SingleCellAI · Deep-learning models of CRISPR-engineered cells define a rulebook of cellular transdifferentiation
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2019-07-01 → 2021-08-31
- EU contribution
- €186,167
- Participants
- 1
- Scheme
- MSCA-IF-EF-ST
Lines connect the coordinator with its partners.
Results in brief
Deep-learning models of CRISPR-engineered cells define a rulebook of cellular transdifferentiation
Over-expression of just a few transcription factors (TFs) can transform a fully differentiated cell into a state of induced pluripotency (iPS). Even more astoundingly, a similar strategy can perform cellular transdifferentiation, i.e. direct conversion of a cell from one type to another. However, the selection of TFs to overexpress or repress to induce transdifferentiation is typically found unsystematically through a combination of biological intuition and extensive trial-and-error required for each combination of source and target cell types. This approach does not scale to transdifferentiation of any cell type into any other cell type, which would be desirable not only for regenerative medicine but also to answer fundamental questions about the biology of cellular identity. A premise for an unbiased, quantitative approach to designing TF transdifferentiation cocktails is the knowledge of the impact of each of the TFs on gene expression with a single-cell resolution. Generating such a resource is now possible thanks to the advances in single-cell sequencing and pooled CRISPR gene editing, which enable coupling gene editing with transcriptome readout. At the same time, the analysis and data-driven prediction for single-cell transcriptomic datasets is currently being revolutionized by deep learning. This is due to the ability to scale the learning process to very large datasets and to extract informative data representations directly from the data. Considering the above, we formulated the following objectives: Objective 1: Development of deep-learning models for CRISPR-engineered cells; Objective 2: Computational modeling of transdifferentiation using the deep-learning models; Objective 3: Experimental validation of fibroblast transdifferentiation into hematopoietic cells.
Data: CORDIS, © European Union
Project objective
Cellular identity is controlled by cell type specific expression of transcription factors (TFs), and it is reflected in the cell’s epigenetic landscape maintained by epigenetic regulator proteins (ERs). Functional dissection of cellular identity has focused mainly on a small number of lineage-defining master regulators, yet there is increasing evidence that multiple TFs and ERs work together to establish and retain the vast number of different cell types and cell states in the human body. For a more quantitative understanding of cellular identity, and of the complexities of its regulation, I propose to develop a machine-learning approach for in silico prediction of TF/ER cocktails that can transdifferentiate any human cell type into any other cell type, thus defining an operational rulebook of cellular transdifferentiation. To this end, I will train a machine-learning model called generative adversarial networks (GANs) on large-scale CRISPR single-cell sequencing (CROP-seq) datasets generated in the host lab. Exploiting unique features of the deep-learning generative approach, the resulting model will be able to generalize the learned genetic perturbations across cell types in silico. I will experimentally validate several of these predicted TF/ER transdifferentiation cocktails in the context of the human hematopoietic system. Importantly, the proposed approach is hypothesis-free and data-driven, exploiting recent advances in machine learning to infer fundamental aspects of the regulation of cellular identity from high-throughput functional CRISPR single-cell sequencing data.
Original text from CORDIS.
Participants
- CEMM - FORSCHUNGSZENTRUM FUER MOLEKULARE MEDIZIN GMBH · WienCoordinatorAustria
Links
Data: CORDIS, © European Union