Calibration-free estimation of field-dependent aberrations for single-molecule localization microscopy across large fields of view
I.E.A.C. Droste (TU Delft - ImPhys/Rieger group)
E. Schuitema (ESSD Software Engineering)
Sajjad A. Khan (University of New Mexico)
M.N.F. Hensgens (TU Delft - ImPhys/Geertsema group)
Stijn Heldens (The Netherlands eScience Center)
C.S. Smith (TU Delft - Team Carlas Smith)
Ben Van Werkhoven (Universiteit Leiden)
H.J. Geertsema (TU Delft - ImPhys/Geertsema group)
Keith A. Lidke (University of New Mexico)
S. Stallinga (TU Delft - ImPhys/Stallinga group)
B. Rieger (TU Delft - ImPhys/Rieger group, TU Delft - ImPhys/Computational Imaging)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Image quality in single-molecule localization microscopy depends largely on the accuracy and precision of the localizations. While under ideal imaging conditions, the theoretically obtainable precision and accuracy are achieved; in practice, this changes if (field-dependent) aberrations are present. Currently, there is no simple way to measure and incorporate these aberrations into the point-spread function (PSF) fitting; therefore, the aberrations are often taken as constant or neglected altogether. Here we introduce a model-based approach to estimate the field-dependent aberration directly from single-molecule data without a calibration step. This is made possible by using nodal aberration theory to incorporate the field dependency of aberrations into our fully vectorial PSF model. This results in a limited set of aberration fit parameters that can be extracted from the raw frames without a bead calibration measurement, also in retrospect. The software implementation is computationally efficient, enabling the fitting of a full 2D or 3D dataset within a few minutes. We demonstrate our method on 2D and 3D localization data of microtubuli, nuclear pore complexes, and nuclear lamina over fields of view of up to 180 µm and compare it with Gaussian fitting, spline-based fitting, and a deep-learning-based approach.
help_outline