Abstract
Astigmatism imaging approach has been widely used to encode the fluorophore’s 3D position in single-particle tracking and super-resolution localization microscopy. Here, we present a new high-speed localization algorithm based on gradient fitting to precisely decode the 3D subpixel position of the fluorophore. This algebraic algorithm determines the center of the fluorescent emitter by finding the position with the best-fit gradient direction distribution to the measured point spread function (PSF), and can retrieve the 3D subpixel position of the fluorophore in a single iteration. Through numerical simulation and experiments with mammalian cells, we demonstrate that our algorithm yields comparable localization precision to the traditional iterative Gaussian function fitting (GF) based method, while exhibits over two orders-of-magnitude faster execution speed. Our algorithm is a promising high-speed analyzing method for 3D particle tracking and super-resolution localization microscopy.
Highlights
Localization microscopy, known as different names including photo-activated localization microscopy [(f) PALM]1,2 and stochastic optical reconstruction microscopy [(d) STORM]3,4, has become a powerful imaging tool to reveal the ultra-structures and understand the complicated mechanisms behind cellular function
We present an algebraic algorithm based on gradient fitting for fast 3D fluorophore localization in astigmatism-based microscopy
We found that our gradient fitting based algorithm achieves a localization precision similar to the Gaussian function fitting (GF) based algorithm (NLLS-WD, NLLS-WA, and MLE-WA) in both lateral (Fig. 2a–b) and axial dimensions (Fig. 2c), at different axial positions
Summary
Localization microscopy, known as different names including (fluorescence) photo-activated localization microscopy [(f) PALM]1,2 and (direct) stochastic optical reconstruction microscopy [(d) STORM]3,4, has become a powerful imaging tool to reveal the ultra-structures and understand the complicated mechanisms behind cellular function. The slow execution speed of such algorithm that often takes several hours to reconstruct a standard super-resolution image is an intrinsic disadvantage of the GF based methods They do not apply to the cases when fast image reconstruction and online data analysis are needed, such as real-time optimization of imaging parameters. For this purpose, several single-iteration algorithms have been developed in the past few years to accelerate the execution speed while providing comparable precision to the GF based algorithm[14,15,16,17,18]. Through numerical simulation and experiments with fluorescent nanospheres and mammalian cells, we demonstrate that the proposed single-iteration algorithm can achieve localization precision close to multiple iterative GF based algorithm in all three dimensions, while yielding over 100 times faster computation speed
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
