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Your search keyword '"Bernex, R."' showing total 8 results
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8 results on '"Bernex, R."'

1. Fast superresolution imaging of live cells

Author

Ruckebusch, C., Hugelier, S., Bernex, R., Sliwa, M., Dedecker, P., Rooi, J. de, Eilers, P.H.C, Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 (LASIRE), Institut de Chimie du CNRS (INC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Laboratory of Photochemistry and Spectroscopy, Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Department of Biostatistics, Erasmus University Medical Center [Rotterdam] (Erasmus MC), Institut de Chimie du CNRS (INC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille Institut (CLIL), Department of Chemistry - KU Leuven, and Centre National de la Recherche Scientifique (CNRS)-Centrale Lille Institut (CLIL)-Institut de Chimie du CNRS (INC)-Université de Lille

Subjects
[CHIM.THEO]Chemical Sciences/Theoretical and/or physical chemistry
Published
2017

2. SPIDER. An algorithm for dynamic superresolution imaging

Author

Hugelier, S., Bernex, R., Sliwa, M., Dedecker, P., de Rooi, J., Eilers, P.H.C., Ruckebusch, C., Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 (LASIRE), Institut de Chimie du CNRS (INC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille Institut (CLIL), Laboratory of Photochemistry and Spectroscopy, Department of Chemistry - KU Leuven, Department of Biostatistics, Erasmus University Medical Center [Rotterdam] (Erasmus MC), Swammerdam Institue for Life Science, Universiteit van Amsterdam (UvA), Institut de Chimie du CNRS (INC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), and Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven)

Subjects
[CHIM.THEO]Chemical Sciences/Theoretical and/or physical chemistry
Published
2016

4. Single particle and ultrafast spectroscopy of new photochromic nanoparticles

Author

Sliwa, M., Debus, B., Bernex, R., Ruckebusch, C., Pavageau, C., Métivier, R., Yu, P., Miyasaka, H., Abe, J., Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 (LASIRE), Institut de Chimie du CNRS (INC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Institute of Chemistry, St Petersburg State University (SPbU), Laboratoire de Photophysique et Photochimie Supramoléculaires et Macromoléculaires (PPSM), École normale supérieure - Cachan (ENS Cachan)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Division of Frontier Materials Science, Graduate School of Engineering Science, Osaka University [Osaka], and Institut de Chimie du CNRS (INC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille Institut (CLIL)

Subjects
[CHIM.THEO]Chemical Sciences/Theoretical and/or physical chemistry
Published
2015

5. Sparse deconvolution of high-density super-resolution images

Author

Hugelier, S, Rooi, Jacob, Bernex, R, Duwe, S, Devos, O, Sliwa, M, Dedecker, P, Eilers, Paul, Ruckebusch, C, Hugelier, S, Rooi, Jacob, Bernex, R, Duwe, S, Devos, O, Sliwa, M, Dedecker, P, Eilers, Paul, and Ruckebusch, C

Abstract

In wide-field super-resolution microscopy, investigating the nanoscale structure of cellular processes, and resolving fast dynamics and morphological changes in cells requires algorithms capable of working with a high-density of emissive fluorophores. Current deconvolution algorithms estimate fluorophore density by using representations of the signal that promote sparsity of the super-resolution images via an L-1-norm penalty. This penalty imposes a restriction on the sum of absolute values of the estimates of emitter brightness. By implementing an L-0-norm penalty - on the number of fluorophores rather than on their overall brightness - we present a penalized regression approach that can work at high-density and allows fast super-resolution imaging. We validated our approach on simulated images with densities up to 15 emitters per mu m(-2) and investigated total internal reflection fluorescence (TIRF) data of mitochondria in a HEK293-T cell labeled with DAKAP-Dronpa. We demonstrated super-resolution imaging of the dynamics with a resolution down to 55 nm and a 0.5 s time sampling.

Published
2016

7. Sparse deconvolution of high-density super-resolution images.

Author

Hugelier S, de Rooi JJ, Bernex R, Duwé S, Devos O, Sliwa M, Dedecker P, Eilers PH, and Ruckebusch C

Subjects
Fluorescent Dyes chemistry, Fluorescent Dyes metabolism, HEK293 Cells, Humans, Image Processing, Computer-Assisted, Microscopy, Fluorescence, Mitochondria pathology, Algorithms
Abstract

In wide-field super-resolution microscopy, investigating the nanoscale structure of cellular processes, and resolving fast dynamics and morphological changes in cells requires algorithms capable of working with a high-density of emissive fluorophores. Current deconvolution algorithms estimate fluorophore density by using representations of the signal that promote sparsity of the super-resolution images via an L1-norm penalty. This penalty imposes a restriction on the sum of absolute values of the estimates of emitter brightness. By implementing an L0-norm penalty--on the number of fluorophores rather than on their overall brightness--we present a penalized regression approach that can work at high-density and allows fast super-resolution imaging. We validated our approach on simulated images with densities up to 15 emitters per μm(-2) and investigated total internal reflection fluorescence (TIRF) data of mitochondria in a HEK293-T cell labeled with DAKAP-Dronpa. We demonstrated super-resolution imaging of the dynamics with a resolution down to 55 nm and a 0.5 s time sampling.

Published
2016
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8. Mapping pixel dissimilarity in wide-field super-resolution fluorescence microscopy.

Author

Ruckebusch C, Bernex R, Allegrini F, Sliwa M, Hofkens J, and Dedecker P

Abstract

Recent advances in fluorescence bioimaging with single-molecule sensitivity have relied on the analysis and visualization of single-molecule data obtained on smart fluorophores. We describe an alternative method to enhance the information content of densely labeled fluorescence images. Visualization is improved by representing pixels as the dissimilarities of the fluctuations of the fluorescence signals, with the dissimilarity being taken to the mean of the signals over all the pixels. Mapping pixel dissimilarity (Mappix) results in signal and information enhancement of the output images. In addition, the spatial distribution of the fluorescence brightness of the original image is not skewed. This allows large differences of molecular brightness to be handled which turns out to be critical to the fidelity of the final image. In this work, we provide testing of the Mappix approach with both simulated and real data. The results obtained on HEK cells expressing Dronpa photoswitchable fluorescent protein show that, for densely labeled samples, improvement can be obtained on fluorescence images allowing the observation of structural information. Despite some limitations, comparison to state of art methods reveals that Mappix can be very useful for biological imaging applications.

Published
2015
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