Discover the Power of Untargeted Metabolomics:

When You Don't Know What You're Looking For Unveiling the Complete Story of Life's Chemistry

Ever wonder about the invisible chemical landscape that shapes our world? From the food we eat to the air we breathe, from soil health to drug metabolism in the body, countless small molecules influence health, disease, and environmental processes.

Untargeted metabolomics is a combination of approaches and methodologies looking to reveal thousands of these chemical signals in any biological sample.

Untargeted metabolomics is primarily powered by mass spectrometry – a powerful analytical technique that can detect, identify, and measure thousands of different molecules in a single analysis. Unlike targeted approaches that focus on specific compounds, untargeted analysis captures the complete picture – both known and unknown molecules – giving researchers unprecedented insights into complex biological systems.

Why Choose Untargeted Analysis?

Traditional targeted methods are like searching your house with a flashlight – you’ll find what you’re looking for, but might miss everything else in the shadows.

Untargeted metabolomics, on the other hand, turns on all the lights at once, revealing the complete picture of chemical activity in your samples.

semi-targeted approach

This comprehensive approach can help in multiple ways, such as:

  • Discover unexpected compounds in various samples
  • Map complete biochemical responses
  • Track unknown contaminants and their breakdown products
  • Understand complex interactions between organisms and their environment
  • Identify new biomarkers for health and disease

Applications Across Industries

Our untargeted metabolomics platform drives innovation across diverse fields, tackling some of today's most challenging scientific questions:

Metabolomics in Biotechnology

In biotech, untargeted metabolomics reveals the intricate chemistry of living factories. From optimizing microbial strains for better yields to monitoring complex fermentation processes in real-time, this technology helps companies develop more efficient and sustainable production methods. Whether you’re producing pharmaceuticals, sustainable materials, or novel food ingredients, metabolomics provides the molecular insights needed to perfect your processes.

Metabolomics in Food Science

Food science demands deep understanding of complex chemical processes. Untargeted metabolomics maps the molecular journey from raw ingredients to finished products, helping producers ensure authenticity, monitor fermentation, extend shelf life, and develop better-tasting products. This technology even helps uncover the chemical signatures of quality in premium products like wine, cheese, and coffee.

Metabolomics in Environmental Science

Environmental scientists use untargeted metabolomics to read nature’s chemical signals. This powerful approach tracks how ecosystems respond to climate change, monitors pollutant transformation in soil and water, and reveals how organisms adapt to environmental stress. By capturing thousands of molecular changes simultaneously, metabolomics helps researchers understand and protect our planet’s delicate chemical balance.

Metabolomics in Cosmetics

Modern cosmetics development relies on understanding the skin’s complex biochemistry. Untargeted metabolomics reveals how products interact with skin
at the molecular level, helps validate product claims with solid science, and shows how formulations affect the skin’s natural ecosystem. This deep understanding leads to more effective, science-backed beauty and personal care products.

Metabolomics in Clinical Studies

In medicine, untargeted metabolomics illuminates the chemistry of health and disease. By analyzing thousands of molecules in patient samples, researchers discover new disease biomarkers, track treatment responses, and understand drug metabolism. This comprehensive approach supports the development of precision medicine, helping doctors choose the right treatments for individual patients based on their unique molecular profiles.

Let Arome Science help you turn specimens into insights

metamolite

Up to 1250 microbiome and human-related metabolites (Level 1) Up to 6500 IDs (Level 2)

State-of-the-art metabolomics
Biomarker elucidation, analysis, & microbiome multiomics integration bioinformatics

Drug & consumer product development and clinical biomarkers

Discover and validate metabolomic biomarkers with a customizable data and analysis package that meets your needs and budget

We recognize that every study is different. Choose the metabolite coverage, specimen type, study design, and bioinformatics analysis you need to succeed. Costs are approximate, and the offerings list is not exhaustive. Metabolite annotations include:

  • Known molecules (Level 1; mass, retention time, MS/MS): 1250 compound authentic standard library
  • Known molecules (Level 2; mass, MS/MS match to spectral library) typically IDs ≤10% of the 1000s of detected features
  • Known and novel molecules (Level 3 & 4; propagated libraries and in silico prediction of MS/MS) typically IDs ≤30% and ≤80% of features with proposed structures and chemical class, respectively

