Our group develops computational techniques to describe and model cell biological processes based on imaging data. We currently focus on the three-dimensional organization of the genome inside the nucleus, which plays a central role in gene expression, DNA repair and replication. Despite its functional importance, nuclear organization remains largely uncharted, owing to several technical limitations. We therefore develop new techniques to describe the nuclear architecture with higher accuracy, using a combination of high-throughput image analysis and state of the art optical microscopy, in close collaboration with cell biologists and geneticists. Our goal, beyond a quantitative description, is a mechanistic and predictive model of chromosome organization, dynamics and function.

We have developed and validated a computational technique, which automatically analyses images of thousands of nuclei and generates high resolution maps of gene territories in the yeast Saccharomycescerevisiae (Berger et al., Nat. Meth., 2008). Applying this method, we found that several genomic loci are confined to ’gene territories’ much smaller than the nucleus, which can be remodelled during transcriptional activation. We revealed a territorial organization of genes significantly more pronounced than observed before (see Figure 1). More recently, we have applied this technique to analyze the subnuclear localization of most yeast telomeres, and their localization relative to each other. We found that both are strongly determined by chromosome arm length, the size of the nucleolus, and tethering to the nuclear envelope (Thérizols et al., PNAS, 2010). These results suggest that chromosome organization is largely governed by mechanical constraints, which might be predicted quantitatively.

In addition, our group is actively interested in the development of super-resolution microscopy techniques based on the accurate positioning of individual photoswitchable molecules. We have recently implemented a single molecule super-resolution microscope, which is now applied to image subcellular structures in several collaborations. We are currently developing computational and experimental approaches to further enhance spatial and temporal resolution.

Keywords: Computational imaging, nuclear organization, yeast, super-resolution microscopy

Berger, A.B., Cabal, G.G., Fabre, E., Duong, T., Buc, H., Nehrbass, U., Olivo-Marin, J.C., Gadal, O., and Zimmer, C. (2008). High-resolution statistical mapping reveals gene territories in live yeast. Nature Methods, vol. 5 , pp. 1031-37. PMID: 18978785.

M. deMoraes Marim, B. Zhang, J-C. Olivo-Marin, C. Zimmer (2008). Improving single particle localization with an empirically calibrated Gaussian kernel. IEEE International Symposium on Biomedical Imaging, pp. 1003-1006.

Wong, H., Winn, P.J., Mozziconacci, J. (2009). A molecular model of chromatin organisation and transcription: how a multi-RNA polymerase II machine transcribes and remodels the -globin locus during development. BioEssays, vol. 31, issue 12, pp. 1357-1366. doi: 10.1002/bies.200900062.

Duong,T., Koch, I., Wand, M. P. (2009).Highest Density Difference Region Estimation with Application to Flow Cytometric Data. Biometrical Journal, vol. 5, no. 3, pp. 504-521. doi: 10.1002/bimj.200800201

B. Zhang, J. Enninga, J-C. Olivo-Marin and C. Zimmer(2006).Automated super-resolution detection of Fluroescent Rods in 2D, Proc.IEEE International Symposium on Biomedical Imaging, 1296-99.