Parameter-free molecular super-structures quantification in single-molecule localization microscopy

Mattia Marenda, Elena Lazarova, Sebastian van de Linde, Nick Gilbert, Davide Michieletto

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
14 Downloads (Pure)


Understanding biological function requires the identification and characterization of complex patterns of molecules. Single-molecule localization microscopy (SMLM) can quantitatively measure molecular components and interactions at resolutions far beyond the diffraction limit, but this information is only useful if these patterns can be quantified and interpreted. We provide a new approach for the analysis of SMLM data that develops the concept of structures and super-structures formed by interconnected elements, such as smaller protein clusters. Using a formal framework and a parameter-free algorithm, (super-)structures formed from smaller components are found to be abundant in classes of nuclear proteins, such as heterogeneous nuclear ribonucleoprotein particles (hnRNPs), but are absent from ceramides located in the plasma membrane. We suggest that mesoscopic structures formed by interconnected protein clusters are common within the nucleus and have an important role in the organization and function of the genome. Our algorithm, SuperStructure, can be used to analyze and explore complex SMLM data and extract functionally relevant information.

Original languageEnglish
Article numbere202010003
Number of pages20
JournalJournal of Cell Biology
Issue number5
Early online date18 Mar 2021
Publication statusPublished - 3 May 2021


  • biophysics
  • DNA biology
  • RNA biology
  • systems and computational biology


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