Thomas Gade Koefoed
MSc in Bioinformatics
PhD @ University of Copenhagen
Resolving Insulin Resistance Heterogeneity in Human Skeletal Muscle Using Multimodal Single-nucleus Sequencing
Insulin resistance (IR) is a key characteristic of type 2 diabetes (T2D) – a common and severe condition characterized by dysregulated blood glucose levels. Despite considerable efforts to map the complex characteristics of IR and T2D, detailed characterizations of IR in some important metabolic tissues, such as skeletal muscle, are still lacking.
In this project, we propose to use a high-throughput, state-of-the-art single-nucleus sequencing assay to gain cutting-edge biological insight into the transcriptomic, epigenetic, and cellular characteristics of IR in skeletal muscle. Furthermore, we will use the generated data to investigate the pivotal role of this tissue in the development of IR and T2D. Specifically, we will determine which muscle cell types mediate the most heritable risk of IR and T2D, potentially elucidating novel targets for treatment. Finally, we will investigate whether cell-type-specific polygenic risk scores can enable better prediction of a patient’s disease comorbidities and drug responses when compared to the use of traditional, non-cell-type-specific polygenic risk scores. No such analysis has yet been performed for human skeletal muscle, and the resulting stratification of heterogenous IR and T2D patient groups would constitute an important advancement in precision medicine.
The single-nucleus assay will be performed by the Hansen group for Genomic Physiology and Translation in collaboration with the Single-Cell Omics Platform at the Novo Nordisk Foundation Center for Basic Metabolic Research. The full dataset will be generated before the start of the project in Q3 2023, at which point the PhD-candidate will start computationally analyzing the data, drawing upon state-of-the-art bioinformatic tools and machine learning models. Importantly, the proposal is based on proof-of-concept data from one skeletal muscle sample, which is included in the project description. Additionally, the project is based on multiple national and international interdisciplinary collaborations, including supervisors from both clinical and technical backgrounds and a six-month research stay at the Broad Institute of Harvard and MIT, Boston, USA.
Finally, it should be noted that the bioinformatic analyses in this project can be generalized to any heritable disease and tissue. We, therefore, believe that the knowledge and methodological advancements gained from the project will have a wider clinical impact beyond skeletal muscle and metabolic diseases.