Evidence map›Paper›PMID 41756771›Full record

ReviewChemical & biomedical imaging2026

Seeing the Unseen: Super-Resolution Microscopy in Protein Aggregation Research.

Molly J M Turner, Junsheng Chen, Nikos S Hatzakis, Min Zhang

Abstract readReview
In one paragraph

Review in Chemical & biomedical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
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

4 authors.

Molly J M TurnerDepartment of Chemistry, Faculty of Science, University of Copenhagen, Copenhagen 2100, Denmark.
Junsheng ChenDepartment of Chemistry, Faculty of Science, University of Copenhagen, Copenhagen 2100, Denmark.ORCID https://orcid.org/0000-0002-2934-8030
Nikos S HatzakisDepartment of Chemistry, Faculty of Science, University of Copenhagen, Copenhagen 2100, Denmark.ORCID https://orcid.org/0000-0003-4202-0328
Min ZhangDepartment of Chemistry, Faculty of Science, University of Copenhagen, Copenhagen 2100, Denmark.ORCID https://orcid.org/0000-0002-2797-5049

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Super-resolution microscopy surpasses the diffraction limit and enables the visualization of biomolecular structures with unprecedented detail. These techniques have been widely used in many scientific areas, including cell biology, genomics, microbiology, and material science. In the field of protein aggregation, a process intimately linked to numerous neurodegenerative diseases, the high spatial resolution of super-resolution microscopy enables the direct observation of the fine structure of different species, ranging from small oligomers to mature aggregates, providing insights into molecular aggregation mechanisms and the pathology of neurodegenerative diseases, such as Parkinson's, Alzheimer's, and Huntington's disease. In this review, we outline the principles of three major super-resolution microscopy techniques, including stimulated emission depletion (STED), structured illumination microscopy (SIM), and single-molecule localization microscopy (SMLM), and compare their respective strengths and limitations in studying protein aggregation. We then highlight the recent applications of these techniques in studying protein aggregation, with a focus on aggregate morphology, dynamic formation processes, and interactions with cellular components.

Indexed as

aggregation pathwaysdistributionmorphologyneurodegenerative diseasepathologyprotein aggregationsuper-resolution microscopytoxicity

Identifiers

PMID41756771
PMCPMC12933489

What OpenQuestion holds

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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.