With recent advances in multi-color super-resolution light microscopy, it is possible to simultaneously visualize multiple subunits within biological structures at nanometer resolution. To optimally evaluate and interpret spatial proximity of stainings on such an image, colocalization analysis tools have to be able to integrate prior knowledge on the local geometry of the recorded biological complex. We present MultiMatch to analyze the abundance and location of chain-like particle arrangements in multi-color microscopy based on multi-marginal optimal unbalanced transport methodology. Our object-based colocalization model statistically addresses the effect of incomplete labeling efficiencies enabling inference on existent, but not fully observable particle chains. We showcase that MultiMatch is able to consistently recover existing chain structures in three-color STED images of DNA origami nanorulers and outperforms geometry-uninformed triplet colocalization methods in this task. MultiMatch generalizes to an arbitrary number of color channels and is provided as a user-friendly Python package comprising colocalization visualizations.

Nanoruler SamplesCustom-made DNA nanoruler samples featuring one, two, or three fluorophore spots, each consisting of ~20 fluorophores (Alexa Fluor488, Alexa Fluor594, Star Red), with a distance between the spots of ~70 nm, were purchased from Gattaquant - DNA Nanotechnologies (Gräfelfing, Germany). The biotinylated nanorulers were immobilized on a BSA-biotin-neutravidin surface according to the manufacturer’s specifications. Stimulated Emission Depletion (STED) Super-Resolution Light MicroscopyImage acquisition was done using a quad scanning STED microscope (Abberior Instruments, Göttingen, Germany) equipped with a UPlanSApo 100x/1,40 Oil objective (Olympus, Tokyo, Japan). Excitation of Alexa Fluor 488, Alexa Fluor 594 and Star Red was achieved by laser beams featuring wave lengths of 485 nm, 561 nm and 640 nm, respectively. For STED imaging, a laser beam with an emission wavelength of 775 nm was applied. For all experimental STED images, a pixel size of 25 nm was utilized. 

Experimental and simulated STED data and scripts associated with Naas et al. "MultiMatch: Geometry-Informed Colocalization in Multi-Color Super-Resolution Microscopy." bioRxiv (2024): 2024-02. The MultiMatch Python package and further illustrative examples are available on GitHub repository https://github.com/gnies/multi_match.