Project No. 2414
PRIORITY STANDARD PROJECT
Dr Susanne Dietrich – University of Portsmouth
Dr Roja Hadianamrei- University of Portsmouth
Dr Matthew Rose-Zerilli- University of Southampton
This basic sciences project will develop novel biomaterials/ formulations to introduce molecular constructs into hard-to-target heart progenitors.
The project is aligned with the BBSRC-theme Transformative Technologies Projects.
In detail: Strategies to repair an infarcted heart with replacement cardiomyocytes have so far been unsuccessful. However, the recruitment of new cardiomyocytes into the beating heart is an integral part of normal heart development. Yet how we might be able to ‘copy’ the embryo is unclear. A key obstacle in cardiac research is that in vivo, heart progenitor cells are organised as a mesenchyme which is difficult to target.
We will design GFP-expressing and experimental mRNA constructs to be formulated in lipidoid nanoparticles (with and without cell-type-specific docking molecules) and micro-inject them into the mesenchyme holding the cardiac precursors. We then will measure specificity and efficacy of construct uptake (fluorescence and confocal microscopy), responses of cells at singe-cell-level (scRNAseq), and any changes to heart development (changes in marker gene expression and anatomy). We will use the chicken embryo, an established model for human heart development and disease, large and easily accessible in the egg, and at the early stages required here, not protected by ASPA.
The principles of nanoparticle-based mRNA delivery strategies to mesenchymal heart precursors will be applicable to any mesenchyme, both in vivo or in organoid culture, thus overcoming a significant technical impasse. Since we are using the heart as model, the research will also pave the way to develop/ improve therapeutic cells for human heart disease.
The candidate needs a background in biochemistry and in cell and developmental biology, an ability to handle small embryo and tissue samples as well as delicate devices to prepare cells for scRNAseq. The candidate needs to be apt or willing to learn R-programming and the machine-learning strategies for the interpretation of scRNAseq data.