ScreenSeed as a novel high throughput seed germination phenotyping method

Abstract

A high throughput phenotyping tool for seed germination, the ScreenSeed technology, was developed with the aim of screening genotype responsiveness and chemical drugs. This technology was presently used with Arabidopsis thaliana seeds to allow characterizing seed samples germination behavior by incubating seeds in 96-well microplates under defined conditions and detecting radicle protrusion through the seed coat by automated image analysis. This study shows that this technology provides a fast procedure allowing to handle thousands of seeds without compromising repeatability or accuracy of the germination measurements. Potential biases of the experimental protocol were assessed through statistical analyses of germination kinetics. Comparison of the ScreenSeed procedure with commonly used germination tests based upon visual scoring displayed very similar germination kinetics.

Seed Transmission of Pathogens: Non-Canonical Immune Response in Arabidopsis Germinating Seeds Compared to Early Seedlings against the Necrotrophic Fungus Alternaria brassicicola

“…Seed germination analyses were performed in microplates using the ScreenSeed automate according to the conditions described by Merieux et al [77]. Incubation was performed inside a thermo-regulated incubator (Memmert ICP 750) regulated at 22 • C (±1 • C).…”

Molecular dynamics of seed priming at the crossroads between basic and applied research

“…The first level for assessing the impact of seed priming is the accurate phenotyping of the germination process. Scoring can be carried out by operators through visual observation; however, this affects accuracy and for this reason, several tools, e.g., Germinator (Joosen et al 2010 ), pheno Seeder (Jahnke et al 2016 ), MultiSense (Keil et al 2017 ), SeedGerm (Colmer et al 2020 ), ScreenSeed (Merieux et al 2021 ), have been so far developed to automatize seed diagnostics and associated phenotypic analysis. Spatiotemporal dynamics of seed germination can be measured through digital analysis based on seed color, texture, morphology, and growth patterns, with the added value of standardization.…”

“…The technological platform known as MultiSense tool allows the parallel monitoring of respiration in imbibing seeds (up to 100 samples) over an extended period, tracking oxygen (O 2 ), carbon dioxide (CO 2 ), and/or pH (Keil et al 2017 ). The ScreenSeed technology, developed for Arabidopsis , provides a fast procedure allowing to handle thousands of seeds without compromising the repeatability or accuracy of the germination measurements (Merieux et al 2021 ). An approach for determining seed quality was developed using FT-NIR spectroscopy and X-ray imaging data by FT-NIR spectroscopy that can be used in conjunction with machine learning algorithms to improve seed germination and vigor prediction (Dantas de Medeiros et al 2020 ).…”

POMONA: a multiplatform software for modeling seed physiology

“…Taking this into account, robust algorithms, using artificial intelligence and machine learning, have been considered the future for quality operations, specially by integrating image analysis and processing with classical physiological measurements ( Deal et al., 2020 ; de Medeiros et al., 2020 ; Galletti et al., 2020 ; Ribeiro-Oliveira et al., 2020 ; Barboza da Silva et al., 2021 ; Batista et al., 2022 ; Oliveira et al., 2022 ). This, for example, can also be observed in platforms for phenotype analysis during the seed-seedling transition, such as SeedGerm ( Colmer et al., 2020 ), or ScreenSeed, a novel high throughput seed germination phenotyping method based on computer vision ( Merieux et al., 2021 ). It is possible to mention other examples of this technology transference such as the GERMINATOR, a high throughput scoring and curve fitting software for seed germination ( Joosen et al., 2010 ; Ligterink and Hilhorst, 2017 ), and the SeedStor, a publicly available database for the seed collections held by the Germplasm Resources Unit (GRU) ( Horler et al., 2018 ), It’s important to highlight that other phenotyping high throughput systems have been proposed over the years, including a chlorophyll fluorescence-based imaging (ChIF) system to detect emerging cotyledons ( Pavicic et al., 2019 ), and an automatic computer vision system using RGB image-based analysis to detect radicle emergence ( Ducournau et al., 2005 ).…”