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

Abstract

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.

Plant phenotyping relevance

植物のライブセル画像から粒子密度・寿命・変位を自動抽出するソフトウェアを開発し、Fiji-ImageJと比較検証しており、表現型取得・解析手法が中心である。

Code and data availability

The paper's own computational analysis tool, Dot Scanner (Python software for quantifying densities, lifetimes, and displacements of fluorescently labeled particles in plant tissues), is explicitly stated to be publicly available on GitHub with a full URL. No public phenotype/trait datasets or raw imaging data deposits