Two images in circles, one with a lab tech putting a sample into a vial and the other of a vial

ProductHeadline

Trained on the world’s largest quantitative mass spec dataset and built upon our Large Spectral Model (LSM), PyxisTM deep-learns how to map raw mass spec data to absolute concentrations. Harnessing a set of universal calibrants called StandardCandlesTM, Pyxis produces untargeted absolute quantitative results in minutes, without expertise or manual data processing.

How it works

Vials in a line

Standardized consumables
for sample preparation and LC-MS methods

image of an instrument collecting date

Acquire data
on your own instrument

Computer monitor with data on screen

Cloud-enabled software
for automated raw data processing

What makes Pyxis unique

Illustrated magnifying glassUntargeted: ability to broadly identify and quantify as many features as possible from raw untargeted data


Illustrated bar graphQuantitative: absolute quantitation makes it possible to compare data across samples, experiments, studies, and even across laboratories


Illustrated stopwatchFast: results allow for decision as soon as data acquisition is complete

Illustrated cog in motionAutomated: no need for PhD-level expertise to manually curate features and analyze data


Illustrated double sided arrow inside a squareScalable: a small set of universal methods and standards replaces laborious method development and calibrations that yield limited analytical coverage


Illustrated arrows forming a circleReproducible: a single sample preparation kit and software pipeline to ensure uniformity and standardization

Image of the slider kit box


Consumables and Methods for Rapid Deployment

Easy setup and onboarding
Consumables, standardized sample preparation, and LC-MS methods ensure uniformity
StandardCandles: Universal calibrators replace isotopically labeled individual standards, thereby broadening analytical coverage
Computer monitor with data moving. on screen


Web Application

Fast & Automated Data Processing: Our cloudbased application streamlines data workflows with rapid, automated processing
Secure Infrastructure: Secure data storage and transfer
Scalable Sample Handling: Analyze up to 384 samples in under 15 minutes
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Coverage & Applications

Coverage
Amino Acids, Glycolysis, Sugars, TCA, Urea Cycle, Vitamins, Glycosylation Precursors, One-Carbon Metabolism, Purines, Energy Carriers, Fatty Acid Metabolism, Pyrimidines, Redox, Choline Metabolism
Applications & Use Cases
Synthetic Biology, Drug Discovery, Metabolomics, Target Screening, Hypothesis Testing, Media Analysis, Microbiome Analysis
Image of pyxis logo on dark gray background

Product Details

Dynamic Range
Analyte dependent: From LOQ to 50µM
LC Method
6 min HILIC method
ACN gradient
Fast polarity switching
StandardCandle™ technology
Supported Matrices*
Cell lysates, culture media, plasma, saliva
Processing Time
<5 min (96-well plate)
<10 min (192-well plate)
<15 min (384-well plate)
* Potentially generalizable to other matrices. Contact us if your matrix of interest is not listed.

Resources

Image of Unlocking Scalable Quantitative Metabolomics with AI video cover

Unlocking Scalable Quantitative Metabolomics with AI

Jennifer M. Campbell, PhD, Chief Scientific Officer, discusses how Matterworks is unlocking scalable quantitative metabolomics with AI. This talk was originally given at a breakfast workshop hosted by Thermo Fisher Scientific at ASMS 2023.

VIDEO

 

Image of Absolute Quantitation of Metabolites Using Machine Learning and StandardCandles as Universal Calibrators - the second-generation model

Absolute Quantitation of Metabolites Using Machine Learning and StandardCandles as Universal Calibrators - the second-generation model

Jennifer Campbell, PhD, Chief Scientific Officer, gives a brief overview of how Matterworks is using machine learning and universal calibrators to enable whole metabolome reading.

VIDEO AND PDF

 

Image of Absolute Quantification of Key Cellular Metabolites in Bioprocessing Samples Using Machine Learning

Absolute Quantification of Key Cellular Metabolites in Bioprocessing Samples Using Machine Learning

Luke Ferro, PhD, gives a short poster overview of a collaboration between Matterworks and Eli Lilly for the quantification of metabolites from bioreactors

VIDEO AND PDF

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