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Faster STORM using compressed sensing


In super-resolution microscopy methods based on single-molecule switching, the rate of accumulating single-molecule activation events often limits the time resolution. Here we developed a sparse-signal recovery technique using compressed sensing to analyze images with highly overlapping fluorescent spots. This method allows an activated fluorophore density an order of magnitude higher than what conventional single-molecule fitting methods can handle. Using this method, we demonstrated imaging microtubule dynamics in living cells with a time resolution of 3 s.

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Figure 1: STORM image analysis using compressed sensing.
Figure 2: Experimental STORM images using compressed sensing.


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We thank E. Griffis and R. Vale (University of California, San Francisco) for generously providing the mEos2-tubulin S2 cells, and Q. Fan (Georgia Institute of Technology) for running the DAOSTORM code. L.Z. receives support from US National Institutes of Health 1R21EB012700-01 A1. B.H. receives support from the UCSF Program for Breakthrough Biomedical Research, Searle Scholarship, and Packard Fellowship for Science and Engineering.

Author information

Authors and Affiliations



L.Z. and B.H. conceived the project and developed the algorithms, W.Z. performed the experiments, D.E. and B.H. analyzed the data, and L.Z. and B.H. wrote the manuscript.

Corresponding authors

Correspondence to Lei Zhu or Bo Huang.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–9 and Supplementary Note (PDF 725 kb)

Supplementary Video 1

STORM 'movie' of microtubules in a living Drosophila S2 cell stably expressing mEos2-fused tubulin, with a time resolution of 3 seconds. The movie is reconstructed from 4,349 camera frames (77 seconds) and plays 11 times as fast as real time. Three snapshots from the movie are shown in Figure 2b. Scale bar, 1 μm. (MOV 1944 kb)

Supplementary Software

Matlab code for STORM data analysis using compressed sensing. (ZIP 221 kb)

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Zhu, L., Zhang, W., Elnatan, D. et al. Faster STORM using compressed sensing. Nat Methods 9, 721–723 (2012).

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