Lachlan Stuart

METASPACE-ML improves metabolite annotation in imaging mass spectrometry by using machine learning, achieving higher precision and throughput than a rule-based method.

This paper introduces 13C-SpaceM, a method for spatial single-cell isotope tracing of de novo fatty acid synthesis in cancer cells, revealing metabolic heterogeneity in liver and glioma tissues.

The paper evaluates spatial metabolomics protocols and technologies, showing extensive metabolite coverage and comparable results across imaging MS methods.

ColocAI quantifies co-localization between mass spectrometry images using AI, outperforming existing methods and providing insights into 10,273 molecules from public datasets.

An open-source cloud engine annotates imaging mass spectrometry data at controlled false discovery rate; publicly shared results reached over 3200 datasets and 6 million metabolite images by January 2019.

The paper develops machine and deep learning methods to identify off-sample ions in mass spectrometry images, achieving high F1 scores.