Zimmer Group / Machine Biophotonics Lab

 Our new lab will initially focus on two main projects:

• High throughput imaging, super-resolution and AI for drug discovery: Diseases such as cancer or those caused by antibiotic-resistant bacteria urgently call for new drugs. However, the pipelines delivering drug molecules with new modes of action are drying up. High-throughput imaging of cells exposed to large chemical libraries has been successful at identifying new drug candidates but requires labor-intensive and time-consuming follow up assays to identify drug targets. We aim to speed up phenotypic drug discovery by using experimental and computational methods to predict drug targets from imaging data alone. This project combines high throughput and adaptive super-resolution imaging with deep learning-based image analysis. It is based on a close collaboration with teams of microbiologists at the Pasteur Institutes of Paris, Korea and elsewhere. Relevant publication: Krentzel et al. Trends in Cell Biol 2023.

• Live cell nanoscopy through AI: A holy grail in biological research is to characterize the structure and dynamics of molecular structures within cells at the best possible spatial and temporal resolution. Super-resolution microscopy based on single molecule localization (SMLM) can now reach nanometric scales or better, but requires extremely long acquisition times. Consequently, live cell super-resolution imaging remains mostly out of reach, preventing the detailed analysis of molecular dynamics. We are pursuing approaches based on state-of-the-art AI (deep learning) methods to speed up SMLM imaging with the goal of enabling molecular-scale resolution imaging at high temporal resolution in live cells. Achieving this goal will open the door to a better understanding of fundamental processes of cellular life and pathologies, from cytoskeletal dynamics to mitochondrial reorganization and chromatin organization. Relevant publications: Ouyang et al. Nat Biotech 2018; Ouyang et al. Nat Meth 2022.

For a broader view of our past and current research topics at the Pasteur Institute, see this website. 

• Deep learning in image-based phenotypic drug discovery. Daniel Krentzel*, Spencer S. Shorte, Christophe Zimmer*. Trends in Cell Biology, 2023. https://doi.org/10.1016/j.tcb.2022.11.011

• Polymer simulations guide the detection and quantification of chromatin loop extrusion by imaging. Thomas Sabaté, Benoît Lelandais, Edouard Bertrand, Christophe Zimmer. Nucleic Acids Research. 2023, gkad034, https://doi.org/10.1093/nar/gkad034

• ShareLoc-an open platform for sharing localization microscopy data. Wei Ouyang#*, Jiachuan Bai#, Manish Kumar Singh, Christophe Leterrier, Paul Barthelemy, Samuel F.H. Barnett, Teresa Klein, Markus Sauer, Pakorn Kanchanawong, Nicolas Bourg, Mickael M. Cohen, Benoît Lelandais, Christophe Zimmer*. Nature Methods. 2022. https://doi.org/10.1038/s41592-022-01659-0

• Sensitive visualization of SARS-CoV-2 RNA with CoronaFISH. E Rensen, S Pietropaoli, F Mueller, C Weber, S Souquere, S Sommer, P Isnard, M Rabant, J-B Gibier, F Terzi, E Simon-Loriere, M-A Rameix-Welti, G Pierron, G Barba-Spaeth, C Zimmer. Life Science Alliance. 2022 Jan 7;5(4):e202101124

• Single-molecule localization microscopy. M Lelek, M Gyparaki, G Beliu, F Schueder, J Griffié, S Manley, R Jungmann, M Sauer, M Lakadamyali, and C Zimmer. Nature Reviews Methods Primers, 1:39 (2021).

• Super-resolution visualization and modeling of human chromosomal regions reveals cohesin-dependent loop structures. X Hao, J Parmar, B Lelandais, A Aristov, W Ouyang, C Weber, C Zimmer. Genome Biology, 22:150  (2021). https://doi.org/10.1186/s13059-021-02343-w

• Clustering and reverse transcription of HIV‐1 genomes in nuclear niches of macrophages. E Rensen, F Mueller*, V Scoca, J J Parmar, P Souque, C Zimmer*, F Di Nunzio*. EMBO J, 40:e105247 (2021). 

• ImJoy: an open-source computational platform for the deep learning era. W. Ouyang, F. Mueller, M. Hjelmare, E. Lundberg, C. Zimmer. Nature Methods, 16, 1199–1200 (2019). 

• ZOLA-3D allows flexible 3D localization microscopy over an adjustable axial range. A. Aristov, B. Lelandais, E. Rensen, C. Zimmer. Nature Communications, 8:2409 (2018). doi: 10.1038/s41467-018-04709-4

• Deep learning massively accelerates super-resolution localization microscopy. W. Ouyang, A. Aristov, M. Lelek, X. Hao, C. Zimmer. Nature Biotechnology, Apr 16, 2018. 36(5):460-468. doi:10.1038/nbt.4106

For a full list of publications, see the Google Scholar profile here

• starting 2024: W3 Professor, Rudolf Virchow Center, University of Würzburg, Germany
• since 2021: Professor, Institut Pasteur, Paris, France
• 2020 – 2023: Director of the Computational Biology department, Institut Pasteur, France
• 2015 – 2016: Visiting Scholar, University of California at Berkeley, USA
• 2010 – 2021: Research Director, Institut Pasteur, France
• 2009: Habilitation to direct research (HDR), University Paris 11, France
• since 2008: Head of Imaging and Modeling Unit, Institut Pasteur
• 2003 – 2007:  Staff scientist, Institut Pasteur, Paris, France
• 2000 – 2002:  Postdoctoral researcher, Institut Pasteur, Paris, France
• 1998 – 2000: Assistant research geophysicist, Space Physics Group, University of California Los Angeles, USA
• 1997: PhD, Astrophysics and Space Techniques, University Paris 7, France

As of April 2024, we have several openings for Postdocs (4),  PhD students (2) and Master students (2-4). We expect to fill most of them by the summer of 2024.

We are looking for highly motivated coworkers with a solid background in one or more of the following fields (or related fields):

• AI, computer science, applied mathematics, computational biology, bioinformatics
• Physics, biophysics, engineering, microscopy, optics 
• Cell biology, microbiology

We expect strong commitment, a good team spirit and fluency in English (written and spoken).

If this applies to you and you are interested in joining our lab, please send the following as a single PDF file to: christophe[dot]zimmer[at]uni-wuerzburg[dot]de

• A cover letter explaining why you would like to join us and have the relevant skills
• A detailed CV including a publication list and a summary of your past research achievements, if relevant (5 pages max)
• The names of at least three references
• Copies of transcripts and diplomas

Applications will be reviewed on a rolling basis until the positions are filled.

We gratefully acknowledge generous funding by the University of Würzburg, the Rudolf Virchow Center and the Bavarian Distinguished Professorship Programm.

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