OPEN leaf : an open‐source cloud‐based phenotyping system for tracking dynamic changes at leaf‐specific resolution in Arabidopsis

Swartz, Landon G.; Liu, Suxing; Dahlquist, Drew; Kramer, Skyler T.; Walter, Emily S.; McInturf, Samuel A.; Bucksch, Alexander; Mendoza‐Cózatl, David G.

doi:10.1111/tpj.16449

Title: OPEN leaf : an open‐source cloud‐based phenotyping system for tracking dynamic changes at leaf‐specific resolution in Arabidopsis

Journal Article · 21 September 2023 · The Plant Journal

DOI: https://doi.org/10.1111/tpj.16449 · OSTI ID:2242536

^[1];

^[2]; Dahlquist, Drew ^[3]; Kramer, Skyler T. ^[4]; Walter, Emily S. ^[5]; McInturf, Samuel A. ^[5];

^[2];

^[1]

Department of Electrical Engineering and Computer Science University of Missouri 411 S 6th St. Columbia Missouri 65201 USA, Division of Plant Science and Technology, C.S. Bond Life Sciences Center University of Missouri 1201 Rollins St. Columbia Missouri 65211 USA
School of Plant Sciences University of Arizona 1140 E South Campus Tucson Arizona 85721 USA
Department of Electrical Engineering and Computer Science University of Missouri 411 S 6th St. Columbia Missouri 65201 USA
MU Institute of Data Science and Informatics, C.S. Bond Life Sciences Center University of Missouri 1201 Rollinst St. Columbia Missouri 65211 USA
Division of Plant Science and Technology, C.S. Bond Life Sciences Center University of Missouri 1201 Rollins St. Columbia Missouri 65211 USA

SUMMARY The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., the dark genome ). High‐throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, access to commercial HTPP platforms remains limited. Here we describe the design and implementation of OPEN leaf , an open‐source phenotyping system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing. OPEN leaf , coupled with our SMART imaging processing pipeline was able to consistently document and quantify dynamic changes at the whole rosette level and leaf‐specific resolution when plants experienced changes in nutrient availability. Our data also demonstrate that VIS sensors remain underutilized and can be used in high‐throughput screens to identify and characterize previously unidentified phenotypes in a leaf‐specific time‐dependent manner. Moreover, the modular and open‐source design of OPEN leaf allows seamless integration of additional sensors based on users and experimental needs.

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Sponsoring Organization:: USDOE

OSTI ID:: 2242536

Journal Information:: The Plant Journal, Journal Name: The Plant Journal Vol. 116 Journal Issue: 6; ISSN 0960-7412

Publisher:: Wiley-BlackwellCopyright Statement

Country of Publication:: United Kingdom

Language:: English

References (43)

