for discovery, targeted for quantitation.

Plan Your Project → Learn the Basics
2
ionisation modes (+/−)
MSI
graded annotation confidence
RP + HILIC
dual UHPLC separations
FIA-MS
high-throughput option
Learning Path

From first principles to expert-level analysis

New here? Start with Foundations. Planning a study or reviewing results? Jump to Practitioner or Expert. Each level builds on the one before it, so you can read this page as a short course or dip in where you need to.

Start here if you are new to this service — six ideas that explain what it is and why it matters.

LEVEL 1

What is metabolomics?

The systematic measurement of small molecules (metabolites) in a biological sample — sugars, amino acids, lipids, organic acids, and more. The metabolome is the closest molecular layer to the phenotype.

LEVEL 1

Untargeted vs targeted — which do I need?

Untargeted profiles everything ionisable to discover unexpected differences; targeted quantifies defined metabolite panels with absolute concentrations. Discovery projects start untargeted; validation and biomarker studies go targeted.

LEVEL 1

What is an Orbitrap?

A high-resolution mass spectrometer that measures ion masses with very high accuracy — enough to separate compounds with identical nominal masses and assign molecular formulas confidently.

LEVEL 1

What does LC-MS/MS mean?

Liquid chromatography separates metabolites before they enter the mass spectrometer; MS/MS fragments them for structural information. The combination is the workhorse of modern metabolomics.

LEVEL 1

What samples can I analyse?

Plasma, serum, urine, tissue, cells, plants, and microbial cultures — with sample-preparation methods matched to the matrix and question.

LEVEL 1

What will I get?

A feature table with annotations and confidence levels, statistics, pathway maps, and a written report — plus raw data for your records.

For scientists planning a project or reading a report: how each step works and how it can mislead.

LEVEL 2

Study design and QC

Randomised acquisition order, pooled quality-control samples at regular intervals, extraction blanks, and isotope-labelled standards where available — the QC framework that makes metabolomics data trustworthy.

LEVEL 2

Sample preparation

Protein precipitation or extraction matched to the matrix; for lipids, dedicated extraction (MTBE/Bligh–Dyer-class) methods. Preparation is documented per batch.

LEVEL 2

Acquisition

Full-scan HRMS with data-dependent MS/MS in both ionisation modes; targeted assays use scheduled PRM/MRM windows for sensitivity and reproducibility.

LEVEL 2

Feature detection and annotation

Peak detection, retention-time alignment, and gap filling produce the feature table; annotations are assigned by accurate mass, isotope pattern, and MS/MS spectral matching — with MSI confidence levels attached.

LEVEL 2

Statistics

Univariate testing with multiple-testing correction; PCA for structure; PLS-DA only with permutation validation — we avoid the over-fitted PLS-DA plots that plague the literature.

LEVEL 2

Batch correction

QC-based signal correction and drift monitoring are documented; batch effects are reported, not hidden.

For study design: the decisions that determine whether the data answers your question.

LEVEL 3

Coverage honesty

LC-MS metabolomics measures ionisable metabolites within the chosen separation space — it is not exhaustive. We report estimated coverage per method and recommend complementary approaches where needed.

LEVEL 3

Biomarker validation pathway

Untargeted discovery → targeted quantitation with authentic standards → independent-cohort validation. We support the full pathway, including custom targeted assay development.

LEVEL 3

Isotope tracing

Flux and tracer studies (e.g. ¹³C-glucose labelling) can be layered onto the platform for metabolic-flux questions — discuss at design stage.

LEVEL 3

Multi-omics integration

Metabolomics integrates with transcriptomics, proteomics, and genomics through our Bioinformatics service (OmniIntegrate pipeline) — joint pathway analysis, correlation networks, and machine-learning models.

LEVEL 3

Reporting standards

We follow metabolomics reporting norms (MSI confidence levels, QC metrics, acquisition parameters) and support MetaboLights/Metabolomics Workbench-style data submission.

LEVEL 3

Confounders to plan for

Diet, medication, circadian rhythm, and sample-handling time dominate human metabolomics. Pre-register covariates and standardise collection — we advise at design stage.

MSI levels
Metabolomics Standards Initiative annotation confidence levels (1 = authentic standard, 4 = unknown).
HILIC
hydrophilic interaction liquid chromatography; separates polar metabolites.
PRM
parallel reaction monitoring; targeted high-resolution quantitation.
FIA-MS
flow-injection analysis MS; high-throughput acquisition without chromatography.
Feature
a detected m/z–retention-time entity, annotated to a metabolite where possible.
PLS-DA
partial least squares discriminant analysis; a supervised model requiring permutation validation.
About the Service

Metabolomics Services at Nucleome Informatics

Metabolomics profiling on Thermo Scientific Orbitrap high-resolution mass spectrometers coupled to UHPLC (LC-MS/MS), supporting both discovery and quantitation workflows.

Discovery

Untargeted metabolomics

Profile everything ionisable to discover unexpected differences between groups.

  • Broad ionisable-metabolite profiling
  • MSI annotation confidence levels on every call
  • Hypothesis-free discovery of differences
Quantitation

Targeted metabolomics

Quantify defined metabolite panels with absolute concentrations for validation and biomarker studies.

