A Periodic Table for Cellular Metabolism: From Single-Cell Technologies to Systematic Discovery of Metabolic States
Event details
| Date | 16.12.2025 |
| Hour | 11:00 › 12:00 |
| Speaker | Theodore Alexandrov, Ph.D., UC San Diego, CA (USA) |
| Location | Online |
| Category | Conferences - Seminars |
| Event Language | English |
3-DAY BIOE MINI-SYMPOSIUM on Life Science Engineering
(DAY THREE: talk seven / previous talk)
Abstract:
Metabolism has emerged as a central regulatory hub in biology: metabolites function as signaling molecules controlling cell fate, immune responses, and disease progression. Metabolic profiles can outperform genomic and transcriptomic data in predicting clinical outcomes in some of the largest cohort studies ever performed. Yet understanding of metabolism is hampered by challenges of data interpretation and our limited knowledge about metabolic states beyond the classical dichotomy of glycolysis vs respiration. Can we systematically discover the full repertoire of metabolic states of human cells—a "periodic table" for cellular metabolism?
I will present integrated technologies and computational approaches addressing this challenge. The foundation is MALDI imaging mass spectrometry, enabling spatial and single-cell metabolomics by detecting hundreds of metabolites and lipids at cellular resolution. We addressed a critical bottleneck—metabolite identification—by developing METASPACE, a cloud platform that translates mass spectra into molecular identities with statistical confidence. METASPACE now serves over 5,000 scientists globally and has enabled hundreds of publications. By motivating public data sharing, METASPACE created a foundational resource of tens of thousands of datasets for training AI and machine learning models, exemplified by METASPACE-ML.
Building on this foundation, we developed the SpaceM family of single-cell metabolomics methods integrating MALDI imaging mass spectrometry with microscopy. HT SpaceM detects 100+ metabolites and 500+ lipids per cell at ten times faster and one hundred times cheaper than scRNA-seq. 13C-SpaceM enables spatial isotope tracing at single-cell resolution, revealing flux heterogeneity invisible to bulk methods.
These technologies advanced our knowledge about human metabolism and enabled systematic discoveries across biological contexts. In cancer, single-cell lipidomics distinguished epithelial versus mesenchymal phenotypes with signatures preserved across patient-derived models. In immune cells, profiling hundreds of thousands of T cells across systematic perturbations revealed mechanistically explainable metabolic rewiring. Deep generative models trained on this atlas predicted functional phenotypes, discovering drugs with convergent metabolic effects.
These and other discoveries establish metabolic states as an organizing principle—a potential periodic table that can transform the complexity of metabolism into predictive understanding.
Bio:
Theodore Alexandrov is an Assistant Professor in the Departments of Pharmacology and Bioengineering at UC San Diego School of Medicine (https://ateam.ucsd.edu). His research spans mathematics, omics, computational biology, and pharmacology. He obtained his PhD in Mathematics and transitioned through mass spectrometry imaging and spatial metabolomics to therapeutic applications.
At EMBL, where he was a research team leader and head of the Metabolomics Core Facility, he led the development of diverse technologies including METASPACE—a cloud platform for spatial metabolomics serving over 5,000 scientists—and SpaceM, a family of methods for single-cell profiling of metabolism. His team combines biologists, experimental scientists, data scientists, and software engineers. He has co-founded three companies which develop software products and platforms in spatial and single-cell metabolomics.
His current research program focuses on discovering metabolic states across biological contexts, developing technologies and AI models to predict cellular responses to perturbations. This work integrates systems biology, machine learning, and bioengineering approaches to establish metabolism as a predictable and controllable cellular property.
Zoom link for attending remotely, if needed: https://epfl.zoom.us/j/69216732793
(DAY THREE: talk seven / previous talk)
Abstract:
Metabolism has emerged as a central regulatory hub in biology: metabolites function as signaling molecules controlling cell fate, immune responses, and disease progression. Metabolic profiles can outperform genomic and transcriptomic data in predicting clinical outcomes in some of the largest cohort studies ever performed. Yet understanding of metabolism is hampered by challenges of data interpretation and our limited knowledge about metabolic states beyond the classical dichotomy of glycolysis vs respiration. Can we systematically discover the full repertoire of metabolic states of human cells—a "periodic table" for cellular metabolism?
I will present integrated technologies and computational approaches addressing this challenge. The foundation is MALDI imaging mass spectrometry, enabling spatial and single-cell metabolomics by detecting hundreds of metabolites and lipids at cellular resolution. We addressed a critical bottleneck—metabolite identification—by developing METASPACE, a cloud platform that translates mass spectra into molecular identities with statistical confidence. METASPACE now serves over 5,000 scientists globally and has enabled hundreds of publications. By motivating public data sharing, METASPACE created a foundational resource of tens of thousands of datasets for training AI and machine learning models, exemplified by METASPACE-ML.
Building on this foundation, we developed the SpaceM family of single-cell metabolomics methods integrating MALDI imaging mass spectrometry with microscopy. HT SpaceM detects 100+ metabolites and 500+ lipids per cell at ten times faster and one hundred times cheaper than scRNA-seq. 13C-SpaceM enables spatial isotope tracing at single-cell resolution, revealing flux heterogeneity invisible to bulk methods.
These technologies advanced our knowledge about human metabolism and enabled systematic discoveries across biological contexts. In cancer, single-cell lipidomics distinguished epithelial versus mesenchymal phenotypes with signatures preserved across patient-derived models. In immune cells, profiling hundreds of thousands of T cells across systematic perturbations revealed mechanistically explainable metabolic rewiring. Deep generative models trained on this atlas predicted functional phenotypes, discovering drugs with convergent metabolic effects.
These and other discoveries establish metabolic states as an organizing principle—a potential periodic table that can transform the complexity of metabolism into predictive understanding.
Bio:
Theodore Alexandrov is an Assistant Professor in the Departments of Pharmacology and Bioengineering at UC San Diego School of Medicine (https://ateam.ucsd.edu). His research spans mathematics, omics, computational biology, and pharmacology. He obtained his PhD in Mathematics and transitioned through mass spectrometry imaging and spatial metabolomics to therapeutic applications.
At EMBL, where he was a research team leader and head of the Metabolomics Core Facility, he led the development of diverse technologies including METASPACE—a cloud platform for spatial metabolomics serving over 5,000 scientists—and SpaceM, a family of methods for single-cell profiling of metabolism. His team combines biologists, experimental scientists, data scientists, and software engineers. He has co-founded three companies which develop software products and platforms in spatial and single-cell metabolomics.
His current research program focuses on discovering metabolic states across biological contexts, developing technologies and AI models to predict cellular responses to perturbations. This work integrates systems biology, machine learning, and bioengineering approaches to establish metabolism as a predictable and controllable cellular property.
Zoom link for attending remotely, if needed: https://epfl.zoom.us/j/69216732793
Practical information
- Informed public
- Free
Organizer
- Prof. Matteo Dal Peraro, Institute of Bioengineering, School of Engineering, EPFL
Contact
- Institute of Bioengineering (IBI), Dietrich REINHARD