DesignerCells · Building a causal regulatory network of mesenchymal cells using targeted programming experiments
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2021-09-01 → 2023-08-31
- EU contribution
- €203,149
- Participants
- 1
- Scheme
- MSCA-IF
Lines connect the coordinator with its partners.
Results in brief
Building a causal regulatory network of mesenchymal cells using targeted programming experiments
We addressed the problem of how transcription factors, the central proteins involved in gene regulation, shape cell identity when they are activated in a stem cell. To do this, we overexpressed the transcription factors and analyzed single-cell gene expression data along with the dosage that each cell received of the transcription factor. This is important for society because transcription factor overexpression is a main way in which cells can be manipulated to develop new cell therapies. Furthermore, most diseases involve some dysregulation in transcription factors, and as such understanding this is important to understand the mode-of-action of existing and future drugs. The overall objective is to assess how various cellular processes, such as differentiation and cell proliferation, interact with the dose of transcription factors, and how cellular heterogeneity can be formed. Overall, we identified extensive interactions between various transcription factors and these cellular processes. For example, we found that nearly every TF interacts strongly with the cell cycle. Extensive dosage-dependencies also exist for many transcription factors.
Data: CORDIS, © European Union
Project objective
Gene regulation is one of the main drivers of a cell's fate and function, but current methods that infer genome-wide regulatory networks have problems with inferring causal and/or combinatorial interactions. Pooled single-cell screening technologies, such as those developed in the host lab, now provide a powerful alternative to regulatory network inference. However, as this technology matures, data analysis is now more and more becoming the rate-limiting step in inferring causal and combinatorial regulatory networks.In this project, I will develop a combined computational and experimental workflow to infer regulatory networks. At the computational side, I will develop an algorithm that models how combinations of transcription factors affect a cell's state. The model will go beyond a ball-stick model, and investigate combinatorial logic, dosage effects, cell proliferation and cellular heterogeneity. The algorithm will also propose new combinations that may move a cell towards a fate of interest. Through multiple rounds of modelling and experimentation, I will iteratively improve our understanding of the causality and combinatorics of a regulatory network.I will validate the workflow on mesenchymal stem cells (MSCs), a cell type for which some (combinations of) transcription factors that drive its differentiation are already known, but for which a combinatorial view across lineages is still missing. I will first start with easier well-studied systems, such as adipocyte differentiation, and later in the project move on to less-studied systems such as adipocyte progenitors.By integrating experimental and computational techniques, the proposed project will provide a causal regulatory network across MSC lineages, and yield a framework to generate such a network in other systems. Moreover, the project will allow me to expand my computational skill set with emerging experimental techniques and management skills which will be invaluable for advancing my scientific career.
Original text from CORDIS.
Participants
- ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE · LausanneCoordinatorSwitzerland
Links
Data: CORDIS, © European Union