To fully understand living systems we need (i) experimental techniques to describe them as accurately and comprehensively as possible, and (ii) computational models able to predict their evolution from a given state and in response to external signals.
The Imaging and Modeling Unit is based at Institut Pasteur in the Department of Cell Biology and Infections (with a secondary affiliation to the Department of Structural Biology and Chemistry). Our lab develops computational and experimental approaches to characterize and quantitatively predict selected cellular processes. Our current projects concentrate on : (i) investigating the spatial organization of the genome and its functional consequences, and (ii) developing high resolution or high throughput imaging techniques, and applying them to a range of studies, with a focus on host-pathogen interactions. Our lab mobilizes a spectrum of expertise including biophysics, microscopy, informatics and cell biology, and works in close collaboration with several experimental groups, many of them at Institut Pasteur.
Figure 1: Computationally imaging the territorial organization of the yeast nucleus. Maps of the intranuclear territories of selected loci (each shown with a distinct color) are generated automatically from fluorescence microscopy images of thousands of cells (background). From .
The one-dimensional sequence of the genome is carried by long polymers (chromosomes), which fold in the three dimensions of the nucleus. How this folding occurs and what it implies for genome function remains largely unknown. To better describe genome architecture in yeast, we developed a method that maps the nuclear territories of fluorescently tagged chromatin loci by computationally analyzing images of thousands of cells . This revealed a strong territorial organization of the yeast nucleus (Figure 1). Application of this method to most yeast telomeres allowed us to identify some of the main determinants of locus positioning: genomic distance to centromere and hindrance by the nucleolar compartment .
To better understand the mechanisms of chromosome folding, we developed a simple predictive model of chromosome dynamics based on polymer physics (Figure 2). The model’s predictions are in very good quantitative agreement with our imaging data and with genome-wide DNA contact frequencies measured biochemically . This suggests that large-scale genome architecture in yeast is governed mainly by generic properties of randomly moving chromosomes rather than by DNA-specific factors. We now explore the consequences of this model for a quantitative understanding of functional processes such as DNA repair . We also investigate the universality of our model’s assumptions by extending it to other organisms.
Figure 2: Predictive computational model of the yeast nucleus (a snapshot of the dynamic simulation is shown). Model predictions agree very well with experimental measurements by imaging and genome-wide chromosome conformation capture. From .
Recently, we started working towards quantitative models of gene expression and RNA processing, and started by developing methods to localize and count individual transcripts in single cells .
Fluorescence microscopy is a defining technology in cellular biology but the diffraction of light limits the resolution of standard microscopes to ~200 nm, thus preventing detailed analyses of molecular structures. We are actively interested in microscopy methods based on stochastic photoswitching and computational localization of single fluorophores (“pointillism”), which enable greatly improved resolution. We have implemented appropriate microscopy hardware and reconstruction software (Figure 3) and applied pointillist super-resolution imaging to various studies of pathogens in their cellular hosts . Beyond, we aim to push some of the important limitations of existing super-resolution methods. One of these limitations is the use of invasive fluorescent tags, which can perturb biological functions. We recently demonstrated FlAsH-PALM, which combines FlAsH-tetracysteine tagging with stochastic localization microscopy. FlAsH-PALM allows to obtain <30 nm resolution images of delicate microbial proteins such as the HIV integrase, without disrupting their function . We now explore different experimental and computational strategies to further improve the information content and the reliability of pointillist microscopy.
Figure 3: Super-resolution PALM/STORM imaging. Left: home-made imaging system. Middle: Dual color 3D STORM image of an axonal track, with a resolution of ~20 nm. Right: magnified view shows an the hollow structure of an individual ~50 nm diameter synaptic vesicle. From .
Key words: super-resolution microscopy, computational biology, nuclear architecture, yeast, host-pathogen interactions, HIV.
 A. B. Berger, G. G. Cabal, E. Fabre, T. Duong, H. Buc, U. Nehrbass, J. C. Olivo-Marin, O. Gadal, and C. Zimmer, “High-resolution statistical mapping reveals gene territories in live yeast,” Nature Methods, vol. 5, no. 12, pp. 1031–1037, 2008.
 P. Thérizols, T. Duong, B. Dujon, C. Zimmer, and E. Fabre, “Chromosome arm length and nuclear constraints determine the dynamic relationship of yeast subtelomeres,” Proceedings of the National Academy of Sciences, vol. 107, no. 5, p. 2025, 2010.
 H. Wong, H. Marie-Nelly, S. Herbert, P. Carrivain, H. Blanc, R. Koszul, E. Fabre, and C. Zimmer, “A Predictive Computational Model of the Dynamic 3D Interphase Yeast Nucleus.,” Current biology : CB, vol. 22, no. 20, pp. 1881–90, Oct. 2012.
 N. Agmon, B. Liefshitz, C. Zimmer, E. Fabre, and M. Kupiec, “Effect of nuclear architecture on the efficiency of double-strand break repair,” Nature Cell Biology, vol.15, no. 6, pp. 694-9 (2013).
 F. Mueller, A. Senecal, K. Tantale, H. Marie-Nelly, N. Ly, O. Collin, E. Basyuk, E. Bertrand, X. Darzacq, and C. Zimmer, “FISH-QUANT: automatically counting transcripts in 3D FISH images,” Nature Methods, Apr; 10(4):277-8, 2013.
 R. Henriques, M. Lelek, E. F. Fornasiero, F. Valtorta, C. Zimmer, and M. M. Mhlanga, “QuickPALM: 3D real-time photoactivation nanoscopy image processing in ImageJ,” Nature Methods, vol. 7, no. 5, pp. 339–340, 2010.
 S. Mostowy, M. Bonazzi, M. A. Hamon, T. N. Tham, A. Mallet, and M. Lelek, “Entrapment of Intracytosolic Bacteria by Septin Cage-like Structures,” Cell Host & Microbe, vol. 8, no. 5, pp. 433–444, 2010.
 S. Ehsani, J. C. Santos, C. D. Rodrigues, R. Henriques, L. Audry, C. Zimmer, P. Sansonetti, G. Tran Van Nhieu, and J. Enninga, “Hierarchies of host factor dynamics at the entry site of Shigella flexneri during host cell invasion,” Infect Immun, 2012.
 D. Judith, S. Mostowy, M. Bourai, N. Gangneux, M. Lelek, M. Lucas-Hourani, N. Cayet, Y. Jacob, M.-C. Prévost, P. Pierre, F. Tangy, C. Zimmer, P.-O. Vidalain, T. Couderc, and M. Lecuit, “Species-specific impact of the autophagy machinery on Chikungunya virus infection.,” EMBO reports, Jun 3;14(6):534-44 (2013).
 M. Lelek, F. Di Nunzio, R. Henriques, P. Charneau, N. Arhel, and C. Zimmer, “Superresolution imaging of HIV in infected cells with FlAsH-PALM.,” Proceedings of the National Academy of Sciences of the United States of America, vol. 109, no. 22, pp. 8564–9, May 2012.
 S. Herbert, H. Soares, C. Zimmer, and R. Henriques, “Single-Molecule Localization Super-Resolution Microscopy: Deeper and Faster,” Microscopy and Microanalysis, vol. 18, no. 6, p. 1419, 2012.