High-efficiency procedure to characterize, segment, and quantify complex multicellularity in raw micrographs in plants Zhang, Xi; Hu, Zijian; Guo, Yayu Plant Methods, Vol. 16, Issue 1 https://doi.org/10.1186/s13007-020-00642-0	journal	July 2020
Sharing the Right Data Right: A Symbiosis with Machine Learning Tsaftaris, Sotirios A.; Scharr, Hanno Trends in Plant Science, Vol. 24, Issue 2 https://doi.org/10.1016/j.tplants.2018.10.016	journal	February 2019
Data management pipeline for plant phenotyping in a multisite project Billiau, Kenny; Sprenger, Heike; Schudoma, Christian Functional Plant Biology, Vol. 39, Issue 11 https://doi.org/10.1071/FP12009	journal	January 2012
Shoot to root communication is necessary to control the expression of iron-acquisition genes in Strategy I plants García, María J.; Romera, Francisco J.; Stacey, Minviluz G. Planta, Vol. 237, Issue 1 https://doi.org/10.1007/s00425-012-1757-0	journal	September 2012
High-Throughput Computer Vision Introduces the Time Axis to a Quantitative Trait Map of a Plant Growth Response Moore, Candace R.; Johnson, Logan S.; Kwak, Il-Youp Genetics, Vol. 195, Issue 3 https://doi.org/10.1534/genetics.113.153346	journal	August 2013
scikit-image: image processing in Python van der Walt, Stéfan; Schönberger, Johannes L.; Nunez-Iglesias, Juan PeerJ, Vol. 2 https://doi.org/10.7717/peerj.453	journal	January 2014
Welcome to the Tidyverse Wickham, Hadley; Averick, Mara; Bryan, Jennifer Journal of Open Source Software, Vol. 4, Issue 43 https://doi.org/10.21105/joss.01686	journal	November 2019
Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives Yang, Wanneng; Feng, Hui; Zhang, Xuehai Molecular Plant, Vol. 13, Issue 2 https://doi.org/10.1016/j.molp.2020.01.008	journal	February 2020
Phenomics for photosynthesis, growth and reflectance in Arabidopsis thaliana reveals circadian and long-term fluctuations in heritability Flood, Pádraic J.; Kruijer, Willem; Schnabel, Sabine K. Plant Methods, Vol. 12, Issue 1 https://doi.org/10.1186/s13007-016-0113-y	journal	February 2016
Live Plant Cell Tracking: Fiji plugin to analyze cell proliferation dynamics and understand morphogenesis Hernández-Herrera, Paul; Ugartechea-Chirino, Yamel; Torres-Martínez, Héctor H. Plant Physiology, Vol. 188, Issue 2 https://doi.org/10.1093/plphys/kiab530	journal	November 2021
Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for maize QTL mapping Xie, Jiayang; Fernandes, Samuel B.; Mayfield-Jones, Dustin Plant Physiology, Vol. 187, Issue 3 https://doi.org/10.1093/plphys/kiab299	journal	July 2021
Image-based methods for phenotyping growth dynamics and fitness components in Arabidopsis thaliana Vasseur, François; Bresson, Justine; Wang, George Plant Methods, Vol. 14, Issue 1 https://doi.org/10.1186/s13007-018-0331-6	journal	July 2018
Perspectives for Remote Sensing with Unmanned Aerial Vehicles in Precision Agriculture Maes, Wouter H.; Steppe, Kathy Trends in Plant Science, Vol. 24, Issue 2 https://doi.org/10.1016/j.tplants.2018.11.007	journal	February 2019
An Improved Grafting Technique for Mature Arabidopsis Plants Demonstrates Long-Distance Shoot-to-Root Transport of Phytochelatins in Arabidopsis Chen, Alice; Komives, Elizabeth A.; Schroeder, Julian I. Plant Physiology, Vol. 141, Issue 1 https://doi.org/10.1104/pp.105.072637	journal	March 2006
Field-based phenomics for plant genetics research White, Jeffrey W.; Andrade-Sanchez, Pedro; Gore, Michael A. Field Crops Research, Vol. 133 https://doi.org/10.1016/j.fcr.2012.04.003	journal	July 2012
Automated Imaging, Tracking, and Analytics Pipeline for Differentiating Environmental Effects on Root Meristematic Cell Division Buckner, Eli; Madison, Imani; Chou, Hsuan Frontiers in Plant Science, Vol. 10 https://doi.org/10.3389/fpls.2019.01487	journal	November 2019
Iron Availability within the Leaf Vasculature Determines the Magnitude of Iron Deficiency Responses in Source and Sink Tissues inArabidopsis Nguyen, Nga T.; Khan, Mather A.; Castro–Guerrero, Norma A. Plant and Cell Physiology, Vol. 63, Issue 6 https://doi.org/10.1093/pcp/pcac046	journal	April 2022
Tissue-Specific Regulation of Gibberellin Signaling Fine-Tunes Arabidopsis Iron-Deficiency Responses Wild, Michael; Davière, Jean-Michel; Regnault, Thomas Developmental Cell, Vol. 37, Issue 2 https://doi.org/10.1016/j.devcel.2016.03.022	journal	April 2016
The FAIR Guiding Principles for scientific data management and stewardship Wilkinson, Mark D.; Dumontier, Michel; Aalbersberg, IJsbrand Jan Scientific Data, Vol. 3, Issue 1 https://doi.org/10.1038/sdata.2016.18	journal	March 2016
Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice Yang, Wanneng; Guo, Zilong; Huang, Chenglong Nature Communications, Vol. 5, Issue 1 https://doi.org/10.1038/ncomms6087	journal	October 2014
Sequencing crop genomes: approaches and applications Jackson, Scott A.; Iwata, Aiko; Lee, Suk‐Ha New Phytologist, Vol. 191, Issue 4 https://doi.org/10.1111/j.1469-8137.2011.03804.x	journal	June 2011
SPICY: towards automated phenotyping of large pepper plants in the greenhouse van der Heijden, Gerie; Song, Yu; Horgan, Graham Functional Plant Biology, Vol. 39, Issue 11 https://doi.org/10.1071/FP12019	journal	January 2012
