Summary of ssbd-repos-000407

Name
URL
DOI

Title
Autonomous multicolor bioluminescence imaging
Description

Bioluminescence imaging has become a valuable tool in biological research, offering several advantages over fluorescence-based techniques, including the absence of phototoxicity and photobleaching, along with a higher signal-to-noise ratio. Common bioluminescence imaging methods often require the addition of an external chemical substrate (luciferin), which can result in a decrease in luminescence intensity over time and limit prolonged observations. Since the bacterial bioluminescence system is genetically encoded for luciferase-luciferin production, it enables autonomous bioluminescence (auto-bioluminescence) imaging. However, its application to multiple reporters is restricted due to a limited range of color variants. Here, we report five-color auto-bioluminescence system named Nano-lanternX (NLX), which can be expressed in bacterial, mammalian, and plant hosts, thereby enabling auto-bioluminescence in various living organisms. Utilizing spectral unmixing, we achieved the successful observation of multicolor auto-bioluminescence, enabling detailed single-cell imaging across both bacterial and mammalian cells. We have also expanded the applications of the NLX system, such as multiplexed auto-bioluminescence imaging for gene expression, protein localization, and dynamics of biomolecules within living mammalian cells.

Submited Date
2024-11-14
Release Date
2024-11-25
Updated Date
-
License
Funding information
-
File formats
TIFF
Data size
8.7 MB

Organism
Escherichia coli, Homo sapiens, Nicotiana benthamiana, Mus musculus
Strain
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Cell Line
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Genes
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Proteins
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GO Molecular Function (MF)
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GO Biological Process (BP)
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GO Cellular Component (CC)
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Study Type
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Imaging Methods
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Method Summary

See details in Kusuma et. al. (2024) Proc Natl Acad Sci U S A.

Related paper(s)

Contact(s)
Takeharu Nagai
Organization(s)
Osaka University , SANKEN , Department of Biomolecular Science and Engineering
Image Data Contributors
Quantitative Data Contributors

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