Bertaux, N. | Search bioRxiv

bioRxiv2024

QCM: real-time quantitative quality control of single-molecule localization microscopy acquisitions

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. Among them, assessing the quality of SMLM data in real-time, rather than after lengthy post-acquisition analysis, represent a computational challenge. Here, we overcome this difficulty by implementing an innovative mathematical approach to drastically reduce the computational analysis of particle localization. We have therefore designed the Quality Control Map (QCM) workflow to process data at a much higher rate than that limited by the frequency required by current cameras. Moreover, QCM requires no parameters other than the PSF radius characteristic of the optical system and only a GPU card to reach its computational speed. Thus, QCM is robust and adaptable to any type of input data. Finally, the QCM off-line mode can be used to evaluate synthetic or previously acquired data, and as a tool for teaching the basic concepts of the SMLM approach. TeaserQCM, a parameter-free algorithm, calculates indicators for instant feedback on single-molecule localization precision experiments

biophysics↗

bioRxiv2018

A theoretical high-density nanoscopy study leads to the design of UNLOC, an unsupervised algorithm

Among the superresolution microscopy techniques, the ones based on serially imaging sparse fluorescent particles enable the reconstruction of high-resolution images by localizing single molecules. Although challenging, single-molecule localization microscopy (SMLM) methods aim at listing the position of individual molecules leading a proper quantification of the stoichiometry and spatial organization of molecular actors. However, reaching the precision requested to localize accurately single molecules is mainly constrained by the signal-to-noise ratio (SNR) but also the density (Dframe), i.e., the number of fluorescent particles per m2 per frame. Of central interest, we establish here a comprehensive theoretical study relying on both SNR and Dframe to delineate the achievable limits for accurate SMLM observations. We demonstrate that, for low-density hypothesis (i.e. one-Gaussian fitting hypothesis), any fluorescent particle biases the localization of a particle of interest when they are distant by less than {approx} 600 nm. Unexpectedly, we also report that even dim fluorescent particles should be taken into account to ascertain unbiased localization of any surrounding particles. Therefore, increased Dframe quickly deteriorates the localization precision, the image reconstruction and more generally the quantification accuracy. The first outcome is a standardized density-SNR space diagram to determine the achievable SMLM resolution expected with experimental data. Additionally, this study leads to the identification of the essential requirements for implementing UNLOC (UNsupervised particle LOCalization), an unsupervised and fast computing algorithm approaching the Cramer-Rao bound for particles at high-density per frame and without any prior on their intensity. UNLOC is available as an ImageJ plugin.

biophysics↗