“Rhizoponics”: a novel hydroponic rhizotron for root system analyses on mature Arabidopsis thaliana plants Mathieu, Laura; Lobet, Guillaume; Tocquin, Pierre Plant Methods, Vol. 11, Issue 1 https://doi.org/10.1186/s13007-015-0046-x	journal	January 2015
Automated Method to Determine Two Critical Growth Stages of Wheat: Heading and Flowering Sadeghi-Tehran, Pouria; Sabermanesh, Kasra; Virlet, Nicolas Frontiers in Plant Science, Vol. 8 https://doi.org/10.3389/fpls.2017.00252	journal	February 2017
Rhizoslides: paper-based growth system for non-destructive, high throughput phenotyping of root development by means of image analysis Le Marié, Chantal; Kirchgessner, Norbert; Marschall, Daniela Plant Methods, Vol. 10, Issue 1 https://doi.org/10.1186/1746-4811-10-13	journal	January 2014
Genome-wide Association Studies in Maize: Praise and Stargaze Xiao, Yingjie; Liu, Haijun; Wu, Liuji Molecular Plant, Vol. 10, Issue 3 https://doi.org/10.1016/j.molp.2016.12.008	journal	March 2017
Genotype-by-Environment Interaction and Plasticity: Exploring Genomic Responses of Plants to the Abiotic Environment Des Marais, David L.; Hernandez, Kyle M.; Juenger, Thomas E. Annual Review of Ecology, Evolution, and Systematics, Vol. 44, Issue 1 https://doi.org/10.1146/annurev-ecolsys-110512-135806	journal	November 2013
Envirotyping for deciphering environmental impacts on crop plants Xu, Yunbi Theoretical and Applied Genetics, Vol. 129, Issue 4 https://doi.org/10.1007/s00122-016-2691-5	journal	March 2016
Changes in iron availability in Arabidopsis are rapidly sensed in the leaf vasculature and impaired sensing leads to opposite transcriptional programs in leaves and roots Khan, Mather A.; Castro‐Guerrero, Norma A.; McInturf, Samuel A. Plant, Cell & Environment, Vol. 41, Issue 10 https://doi.org/10.1111/pce.13192	journal	June 2018
Predictive biology: modelling, understanding and harnessing microbial complexity Lopatkin, Allison J.; Collins, James J. Nature Reviews Microbiology, Vol. 18, Issue 9 https://doi.org/10.1038/s41579-020-0372-5	journal	May 2020
Defining the core Arabidopsis thaliana root microbiome Lundberg, Derek S.; Lebeis, Sarah L.; Paredes, Sur Herrera Nature, Vol. 488, Issue 7409 https://doi.org/10.1038/nature11237	journal	August 2012
Next-generation DNA sequencing Shendure, Jay; Ji, Hanlee Nature Biotechnology, Vol. 26, Issue 10 https://doi.org/10.1038/nbt1486	journal	October 2008
High-Throughput Non-destructive Phenotyping of Traits that Contribute to Salinity Tolerance in Arabidopsis thaliana Awlia, Mariam; Nigro, Arianna; Fajkus, Jiří Frontiers in Plant Science, Vol. 7 https://doi.org/10.3389/fpls.2016.01414	journal	September 2016
Nicotianamine Functions in the Phloem-Based Transport of Iron to Sink Organs, in Pollen Development and Pollen Tube Growth in Arabidopsis Schuler, Mara; Rellán-Álvarez, Rubén; Fink-Straube, Claudia The Plant Cell, Vol. 24, Issue 6 https://doi.org/10.1105/tpc.112.099077	journal	June 2012
Getting a sense for signals: Regulation of the plant iron deficiency response Hindt, Maria N.; Guerinot, Mary Lou Biochimica et Biophysica Acta (BBA) - Molecular Cell Research, Vol. 1823, Issue 9 https://doi.org/10.1016/j.bbamcr.2012.03.010	journal	September 2012
Moderate to severe water limitation differentially affects the phenome and ionome of Arabidopsis Acosta-Gamboa, Lucia M.; Liu, Suxing; Langley, Erin Functional Plant Biology, Vol. 44, Issue 1 https://doi.org/10.1071/FP16172	journal	January 2017
A Program for Iron Economy during Deficiency Targets Specific Fe Proteins Hantzis, Laura J.; Kroh, Gretchen E.; Jahn, Courtney E. Plant Physiology, Vol. 176, Issue 1 https://doi.org/10.1104/pp.17.01497	journal	November 2017
Phytochrome B integrates light and temperature signals in Arabidopsis Legris, Martina; Klose, Cornelia; Burgie, E. Sethe Science, Vol. 354, Issue 6314 https://doi.org/10.1126/science.aaf5656	journal	October 2016
1,135 ionomes reveal the global pattern of leaf and seed mineral nutrient and trace element diversity inArabidopsis thaliana Campos, Ana Carolina A. L.; Dijk, William F. A.; Ramakrishna, Priya The Plant Journal, Vol. 106, Issue 2 https://doi.org/10.1111/tpj.15177	journal	March 2021
The Arabidopsis immune adaptor SRFR1 interacts with TCP transcription factors that redundantly contribute to effector-triggered immunity Kim, Sang Hee; Son, Geon Hui; Bhattacharjee, Saikat The Plant Journal, Vol. 78, Issue 6 https://doi.org/10.1111/tpj.12527	journal	May 2014
OPENER Is a Nuclear Envelope and Mitochondria Localized Protein Required for Cell Cycle Progression in Arabidopsis Wang, Wei; Zhang, Xueyang; Niittylä, Totte The Plant Cell, Vol. 31, Issue 7 https://doi.org/10.1105/tpc.19.00033	journal	April 2019
A “Do-It-Yourself” phenotyping system: measuring growth and morphology throughout the diel cycle in rosette shaped plants Dobrescu, Andrei; Scorza, Livia C. T.; Tsaftaris, Sotirios A. Plant Methods, Vol. 13, Issue 1 https://doi.org/10.1186/s13007-017-0247-6	journal	November 2017
Cadmium interference with iron sensing reveals transcriptional programs sensitive and insensitive to reactive oxygen species McInturf, Samuel A.; Khan, Mather A.; Gokul, Arun Journal of Experimental Botany, Vol. 73, Issue 1 https://doi.org/10.1093/jxb/erab393	journal	September 2021

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