Chemical evidence of ancient life detected in 3.3-billion-year-old rocks

Researchers combined advanced chemical analysis with artificial intelligence to detect biosignatures in 3.3-billion-year-old rocks, analyzing more than 400 samples from modern organisms, fossils, sediments and meteorites. Published in the Proceedings of the National Academy of Sciences, the study reports biotic signatures with high accuracy and molecular evidence that oxygen-producing photosynthesis existed at least 2.5 billion years ago, pushing back the chemical record by about 800 million years. The approach shows life leaves enduring chemical echoes even after biomolecules degrade and suggests new ways to search for life on other worlds, while noting limitations and the need for broader sampling. Source: Proceedings of the National Academy of Sciences (PNAS).

Overview

A multidisciplinary team combined high-tech chemical analysis with machine learning to search for biosignatures in rocks dating back up to 3.3 billion years. Analyzing more than 400 samples spanning modern plants and animals, fossil materials, ancient sediments, meteorites and lab-made organics, the researchers trained a random-forest model to distinguish biotic from abiotic sources based on degraded organic fragments preserved in rocks.

Biology meets chemistry and AI: The study treats life as a selective chemical system, where living processes imprint characteristic distributions of molecular fragments that persist even after original biomolecules are destroyed. The team’s approach demonstrates that biosignatures can survive geological processing for billions of years, and AI can read these faint echoes with high accuracy. “This study represents a major leap forward in our ability to decode Earth's oldest biological signatures,” said Robert Hazen of the Carnegie Institution for Science.

Methods and Data

Using sophisticated spectrometry to release trapped chemical fragments, the researchers applied AI to interpret complex data. The model achieved up to 98 percent accuracy when distinguishing life-derived matter from meteoritic or synthetic carbon, and around 93 percent in identifying photosynthetic signatures in rocks as old as 2.52 billion years. The team also demonstrated the ability to classify samples as plant- or animal-based life with about 95 percent accuracy. “A.I. didn’t just help us analyze data faster, it allowed us to make sense of messy, degraded chemical data,” noted Michael L. Wong of the Carnegie team.

“These samples and the spectral signatures they produce have been studied for decades, but AI offers a powerful new lens that allows us to extract critical information and better understand their nature,” – Anirudh Prabhu, first author

Key Findings

Significant signals of biotic material were found in 3.33-billion-year-old Josefsdal Chert sediments in South Africa, and clear evidence of ancient photosynthesis emerged in 2.52-billion-year-old rocks from the Gamohaan Formation. The results push the useful window for reading biosignatures back beyond earlier limits and show that oxygenic photosynthesis was already in operation well before the Great Oxidation Event.

“Understanding when photosynthesis emerged helps explain how Earth's atmosphere became oxygen-rich, a key milestone that allowed complex life, including humans, to evolve,” – Anirudh Prabhu, first author

Implications for Earth and Beyond

Beyond enriching our understanding of Earth's early biosphere, the study hints at new strategies for detecting life on other worlds. If AI can extract meaningful biosignatures from degraded Earth rocks, similar methods might be applied to Martian samples or icy worlds like Europa to search for remnants of life. The authors caution that results depend on sample quality and that the approach complements, not replaces, traditional isotope analyses and fossil morphology.

Future work will seek larger, more balanced sample sets and test the approach on Mars-like desert rocks, refining the models to reduce gray areas where probability scores are mid-range. The authors emphasize that the method should be used in concert with established techniques to strengthen conclusions about ancient life.