DNA Nanostructures as Probes for Multi-omic Analyses
Lead Research Organisation:
University of Edinburgh
Department Name: Sch of Molecular. Genetics & Pop Health
Abstract
Multi-omic analyses are expected to revolutionize medicine by increasing our understanding of disease and enabling treatments to be personalized. In multi-omic studies, experimental data is obtained using various high-throughput methods that probe a wide range of biomarkers. However, in many cases, different techniques must be used for each type of marker to be monitored. The apparatus required is often expensive and the procedures can be complex. The goal of this project is to create a low-cost easy-to-use platform that will allow many different biomarkers to be investigated in parallel in a single measurement.
In this system, DNA nanostructures will be used as customized molecular barcodes. Synthetic DNA molecules will be designed to self-assemble into nanoscale objects, and a set of such structures will be formed. Through chemical or biological modification of specific DNA molecules, each species of nanostructure will be designed to interact with one particular target. An assay will be established for the study of complex samples and streamlined quantitative methods will be developed for processing the large datasets that will be generated.
The technology resulting from this project has potential applications in various areas of medicine. It could be used in a clinical setting as a diagnostic tool or in a laboratory environment to enhance our understanding of the causes and mechanisms of disease.
In this system, DNA nanostructures will be used as customized molecular barcodes. Synthetic DNA molecules will be designed to self-assemble into nanoscale objects, and a set of such structures will be formed. Through chemical or biological modification of specific DNA molecules, each species of nanostructure will be designed to interact with one particular target. An assay will be established for the study of complex samples and streamlined quantitative methods will be developed for processing the large datasets that will be generated.
The technology resulting from this project has potential applications in various areas of medicine. It could be used in a clinical setting as a diagnostic tool or in a laboratory environment to enhance our understanding of the causes and mechanisms of disease.
Organisations
People |
ORCID iD |
Katherine Dunn (Primary Supervisor) | |
Matthew Aquilina (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
MR/N013166/1 | 01/10/2016 | 30/09/2025 | |||
2259148 | Studentship | MR/N013166/1 | 01/09/2019 | 31/05/2023 | Matthew Aquilina |