DOI: 10.5061/dryad.2v6wwpzw3
title: Data from: Towards drift-free high-throughput nanoscopy through adaptive intersection maximization

description: Single-molecule localization microscopy (SMLM) often suffers from
 suboptimal resolution due to imperfect drift correction. Existing
 marker-free drift-correction algorithms often struggle to reliably track
 high-frequency drift and lack the computational efficiency to manage
 large, high-throughput localization datasets. We present an adaptive
 intersection maximization-based method (AIM) that leverages the entire
 dataset's information content to minimize drift correction errors,
 particularly addressing high-frequency drift, thereby enhancing the
 resolution of existing SMLM systems. We demonstrate that AIM can robustly
 and efficiently achieve an angstrom-level tracking precision for
 high-throughput SMLM datasets under various imaging conditions, resulting
 in an optimal resolution in simulated and biological experimental
 datasets. We offer AIM as simple and model-free software for instant
 resolution enhancement with standard CPU devices.
description: We provided the following dataset. 
 Two-dimensional single-molecule localization point list from an
 DNA origami structure (Origami_PAINT.mat). This data is used to produce
 Fig. 3 in the main text. Two-dimensional
 single-molecule localization point list from CTCF of a cell line MCF10A
 treated with DRB (CTCF_MCF10A_DRB_6h.mat). This image size is 2048 x 2048
 pixels with a pixel size of 100 nm. This data is used to produce Fig. 4 in
 the main text. Two-dimensional single-molecule
 localization point list from a colon tissue (Tissue_colon.mat). This image
 size is 2048 x 2048 pixels with a pixel size of 100 nm. This data is used
 to produce Fig. 5 in the main text. Three-dimensional
 single-molecule localization point list for microtubules from COS-7
 (microtubule Microtublue_3d.mat). This image size is 2048 x 2048 pixels
 with a pixel size of 100 nm. This data is used to produce Fig. 6 in the
 main text. Simulated three-dimensional single
 molecule localization point list for DNA origami structure
 (simulationSMLM.mat). This dataset is used to produce Fig. 2 in the main
 text. It can also be generated using the provided code
 "simulationSMLM.m"
description: # Towards drift-free high-throughput nanoscopy through adaptive
 intersection maximization ## AIM Adaptive Intersection Maximization (AIM)
 is a high-speed drift correction algorithm for single molecule
 localization microscopy. The details are presented in our paper entitled
 "Towards drift-free high-throughput nanoscopy through adaptive
 intersection maximization". All the codes under \DME_RCC are from
 [https://github.com/qnano/drift-estimation](https://github.com/qnano/drift-estimation) published in Jelmer Cnossen, Tao Ju Cui, Chirlmin Joo, and Carlas Smith, "Drift correction in localization microscopy using entropy minimization," Opt. Express 29, 27961-27974 (2021). ## Hardware requirement: AIM requires only a standard computer with a minimum of 16GB of RAM. RCC and DME require a minimum of 32GB of RAM to handle the large datasets generated from systems with a large field of view (e.g., 2048 x 2048 pixels). ## Software requirement: The provided codes have been tested on MATLAB version 2020b to 2023a on Windows 10 Operating System. ## Installation: Users can direacly download the codes and run the demo code on MATLAB. Users need to replace the file name when processing users' own datasets. ## Demo datasets: We provided four experimental datasets (Origami_PAINT, Microtublue_3d, Tissue_colon and CTCF_MCF10A_DRB_6h) along with one simulated dataset (simulationSMLM) in MATLAB '.mat' format available at [Dryad](https://doi.org/10.5061/dryad.2v6wwpzw3). Please download these datasets and put them in the **Data** folder. The 'simulationSMLM.mat' (Figure 2) and 'Microtubule_3D.mat' (Figure 6) datasets contain 3D localization lists, consisting of four variables: 'F', 'X', 'Y', and 'Z'. '**F**': Frame index of the localization (unit: frame). Note that the time duration for each frame is 10 ms for both 'simulationSMLM.mat' and 'Microtubule_3D.mat' '**X**': X position of the localization (unit: pixels) '**Y**': Y position of the localization (unit: pixels) '**Z**': Z position of the localization (unit: pixels) The remaining datasets, 'Origami_PAINT.mat' (Figure 3), 'CTCF_MCF10A_DRB_6h.mat' (Figure 4), and 'Tissue_colon.mat' (Figure 5), contain 2D localization lists with three variables: 'F', 'X', and 'Y'. '**F**': Frame index of the localization (unit: frame). Note that the time duration for each frame is 50 ms for 'Origami_PAINT.mat', 20 ms for 'CTCF_MCF10A_DRB_6h.mat', and 10 ms for 'Tissue_colon.mat' '**X**': X position of the localization (unit: pixels) '**Y**': Y position of the localization (unit: pixels) ## Example files: We provide four MATLAB codes as examples to demonstrate how to use AIM. **example_ExperimentalData.m**: This code performs drift correction with AIM on 2D or 3D localization coordinates of experimental data. Sample experimental data are available at [Dryad](https://doi.org/10.5061/dryad.2v6wwpzw3) **example_code_2D.m**: This code compares the performance of drift correction for AIM, RCC and DME using 2D localization coordinates for the experimental dataset of DNA origami (Origami_PAINT.mat) or simulated data (simulationSMLM.mat) available at [Dryad](https://doi.org/10.5061/dryad.2v6wwpzw3) **example_code_3D.m**: This code compares the performance of drift correction for AIM, RCC, and DME using 3D localization coordinates of experimental data of simulated data or experimental data of microtubules (Microtublue_3d.mat) available at [Dryad](https://doi.org/10.5061/dryad.2v6wwpzw3) **example_code_FigureS1.m**: This code is used to reproduce Supplementary Figure S1, which shows drift tracking precision under a wide range of image sizes from 128×128 pixels to 2048×2048 pixels. ## Other files: **simulationSMLM.m**: This code is used to generate the simulated SMLM dataset from DNA origami structures used in Figure 2 in the main text. **save_imSR.m**: This MATLAB function is used to save the SMLM dataset into a tif image. **Load_ThunderSTORM.m**: This code is used to provide a MATLAB function to read the localization dataset (csv files) from the commonly used ThunderSTORM software.