4.6 Article

Classification of the Biogenicity of Complex Organic Mixtures for the Detection of Extraterrestrial Life

Journal

LIFE-BASEL
Volume 11, Issue 3, Pages -

Publisher

MDPI
DOI: 10.3390/life11030234

Keywords

astrobiology; life detection; biosignatures; machine learning; mass spectrometry; complexity; prebiotic chemistry

Funding

  1. JSPS KAKENHI [JP26106003]
  2. John Templeton Foundation
  3. National Science Foundation [DMR 1157490, DMR-1644779]
  4. state of Florida
  5. Gulf of Mexico Research Initiative

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The search for extraterrestrial life in the Solar System relies on distinguishing biological features from background signals using techniques like mass spectrometry. This method can effectively differentiate organic compounds produced by biological and abiological processes. It presents a general approach for detecting evidence of biology independently of the specific molecular makeup of samples, and may have implications for studying emergent living phenomena and paleobiological samples.
Searching for life in the Universe depends on unambiguously distinguishing biological features from background signals, which could take the form of chemical, morphological, or spectral signatures. The discovery and direct measurement of organic compounds unambiguously indicative of extraterrestrial (ET) life is a major goal of Solar System exploration. Biology processes matter and energy differently from abiological systems, and materials produced by biological systems may become enriched in planetary environments where biology is operative. However, ET biology might be composed of different components than terrestrial life. As ET sample return is difficult, in situ methods for identifying biology will be useful. Mass spectrometry (MS) is a potentially versatile life detection technique, which will be used to analyze numerous Solar System environments in the near future. We show here that simple algorithmic analysis of MS data from abiotic synthesis (natural and synthetic), microbial cells, and thermally processed biological materials (lab-grown organisms and petroleum) easily identifies relational organic compound distributions that distinguish pristine and aged biological and abiological materials, which likely can be attributed to the types of compounds these processes produce, as well as how they are formed and decompose. To our knowledge this is the first comprehensive demonstration of the utility of this analytical technique for the detection of biology. This method is independent of the detection of particular masses or molecular species samples may contain. This suggests a general method to agnostically detect evidence of biology using MS given a sufficiently strong signal in which the majority of the material in a sample has either a biological or abiological origin. Such metrics are also likely to be useful for studies of possible emergent living phenomena, and paleobiological samples.

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