SMILE: a universal tool for modulated-enhanced localization microscopy to achieve minimal three-dimensional resolution

TL;DR

SMILE is a versatile 3D localization microscopy tool that combines lateral modulation and PSF engineering to achieve near-theoretical localization precision, significantly improving 3D imaging resolution.

Contribution

SMILE introduces a unified framework integrating lateral modulation with PSF engineering for enhanced 3D localization microscopy.

Findings

Achieves 4-fold improvement in lateral precision over conventional 3D-SMLM.

Compatible with diverse PSFs and illumination patterns, including 4Pi configurations.

Demonstrates potential for sub-10 nm axial resolution with 4Pi-SMILE.

Abstract

Modulation-enhanced localization microscopy (MELM) has demonstrated significant improvements in both lateral and axial localization precision compared to conventional single-molecule localization microscopy (SMLM). However, lateral modulated illumination based MELM (MELMxy) remains fundamentally limited to two-dimensional imaging. Here we present three-dimensional Single-Molecule Modulated Illumination Localization Estimator (SMILE) that synergistically integrates lateral illumination modulation with point spread function engineering. By simultaneously exploiting lateral modulation patterns and an accurate point spread function (PSF) model for 3D localization, SMILE achieves near-theoretical-minimum localization uncertainty, demonstrating an average 4-fold enhancement in lateral precision compared to conventional 3D-SMLM. Crucially, SMILE exhibits exceptional compatibility with diverse PSFs and different illumination patterns with various structures including 4Pi configurations, making it a versatile tool that can be easily adapted for different experimental setups. When integrated with 4Pi microscopy, 4Pi-SMILE shows particular promise for achieving sub-10 nm axial resolution and approaching isotropic resolution. From the simulations and proof-of-concept experiments, we verified the superiority of SMILE over 3D-SMLM and ordinary MELM. We highlight SMILE as a novel methodology and robust framework that holds great potential to significantly promote the development of MELM.