Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 67,316 bioRxiv papers from 296,340 authors.
Interactions between biomolecules such as proteins underlie most cellular processes. It is crucial to visualize these molecular-interaction complexes directly within the cell, to show precisely where these interactions occur and thus improve our understanding of cellular regulation. Currently available proximity-sensitive assays for in situ imaging of such interactions produce diffraction-limited signals and therefore preclude obtaining information on the nanometer-scale distribution of interaction complexes. By contrast, optical super-resolution imaging provides information about molecular distributions with nanometer resolution which has greatly advanced our understanding of cell biology. However, current colocalization analysis of super-resolution fluorescence imaging is prone to false positive signals as the detection of protein proximity is directly dependent on the local optical resolution. Here we present Proximity-Dependent PAINT (PD-PAINT), a method for sub-diffraction imaging of protein pairs, in which proximity detection is decoupled from optical resolution. Proximity is detected via the highly distance-dependent interaction of two DNA labels anchored to the target species. Labelled protein pairs are then imaged with high contrast and nanoscale resolution using the super-resolution approach of DNA-PAINT. The mechanisms underlying the new technique are analyzed by means of coarse-grained molecular simulations and experimentally demonstrated by imaging high proximity biotin binding sites of streptavidin and epitopes of ryanodine receptor proteins in cardiac tissue samples. We show that PD-PAINT can be straightforwardly integrated in a multiplexed super-resolution imaging protocol and benefits from advantages of DNA-based super-resolution localization microscopy, such as high specificity, high resolution and the ability to image quantitatively.
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