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

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, Boutros M, Camp JG, Clarke L, Clevers H, Knoblich JA, Liberali P, Regev A, Rios AC, Stegle O, Stunnenberg HG, Teichmann SA, Treutlein B, Vries RGJ, the Human Cell Atlas ‘Biological Network’ Organoids (2020). The Organoid Cell Atlas. Nature Biotechnology, doi: 10.1038/s41587-020-00762-x
2. Krausgruber T, Fortelny N, Fife-Gernedl V, Senekowitsch M, Schuster LC, Nemc A, Schmidl C, Rendeiro AF, Lercher A, Bergthaler A, Bock C (2020). Structural cells are key regulators of organ-specific immune response. Nature 583, 296-302.
3. Fortelny N, Bock C (2020). Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data. Genome Biology 21, 190.
4. Klughammer J, Kiesel B, Roetzer T, Fortelny N, Nemc A, Nenning KH, Furtner J, Sheffield NC, Datlinger P, Peter N, Nowosielski M, Augustin M, Mischkulnig M, Strobel T, Alpar D, Ergüner B, Senekowitsch M, Moser P, Freyschlag CF, Kerschbaumer J, Thomé C, Grams AE, Stockhammer G, Kitzwoegerer M, Oberndorfer S, Marhold F, Weis S, Trenkler J, Buchroithner J, Pichler J, Haybaeck J, Krassnig S, Mahdy Ali K, von Campe G, Payer F, Sherif C, Preiser J, Hauser T, Winkler PA, Kleindienst W, Wurtz F, Brandner-Kokalj T,
5. Datlinger P, Schmidl C, Rendeiro A, Krausgruber T, Traxler P, Klughammer J, Schuster L, Kuchler A, Alpar D, Bock C (2017). Pooled CRISPR screening with single-cell transcriptome readout. Nature Methods, 14, 297-301.