Bioinformatician – Single-Cell Omics in Vascular & Skeletal Muscle Biology

Bioinformatician – Single-Cell Omics in Vascular & Skeletal Muscle Biology

ETH Zürich is well known for its excellent education, ground-breaking fundamental research and for implementing its results directly into practice.

The Laboratory of Exercise and Health, headed by Prof. Dr. Katrien De Bock at the Department of Health Sciences and Technology (D-HEST), ETH Zurich, is offering a fixed-term position for a bioinformatician (postdoctoral level) to lead the single-cell and spatial transcriptomics analyses within the laboratory of Exercise and Health.

The Laboratory of Exercise and Health investigates how the muscle microvascular niche – and in particular heterogeneous and specialized endothelial cell (EC) subpopulations – interacts with other niche cells (e.g. macrophages, pericytes, fibro-adipogenic progenitors) to maintain skeletal muscle homeostasis, and how disruption of this crosstalk contributes to muscle dysfunction. The research of the Laboratory of Exercise and Health combines human patient samples, mouse genetic models, single-cell and spatial transcriptomics, and in vivo cell-cell interaction tracing with CRISPR perturbation to mechanistically dissect EC-niche communication.

The lab offers a highly collaborative, international research environment at the interface of vascular biology, muscle physiology, and metabolism. For more information on the lab, visit (Use the "Apply for this Job" box below)..ch

You will lead all single-cell and spatial transcriptomics work, working closely with an international team of PhD students and postdocs and in direct collaboration with Prof. De Bock. Specific responsibilities include:

• Design, execute, and interpret scRNAseq analyses of human (patient-derived) and mouse skeletal muscle samples, identifying and annotating cell (sub) populations. Discuss and design follow-up wet lab experiments with colleagues.
• Integrate scRNAseq datasets with other omics information, including spatial transcriptomics, ATAC sequencing, metabolomics and/or multiplex imaging data.
• Perform cross-species (human–mouse) data integration to identify conserved and divergent cellular and molecular signatures of PAD.
• Apply and further develop computational tools for cell-cell communication analysis (ligand-receptor interactions and signaling pathways) and constraint based metabolic modeling tools (e.g. COMPASS, scFEA) to link transcriptional states to cell metabolism.
• Analyze single-cell datasets generated from in vivo cell-cell interaction tracing models.
• Perform pseudo bulk and differential expression analyses (e.g. edgeR) of scRNAseq/5’ECCITE-seq data from CRISPR perturbation screens.
• Apply in silico perturbation approaches to nominate candidate regulators of cellular crosstalk for downstream functional validation.
• Build, document, and maintain reproducible bioinformatics pipelines, and support other lab members with transcriptomics and genomics data.
• Designing Shiny apps for data visualization for internal and possibly external use in publications.
• Contribute to manuscript preparation, data visualization, and project reporting, and stay current with emerging single-cell and spatial genomics methods.

• PhD degree in bioinformatics, computational biology, computational genomics, or a related quantitative field.
• Demonstrated hands-on experience analyzing single-cell RNA sequencing data (e.g. Seurat, Scanpy) from raw data processing through downstream analysis; experience with spatial transcriptomics is a strong plus.
• Excellent scripting and data analysis skills in R and/or Python, with in-depth knowledge of relevant Bioconductor/scverse packages.
• Strong experience with collaborative development environments (e.g. Git…