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