Evidence map›Paper›PMID 42145706›Full record

ArticleBiomedical optics express2026

NanorulerQA: quantitative quality analysis of dual-color DNA nanorulers via single-molecule photobleaching step counting and spatio-temporal colocalization.

Jingjing Wu, Chong Li, Yaolong Li, Xingguang Chen, Qihuan Li, Yingjun Zhang, Wei Ji, Nana Ma, Zhen-Li Huang

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Article in Biomedical optics express, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Jingjing WuState Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China.ORCID https://orcid.org/0009-0003-7205-8885
Chong LiInstitute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
Yaolong LiState Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China.ORCID https://orcid.org/0009-0007-9585-7750
Xingguang ChenState Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China.
Qihuan LiState Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China.
Yingjun ZhangState Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China.ORCID https://orcid.org/0000-0001-5703-3610
Wei JiInstitute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
Nana MaInstitute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
Zhen-Li HuangState Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China.ORCID https://orcid.org/0000-0003-2400-966X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dual-color DNA nanorulers are critical tools for characterizing the resolution of dual-color super-resolution imaging systems. However, existing quality analysis methods struggle to directly quantify the number of dye molecules (i.e., the degree of labeling) within these nanorulers, which precludes precise guidance for optimizing fabrication processes. Here, we present a novel quality analysis framework for dual-color DNA nanorulers, termed NanorulerQA. This framework employs a spatio-temporal colocalization algorithm to reduce the color crosstalk rate in dual-color nanoruler identification while improving throughput, and it utilizes single-molecule photobleaching step counting to calculate the absolute number of dye molecules. Experimental results demonstrate that our framework achieves a color crosstalk rate of less than 2%, comparable to state-of-the-art performance. Furthermore, applying this framework to compare fabrication protocols for dual-color DNA nanorulers (Cy3 & Atto647N) reveals that one protocol yields a significantly higher dual-color labeling degree (69.11% vs. 21.34%) and a greater proportion of intensity-matched nanorulers (34.15% vs. 11.30%). This work establishes a direct correlation between fabrication conditions and quality, providing precise feedback for optimizing preparation protocols and enabling practical quality spot-checking.

Identifiers

PMID42145706
PMCPMC13178635

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.