Natural Product Metabolomics Analysis Service

Why Natural Product Metabolomics?

Natural product matrices are dominated by secondary metabolites and are highly sensitive to variables such as origin, harvest season, processing, and extraction. LC–MS/MS metabolomics provides an efficient way to compare profiles, prioritize key compounds, and link chemical changes to biological outcomes.

Questions this analysis can help answer

• What are the key secondary metabolites in my medicinal plant, extract, or formula?
• How do batches differ across origin, cultivation conditions, processing, or storage?
• Which compounds are absorbed into serum or distributed into target tissues after dosing?
• Which endogenous pathways shift after treatment (e.g., lipids, amino acids, bile acids) and what does that suggest mechanistically?
• Which differential metabolites and pathways can support publishable conclusions and follow-up validation?

What We Analyze: Service Menu

Medicinal Plant & Natural Product Profiling

• Plant secondary metabolite profiling (broad chemical coverage)
• Comparative metabolomics (cultivar/variety, geography, cultivation environment)
• Tissue-/organ-specific accumulation (plant tissues, parts, developmental stages)
• Batch-to-batch consistency and natural product quality assessment
• Processing impact studies (e.g., drying, extraction, and other preparation variables)

Traditional Chinese Medicine (TCM) Application

If your project involves herbal formulas or TCM materials, we can provide dedicated workflows for:

• Herbal formula metabolomics (formula-level constituent profiling)
• Processing (Paozhi) comparison and consistency evaluation
• Formula deconvolution / single-herb attribution (supporting "which herb contributes which compounds")
• TCM quality control and compositional comparison across manufacturers/batches

In Vivo Exposure & Mechanism-of-Action Support

• Serum pharmacochemistry / absorbed constituents profiling
• Tissue exposure profiling (target organ distribution)
• Host endogenous metabolomics after dosing (pathway-level readouts)
• Pathway enrichment and biological interpretation
• Optional: network pharmacology integration (mechanism hypothesis generation)

Detectable Metabolite Coverage: Natural Product LC–MS/MS

This service profiles plant secondary metabolites in natural products and herbal materials, with optional endogenous metabolomics for in vivo studies. Detection is influenced by matrix, extraction, and ionization response.

Typical detection: ~600–1000+ plant secondary metabolites per run (matrix-dependent).

Plant & Natural Product Secondary Metabolites (Standard Coverage)

Typical number of detected plant secondary metabolites per run: ~600–1000+ (sample-type dependent).

Endogenous Metabolites for In Vivo / Host-Response Studies (Optional)

Advantages of Our Natural Product Metabolomics Service

• Deep reference libraries for confident annotation
  TCM-focused library capacity: 39,000+ total entries, including 5,000+ TCM standards, 14,000+ manually curated active constituents, and 20,000+ metabolite spectra generated from in vitro incubation models (for metabolite interpretation).
• Endogenous/metazoan metabolite library
  10,000+ metabolites with 2,500+ standard-supported entries providing RT + MS1 + MS2 evidence (when applicable to the module).
• Standard-supported, multi-dimensional identification options
  Highest-confidence reporting can include RT + accurate mass (MS1) + MS/MS (LC–MS/MS) evidence, with match scoring and spectra visualization for key findings.
• Quality control built into routine production
  Multi-layer QC strategy (e.g., pooled QC injections, blanks, internal standards, signal stability monitoring, RT drift checks, mass accuracy checks) to improve reproducibility and interpretability.
• Scalable production experience
  High annual sample throughput supports consistent execution for both pilot studies and larger experimental designs (capacity planning available on request).

Study Design Support: Recommended Comparisons and Experimental Strategies

We provide practical study design guidance to help you obtain interpretable, publication-ready metabolomics results.

• Group comparison: Control vs treatment, batch/origin, processing (Paozhi), or extraction method.
• Dose–response / time-course: Best for absorbed constituents and mechanism-focused pathway changes.
• Multi-factor design: Separate effects such as origin × processing or cultivar × environment with balanced sampling.
• QC & replicates: Pooled QC, blanks, internal standards, and randomized run order to control drift and batch effects.

Replicate tip: For discovery studies, prioritize biological replicates (a common starting point is ≥6 per group when feasible).