  • Defined panels with absolute concentrations
  • Isotope-labelled internal standards
  • Biomarker validation pathway

Why Orbitrap LC-MS?

Mass accuracy & resolutionSeparates isobaric species and supports confident formula assignment — the foundation of reliable annotation.
MS/MS on every platformFragmentation spectra enable structural annotation, not just mass matching.
Dual-mode coveragePositive and negative ionisation with reversed-phase + HILIC separations widen metabolite coverage.

The analysis pipeline — included with every project

Profiling is only half the job; the statistics decide what you can claim. Every dataset goes from raw feature tables to biological interpretation:

Feature lists & annotationBoth ionisation modes processed in parallel, with HMDB-based compound annotation
Multi-layered filteringIncluding an RSD cut-off (commonly 20%) on pooled-QC injections to remove noise-dominated features
Imputation & scalingLeft-censored imputation for missing values, then median centring and log10 transformation
Unsupervised explorationPCA and UMAP to check outliers, batch effects and QC-cluster tightness before any comparison
Differential abundanceFDR correction and fold-change thresholds, reported as volcano plots and heatmaps
Pathway enrichmentPathway and interaction-network visualisation to turn a metabolite list into biology
Already have data?Our data-analysis-only service processes feature or abundance tables from another lab or instrument through the same pipeline. Raw data must be supplied by the client or generated by Nucleome.
Bioinformatics services →
Sample Requirements

What to send us

Typical starting requirements per sample type. Final requirements are confirmed at project intake and depend on the workflow and matrix — amounts below are starting points, not strict minimums.

Sample typeTypical amountNotes
Plasma / serum50–100 µL per aliquotEDTA plasma or serum tube; avoid haemolysed samples
Urine0.5–1 mLNormalise to creatinine or osmolality at analysis
Tissue10–50 mgSnap-frozen; record collection time and location
Cultured cells1–5 × 10⁶ cellsWash thoroughly to remove media metabolites
Plant / microbial material10–50 mgFlash-frozen; document growth/culture conditions
Handling and shipping: aliquot to avoid freeze–thaw cycles, store at −80 °C, and ship on dry ice. For human studies, record diet, medication, fasting status, and collection time — these confounders dominate metabolite variation and must be captured as metadata.
Workflow

How the service works, step by step

Orbitrap-based LC-MS/MS is the established platform for high-resolution metabolomics; the Metabolomics Standards Initiative defines the annotation-confidence framework we apply; QC-based batch correction practices are established in the metabolomics literature. See references below.

Figure 1 Untargeted vs targeted workflows. schematic
Untargeted discovery Targeted quantitation Broad ionisable-metabolite profiling MSI annotation confidence levels Unexpected-difference discovery Defined panels, absolute concentrations Isotope-labelled internal standards Biomarker validation pathway
Figure 1 | Untargeted vs targeted workflows. (Illustrative schematic, not sample data.)
Figure 2 Workflow. schematic
Study design & QC plan Matrix-matched extraction UHPLC separation (RP + HILIC) Orbitrap HRMS/MS (pos/neg) Feature detection & MSI annotation Statistics & pathway report
Figure 2 | Workflow. (Illustrative schematic, not sample data.)
Technology Highlights

Built for the questions you are asking

The platforms, pipelines and standards behind this service.

Thermo Orbitrap high-resolution MS

High mass accuracy and resolving power for confident feature detection, isotope-pattern matching, and formula assignment.

UHPLC separations

Reversed-phase (lipid-like and hydrophobic metabolites) and HILIC (polar metabolites) methods, with positive/negative mode switching per project design.

Untargeted discovery workflow

Feature detection, alignment, and gap filling across batches; MS/MS-based annotation against spectral libraries and in-house standards.

Targeted quantitation

PRM/MRM-class assays with isotopically-labelled internal standards for absolute concentrations in defined panels (amino acids, central carbon, lipids, bile acids, and more).

MSI-honest annotation

Every metabolite annotation carries its Metabolomics Standards Initiative confidence level — Level 1 identifications require authentic standards, and we say so when a feature is only class-level.

Statistical and pathway analysis

PCA, PLS-DA with permutation validation, differential abundance testing, and pathway enrichment mapped to KEGG/HMDB — with multi-omics integration available through our Bioinformatics service.

Deliverables

What you receive

Every project ships with the complete deliverable set — data, analysis, and documentation.

Raw data filesVendor format plus mzML, with full acquisition parameters
Processed feature tablem/z, retention time and abundance per sample, with QC metrics
Annotated metabolitesEvery compound with its MSI confidence level
Statistical plotsPCA/UMAP, volcano plots and heatmaps of differential metabolites
Pathway resultsPathway enrichment and pathway-interaction networks
Written reportMethods, software versions and interpretation notes
Case Studies

Data analysis in pictures

Illustrative analyses showing the outputs your project report contains. All data below are simulated for illustration — not client results.

