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Ancient chemical clues reveal Earth’s earliest life 3.3 billion years ago

MSU researcher contributes rare fossils that help train AI to detect life’s oldest molecular signature

2025-11-17
(Press-News.org) A new study uncovered fresh chemical evidence of life in rocks more than 3.3 billion years old, along with molecular traces showing that oxygen-producing photosynthesis emerged nearly a billion years earlier than previously thought.

The international team, led by researchers at the Carnegie Institution for Science, paired cutting-edge chemistry with artificial intelligence to reveal faint chemical “whispers” of biology locked inside ancient rocks. Using machine learning, the researchers trained computers to recognize subtle molecular fingerprints left behind by living organisms, even when the original biomolecules have long since degraded.

Among the collaborators was Michigan State University’s Katie Maloney, an assistant professor in the Department of Earth and Environmental Sciences, who studies the evolution of early complex life and its impact on ancient ecosystems. Maloney contributed samples of exceptionally well-preserved one-billion-year-old seaweed fossils from Yukon Territory, Canada. These samples represent one of the first seaweeds known in the fossil record, when most life can only be viewed through a microscope.

The study, published in the Proceedings of the National Academy of Sciences, not only deepens understanding of Earth’s earliest biosphere but also has implications for the search for life beyond Earth. The same approach could be used to analyze samples from Mars or other planetary bodies to detect whether they once harbored living organisms.

“Ancient rocks are full of interesting puzzles that tell us the story of life on Earth, but a few of the pieces are always missing,” Maloney said. “Pairing chemical analysis and machine learning has revealed biological clues about ancient life that were previously invisible.”

Earth’s earliest life left behind little in the way of molecular traces. The few fragile remnants such as ancient cells and microbial mats were buried, crushed, heated, and fractured within Earth’s restless crust before being thrust back to the surface. These transformations all but obliterated biosignatures holding vital clues to the origins and early evolution of life.

The new work suggests that the distribution of biomolecular fragments found in old rocks still preserves diagnostic information about the biosphere, even if no original biomolecules remain. 

Indeed, this new research shows that life left behind more than anyone ever realized – faint chemical “whispers” locked deep inside ancient rocks. 

The team used high-resolution chemical analysis to break down organic and inorganic materials into molecular fragments, then trained an artificial intelligence system to recognize the chemical “fingerprints” left behind by life. Scientists examined more than 400 samples from plants and animals to billion-year-old fossils and meteorites. The AI model distinguished biological from non-biological materials with over 90% accuracy and detected signs of photosynthesis in rocks at least 2.5 billion years old.

Until now, molecular traces that reliably indicated life had only been found in rocks younger than 1.7 billion years. This new method roughly doubles the window of time scientists can study using chemical biosignatures.

“Ancient life leaves more than fossils; it leaves chemical echoes,” said Dr. Robert Hazen, senior staff scientist at Carnegie and a co-lead author. “Using machine learning, we can now reliably interpret these echoes for the first time.”

For Maloney, whose research focuses on how early photosynthetic life transformed the planet, the implications are profound.

“This innovative technique helps us to read the deep time fossil record in a new way,” she said. “This could help guide the search for life on other planets."

END


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[Press-News.org] Ancient chemical clues reveal Earth’s earliest life 3.3 billion years ago
MSU researcher contributes rare fossils that help train AI to detect life’s oldest molecular signature