Abstract
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.
Highlights
We demonstrate the capabilities of SuperStructure on simulated datasets and use it to analyze two groups of experimental datasets: (1) nuclear proteins involved in RNA processing, namely SAF-A, heterogeneous nuclear ribonucleoprotein particles (hnRNPs)-C, and SC35; and (2) ceramide lipids involved in cellular trafficking at the membrane
We find that interconnections between clusters are abundant in classes of proteins in the hnRNP family and that they are surprisingly absent from ceramides, suggesting this feature is relevant for the biological function of SAF-A and hnRNP-C
Quantification of super-structures in nuclear proteins We examine biological data and apply SuperStructure to dSTORM data acquired for three different nuclear proteins (Fig. 3, A and B): the serine/arginine-rich splicing factor SC35, hnRNP-C, and hnRNP-U
Summary
Single-molecule localization microscopy (SMLM; van de Linde et al, 2011; Schermelleh et al, 2010; Henriques et al, 2011; Sauer and Heilemann, 2017) is commonly employed for quantitative analysis of molecular structures and interactions in both cell-based (Cisse et al, 2013; Kapanidis et al, 2018; Chong et al, 2018) and in vitro experiments (Revyakin et al, 2006; Deniz et al, 2008). Traditional clustering algorithms rely on userdefined parameters that are intrinsically intertwined with the notion of similarity that is necessary to define a cluster These parameters can be either hypothesized by physical intuition or inferred via preemptive analysis (Burgert et al, 2017; Williamson et al, 2020; Malkusch and Heilemann, 2016), yet their choice has a significant impact on the results, in turn hindering the portability of clustering algorithms and the comparison between different datasets. Recent evidence suggest that assemblies of proteins (Brangwynne et al, 2015; Larson et al, 2017; Strom et al, 2017; Sabari et al, 2018; Cho et al, 2018; Maharana et al, 2018; Chong et al, 2018) and chromatin (Bintu et al, 2018; Boettiger et al, 2016; Frank and Rippe, 2020) form functional complex structures that are not fully captured by standard clustering algorithms. Recent super-resolution studies indicate that chromatin is functionally organized in connected nano-scale compartments
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