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bioRxiv · 10.1101/2024.05.10.593629

Robotic Imaging and Machine Learning Analysis of Seed Germination: Dissecting the Influence of ABA and DOG1 on Germination Uniformity

Cutler, S.·Eckhardt, J.·Subramanian, V.·Xing, Z.·Vaidya, A.
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Abstract

Seed germination research has evolved over the years, increasingly incorporating technology. Recent advances in phenotyping platforms have increased the accessibility of high throughput phenotyping technologies to more labs, leading to valuable insights into germination biology. These platforms benefit researchers by limiting manual labor and increasing the temporal resolution of imaging. Each of the platforms developed presents unique benefits and challenges, from scalability to price to computing resources. Performing experiments involving thousands of seeds remains a daunting task due to the limitations of current phenotyping platforms and image analysis pipelines. To overcome these challenges, we introduce SPENCER (Seed Phenotype Evaluation and Germination Curve Estimation Robot), a high-throughput phenotyping platform. SPENCER accommodates 32 rectangular petri plates, capable of assessing up to 8000 Arabidopsis seeds per experiment. Our design allows for high quality images while maintaining optimal humidity, crucial for precise germination assessment over longer experiments. The image analysis workflow incorporates advanced image analysis using semantic segmentation models trained for Arabidopsis and lettuce, providing researchers with accessible, reproducible, and efficient tools. We applied SPENCER to investigate the relative roles of DELAY OF GERMINATION 1 (DOG1) and abscisic acid (ABA) in Arabidopsis dormancy. DOG1 mutants exhibited rapid germination, whereas ANT application had a greater impact on the slower-germinating Ler ecotype. Our findings suggest that DOG1 plays a significant role in dormancy, particularly in non-dormant accessions, while ABAs influence is more pronounced under stress conditions. Additionally, we explored germination uniformity, another agriculurally relevant trait, observing parallels with germination timing. SPENCER offers a powerful and accessible tool for dissecting complex biological traits in conjunction with chemical and genetic manipulations. Its scalability and versatility make it suitable for large-scale genetic and chemical germination screens.

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Cutler, S., Eckhardt, J., Subramanian, V., Xing, Z., Vaidya, A.. 2024-05-13. Robotic Imaging and Machine Learning Analysis of Seed Germination: Dissecting the Influence of ABA and DOG1 on Germination Uniformity. https://doi.org/10.1101/2024.05.10.593629

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