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Plants
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High Throughput Methods in Monitoring Arabidopsis Thaliana Growth and Development
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High Throughput Methods in Monitoring Arabidopsis Thaliana Growth and Development

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Cell Biology".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 10383

Special Issue Editor

Dr. Kristiina Himanen

E-Mail Website
Guest Editor
Department of Biosciences / Department of Agricultural Sciences, University of Helsinki, Latokartanonkaari 7, PL 27, 00790 Helsinki, Finland
Interests: high-throughput phenotyping

Special Issue Information

Dear Colleagues,

Arabidopsis is still one of the most important model plants. It has fully sequenced genomes, stock centers for genetic material and many well established research technologies available. One of the recent technologies under intensive development is high throughput, imaging sensor-based phenotyping. Phenotyping with multiple imaging sensors allows non-invasive monitoring of plant growth, development and physiological responses in time series over the whole plant life cycle. Digitization and automation of plant phenotyping is in many ways research enabling and allows significantly increasing the analysis throughput. High throughput methods facilitate plant phenomics approaches that assess phenotypes in different environments and in different genetic backgrounds. The available knowledge and resources of Arabidopsis allow integration of molecular omics data (genetics, transcriptomics, proteomics, metabolomics) with the phenomics data. Such integrated omics analysis will expand our understanding of plant growth, development and responses with the environment. The Plants Special Issue of “High Throughput Methods in Monitoring Arabidopsis Thaliana Growth and Development” welcomes primary research papers and reviews addressing phenomics approaches, possibly in combination with molecular omics analysis, unraveling different aspects of Arabidopsis life cycle also in interaction with different environmental conditions.

Dr. Kristiina Himanen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Plants is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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14 pages, 2493 KiB  
Article
Image-Based Methods to Score Fungal Pathogen Symptom Progression and Severity in Excised Arabidopsis Leaves
by Mirko Pavicic, Kirk Overmyer, Attiq ur Rehman, Piet Jones, Daniel Jacobson and Kristiina Himanen
Plants 2021, 10(1), 158; https://0-doi-org.brum.beds.ac.uk/10.3390/plants10010158 - 15 Jan 2021
Cited by 16 | Viewed by 6337
Abstract
Image-based symptom scoring of plant diseases is a powerful tool for associating disease resistance with plant genotypes. Advancements in technology have enabled new imaging and image processing strategies for statistical analysis of time-course experiments. There are several tools available for analyzing symptoms on [...] Read more.
Image-based symptom scoring of plant diseases is a powerful tool for associating disease resistance with plant genotypes. Advancements in technology have enabled new imaging and image processing strategies for statistical analysis of time-course experiments. There are several tools available for analyzing symptoms on leaves and fruits of crop plants, but only a few are available for the model plant Arabidopsis thaliana (Arabidopsis). Arabidopsis and the model fungus Botrytis cinerea (Botrytis) comprise a potent model pathosystem for the identification of signaling pathways conferring immunity against this broad host-range necrotrophic fungus. Here, we present two strategies to assess severity and symptom progression of Botrytis infection over time in Arabidopsis leaves. Thus, a pixel classification strategy using color hue values from red-green-blue (RGB) images and a random forest algorithm was used to establish necrotic, chlorotic, and healthy leaf areas. Secondly, using chlorophyll fluorescence (ChlFl) imaging, the maximum quantum yield of photosystem II (Fv/Fm) was determined to define diseased areas and their proportion per total leaf area. Both RGB and ChlFl imaging strategies were employed to track disease progression over time. This has provided a robust and sensitive method for detecting sensitive or resistant genetic backgrounds. A full methodological workflow, from plant culture to data analysis, is described. Full article
(This article belongs to the Special Issue High Throughput Methods in Monitoring Arabidopsis Thaliana Growth and Development)
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16 pages, 1221 KiB  
Technical Note
Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences
by Ioannis Spyroglou, Jan Skalák, Veronika Balakhonova, Zuzana Benedikty, Alexandros G. Rigas and Jan Hejátko
Plants 2021, 10(2), 362; https://0-doi-org.brum.beds.ac.uk/10.3390/plants10020362 - 13 Feb 2021
Cited by 7 | Viewed by 3314
Abstract
Plants adapt to continual changes in environmental conditions throughout their life spans. High-throughput phenotyping methods have been developed to noninvasively monitor the physiological responses to abiotic/biotic stresses on a scale spanning a long time, covering most of the vegetative and reproductive stages. However, [...] Read more.
Plants adapt to continual changes in environmental conditions throughout their life spans. High-throughput phenotyping methods have been developed to noninvasively monitor the physiological responses to abiotic/biotic stresses on a scale spanning a long time, covering most of the vegetative and reproductive stages. However, some of the physiological events comprise almost immediate and very fast responses towards the changing environment which might be overlooked in long-term observations. Additionally, there are certain technical difficulties and restrictions in analyzing phenotyping data, especially when dealing with repeated measurements. In this study, a method for comparing means at different time points using generalized linear mixed models combined with classical time series models is presented. As an example, we use multiple chlorophyll time series measurements from different genotypes. The use of additional time series models as random effects is essential as the residuals of the initial mixed model may contain autocorrelations that bias the result. The nature of mixed models offers a viable solution as these can incorporate time series models for residuals as random effects. The results from analyzing chlorophyll content time series show that the autocorrelation is successfully eliminated from the residuals and incorporated into the final model. This allows the use of statistical inference. Full article
(This article belongs to the Special Issue High Throughput Methods in Monitoring Arabidopsis Thaliana Growth and Development)
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