2016-02-25
Sparse deconvolution of high-density super-resolution images
Publication
Publication
Scientific Reports (Nature) , Volume 6 - Issue 21413 p. 1- 10
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.
| Additional Metadata | |
|---|---|
| doi.org/10.1038/srep21413, hdl.handle.net/1765/79887 | |
| Scientific Reports (Nature) | |
| Organisation | Department of Biostatistics |
|
Hugelier, S., de Rooi, J., Bernex, R., Duwé, S., Devos, O., Sliwa, M., … Ruckebusch, C. (2016). Sparse deconvolution of high-density super-resolution images. Scientific Reports (Nature), 6(21413), 1–10. doi:10.1038/srep21413 |
|
