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High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

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Toward drift-free high-throughput nanoscopy through adaptive intersection maximization.

Hongqiang Ma^1,2, Maomao Chen¹, Phuong Nguyen^1,2

¹Department of Medicine, Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA.

Science Advances

|May 23, 2024

Summary

This summary is machine-generated.

We developed Adaptive Intersection Maximization (AIM), a new method to improve single-molecule localization microscopy (SMLM) resolution by precisely correcting high-frequency drift. AIM enhances SMLM imaging quality efficiently on standard hardware.

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Area of Science:

Biophysics
Microscopy
Computational Biology

Background:

Single-molecule localization microscopy (SMLM) is crucial for nanoscale biological imaging.
Suboptimal resolution in SMLM is often caused by uncorrected sample drift.
Existing drift correction methods struggle with high-frequency drift and large datasets.

Purpose of the Study:

To develop a novel, efficient, and model-free algorithm for drift correction in SMLM.
To enhance the resolution of SMLM by accurately tracking high-frequency drift.
To provide a user-friendly software solution for improving SMLM data quality.

Main Methods:

Adaptive Intersection Maximization (AIM) algorithm leveraging entire dataset information.
Model-free approach for robust drift tracking.
Application to simulated and experimental high-throughput SMLM datasets.

Main Results:

AIM achieves angstrom-level tracking precision for high-frequency drift.
Demonstrated robust and efficient performance across various imaging conditions.
Significantly enhanced resolution in both simulated and biological SMLM data.

Conclusions:

AIM effectively minimizes drift correction errors, particularly high-frequency drift.
The method provides instant resolution enhancement for SMLM systems.
AIM is a computationally efficient, accessible software solution for standard CPUs.