Gas Chromatography Reveals Clues to Martian Amino Acids: A Step Towards Understanding the Red Planet's Potential for Life

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Researchers from the University of Poitiers and CNES have developed a groundbreaking gas chromatography method, detailed in the journal Chemosensors, which enables the detection of amino acid-like compounds on Mars, offering a vital tool in the quest for extraterrestrial life.

In the ongoing search to unlock the mysteries of the universe and explore the potential for life beyond Earth, researchers from the University of Poitiers in Poitiers, France, and the Centre National d’Etudes Spatiales (CNES) in Toulouse, France, have made significant strides in the search for extraterrestrial life by developing a pioneering technique for the detection of amino acids on Mars.

Mars red planet black background | Image Credit: © Martin - stock.adobe.com

Mars red planet black background | Image Credit: © Martin - stock.adobe.com

Published in the journal Chemosensors, the study, led by Claude Geffroy-Rodier, a lecturer at the University of Poitiers, focuses on using gas chromatography coupled to mass spectrometry (GC–MS) to identify amino acid-like compounds on the Martian surface. This breakthrough technique could prove instrumental in future missions aimed at searching for signs of life on the Red Planet (1).

Amino acids, the fundamental building blocks of life on Earth, are crucial targets in the hunt for extraterrestrial life. Mars, with its harsh conditions, has long piqued the curiosity of scientists looking for indicators of life's potential beyond our planet. The study's authors aimed to adapt GC–MS to withstand the rigors of space exploration and detect these organic compounds on Mars.

Gas chromatography coupled with mass spectrometry has already demonstrated its capability for molecular-level detection in space instrumentation, but its limitation lies in its ability to analyze only thermally stable volatiles. However, the researchers found a solution by derivatizing amino acids, making them compatible with remote GC–MS experiments. This derivatization method, known as silylation, was previously successful on NASA's Curiosity rover, leading to the identification of benzoic acid and ammonia on the Martian surface.

In this latest study, the team not only optimized the derivatization process but also introduced an extraction step using a water and methanol mixture, a method previously unexplored on Mars. This one-pot extraction-derivatization process promises to improve the recovery and interpretation of chromatograms, potentially opening new avenues for detecting amino acids and other complex molecules on the Martian surface.

Additionally, the researchers proposed using the total ion chromatogram (TIC) method as a recognition response pattern to detect specific chemical compounds, treating GC–MS as sensors. This approach could significantly simplify the selection of sampling sites for future Mars sample return missions.

The TIC method is an analytical technique commonly used in mass spectrometry coupled with gas chromatography (GC–MS). It involves monitoring and recording the total ion current, which is the sum of all ion signals generated by molecules as they pass through the mass spectrometer detector during a GC–MS analysis. The TIC provides a comprehensive overview of the entire sample composition, allowing for the detection and identification of various compounds present in a complex mixture. Researchers can use the TIC as a fingerprint or recognition pattern to detect specific chemical compounds or changes in a sample's composition, making it a valuable tool in analytical chemistry for identifying biomarkers in complex samples.

This article was written with the help of artificial intelligence and has been edited to ensure accuracy and clarity. You can read more about our policy for using AI here.

Reference

(1) Fkiri, R.; Timoumi, R.; Rioland, G.; Poinot, P.; Baron, F.; Gregoire, B.; Geffroy-Rodier, C. Gas Chromatography Fingerprint of Martian Amino Acids before Analysis of Return Samples. Chemosensors 2023, 11 (2), 76. DOI: 10.3390/chemosensors11020076

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