Quality Control Maps: real-time quantitative quality control of single-molecule localization microscopy data.

Abstract

Single molecule localization microscopy (SMLM) has revolutionized the understanding of cellular organization by reconstructing informative images with quantifiable spatial distributions of molecules far beyond the optical diffraction limit. Much effort has been devoted to optimizing localization accuracy. One such approach is the assessment of SMLM data quality in real-time, rather than after lengthy post-acquisition analysis, which nevertheless represents a computational challenge. We overcame this difficulty by implementing an innovative mathematical approach we designed to drastically reduce the computational analysis of particle localization. Our Quality Control Maps (QCM) workflow enables a much higher rate of data processing compared to that limited by the frequency required by current cameras. Accordingly, by using an innovative computational approach for the detection step and an estimator based on a Gaussian model of the point spread function (PSF), sub-pixel particle locations and their accuracy can be determined through a straightforward analytical calculation, without the need for iterations. As a true parameter-free algorithm, QCM is robust and adaptable to all types of SMLM data, with high speed enabling the real-time calculation of quantitative quality control indicators. Such features are compatible with smart microscopy, the concept of which depends on the adjustment of acquisition parameters in real-time according to analytical results. Finally, the offline QCM mode can be used as a tool to evaluate synthetic or previously acquired data, as well as to teach the basic concepts of SMLM.