Summary of ssbd-repos-000346

Name
URL
DOI

Title
Imaging data in the paper, "Automated neuronal reconstruction with super-multicolour Tetbow labelling and threshold-based clustering of colour hues" (Leiwe et al., 2024).
Description

Images of HEK293 cells, and neurons in the cerebral cortex and the olfactory bulb in mice, which are labeled with seven-color fluorescent protein. Both original and color-unmixed images are included.

Submited Date
2024-05-10
Release Date
2024-06-05
Updated Date
-
License
Funding information
Brain/MINDS project (JP20dm0207055 to TI) and iBrain/MINDS project (JP23wm0525012 to TI) from AMED, JST CREST program (JPMJCR2021 to TI)
File formats
lif, tif
Data size
623.2 GB

Organism
Mus musculus, Homo Sapiens
Strain
ICR, C57BL/6 (Mus musculus)
Cell Line
HEK293T (Homo Sapiens)
Genes
-
Proteins
-

GO Molecular Function (MF)
-
GO Biological Process (BP)
-
GO Cellular Component (CC)
-
Study Type
-
Imaging Methods
-

Method Summary
-
Related paper(s)

Marcus N Leiwe, Satoshi Fujimoto, Toshikazu Baba, Daichi Moriyasu, Biswanath Saha, Richi Sakaguchi, Shigenori Inagaki, Takeshi Imai (2024) Automated neuronal reconstruction with super-multicolour Tetbow labelling and threshold-based clustering of colour hues., Nature communications, Volume 15, Number 1, pp. 5279

Published in 2024 Jun 25 (Electronic publication in June 25, 2024, midnight )

(Abstract) Fluorescence imaging is widely used for the mesoscopic mapping of neuronal connectivity. However, neurite reconstruction is challenging, especially when neurons are densely labelled. Here, we report a strategy for the fully automated reconstruction of densely labelled neuronal circuits. Firstly, we establish stochastic super-multicolour labelling with up to seven different fluorescent proteins using the Tetbow method. With this method, each neuron is labelled with a unique combination of fluorescent proteins, which are then imaged and separated by linear unmixing. We also establish an automated neurite reconstruction pipeline based on the quantitative analysis of multiple dyes (QDyeFinder), which identifies neurite fragments with similar colour combinations. To classify colour combinations, we develop unsupervised clustering algorithm, dCrawler, in which data points in multi-dimensional space are clustered based on a given threshold distance. Our strategy allows the reconstruction of neurites for up to hundreds of neurons at the millimetre scale without using their physical continuity.
(MeSH Terms)

Contact(s)
Takeshi Imai
Organization(s)
Kyushu Univerisity
Image Data Contributors
Satoshi Fujimoto, Toshikazu Baba, Daichi Moriyasu
Quantitative Data Contributors

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