Dot Scanner: open‐source software for quantitative live‐cell imaging in planta

SUMMARY Confocal microscopy has greatly aided our understanding of the major cellular processes and trafficking pathways responsible for plant growth and development. However, a drawback of these studies is that they often rely on the manual analysis of a vast number of images, which is time‐consumi...

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Vydané v:The Plant journal : for cell and molecular biology Ročník 118; číslo 5; s. 1689 - 1698
Hlavní autori: Allen, Holly, Davis, Brian, Patel, Jenna, Gu, Ying
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Blackwell Publishing Ltd 01.06.2024
Predmet:
automated data analysis software
Automation
Bias
Biosynthesis
Bleaching
Cellulose
cellulose synthesis
computer software
Confocal microscopy
growth and development
live‐cell imaging
Plant growth
Scanners
technical advance
Workflow
Fiji
automated data analysis software
cellulose synthesis
confocal microscopy
technical advance
live-cell imaging
ISSN:0960-7412, 1365-313X, 1365-313X
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Shrnutí:SUMMARY Confocal microscopy has greatly aided our understanding of the major cellular processes and trafficking pathways responsible for plant growth and development. However, a drawback of these studies is that they often rely on the manual analysis of a vast number of images, which is time‐consuming, error‐prone, and subject to bias. To overcome these limitations, we developed Dot Scanner, a Python program for analyzing the densities, lifetimes, and displacements of fluorescently tagged particles in an unbiased, automated, and efficient manner. Dot Scanner was validated by performing side‐by‐side analysis in Fiji‐ImageJ of particles involved in cellulose biosynthesis. We found that the particle densities and lifetimes were comparable in both Dot Scanner and Fiji‐ImageJ, verifying the accuracy of Dot Scanner. Dot Scanner largely outperforms Fiji‐ImageJ, since it suffers far less selection bias when calculating particle lifetimes and is much more efficient at distinguishing between weak signals and background signal caused by bleaching. Not only does Dot Scanner obtain much more robust results, but it is a highly efficient program, since it automates much of the analyses, shortening workflow durations from weeks to minutes. This free and accessible program will be a highly advantageous tool for analyzing live‐cell imaging in plants. Significance Statement Live‐cell imaging analyses of fluorescently tagged particles in biological systems often require significant manual input that is time‐consuming and subject to bias. We have developed a free Python program, Dot Scanner, to allow users without any programming experience to analyze large datasets rapidly, efficiently, and with reduced bias.
Bibliografia:These authors contributed equally to this work.
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ISSN:0960-7412
1365-313X
1365-313X
DOI:10.1111/tpj.16662