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.

Methods
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"