- Development of bioinformatics software, tools and databases
- Single-cell data analysis
Agricultural and food sciences
- Agricultural plant breeding and biotechnology
The rapid adoption of single-cell profiling methods, such as single-cell RNA sequencing (scRNA-seq), in life sciences confirms it is revolutionizing the details of gene expression profiling from a tissue to a cell. While singe-cell profiling is booming in the medical field, technology optimization focusing on plant cells is ongoing. In the UGent/VIB Department of Plant Biotechnology and Bioinformatics, a single-cell platform was established for academic and industrial collaborations, focusing on single-cell technology implementation/benchmarking and end-to-end services. Additionally, the AgBio Accelerator was created to allow interested industrial partners to join forces with the department, unite expertise, and evaluate and de-risk novel technologies for plant breeding companies. The next challenge is gaining access to cost-effective and efficient methods to extract biological insights from these complex single-cell data sets. Given that gene discovery is an essential aspect of plant breeding programs in agricultural biotechnology (AgBio), there is urgent need for easy-to-use and efficient tools to combine single-cell RNA-Seq data and gene-trait information to identify new genes for crop improvement.
In this project we aim to develop a software tool (scTrait) to improve gene-trait discovery and prioritization starting from scRNA-seq data. First, we will collect, integrate, and curate gene-trait information from different sources. Next, we will apply network biology algorithms to leverage single-cell data, gene trait information and orthology data, with the final goal to improve and speed-up gene discovery and prioritization for specific plant traits. Apart from building a curated plant gene-trait database and developing the scTrait software tool (two main targets for valorization), the tool performance to identify and prioritize genes covering specific plant traits will be evaluated in a first proof-of-concept (PoC). In a second PoC, we will generate new scRNA-seq datasets for a selection of economically important plants to evaluate the power and applicability of our scTrait software in crops. Here, the goal is to demonstrate the potential of the software to attract and convince AgBio companies.
Taken together, the development of efficient tools for plant data analysis, embracing the power of cutting-edge new technologies like single-cell sequencing and semantic data integration, will improve and speed-up gene discovery and prioritization for plant breeding. Providing PoC for a variety of crops offers ample opportunities for valorization, either via licenses, research collaborations or spinning out of a company.
A graphical abstract was uploaded showing the relationships between the different WPs, Milestones and PoCs.