Discriminating abiotic and biotic organics in meteorite and terrestrial samples using machine learning on mass spectrometry data
Year: 2025
Type: article
Abstract: With the upcoming sample return missions to the Solar System where traces of past, extinct, or present life may be found, there is an urgent need to develop unbiased methods that can distinguish molecular distributions of organic compounds synthesized abiotically from those produced biotically but were subsequently altered through diagenetic processes. We conducted untargeted analyses on a collection of meteorite and terrestrial geologic samples using 2D gas chromatography coupled with high-resolution time-of-flight mass spectrometry and compared their soluble nonpolar and semipolar organic species. To deconvolute the resulting large dataset, we developed LifeTracer, a computational framework for processing and downstream machine learning analysis of mass spectrometry data. LifeTracer identified predictive molecular features that distinguish abiotic from biotic origins and enabled a robust classification of meteorites from terrestrial samples based on the composition of their nonpolar soluble organics.
Source: PNAS Nexus
Authors: Daniel Saeedi, Denise Buckner, Thomas A Walton, José C Aponte, Amirali Aghazadeh
Institutions: Georgia Institute of Technology, Goddard Space Flight Center
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,202
Funders: National Science Foundation, Smithsonian Institution, National Aeronautics and Space Administration, Institute for Bioengineering and Biosciences, Goddard Space Flight Center, Johnson Space Center, National Museum of Natural History, Parker H. Petit Institute for Bioengineering and Bioscience, Planetary Science Division
OpenAlex ID: W4416314227