Detail

Keywords

Artificial Intelligence; Biomedical Research; Biomedical Technology; Computational Biology; Epigenomics; Hematology; High-Throughput Nucleotide Sequencing; Immunity; Oncology; Organoids; Rare Diseases; Single-Cell Analysis; Stem Cells

Research group(s)

• Medical Epigenomics Lab
  Head: Christoph Bock
  Research Area: The Medical Epigenomics Lab at CeMM seeks to advance precision medicine through collaborative, technology-driven biomedical research, developing wet-lab and computational methods and investigating the epigenetic (de)regulation underlying cancer and immunity.
  Members:
  Christoph Bock
  Raphael Bednarsky

Research interests

Scientific goal: To advance biology and biomedicine through computationally driven research, combining bioinformatic methods with genome technology and machine learning / artificial intelligence for the discovery of unexpected biology.

Areas of research:

1. Computational biology. Bioinformatic methods are essential for advancing biomedical research. We develop algorithms and software for large-scale data analysis, and we pursue clinical collaborations to demonstrate health impact.

2. Single-cell genomics. Many diseases show deregulation of epigenetic cell states. As a member of the Human Cell Atlas, we use single-cell sequencing and human organoids to dissect the gene-regulatory foundations of cancer & immunity.

3. High-throughput technology. Many groundbreaking discoveries are driven by new technologies. We invest heavily into tech development, including single-cell sequencing, CRISPR screens, epigenome editing, and synthetic biology.

4. Machine learning. Huge datasets pose new analytical challenges. As a fellow of the European Laboratory for Learning and Intelligent Systems, I develop methods for interpretable deep learning and artificial intelligence in biology.

5. Immune cell engineering. CAR T cells have shown dramatic efficacy for blood cancers and may spearhead a broad shift toward personalized, cell-based therapies. We use high-throughput technology to design synthetic immune cells.

Techniques, methods & infrastructure

The Biomedical Sequencing Facility (BSF) is an academic core facility and center of expertise for next generation sequencing (NGS) services and technology. The BSF is a joint project of the CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences and the Medical University of Vienna. Coordinated by Christoph Bock and with a dedicated team of staff scientists, the BSF contributes to biomedical research and whole genome medicine in Vienna, Austria, and internationally. More information: https://www.biomedical-sequencing.org/  

Grants

• Understanding and exploiting epigenetic regulation in CAR T cell therapy (2020)
  Source of Funding: EU, European Research Council (ERC) Consolidator Grant
  Principal Investigator
• HCA|Organoid: Establish a multi-tissue organoid platform within the Human Cell Atlas (2019)
  Source of Funding: EU, EU Horizon 2020 Health Programme
  Coordinator of the collaborative project
• An experimental and bioinformatic toolbox for functional epigenomics (2015)
  Source of Funding: EU, European Research Council (ERC) Starting Grant
  Principal Investigator
• High-throughput dissection and reprogramming of epigenetic drug resistance in leukemia (2013)
  Source of Funding: OeAW (Austrian Academy of Sciences), New Frontiers Group
  Principal Investigator
• BLUEPRINT - A Blueprint of Haematopoietic Epigenomes (project partner) (2011)
  Source of Funding: EU, FP7-HEALTH-2011
  Principal Investigator

Selected publications

1. Bock, C. et al. (2020) ‘The Organoid Cell Atlas’, Nature Biotechnology, 39(1), pp. 13–17. Available at: http://dx.doi.org/10.1038/s41587-020-00762-x.
2. Krausgruber, T. et al. (2020) ‘Structural cells are key regulators of organ-specific immune responses’, Nature, 583(7815), pp. 296–302. Available at: http://dx.doi.org/10.1038/s41586-020-2424-4.
3. Fortelny, N. and Bock, C. (2020) ‘Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data’, Genome Biology, 21(1). Available at: http://dx.doi.org/10.1186/s13059-020-02100-5.
4. Klughammer, J. et al. (2018) ‘The DNA methylation landscape of glioblastoma disease progression shows extensive heterogeneity in time and space’, Nature Medicine, 24(10), pp. 1611–1624. Available at: http://dx.doi.org/10.1038/s41591-018-0156-x.
5. Datlinger, P. et al. (2017) ‘Pooled CRISPR screening with single-cell transcriptome readout’, Nature Methods, 14(3), pp. 297–301. Available at: http://dx.doi.org/10.1038/nmeth.4177.