Three-dimensional localization microscopy with increased axial precision through TIRF angle modulation

Journal article (2023)

Authors

D. Fan Team Carlas Smith - Mechanical, Maritime and Materials Engineering
J.P. Cnossen Team Carlas Smith - Mechanical, Maritime and Materials Engineering
S. Hung Team Carlas Smith - Mechanical, Maritime and Materials Engineering
D. Kromm Support Delft Center for Systems and Control - Mechanical, Maritime and Materials Engineering
N.H. Dekker BN/Nynke Dekker Lab - AS
G.J. Verbiest Dynamics of Micro and Nano Systems - Mechanical, Maritime and Materials Engineering
C.S. Smith Team Carlas Smith - Mechanical, Maritime and Materials Engineering
Research Group
Team Carlas Smith (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2023 D. Fan, J.P. Cnossen, S. Hung, D. Kromm, N.H. Dekker, G.J. Verbiest, C.S. Smith
DOI: https://doi.org/10.1016/j.optcom.2023.129548
More Info
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Published Date

2023

Language

English

Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Carlas Smith

Abstract

To better understand the interactions between biological molecules, a high optical resolution in all three dimensions is crucial. The intrinsically lower axial resolution of microscopes however, is a limiting factor in fluorescence imaging, correspondingly in fluorescence based single molecule localization microscopy (SMLM). Here, we present a method to improve the axial localization precision in SMLM by combining point-spread-function engineering with total internal reflection fluorescence (TIRF) fields with decay lengths that vary within the on-time of a fluorophore. Such time-varying illumination field intensity allows one to extract additional axial location information from the emitted photons. With this time varying illumination approach, we show that axial localization is improved two-fold over TIRF-based SMLM using astigmatic PSFs. We calculate theoretical resolution gains for various imaging conditions via the Cramér Rao Lower Bound (CRLB), a commonly used metric to compute the best attainable localization precision in SMLM.