1

Project design & study goals (comparisons, sample types, endpoints)

2

Sample receipt & integrity check (temperature, labeling, volume/mass)

3

Extraction & internal standard spiking (matrix-optimized protocols)

4

UPLC separation + HRMS acquisition (positive/negative ion modes as needed; randomized sequence with QC)

5

Data preprocessing (peak detection, alignment, deconvolution, normalization, QC filtering)

6

Compound identification & annotation (library match + scoring; optional expert review)

7

Statistics & differential analysis (PCA, clustering, differential metabolites; optional supervised models upon request)

8

Pathway/biological interpretation (enrichment, pathway mapping; optional network pharmacology module)

9

Reporting & delivery (data package + figures + consultation)

Analytical Platform: UPLC–Orbitrap LC–MS/MS and Key Parameters

We use an industry-standard UPLC–high-resolution Orbitrap LC–MS/MS workflow optimized for natural product complexity.

UPLC System

• Waters ACQUITY UPLC I-Class (or equivalent UPLC platform)
• Why it matters: high-pressure UPLC improves separation of isomers and co-eluting phytochemicals and supports stable retention times.

High-Resolution Mass Spectrometer (HRMS)

• Thermo Scientific Q Exactive series Orbitrap (configuration dependent)
  Common acquisition characteristics (method-optimized per project):
• Ionization: ESI/HESI in positive and negative modes
• Mass accuracy: typically ≤5 ppm after calibration (project-run dependent)
• MS1 resolution: commonly 70,000–120,000 at m/z 200
• MS/MS resolution: commonly 17,500–30,000 at m/z 200
• Scan range: commonly m/z 70–1,050 (method dependent)
• Acquisition modes: DDA and/or DIA (selected based on coverage vs. annotation goals)

Final acquisition settings are optimized based on matrix complexity (plant tissue vs. extract vs. serum/tissue), expected compound classes, and study objectives.

Waters ACQUITY UPLC System (Figure from Waters)

Thermo Fisher Q Exactive (Figure from Thermo Fisher)

Sample Requirements: Recommended Inputs, Storage, and Shipping

Deliverables: Data Files, Reports, and Identification Confidence Levels

• Raw LC–MS/MS data files for all samples (including QC and blanks), provided in instrument format (e.g., .raw) and/or open format (.mzML).
• A processed peak/feature table (sample × feature matrix) including RT, m/z, adduct (if applicable), and peak intensity/area.
• An identified/annotated compound list with compound name, chemical class, RT, precursor m/z, MS/MS match score, and confidence level.
• A QC summary sheet reporting key run metrics such as internal standard performance, signal stability, RT drift, and missing-value rate.
• Core statistical outputs (PCA scores/loadings and clustering/heatmap files) with the corresponding underlying data tables.
• A differential metabolite results table including fold change and statistical values (p-value and/or FDR, as applicable).
• Pathway enrichment results (tables and pathway mapping outputs) for interpretation, when biological pathway analysis is included.
• A final summary report (PDF or Word) describing methods, QC, key findings, and interpretation aligned to your study objectives.

QC chromatogram overlay and feature RSD distribution demonstrate run stability and reproducibility.

TIC/BPC comparison of blank, pooled QC, and sample highlights signal quality and low background.

MS/MS mirror plot confirms compound identity via matched fragments between sample and reference spectra.

PCA score plot shows tight QC clustering and clear sample group separation after QC filtering.

Applications for Natural Product Metabolomics Analysis

Supplier Qualification

Compare multiple suppliers for the same botanical to confirm chemical consistency before scale-up or long-term purchasing.

Batch Release Trending

Track lot-to-lot variation over time and flag outliers linked to harvest season, storage, or processing drift.

Origin / Cultivar Discrimination

Differentiate geographic origin or cultivar and identify practical chemical markers for authentication.

Processing Optimization

Compare drying methods, extraction solvents, temperature/time, or milling conditions to select parameters that preserve desired compounds.

Adulteration / Substitution Screening

Detect unexpected shifts in secondary-metabolite patterns that suggest substitution or mixed raw materials (risk-screening use).

Active Fraction Prioritization

Rank extract fractions by enriched secondary metabolites to guide bioassay follow-up and compound prioritization.

Herbal Formula Attribution (Optional)

Assign major constituents to specific herbs in a multi-herb formula to support deconvolution and QC planning.

Serum Exposure Confirmation (In Vivo Module)

Verify which plant-derived constituents are detectable in serum/plasma post-dosing to guide exposure-driven follow-up.