Reading metabolites by sequencing - towards a new spatial metabolomics platform
Event details
| Date | 07.10.2025 |
| Hour | 11:00 › 12:00 |
| Speaker | Prof. Andrew G. Fraser, The Donnelly Centre, University of Toronto, Toronto M5S2N1, Canada |
| Location | |
| Category | Conferences - Seminars |
| Event Language | English |
Abstract:
Biography:
Andrew G. (Andy) Fraser is a professor in the Department of Molecular Genetics at the University of Toronto and a member of the Donnelly Centre for Cellular and Biomolecular Research. He studied biochemistry and molecular biology as an undergraduate, completed his PhD in cancer biology, and went on to hold postdoctoral and fellowship positions at Cold Spring Harbor Laboratory and the Gurdon Institute at the University of Cambridge. His research uses the nematode worm Caenorhabditis elegans to understand how genetic variation shapes traits such as metabolism, nervous system function, and disease severity. A central theme of his work is investigating why individuals with the same disease-causing mutation can show very different symptoms, focusing on the role of genetic background in modulating disease. Professor Fraser is a Senior Fellow of the Canadian Institute for Advanced Research and has been supported by multiple grants from the Canadian Institutes of Health Research. His work is widely recognized in the fields of genomics, RNA interference, and quantitative genetics, and his laboratory continues to make important contributions to understanding the interplay between genes, variation, and health.
Metabolites and drugs are at the heart of biology. The levels of sugars, amino acids, hormones and vitamins, give a clear snapshot of our health, and drugs and natural products are critical for treating diseases. However, these molecules are challenging: they are biochemically incredibly diverse and they cannot be amplified. How can we detect them as easily as measuring RNA level? We recently developed a method that uses DNA sequencing as a readout for metabolite and drug concentrations in complex biological settings like cytoplasm. This method, that we call smol-seq (for Small MOLecule sequencing) uses structure-switching aptamers (SSAs) to detect individual targets and to convert target-binding to barcode release. Each SSA detects a single target and is coupled to a unique DNA barcode — when the target binds, this causes the release of the barcode e.g. a glucose SSA ‘sees’ glucose and releases a ‘glucose’ barcode and an SSA that ‘sees’ phenylalanine releases a different ‘phenylalanine’ barcode. Simply by reading and quantifying the released barcoded SROs, it is thus possible to quantify the metabolites in a complex mixture. This harnesses the incredibly power of modern sequencing for metabolomics and opens the way to incorporating metabolomics into multiomics readouts.
We previously showed that these SSAs can be highly specific, distinguishing between closely related targets and even distinguishing between stereoisomers. We also showed that the level of barcode release gives a quantitative readout of target concentration, and that many of these SSAs can be read in parallel since each has a different barcode. This thus allows the multiplexed quantitative measurement of many metabolite or drug targets in parallel by DNA sequencing. I will present this platform along with recent progress we have made in understanding how the SSAs specifically detect their targets and how we aim to generate deep datasets to predict SSAs for a wide range of molecular targets.
We previously showed that these SSAs can be highly specific, distinguishing between closely related targets and even distinguishing between stereoisomers. We also showed that the level of barcode release gives a quantitative readout of target concentration, and that many of these SSAs can be read in parallel since each has a different barcode. This thus allows the multiplexed quantitative measurement of many metabolite or drug targets in parallel by DNA sequencing. I will present this platform along with recent progress we have made in understanding how the SSAs specifically detect their targets and how we aim to generate deep datasets to predict SSAs for a wide range of molecular targets.
Andrew G. (Andy) Fraser is a professor in the Department of Molecular Genetics at the University of Toronto and a member of the Donnelly Centre for Cellular and Biomolecular Research. He studied biochemistry and molecular biology as an undergraduate, completed his PhD in cancer biology, and went on to hold postdoctoral and fellowship positions at Cold Spring Harbor Laboratory and the Gurdon Institute at the University of Cambridge. His research uses the nematode worm Caenorhabditis elegans to understand how genetic variation shapes traits such as metabolism, nervous system function, and disease severity. A central theme of his work is investigating why individuals with the same disease-causing mutation can show very different symptoms, focusing on the role of genetic background in modulating disease. Professor Fraser is a Senior Fellow of the Canadian Institute for Advanced Research and has been supported by multiple grants from the Canadian Institutes of Health Research. His work is widely recognized in the fields of genomics, RNA interference, and quantitative genetics, and his laboratory continues to make important contributions to understanding the interplay between genes, variation, and health.
Practical information
- General public
- Free
Organizer
- Prof. Bart Deplancke