Evidence map›Paper›PMID 41644552›Full record

ArticleNature communications2026

Utilizing the predictable binding kinetics of DNA-PAINT to denoise super-resolution images.

George Sirinakis, Edward S Allgeyer, Jennifer H Richens, Jeanne Lefévère-Laoide, Ewa K Paluch, Daniel St Johnston

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

George SirinakisThe Gurdon Institute & the Department of Genetics, University of Cambridge, Cambridge, UK. gs519@cam.ac.uk.ORCID 0000-0002-4762-422X
Edward S AllgeyerThe Gurdon Institute & the Department of Genetics, University of Cambridge, Cambridge, UK.ORCID 0000-0002-2187-4423
Jennifer H RichensThe Gurdon Institute & the Department of Genetics, University of Cambridge, Cambridge, UK.ORCID 0000-0002-8241-4826
Jeanne Lefévère-LaoideDepartment of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK.ORCID 0009-0004-6775-4787
Ewa K PaluchDepartment of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK.ORCID 0000-0003-4691-2323
Daniel St JohnstonThe Gurdon Institute & the Department of Genetics, University of Cambridge, Cambridge, UK. ds139@cam.ac.uk.ORCID 0000-0001-5582-3301

Funding

RCUK | Biotechnology and Biological Sciences Research Council (BBSRC) BB/V008595/1Wellcome Trust (Wellcome) 224402/Z/21/ZWellcome Trust (Wellcome) 24843
6 · The paper itself

Abstract

DNA-Point Accumulation for Imaging in Nanoscale Topography (DNA-PAINT) is a versatile super-resolution technique that relies on the predictable binding kinetics between fluorescent imager strands and docking strands attached to target proteins. This makes DNA-PAINT particularly suitable for multiplexing and quantitative applications, but its performance is often limited by spurious signals from non-specific binding of imager strands. Here we describe a method to remove these non-specific binding events using a statistical test to distinguish between DNA-specific and non-specific interactions. To demonstrate the method, we imaged mosaic epithelial tissues in Drosophila melanogaster egg chambers and showed that >90% of non-specific and otherwise indistinguishable signal in the super-resolved images can be removed. This denoising improves the quality of DNA-PAINT super-resolved images and is essential for accurate measurements of spatial relationships and protein quantification.

Indexed as

DNAAnimalsDrosophila melanogasterKineticsMicroscopy, FluorescenceDNA

Identifiers

PMID41644552
PMCPMC12982735

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.