Pitfalls and potential of high-throughput plant phenotyping platforms

Poorter, Hendrik; Hummel, Grégoire M.; Nagel, Kerstin A.; Fiorani, Fabio; von Gillhaussen, Philipp; Virnich, Olivia; Schurr, Ulrich; Postma, Johannes A.; van de Zedde, Rick; Wiese-Klinkenberg, Anika

doi:10.3389/fpls.2023.1233794

Cited by 6 publications

(2 citation statements)

References 79 publications

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“…Thus, such technologies might overwhelm (non-data-science-) researchers with high amounts of complex datasets as significant skills are required to derive easy-to-interpret insights relevant to answering biological research questions [28]. A second challenge lies in adapting an established phenotyping system for various pathosystems, i.e., different crops or pathogens [22,29]. Lastly, most high-end phenotyping systems have very high investment and running costs and thus are less available.…”

Section: Phenotyping Technology and Approaches To Quantify Qdrmentioning

confidence: 99%

High-resolution disease phenotyping reveals distinct resistance strategies of wild tomato crop wild relatives againstSclerotinia sclerotiorum

Einspanier,

Tominello-Ramirez,

Hasler

et al. 2024

Preprint

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Besides the well-understood qualitative disease resistance, plants possess a more complex quantitative form of resistance: quantitative disease resistance (QDR). QDR is commonly defined as a partial but more durable form of resistance and, therefore, might display a valuable target for resistance breeding. The characterization of QDR phenotypes, especially of wild crop relatives, displays a major bottleneck in deciphering QDR's genomic and regulatory background. Moreover, the relationship between QDR parameters, such as infection frequency, lag phase duration, and lesion growth rate, remains elusive. High hurdles for applying modern phenotyping technology, such as the low availability of phenotyping facilities or complex data analysis, further dampen progress in understanding QDR. Here, we applied a low-cost phenotyping system to measure lesion growth dynamics of wild tomato species (e.g.,S. pennelliiorS. pimpinellifolium). We provide insight into QDR diversity of wild populations and derive specific QDR strategies and their crosstalk. We show how temporally continuous observations are required to dissect end-point severity into functional resistance strategies. The results of our study show how QDR can be maintained by facilitating different defense strategies during host-parasite interaction and that the capacity of the QDR toolbox highly depends on the host's genetic context. We anticipate that the present findings display a valuable resource for more targeted functional characterization of the processes involved in QDR. Moreover, we show how modest phenotyping technology can be leveraged to help answer highly relevant biological questions.

show abstract

Section: Phenotyping Technology and Approaches To Quantify Qdrmentioning

confidence: 99%

High-resolution disease phenotyping reveals distinct resistance strategies of wild tomato crop wild relatives againstSclerotinia sclerotiorum

Einspanier,

Tominello-Ramirez,

Hasler

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Thus, such technologies might overwhelm (non-data-science) researchers with high amounts of complex datasets as substantial skills are required to derive easy-to-interpret insights relevant to answering biological research questions [ 28 ]. A second challenge lies in adapting an established phenotyping system for various pathosystems, i.e., different crops or pathogens [ 22 , 29 ]. Lastly, most high-end phenotyping systems have very high investment and running costs and thus are less available.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Disease Phenotyping Reveals Distinct Resistance Mechanisms of Tomato Crop Wild Relatives against Sclerotinia sclerotiorum

Einspanier,

Tominello-Ramirez,

Hasler

et al. 2024

Plant Phenomics

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Besides the well-understood qualitative disease resistance, plants possess a more complex quantitative form of resistance: quantitative disease resistance (QDR). QDR is commonly defined as a partial but more durable form of resistance and, therefore, might display a valuable target for resistance breeding. The characterization of QDR phenotypes, especially of wild crop relatives, displays a bottleneck in deciphering QDR’s genomic and regulatory background. Moreover, the relationship between QDR parameters, such as infection frequency, lag-phase duration, and lesion growth rate, remains elusive. High hurdles for applying modern phenotyping technology, such as the low availability of phenotyping facilities or complex data analysis, further dampen progress in understanding QDR. Here, we applied a low-cost (<1.000 €) phenotyping system to measure lesion growth dynamics of wild tomato species (e.g., Solanum pennellii or Solanum pimpinellifolium ). We provide insight into QDR diversity of wild populations and derive specific QDR mechanisms and their cross-talk . We show how temporally continuous observations are required to dissect end-point severity into functional resistance mechanisms. The results of our study show how QDR can be maintained by facilitating different defense mechanisms during host–parasite interaction and that the capacity of the QDR toolbox highly depends on the host’s genetic context. We anticipate that the present findings display a valuable resource for more targeted functional characterization of the processes involved in QDR. Moreover, we show how modest phenotyping technology can be leveraged to help answer highly relevant biological questions.

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GWAS analysis for plant height and stem diameter in sorghum using multiple phenotyping approaches

Boatwright,

Thudi,

Sangireddy

et al. 2024

The Plant Phenome Journal

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Sorghum (Sorghum bicolor (L.) Moench) is an important cereal crop cultivated around the globe. To identify the genomic regions responsible for plant height and stem diameter, a subset of the sorghum association panel was phenotyped using manual, ground‐robot, and drone‐based phenotyping approaches during 2019 and 2020 at Tifton, GA. Manual and ground‐robot‐based plant heights had a lower correlation (r = 0.55) than the manual and drone‐based measurements (r = 0.61). Tukey's mean difference plot analysis indicated that both high‐throughput methods were less accurate with taller accessions. In genome‐wide association studies using single nucleotide polymorphisms (SNPs), insertions and deletions (indels), and copy number variants, a total of 136 and 18 SNP loci associated with plant height were identified using mixed linear model and fixed and random model circulating probability unification models. For the first time, we report eight significant SNPs associated with stem diameter using a ground‐based robot. We were able to detect all four major dwarfing genes (Dw1 through Dw4) using manual phenotyping; in contrast, Dw2 was identified using both manual and ground‐robot‐based phenotyping. Sorbi.3004G093400, a gene located 19 kb upstream of the SNP locus SBI‐04_7987371 and a member of the glycoside hydrolase superfamily 35, plays a role in stem diameter, plant height, and is involved in cellular processes. Novel associations (eight for stem diameter), SNPs, and structural variants identified in this study can be used for manipulating plant height and stem diameter in sorghum.

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Pitfalls and potential of high-throughput plant phenotyping platforms

Cited by 6 publications

References 79 publications

High-resolution disease phenotyping reveals distinct resistance strategies of wild tomato crop wild relatives againstSclerotinia sclerotiorum

High-resolution disease phenotyping reveals distinct resistance strategies of wild tomato crop wild relatives againstSclerotinia sclerotiorum

High-Resolution Disease Phenotyping Reveals Distinct Resistance Mechanisms of Tomato Crop Wild Relatives against Sclerotinia sclerotiorum

GWAS analysis for plant height and stem diameter in sorghum using multiple phenotyping approaches

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