Our knowledge of the molecular inventory of a planetary body can only be as good as the measurement instrument that is deployed on the surface. Flight-ready instruments and sample handling procedures are often not what we would choose for optimal analysis on Earth. For example, a common mass spectrometry instrument is electrospray ionization-liquid chromatography mass spectrometry (ESI-LC-MS), but this is an example of an instrument that is currently not suitable for space flight. Gas
chromatography mass spectrometry (GC-MS) is a slightly different but powerful alternative with space flight heritage. However, while these instruments provide important analytical information about organic molecules, interpretation for life detection is still a challenge. A method to recognize an anomaly in the environment and consider finding life as we don’t know it, by searching for something that we have not seen before, is necessary. Molecular assembly (MA) is an experimental biosignature framework that categorizes molecules based on their likelihood of biological formation.

The underlying theory is based on the idea that when a molecule is of higher complexity and produced selectively, it is more likely to have been made biologically. Based on this idea, molecular assembly could potentially be used as a tool to convert a complex dataset into simpler probability numbers, uncovering anomalies of molecules, when compared to the abiotic background. It can also aid in uncovering
molecules that could have been produced biotically that might be leading to a false negative detection signal otherwise.

Previous work has already demonstrated that MA can be used to distinguish biotic from abiotic samples using ESI-MS/MS. To leverage this agnostic biosignature tool for space exploration, we have developed an MA method for GC-MS. Adapting MA
estimations for GC-MS requires careful consideration of several parameters, including column selection, derivatization methods, and consideration towards sample matrices.

We will present experimental results of MA estimations using GC-MS of known standards and biological samples and discuss how operational choices may impact the performance of MA as a molecular anomaly detection tool. Developing robust data interpretation methods for flight-ready instruments that are already deployed provides new opportunities for advancing life detection and interpreting space mission data of already collected and future analyses.