Scientists from the University of Toronto have developed a new technique for small molecule sequencing named, “smol-seq.” This platform uses DNA sequencing to detect and quantify metabolites including sugars, vitamins, and hormones.
Metabolite Analysis
A major challenge in metabolite analysis comes from the extensive structural diversity and chemical complexity of these molecules. But by using smol-seq, the researchers were able to offer a new alternative that increased the scale and speed of traditional approaches.
"We need to measure metabolites because of the role they play in our health, but it is very challenging to study this wide range of molecules," said June Tan, first author of the study and research associate at the Donnelly Centre for Cellular and Biomolecular Research. “Until now, mass spectrometry has been the gold standard for measuring metabolite levels, but it is not as accessible or as fast as methods that sequence DNA. We wanted to develop a method that detects metabolites using DNA sequencing to make use of that incredible sequencing power.”
How Smol-Seq Works
To identify various metabolites, smol-seq utilizes engineered short strands of DNA known as aptamers. These apatamers that have been designed to carry a unique DNA barcode, which is released when binding occurs. For instance, the aptamer recognizing glucose releases one type of barcode, while the aptamer for cortisol, a stress hormone, releases a different one. The research team can then sequence the released barcodes to determine the presence and concentration of metabolites in a sample.
"Scientists have previously used aptamers to measure metabolites, but mostly through methods that only allow you to measure a few metabolites at a time," Tan explained. "We realized that if we use DNA barcodes as tags for metabolites, we can measure hundreds or even thousands of metabolites simultaneously."
Expanding Platform Capabilities
Now with the core technology established, the next step is to expand the aptamer library to target a broader range of metabolites with biomedical significance. The research team also plans to refine aptamer specificity at the nucleic acid level, thereby improving binding precision as the platform scales. Machine learning is another addition that could further enhance aptamer design and allow the prediction of new sequences that bind to previously uncharacterized metabolites.
"DNA sequencing is millions of times faster than it was 20 years ago, and we wanted to harness that power for metabolite detection," stated Andrew Fraser, principal investigator on the study and professor of molecular genetics at the University of Toronto’s Temerty Faculty of Medicine. "Smol-seq could make metabolite detection as easy and rapid as DNA sequencing."
Publication Details
Tan, J.H., Fraser, A.G. Quantifying metabolites using structure-switching aptamers coupled to DNA sequencing. Nat Biotechnol (2025). https://doi.org/10.1038/s41587-025-02554-7
Metabolite Analysis
A major challenge in metabolite analysis comes from the extensive structural diversity and chemical complexity of these molecules. But by using smol-seq, the researchers were able to offer a new alternative that increased the scale and speed of traditional approaches.
"We need to measure metabolites because of the role they play in our health, but it is very challenging to study this wide range of molecules," said June Tan, first author of the study and research associate at the Donnelly Centre for Cellular and Biomolecular Research. “Until now, mass spectrometry has been the gold standard for measuring metabolite levels, but it is not as accessible or as fast as methods that sequence DNA. We wanted to develop a method that detects metabolites using DNA sequencing to make use of that incredible sequencing power.”
How Smol-Seq Works
To identify various metabolites, smol-seq utilizes engineered short strands of DNA known as aptamers. These apatamers that have been designed to carry a unique DNA barcode, which is released when binding occurs. For instance, the aptamer recognizing glucose releases one type of barcode, while the aptamer for cortisol, a stress hormone, releases a different one. The research team can then sequence the released barcodes to determine the presence and concentration of metabolites in a sample.
"Scientists have previously used aptamers to measure metabolites, but mostly through methods that only allow you to measure a few metabolites at a time," Tan explained. "We realized that if we use DNA barcodes as tags for metabolites, we can measure hundreds or even thousands of metabolites simultaneously."
Expanding Platform Capabilities
Now with the core technology established, the next step is to expand the aptamer library to target a broader range of metabolites with biomedical significance. The research team also plans to refine aptamer specificity at the nucleic acid level, thereby improving binding precision as the platform scales. Machine learning is another addition that could further enhance aptamer design and allow the prediction of new sequences that bind to previously uncharacterized metabolites.
"DNA sequencing is millions of times faster than it was 20 years ago, and we wanted to harness that power for metabolite detection," stated Andrew Fraser, principal investigator on the study and professor of molecular genetics at the University of Toronto’s Temerty Faculty of Medicine. "Smol-seq could make metabolite detection as easy and rapid as DNA sequencing."
Publication Details
Tan, J.H., Fraser, A.G. Quantifying metabolites using structure-switching aptamers coupled to DNA sequencing. Nat Biotechnol (2025). https://doi.org/10.1038/s41587-025-02554-7