Metabolomics offers valuable insights into physiological processes, with applications including biomarker discovery, bioprocessing, drug discovery, personalized medicine and environmental monitoring.
However, untargeted metabolomics generates vast amounts of complex data that are difficult to process, analyze and interpret. As a result, large amounts of routinely collected metabolomic data are discarded due to analytical limitations.
This infographic explores a cutting-edge technology that overcomes these challenges to access previously unreachable metabolomic information. Built on top of a first-of-its-kind Large Spectral Model (LSM), this revolutionary platform allows researchers to achieve untargeted absolute quantitative metabolomic results directly from raw spectra.
Download this infographic to discover:
- The challenges of absolute quantitation in traditional workflows
- The benefits of an innovative a LC-MS workflow without peak analysis, isotopologues or calibration curves
- How to deploy this technology in your lab in only two days
Navigate Your
Way to Precise and
Scalable Untargeted
Metabolomics
Metabolomics provides a deep look into the small molecules present in a sample
at any given time, offering valuable insights into physiological processes. This
analysis has a broad range of applications including biomarker discovery,
bioprocessing, drug discovery, personalized medicine and environmental
monitoring.
However, untargeted metabolomics generates vast amounts of complex data
that are difficult to process, analyze and interpret. As a result, large amounts of
routinely collected metabolomic data are discarded due to analytical limitations.
Advanced artificial intelligence (AI) and machine learning (ML) models promise to
overcome these challenges and access previously unreachable metabolomic
information directly from raw spectra.
The
challenges
of absolute
quantitation
a revolutionary
solution
During traditional untargeted metabolomic
workflows, small-molecule metabolites are
extracted from the sample matrix, separated
using chromatographic methods, then ionized
and analyzed using mass spectrometry (MS).
The raw data containing the chromatographic
trace and underlying mass spectra need to
be pre-processed to remove noise, correct
for instrumental drift and align peaks across
samples to ensure consistency. Following this,
the peaks need to be analyzed to calculate
the concentration of each metabolite. Such
quantitation can be relative or absolute.
The Pyxis™ platform approaches absolute
quantitation in a fundamentally different
way. This groundbreaking system is built
on top of a first-of-its-kind Large Spectral
Model (LSM), for directly interpreting
raw data. Pyxis is based on three main
elements to rapidly deliver absolute
concentration data
