Sparse deconvolution of high-density super-resolution images

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 fluoro...

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Veröffentlicht in:Scientific reports 2016-02, Vol.6 (1), p.21413-21413, Article 21413
Hauptverfasser: Hugelier, Siewert, de Rooi, Johan J., Bernex, Romain, Duwé, Sam, Devos, Olivier, Sliwa, Michel, Dedecker, Peter, Eilers, Paul H. C., Ruckebusch, Cyril
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Sprache:eng
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Zusammenfassung: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 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.
ISSN:2045-2322
2045-2322
DOI:10.1038/srep21413