Achieve Discovery and Biomarker Analysis Simultaneously

with Semi-Targeted Metabolomics

Access biological function by profiling metabolites: don’t settle for possible

To identify biological mechanisms of action or elucidate biomarkers, you can’t afford to settle for risk scores or potential functionality. You need a technology that integrates genetics, lifestyle, and environment in any sample type, while offering the ability to make discoveries and measure quantitative biomarkers.

Across any application area or disease indication, semi-targeted metabolomics and multiomic data integration solutions from Arome Sciences will provide you with the data you need to bring your product to market.

from DNA genome to metabolite metabolome

Semi-targeted metabolomics combines discovery and biomarker capabilities into one powerful technology for multiomic integration and clinical development

When designing a metabolomics study, scientists typically decide between platforms that enable discovery and those that quantitatively measure biomarkers.

Arome Science has pioneered a unique semi-targeted approach, offering both discovery and biomarker analysis in the same experiment.

semi-targeted approach

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

Semi-targeted metabolomics 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