bioRxivBiophysics
Calibration free counting of low molecular copy numbers in single DNA-PAINT localization clusters
Abstract
Single-Molecule Localization Microscopy (SMLM) has revolutionized light microscopy by enabling optical resolutions down to a few nanometer. Yet, localization precisions commonly not suffice to visually resolve single subunits in molecular assemblies or multimeric complexes. Since each targeted molecule contributes localizations during image acquisition, molecular counting approaches to reveal the target copy numbers within localization clusters have been continuously proposed since the early days of SMLM, most of which rely on preliminary knowledge of the dye photo-physics or on a calibration to a reference. Previously, we developed localization-based Fluorescence Correlation Spectroscopy (lbFCS) as an absolute ensemble counting approach for the SMLM-variant DNA-Points Accumulation for Imaging in Nanoscale Topography (PAINT), for the first time circumventing the necessity for reference calibrations. Here, we present a revised framework termed lbFCS+ which allows absolute counting of copy numbers for individual localization clusters in a single DNA-PAINT image. In lbFCS+, absolute counting in individual clusters is achieved via precise measurement of the local hybridization rates of the fluorescently-labeled oligonucleotides ( imagers) employed in DNA-PAINT imaging. In proof-of-principle experiments on DNA origami nanostructures, we demonstrate the ability of lbFCS+ to truthfully determine molecular copy numbers and imager association and dissociation rates in well-separated localization clusters containing up to six docking strands. We show that lbFCS+ allows to resolve heterogeneous binding dynamics enabling the distinction of stochastically generated and a priori indistinguishable DNA assemblies. Beyond advancing quantitative DNA-PAINT imaging, we believe that lbFCS+ could find promising applications ranging from bio-sensing to DNA computing.
- assay: fluorescence / live-cell microscopy
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: cell-cell communication
- approach: statistical model
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