Evidence map›Paper›PMID 42009863›Full record

ArticleScientific reports2026

Labeling and imaging of neurons with a genetically encoded autonomous bioluminescence system.

Theresa Brinker, Anja Günther, Kamila A Kiszka, Jeong Seop Rhee, Carola Gregor

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Theresa BrinkerDepartment of Optical Nanoscopy, Institut für Nanophotonik Göttingen e.V., 37077, Göttingen, Germany.
Anja GüntherDepartment of Molecular Neurobiology, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany.
Kamila A KiszkaDepartment of NanoBiophotonics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany.
Jeong Seop RheeDepartment of Molecular Neurobiology, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany.
Carola GregorDepartment of Optical Nanoscopy, Institut für Nanophotonik Göttingen e.V., 37077, Göttingen, Germany. carola.gregor@ifnano.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The bacterial bioluminescence system, consisting of a luciferase and enzymes for intracellular luciferin synthesis, enables autonomous bioluminescence imaging of mammalian cells. Its continuous light emission without the need for exogenous substrate administration makes this system particularly well-suited for the long-term imaging of living cells. An additional feature is its dependence on metabolic energy, allowing the monitoring of cellular energy status and vitality. However, the system’s relatively low brightness and the requirement of six different genes complicate cellular labeling and imaging. Here, we employed a combination of recombinant adeno-associated viruses containing the involved genes to achieve both bright and cell type-specific bioluminescent labeling of neurons. The high light output obtained with the developed viruses enabled the detailed visualization of individual neurons. Furthermore, monitoring of bioluminescence emission in the presence of neurotoxic agents allowed the observation of cellular decline in models of neurodegenerative diseases. The autonomous light production realized with our labeling approach thus provides a promising tool for investigating neurodegeneration, facilitating continuous and real-time cellular monitoring.

Indexed as

Luminescent MeasurementsNeuronsStaining and LabelingAnimalsDependovirusHumansLuciferasesMiceLuciferasesBioluminescenceluxMicroscopyNeurons

Identifiers

PMID42009863
PMCPMC13096443

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Registered trials

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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.