Assistant Member, Ludwig Princeton Branch
Assistant Professor, Lewis-Sigler Institute for Integrative Genomics, Princeton University
Carl Icahn Laboratory, 148

Michael Skinnider, member of the Ludwig Institute for Cancer Research Princeton Branch, is an assistant professor in the Ludwig Princeton Branch and the Lewis-Sigler Institute for Integrative Genomics at Princeton University.  He earned his MD and PhD from the University of British Columbia, where his doctoral work focused on developing machine-learning tools for mass spectrometry-based proteomics and metabolomics. Previously, he completed his undergraduate studies at McMaster University, where his research with Nathan Magarvey leveraged bacterial genomes and metabolomes to discover new bioactive small molecules.  His research interests are broadly at the intersection of applied machine learning for problems in biology, chemistry, and medicine, with a particular interest in the discovery of novel small molecules.  His work has been recognized by a number of awards including the Forbes 30 Under 30 list, the International Birnstiel Award, the Dan David Scholarship, and the Borealis AI Fellowship. 

The human body contains thousands of small molecules, and is exposed to thousands more during daily life. This complex chemical ecosystem reflects both the endogenous metabolism of human cells, as well as xenobiotic exposures from our diets, our gut flora, and our natural and built environments. Collectively, these small molecules influence our risk of developing disease, determine how we respond to prescription drugs, and provide molecular biomarkers that are used in the clinic to make diagnoses and select treatments.

At present, however, the vast majority of these small molecules remain unknown. Whereas high-throughput techniques can now reliably measure the DNA, RNA, and protein content of any given biospecimen, enumerating the complete complement of small molecules—the metabolome—has proven much more challenging. Mass spectrometry (MS), the workhorse of metabolomics, is capable of detecting thousands of molecules in routine experiments, but the vast majority of these cannot be definitively identified. This profusion of unidentified chemical entities has been dubbed the “dark matter” of the metabolome.

Skinnider is interested in illuminating this metabolic dark matter by developing new computational approaches to identify both known and unknown small molecules using mass spectrometry.  To achieve this aim, Skinnider designs and applies cutting-edge AI technologies to translate mass spectrometric information into chemical structures.  Although Skinnider’s core focus is on developing these metabolic technologies themselves, an ancillary focus is on linking the identified molecules to human disease. Skinnider has a particular focus on the role of unknown metabolites in cancer, via connections with germline risk factors and the human microbiome.

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