PC1 (34% of variance)PC2 (12%)Group AGroup BPooled QC
Cohort separation and QC behaviour. PCA of an untargeted plasma run (simulated: 40 samples + 8 pooled QC injections). The groups separate along PC1 while pooled QCs cluster tightly at the centre — the first QC checkpoint before any statistics.
log2 fold change (B vs A)−log10 adjusted pLower in BHigher in B
Differential metabolites between two groups. Volcano plot of feature-level differential abundance (simulated: 400 features). Features beyond |log2 FC| > 1 and adjusted p < 0.05 are highlighted; each would carry its MSI confidence level in the report.
Why Nucleome

Service highlights

What you can expect from our team in Hyderabad.

Orbitrap HRMS in-house

Thermo Orbitrap high-resolution MS with UHPLC separations — discovery and quantitation on one platform.

MSI-honest annotation

Confidence levels attached to every metabolite call — no overselling of Level 3 features.

Discovery-to-validation pathway

Untargeted discovery, targeted assay development, and independent-cohort validation under one roof.

Multi-omics ready

Integration with transcriptomics, proteomics, and genomics via our Bioinformatics service (OmniIntegrate).

India-relevant matrices

Experience with clinical, nutritional, plant, and microbial metabolomics from Indian programmes.

Publication-grade reporting

Methods, QC metrics, and figures formatted for journals and data repositories.

Scope Note

What this service does and does not cover

Scope note. LC-MS metabolomics measures ionisable metabolites within the chosen separation and ionisation space; it is not exhaustive, and annotation confidence varies — every annotation carries its MSI level, and Level 1 identifications require authentic standards. Relative abundances from untargeted workflows are not absolute concentrations; targeted assays are required for quantitation. Human-metabolomics confounders (diet, medication, circadian effects) must be controlled at study design. Turnaround times and pricing are defined per project.
Questions

Frequently asked, in plain language

From the basics to the finer points of design and interpretation.

basicWhat is the difference between untargeted and targeted metabolomics?
Untargeted profiles everything ionisable to discover unexpected differences; targeted quantifies defined metabolite panels with absolute concentrations using isotope-labelled standards.
basicWhat samples do you accept?
Plasma, serum, urine, tissue, cells, plants, and microbial cultures — with matrix-matched preparation methods.
basicWill I get metabolite identifications?
Yes, with MSI confidence levels attached — Level 1 (authentic standard) where available, down to class-level annotations, stated honestly.
intermediateHow do you control batch effects?
Randomised acquisition order, pooled QC samples at regular intervals, QC-based signal correction, and documented drift monitoring.
intermediateCan you quantify absolute concentrations?
Yes — targeted PRM/MRM-class assays with isotope-labelled internal standards deliver absolute quantitation for defined panels.
intermediateWhat statistics do you provide?
PCA, differential abundance testing with multiple-testing correction, and PLS-DA only with permutation validation — plus pathway enrichment mapped to KEGG/HMDB.
advancedCan you develop a custom targeted assay for my metabolites?
Yes — custom PRM/MRM assay development with authentic standards, including method validation for biomarker studies.
advancedCan metabolomics integrate with my transcriptomics/proteomics data?
Yes — through our Bioinformatics service (OmniIntegrate pipeline): joint pathway analysis, correlation networks, and multi-omics machine-learning models.
advancedDo you support isotope-tracer studies?
Yes — ¹³C/¹⁵N tracer and flux studies can be layered onto the platform; discuss labelling design at project intake.
advancedCan data be submitted to public repositories?
Yes — MetaboLights/Metabolomics Workbench-style submission formatting is supported.
References

Key literature & resources

Selected sources behind the methods and reference values on this page. Figures labelled "schematic" or "illustrative" are generated for this page, not client data.

  1. Johnson CH, Ivanisevic J, Siuzdak G. Metabolomics: beyond biomarkers and towards mechanisms. Nature Reviews Molecular Cell Biology. 2016;17(7):451–459.
  2. Sumner LW, et al. Proposed minimum reporting standards for chemical analysis: Chemical Analysis Working Group of the Metabolomics Standards Initiative (MSI). Metabolomics. 2007;3(3):211–221.
  3. Eliuk S, Makarov A. Evolution of Orbitrap mass spectrometry instrumentation. Annual Review of Analytical Chemistry. 2015;8:61–80.
  4. Dunn WB, et al. Procedures for large-scale integrated metabolomic studies: a compendium of untargeted metabolomics methodology. Metabolomics. 2011;7(1):3–20.
  5. Kirwan JA, et al. Characterizing and controlling variation in metabolomic biomarker discovery and measurement experiments. Analytical Chemistry. 2014;86(10):5040–5048.
  6. Pang Z, et al. MetaboAnalyst 5.0: paving the way for flexible metabolomics data analytics. Nucleic Acids Research. 2021;49(W1):W488–W496.
  7. Kanehisa M, et al. KEGG for taxonomy-based analysis of pathways and genes. Nucleic Acids Research. 2021;49(D1):D545–D551.
  8. Wishart DS, et al. HMDB 5.0: the Human Metabolome Database. Nucleic Acids Research. 2022;50(D1):D622–D631.