Evidence map›Paper›PMID 41028723›Full record

ArticleNature communications2025

Nano-org, a functional resource for single-molecule localisation microscopy data.

S Shirgill, D J Nieves, J A Pike, M A Ahmed, H Abbott, M H H Baragilly, K Savoye, J D Worboys, K S Hazime, E Bruggeman and 8 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. 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

18 authors.

S ShirgillSchool of Infection, Inflammation and Immunology, School of Mathematics, Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK.
D J NievesSchool of Infection, Inflammation and Immunology, School of Mathematics, Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK.ORCID http://orcid.org/0000-0002-0873-4418
J A PikeInstitute for Interdisciplinary Data Science and AI, University of Birmingham, Birmingham, UK.ORCID http://orcid.org/0000-0003-4163-0335
M A AhmedResearch Software Group, Advanced Research Computing, University of Birmingham, Birmingham, UK.
H AbbottResearch Software Group, Advanced Research Computing, University of Birmingham, Birmingham, UK.
M H H BaragillySchool of Infection, Inflammation and Immunology, School of Mathematics, Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK.ORCID http://orcid.org/0000-0003-4775-4742
K SavoyeSchool of Infection, Inflammation and Immunology, School of Mathematics, Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK.ORCID http://orcid.org/0009-0008-0111-8541
J D WorboysLydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK.ORCID http://orcid.org/0000-0002-8242-4901
K S HazimeDepartment of Life Sciences, Imperial College London, London, UK.ORCID http://orcid.org/0000-0001-5125-2011
E BruggemanYusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.
A GarciaResearch Software Group, Advanced Research Computing, University of Birmingham, Birmingham, UK.
D J WilliamsonDepartment of Infectious Diseases, School of Immunology and Microbial Sciences, King's College London, London, UK.ORCID http://orcid.org/0000-0002-6222-6297
P Rubin-DelanchySchool of Mathematics, University of Edinburgh, Edinburgh, UK.
R PetersSchool of Mathematical and Physical Sciences, University of Sheffield, Sheffield, UK.
D M DavisDepartment of Life Sciences, Imperial College London, London, UK.ORCID http://orcid.org/0000-0002-9182-291X
R HenriquesInstituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Lisboa, Portugal.ORCID http://orcid.org/0000-0002-2043-5234
S F LeeYusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0003-4492-5139
D M OwenSchool of Infection, Inflammation and Immunology, School of Mathematics, Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK. d.owen@bham.ac.uk.ORCID http://orcid.org/0000-0002-5284-2782

Funding

European Research Council 101001332Medical Research Council MR/W031698/1RCUK | Biotechnology and Biological Sciences Research Council (BBSRC) BB/X018644/1Wellcome Trust 307027
6 · The paper itself

Abstract

The nanoscale organisation of proteins plays a key role in diverse cellular processes, including signalling, adhesion, and structural integrity. Single-molecule localisation microscopy (SMLM) is a super-resolution imaging technique that captures the spatial distributions of proteins in cells with nanometre precision, enabling detailed studies of protein clustering and architecture. However, comparing such data across experiments remains challenging due to a lack of curated, functional resources. Here we present a publicly accessible, curated, and functional resource, termed "nano-org", containing SMLM data representing the nanoscale distributions of proteins in cells. Nano-org is searchable by comparing the statistical similarity of the datasets it contains. This unique functionality allows the resource to be used to understand the relationships of nanoscale architectures between proteins, cell types or conditions, supporting the development of the field of spatial nano-omics.

Indexed as

ProteinsSingle Molecule ImagingHumansMicroscopy, FluorescenceNanotechnologyProteins

Identifiers

PMID41028723
PMCPMC12484560

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.