High sensitivity and specificity feature detection in liquid chromatography-mass spectrometry data: A deep learning framework

Autonomous CE Mass-Spectra Examination for the Ocean Worlds Life Surveyor

Ocean worlds such as Europa and Enceladus are high priority targets in the search for past or extant life beyond Earth. Evidence of life may be preserved in samples of surface ice by processes such as deposition from active plumes, hydrofracturing, or thermal convection. Terrestrial life produces unique distributions of organic molecules that translate into recognizable biosignatures. Identification and quantification of these organic compounds can be achieved by separation science such as capillary electrophoresis coupled to mass spectrometry (CE-MS). However, the data generated by such an instrument can be multiple orders of magnitude larger than what can be transmitted back to Earth during an ocean world's mission. This requires onboard science data analysis capabilities that summarize and prioritize CE-MS observations with limited computational resources. In response, the autonomous capillary electrophoresis mass-spectra examination (ACME) onboard science autonomy system was created for application to the ocean world's life surveyor (OWLS) instrument suite. ACME is able to compress raw mass spectra by two to three orders of magnitude while preserving most of its scientifically relevant information content. This summarization is achieved by the extraction of raw data surrounding autonomously identified ion peaks and the detection and parameterization of unique background regions. Prioritization of the summarized observations is then enabled by providing estimates of scientific utility, including presence of key target compounds, and the uniqueness of an observation relative to previous observations.