Michael Skinnider Position Assistant Professor, Lewis-Sigler Institute and Ludwig Princeton Branch Title Lewis-Sigler Institute for Integrative Genomics, Department Ludwig Princeton Branch Email [email protected] Office 148 Carl Icahn Laboratory Bio/Description The Skinnider lab develops machine-learning approaches to identify known and unknown small molecules that are relevant to human health and disease, with mass spectrometry-based metabolomics being the primary analytical technique.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.We are 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, we design and apply cutting-edge AI technologies to translate mass spectrometric information into chemical structures. Although the core focus of the lab is on developing these metabolic technologies themselves, an ancillary focus is on linking the identified molecules to human disease. The lab has a particular focus on the role of unknown metabolites in cancer, via connections with germline risk factors and the human microbiome. A second application entails working with forensic laboratories to identify new synthetic drugs of abuse with mass spectrometry. Because many of these objectives share the common technical challenge of learning complex models from small datasets, the lab is also interested in techniques for low-data learning in the setting of chemistry and biology more generally. Selected Publications Kathe, Claudia, Michael Skinnider, Thomas Hutson, Nicola Regazzi, Matthieu Gautier, Robin Demesmaeker, Salif Komi, et al. (2022) 2022. “The Neurons That Restore Walking After Paralysis.”. Nature 611 (7936): 540-47. doi:10.1038/s41586-022-05385-7. Skinnider, Michael, Nichollas Scott, Anna Prudova, Craig Kerr, Nikolay Stoynov, Greg Stacey, Queenie Chan, David Rattray, Jörg Gsponer, and Leonard Foster. (2021) 2021. “An Atlas of Protein-Protein Interactions across Mouse Tissues.”. Cell 184 (15): 4073-4089.e17. doi:10.1016/j.cell.2021.06.003. Squair, Jordan, Matthieu Gautier, Claudia Kathe, Mark Anderson, Nicholas James, Thomas Hutson, Rémi Hudelle, et al. (2021) 2021. “Confronting False Discoveries in Single-Cell Differential Expression.”. Nature Communications 12 (1): 5692. doi:10.1038/s41467-021-25960-2. Skinnider, Michael, and Leonard Foster. (2021) 2021. “Meta-Analysis Defines Principles for the Design and Analysis of Co-Fractionation Mass Spectrometry Experiments.”. Nature Methods 18 (7): 806-15. doi:10.1038/s41592-021-01194-4. Skinnider, Michael, Jordan Squair, Claudia Kathe, Mark Anderson, Matthieu Gautier, Kaya Matson, Marco Milano, et al. (2021) 2021. “Cell Type Prioritization in Single-Cell Data.”. Nature Biotechnology 39 (1): 30-34. doi:10.1038/s41587-020-0605-1. Skinnider, Michael, Chad Johnston, Mathusan Gunabalasingam, Nishanth Merwin, Agata Kieliszek, Robyn MacLellan, Haoxin Li, et al. (2020) 2020. “Comprehensive Prediction of Secondary Metabolite Structure and Biological Activity from Microbial Genome Sequences.”. Nature Communications 11 (1): 6058. doi:10.1038/s41467-020-19986-1. Skinnider, Michael, Chris Dejong, Brian Franczak, Paul McNicholas, and Nathan Magarvey. (2017) 2017. “Comparative Analysis of Chemical Similarity Methods for Modular Natural Products With a Hypothetical Structure Enumeration Algorithm.”. Journal of Cheminformatics 9 (1): 46. doi:10.1186/s13321-017-0234-y. Skinnider, Michael, Chris Dejong, Philip Rees, Chad Johnston, Haoxin Li, Andrew Webster, Morgan Wyatt, and Nathan Magarvey. (2015) 2015. “Genomes to Natural Products PRediction Informatics for Secondary Metabolomes (PRISM).”. Nucleic Acids Research 43 (20): 9645-62. doi:10.1093/nar/gkv1012. Related News Ludwig Princeton Branch and the Lewis-Sigler Institute Welcome Skinnider as Assistant Professor Program(s) QCB Graduate Program Research Area Computational Genomics Systems Biology: Metabolomics/Proteomics LSI Research Lab Skinnider Research Lab