Comprehensive Metabolite Identification and Quantification in Diverse Biological Matrices Enabled by Advanced Machine Learning

Comprehensive Metabolite Identification and Quantification in Diverse Biological Matrices Enabled by Advanced Machine Learning

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Overview

We evaluated the ability of Pyxis™, a machine learning (ML)-based cloud platform, to annotate metabolite identity and absolute concentrations in diverse sample matrices. Absolute quantification is achieved by combining the signal from matrix-independent calibrators (StandardCandles™) with an ML approach, which obviates the requirement for stable isotope-based calibration curves.

In this study, we used conventional stable isotope-labeled standard methodology as a benchmark. The efficient and rapid performance of Pyxis using unprocessed MS data, demonstrates its comparative advantage over the traditional approach. This highlights the potential of this innovative approach to revolutionize metabolomics. Pyxis can facilitate metabolite analysis across biological discovery, drug development, and bioprocessing applications, regardless of the sample type or the researcher's experience.

Key Objectives:

How scalable metabolite identification and absolute quantitation can be achieved from raw LC-MS data without the need for isotopically labeled standards.
The ability of Pyxis, a ML-based technology, to rapidly annotate raw MS data, reducing time to result in quantitative metabolomics to days instead of weeks or months.
The application of this new platform for the identification and quantification of metabolites across multiple sample types.
How this innovative technology can be used for monitoring cell growth, optimizing bioprocesses, and identifying biomarkers in human health studies.

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