New transcriptomic methods to investigate cardiovascular, metabolic and autoimmune disease

NAISS 2023/6-68


NAISS Medium Storage

Principal Investigator:

Joan Camunas Soler


Göteborgs universitet

Start Date:


End Date:


Primary Classification:

10203: Bioinformatics (Computational Biology) (applications to be 10610)



My lab works at the intersection of genomics, biophysics, and precision medicine. To do so, we develop new single-cell technologies and non-invasive molecular diagnostics methods. Our methods are then applied to a wide range of biological questions, but we currently have a focus in using these tools to predict cellular dysfunction associated with cardiovascular, metabolic, and autoimmune disorders. Project Overview: Our lab has developed a ground-breaking method that combines single-cell RNA sequencing and patch-clamp electrophysiology in pancreatic islet cells. This innovative technique allows us to uncover functional distinctions among key cell types, providing insights into diabetes-associated genes and predicting electrophysiological properties based on gene expression profiles. We are now extending this methodology to combine this technology with spatial transcriptomics. Additionally, we are also exploring its use to investigate cell function in other tissues such as the heart. In this way, we want to understand the effect of cell-to-cell interactions and electrical coupling in multicellular structures. Finally, in addition to our work in single-cell genomics, the lab focuses on non-invasive diagnostics using circulating nucleic acid such as cfDNA and cfRNA. In this direction, we are currently collecting large sequencing datasets of transcriptomic data from liquid biopsy assays of autoimmune disorders. Oveeview of Resource Requirements: Our lab is highly focused on bioinformatics and data analysis. Hence, as of right now, there are five members in the group working with computer heavy tasks. Each of these group members analyse their own samples, from raw sequences to more complex data analysis. Given the data-intensive nature of our work and that only in a few months already approximately 10000 GiB of data already utilized, we propose an allocation of 70 000 GiB of storage space to ensure the success of our ongoing and future projects.