Nanoscale pattern extraction from relative positions of sparse 3D localisations

Curd, A.; Leng, J.; Hughes, R.; Cleasby, A.; Rogers, B.; Trinh, C.; Baird, M.; Takagi, Y.; Tiede, C.; Sieben, C.; Manley, S.; Schlichthaerle, T.; Jungmann, R.; Ries, J.; Shroff, H.; Peckham, M.

doi:10.1101/2020.02.13.947135

bioRxiv · 10.1101/2020.02.13.947135

Nanoscale pattern extraction from relative positions of sparse 3D localisations

Curd, A.·Leng, J.·Hughes, R.·Cleasby, A.·Rogers, B.·Trinh, C.·Baird, M.·Takagi, Y.·Tiede, C.·Sieben, C.·Manley, S.·Schlichthaerle, T.·Jungmann, R.·Ries, J.·Shroff, H.·Peckham, M.

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Abstract

We present a method for extracting high-resolution ordered features from localisation microscopy data by analysis of relative molecular positions in 2D or 3D. This approach allows pattern recognition at sub-1% protein detection efficiencies, in large and heterogeneous samples, and in 2D and 3D datasets. We used this method to infer ultrastructure of the nuclear pore, the cardiomyocyte Z-disk, DNA origami structures and the centriole.

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Curd, A., Leng, J., Hughes, R., Cleasby, A., Rogers, B., Trinh, C., Baird, M., Takagi, Y., Tiede, C., Sieben, C., Manley, S., Schlichthaerle, T., Jungmann, R., Ries, J., Shroff, H., Peckham, M.. 2020-02-13. Nanoscale pattern extraction from relative positions of sparse 3D localisations. https://doi.org/10.1101/2020.02.13.947135

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