Evidence map›Paper›PMID 41073388›Full record

ArticleNature communications2025

Fast and robust drift correction for single-molecule localization microscopy.

Mengdi Hou, Jianyu Yang, Mingjie Yang, Fen Hu, Rongge Zhao, Yuhang Pan, Wan Li, Mingxin Chen, Jingjun Xu, Ke Xu and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Mengdi HouKey Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, TEDA Institute of Applied Physics and School of Physics, Nankai University, Tianjin, China.
Jianyu YangKey Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, TEDA Institute of Applied Physics and School of Physics, Nankai University, Tianjin, China.
Mingjie YangKey Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, TEDA Institute of Applied Physics and School of Physics, Nankai University, Tianjin, China.
Fen HuKey Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, TEDA Institute of Applied Physics and School of Physics, Nankai University, Tianjin, China.
Rongge ZhaoKey Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, TEDA Institute of Applied Physics and School of Physics, Nankai University, Tianjin, China.
Yuhang PanKey Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, TEDA Institute of Applied Physics and School of Physics, Nankai University, Tianjin, China.
Wan LiDepartment of Chemistry, University of California, Berkeley, California, USA.
Mingxin ChenKey Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, TEDA Institute of Applied Physics and School of Physics, Nankai University, Tianjin, China.
Jingjun XuKey Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, TEDA Institute of Applied Physics and School of Physics, Nankai University, Tianjin, China.ORCID http://orcid.org/0000-0002-2509-0175
Ke XuDepartment of Chemistry, University of California, Berkeley, California, USA. xuk@berkeley.edu.ORCID http://orcid.org/0000-0002-2788-194X
Leiting PanKey Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, TEDA Institute of Applied Physics and School of Physics, Nankai University, Tianjin, China. plt@nankai.edu.cn.ORCID http://orcid.org/0000-0001-5329-6114

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Owing to its gradual accumulation of molecular positions, single-molecule localization microscopy (SMLM) depends on the proper correction of sample drifts that occur during data acquisition. However, current data-based drift-correction approaches for SMLM are often unreliable and time-consuming, limiting the achieved resolution and throughput. Here we report nearest paired cloud (NP-Cloud), a fast and robust SMLM drift-correction method. By pairing the nearest molecules in SMLM data segments and calculating their displacements within a small search radius, NP-Cloud efficiently utilizes the continuously valued positions of each super-localized molecule while drastically reducing the computational cost. With both simulated and experimental SMLM data, we thus demonstrate substantially improved robustness and fidelity for drift correction in three dimensions, as well as speeds >100-fold faster over traditional single-referenced approaches and >10

Identifiers

PMID41073388
PMCPMC12514242

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