A multi-omics-AI/ML approach to understand the phenotypic diversity in ciliopathies

Dysfunction of cilia causes a broad spectrum of hereditary diseases, but the molecular basis of the phenotypic diversity remains largely unknown. DC1 will address this by using a state-of-the-art molecular toolbox to determine the key similarities and differences of cilium composition during early development of different tissues in health and disease. We will focus on Senior-Løken syndrome, a retinal-renal ciliopathy, and deploy a multi-omics approach to assess and compare ciliary processes during renal and retinal hiPSC-derived organoid development. This approach includes CRISPR/Cas9-based genome editing to generate isogenic cell lines and in-loci tagged target genes, spatial optoproteomics, BioID-based live cell proteomics, and single nucleus RNA-seq. Machine learning-based data integration algorithms will be implemented for in-depth integration of data from with publicly available multi-omics datasets and other Cilia-AI-generated data to pinpoint regulatory pathways and therapeutic ciliopathy targets.

Fellow profile: Master degree in Cell Biology, Molecular Biology, Developmental Biology, Biochemistry or related field. This project is very suitable for students with a keen interest in developmental cell biology who are motivated to develop AI/ML-based data integration and molecular modelling skills.