Modelling 3D supramolecular structure from sparse single-molecule localisation microscopy data

Single-molecule localisation microscopy (SMLM) has the potential to reveal the underlying organisation of specific molecules within supramolecular complexes and their conformations, which is not possible with conventional microscope resolution. However, the detection efficiency for fluorescent molecules in cells can be limited in SMLM, even to below 1% in thick and dense samples. Segmentation of individual complexes can also be challenging. To overcome these problems, we have developed a software package termed PERPL: Pattern Extraction from Relative Positions of Localisations. This software assesses the relative likelihoods of models for underlying patterns behind incomplete SMLM data, based on the relative positions of pairs of localisations. We review its principles and demonstrate its use on the 3D lattice of Z-disk proteins in mammalian cardiomyocytes. We find known and novel features at similar to 20 nm with localisations of less than 1% of the target proteins, using mEos fluorescent protein constructs.

Automated summary

Single-molecule localisation microscopy has the potential to reveal the organisation and conformation of specific molecules within supramolecular complexes, but it has limitations in detecting fluorescent molecules in cells and segmenting individual complexes. To overcome these problems, a software package called PERPL has been developed to assess the likelihood of underlying patterns in incomplete SMLM data based on the relative positions of localisations.