Structure based analysis of protein cluster size for super-resolution microscopy in the nervous system

To overcome the diffraction limit and resolve target structures in greater detail, far-field super-resolution techniques such as stochastic optical reconstruction microscopy (STORM) have been developed, and different STORM algorithms have been developed to deal with the various problems that arise. In particular, the effect of local structure is an important issue. For objects with closely correlated distributions, simple Gaussian-based localization algorithms often used in STORM imaging misinterpret overlapping point spread functions (PSFs) as one and this limits the ability of super-resolution imaging to resolve nanoscale local structures and leading to inaccurate length measurements. In the present study, we proposed a novel, structure-based, super-resolution image analysis method: structure-based analysis (SBA), which combines a structural function and a super-resolution localization algorithm. Using SBA, we estimated the size of fluorescent beads, inclusion proteins, and subtle synaptic structures in both wide-field and STORM images. The results showed that SBA has comparable and often superior performance to commonly used full-width-at-half-maximum parameters. We also demonstrated that SBA provides size estimations that corroborate previously published electron microscopy data.