Untargeted metabolomics testing cost per sample

Mass Spectrometry Methods​

LC-MS
1 (Positive & Negative)
GC-MS

Metabolite Classes​

  • Steroids & Bile Acids
  • Nucleic Acids
  • Vitamins & Cofactors
  • Xenobiotics
  • Polar Lipids
  • Peptides & Analogues
  • Fatty Acids
  • Amino Acids & Amines
  • Small Saccharides & Alcohols
  • Short-Chain Fatty Acids
  • Larger Lipids
  • Polysaccharides & Polyalcohols
  • Energetics
  • Volatiles
  • Terpenes & Terpenoids
  • Esters
  • Phenols, Benzyls, & Naphthalenes Thiols

Sample Types​

  • Cell Culture
  • Any MS-Compatible

Study Design, Data, & Interpretation

  • Pilot Study
  • Raw Data Feature Table with Annotations Molecular Network PCoA Publication-Ready Methods
  • Statistical Analysis (e.g., Supervised Learning & Multivariate)
  • In Silico Metabolite Prediction
  • Pathway Analysis
  • Additional Bioinformatics
  • Multiomics Integration Analysis

Mass Spectrometry Methods​

LC-MS
1 (Positive & Negative)
GC-MS
1 (Derivitized)

Metabolite Classes​

  • Steroids & Bile Acids
  • Nucleic Acids
  • Vitamins & Cofactors
  • Xenobiotics
  • Polar Lipids
  • Peptides & Analogues
  • Fatty Acids
  • Amino Acids & Amines
  • Small Saccharides & Alcohols
  • Short-Chain Fatty Acids
  • Larger Lipids
  • Polysaccharides & Polyalcohols
  • Energetics
  • Volatiles
  • Terpenes & Terpenoids
  • Esters
  • Phenols, Benzyls, & Naphthalenes Thiols

Sample Types​

  • Cell Culture
  • Any MS-Compatible

Study Design, Data, & Interpretation

  • Pilot Study
  • Raw Data Feature Table with Annotations Molecular Network PCoA Publication-Ready Methods
  • Statistical Analysis (e.g., Supervised Learning & Multivariate)
  • In Silico Metabolite Prediction
  • Pathway Analysis
  • Additional Bioinformatics
  • Multiomics Integration Analysis

Mass Spectrometry Methods​

LC-MS
2 (Positive & Negative)
GC-MS
1 (Derivitized or Non-Derivitized)

Metabolite Classes​

  • Steroids & Bile Acids
  • Nucleic Acids
  • Vitamins & Cofactors
  • Xenobiotics
  • Polar Lipids
  • Peptides & Analogues
  • Fatty Acids
  • Amino Acids & Amines
  • Small Saccharides & Alcohols
  • Short-Chain Fatty Acids
  • Larger Lipids
  • Polysaccharides & Polyalcohols
  • Energetics
  • Volatiles
  • Terpenes & Terpenoids
  • Esters
  • Phenols, Benzyls, & Naphthalenes Thiols

Sample Types​

  • Cell Culture
  • Any MS-Compatible

Study Design, Data, & Interpretation

  • Pilot Study
  • Raw Data Feature Table with Annotations Molecular Network PCoA Publication-Ready Methods
  • Statistical Analysis (e.g., Supervised Learning & Multivariate)
  • In Silico Metabolite Prediction
  • Pathway Analysis
  • Additional Bioinformatics
  • Multiomics Integration Analysis

Mass Spectrometry Methods​

LC-MS
3 (Positive & Negative)
GC-MS
2 (Derivitized or Non-Derivitized)

Metabolite Classes​

  • Steroids & Bile Acids
  • Nucleic Acids
  • Vitamins & Cofactors
  • Xenobiotics
  • Polar Lipids
  • Peptides & Analogues
  • Fatty Acids
  • Amino Acids & Amines
  • Small Saccharides & Alcohols
  • Short-Chain Fatty Acids
  • Larger Lipids
  • Polysaccharides & Polyalcohols
  • Energetics
  • Volatiles
  • Terpenes & Terpenoids
  • Esters
  • Phenols, Benzyls, & Naphthalenes Thiols

Sample Types​

  • Cell Culture
  • Any MS-Compatible

Study Design, Data, & Interpretation

  • Pilot Study
  • Raw Data Feature Table with Annotations Molecular Network PCoA Publication-Ready Methods
  • Statistical Analysis (e.g., Supervised Learning & Multivariate)
  • In Silico Metabolite Prediction
  • Pathway Analysis
  • Additional Bioinformatics
  • Multiomics Integration Analysis