Project description
Studying top-down interactions at different biological scales
At different biological scales, new properties in form and function often have an impact on proteins, DNA and RNA, the lower-scale components of the central dogma of biology. It is important to quantify these top-down effects to predict and understand the complexity of cell fate determination. To address this, the EU-funded CROSSINGSCALES project will apply quantitative imaging and genome-wide transcriptomics on stem cell cultures and early zebrafish embryos. Results will create a holistic view of nuclear and chromatin states, gene expression, subcellular organisation and tissue-scale organisation across millions of individual cells. The project will contribute towards the understanding of the dynamic, spatiotemporally controlled interaction of gene expression and cellular signalling that drive life as we know it.
Objective
The central dogma in biology often invokes a bottom-up picture of life. However, at different biological scales, new properties in form and function arise that have a superseding causal impact on the behaviour of the lower-scale components from which these new properties emerge. These top-down or reverse scale-crossing effects must be taken into account in order to make predictions about spatiotemporally controlled single-cell fates, activities, levels of gene expression, or the functional outcome of cellular signalling. They can stem from the multicellular, the cellular, and the intracellular scale, and can be quantified using multiscale and multiplexed RNA and protein state imaging in combination with computer vision and data-driven modelling. The ability to comprehensively map these reverse causal effects across multiple scales has the potential to revolutionize most, if not all domains of biology and medicine. In this project, we will establish the importance of reverse causal effects in human induced pluripotent stem cells and early D. rerio embryos. To achieve this, we will develop a quantitative imaging method beyond the diffraction limit of light without compromising scalability in temporal and spatial dimensions. We will also develop a method that achieves scalable, transcriptome-wide image-based multiplexing of mRNA transcripts, and we will extend our computer vision approaches to higher resolution and to three spatial dimensions. These methods will be systematically applied to stem cell collectives grown in 2D and 3D, as well as to early embryos, achieving comprehensive quantification of nuclear and chromatin states, gene expression, subcellular organization, cellular states, and tissue-scale organization across millions of individual cells within the same dataset. These datasets will be used to quantify how, at different scales, new properties in form and function arise that have a superseding causal impact on the behaviour of the lower-scale components
Fields of science
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteins
- natural sciencescomputer and information sciencesartificial intelligencecomputer vision
- medical and health sciencesmedical biotechnologycells technologiesstem cells
- natural sciencesbiological sciencesgeneticsRNA
- medical and health sciencesclinical medicineembryology
Programme(s)
Topic(s)
Funding Scheme
ERC-ADG - Advanced GrantHost institution
8006 Zurich
Switzerland