Towards drift-free high-throughput nanoscopy through adaptive intersection maximization

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., 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 ..., , # 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 ve...单分子定位显微镜（Single-molecule localization microscopy, SMLM）常因漂移校正不完善而难以达到最优分辨率。现有的无标记漂移校正算法往往难以可靠追踪高频漂移，且计算效率不足以处理大规模、高通量的定位数据集。本研究提出一种基于自适应交点最大化的方法（AIM），该方法利用全数据集的信息含量以最小化漂移校正误差，尤其针对高频漂移进行优化，从而提升现有SMLM系统的分辨率。研究表明，AIM能够在多种成像条件下，对高通量SMLM数据集实现稳健且高效的埃级追踪精度，在模拟数据集与生物实验数据集中均能获得最优分辨率。我们将AIM作为一款简洁且无模型依赖的软件提供，可在标准CPU设备上快速实现分辨率提升。 我们提供了如下数据集： 二维单分子定位点列表，来自DNA折纸结构（Origami_PAINT.mat）。该数据用于制作正文中的图3。 二维单分子定位点列表，来自经DRB处理6小时的MCF10A细胞系的CTCF蛋白（CTCF_MCF10A_DRB_6h.mat）。该图像尺寸为2048×2048像素，像素尺寸为100 nm。该数据用于制作正文中的图4。 二维单分子定位点列表，来自结肠组织（Tissue_colon.mat）。该图像尺寸为2048×2048像素，像素尺寸为100 nm。该数据用于制作正文中的图5。 三维单分子定位点列表，来自COS-7细胞的微管（microtubule Microtublue_3d.mat）。该图像尺寸为2048×2048像素，像素尺寸为100 nm。该数据用于制作正文中的图6。 用于DNA折纸结构的模拟三维单分子定位点列表…… # 基于自适应交点最大化实现无漂移高通量纳米显微成像（Towards drift-free high-throughput nanoscopy through adaptive intersection maximization） ## AIM 自适应交点最大化（Adaptive Intersection Maximization, AIM）是一款面向单分子定位显微镜的高速漂移校正算法。 详细内容请参阅我们题为《基于自适应交点最大化实现无漂移高通量纳米显微成像》（"Towards drift-free high-throughput nanoscopy through adaptive intersection maximization"）的论文。 DME_RCC 下的所有代码均来自[https://github.com/qnano/drift-estimation](https://github.com/qnano/drift-estimation)，该代码源自Jelmer Cnossen、Tao Ju Cui、Chirlmin Joo与Carlas Smith发表于《Optics Express》29卷，第27961-27974页（2021年）的论文《利用熵最小化实现定位显微镜中的漂移校正》（"Drift correction in localization microscopy using entropy minimization"）。 ## 硬件要求 AIM仅需标准计算机，内存最低要求为16GB。 RCC与DME需至少32GB内存，以处理大视场（如2048×2048像素）系统产生的大规模数据集。 ## 软件要求 本次提供的代码已在MATLAB v……上完成测试。