ABSTRACT (250 words)
Micronutrients, such as iron, zinc, and sulfur, play a vital role in both plant and human development. Understanding how plants sense and allocate nutrients within their tissues may offer different venues to develop plants with high nutritional value. Despite decades of intensive research, more than 40% of genes in Arabidopsis remain uncharacterized or have no assigned function. While several resources such as mutant populations or diversity panels offer the possibility to identify genes critical for plant nutrition, the ability to consistently track and assess plant growth in an automated, unbiased way is still a major limitation. High-throughput phenotyping (HTP) is the new standard in plant biology but few HTP systems are open source and user friendly. Therefore, we developed OPEN Leaf, an open source HTP for hydroponic experiments. OPEN Leaf is capable of tracking changes in both size and color of the whole plant and specific regions of the rosette. We have also integrated communication platforms (Slack) and cloud services (CyVerse) to facilitate user communication, collaboration, data storage, and analysis in real time. As a proof-of-concept, we report the ability of OPEN Leaf to track changes in size and color when plants are growing hydroponically with different levels of nutrients. We expect that the availability of open source HTP platforms, together with standardized experimental conditions agreed by the scientific community, will advance the identification of genes and networks mediating nutrient uptake and allocation in plants.
Keywords: High-throughput phenotyping, Cloud-based, CyVerse, Plant Nutrition
INTRODUCTION
Predictive biology, or predicting biological outcomes from a known input, is central to understanding the genome-to-phenome relationship1. However, the phenome is a complex combination of the result of environmental conditions and the ability of the organism to adapt to environmental changes2,3. In the case of plants, predictive biology requires a deep understanding of its genetics and its response to environmental cues, such as light intensity, water availability, and other organisms4,5. Therefore, the prediction of plant responses to changes in environmental conditions has lagged compared to other advances in the understanding of other plant behaviors. The consistent collection of reproducible data at the “-omic” and environmental level are vital towards addressing this issue.
A drastic decline in cost has allowed for large amounts of genomic and environmental data to be collected, yet major bottlenecks still stand in integrating, sharing, and analyzing these datasets. For example, methods characterizing genomes, such as DNA sequencing, have advanced far more quickly than methods for phenomes6,7. Plant phenotyping at scale is costly and inaccessible to the majority of plant researchers8,9.10. However, many components for high-throughput phenotyping, such as sensors and computer vision, have become cheaper and more accessible for development of phenotyping platforms. These platforms can collect information on roots11,12 and shoots13,14 in greenhouses15,16 to whole fields17,18. However data management and cost still stand as major limitations in reproducing abiotic stresses from water and nutrient deficiency7,10,19,20.
In response, we propose OPEN Leaf [Open PhENotyper]. OPEN Leaf is designed to be an open-source plant phenotyping system that tracks rosette growth in Arabidopsis by color and area. This system was built using commercially available materials and uses a high resolution RGB camera and a track system with user-defined positions to capture dynamic changes in Arabidopsis rosette growth over time. Furthermore, complications due to the COVID-19 pandemic, spurred the integration of remote communication to observe data collection in real time with OPEN Leaf. Overall, OPEN Leaf is a modular, scalable, and cloud-based system that will enable researchers across the globe to share and process reproducible experiments in predictive biology.
MATERIALS AND METHODS