Special Issue "High Throughput Methods in Monitoring Arabidopsis Thaliana Growth and Development"
Deadline for manuscript submissions: 31 December 2020.
Interests: high-throughput phenotyping
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
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 papers will be 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 monthly 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 1600 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.
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: A high throughput method to screen Botrytis cinerea symptoms in leaves of the genetic model plant, Arabidopsis thaliana
Authors: Mirko Pavicic; Kirk Overmyer; Kristiina Himanen
Affiliation: 1 Department of Agricultural Sciences, University of Helsinki, Helsinki, Finland; 2 Viikki Plant Science Centre; 3 Organismal and Evolutionary Biology Research Program, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
Abstract: Image-based symptom scoring of plant diseases is a powerful tool to associate resistance or susceptibility to different plant varieties or genotypes. Technological advances have enabled new imaging, image processing, and statistical techniques. Several tools are already available for the analysis of symptoms on leaves and fruits of large agricultural plants but almost none for small genetic model plants such as Arabidopsis thaliana (Arabidopsis). Arabidopsis and the model fungus Botrytis cinerea (Botrytis) form a potent model pathosystem for the identification of signalling pathways conferring immunity against this broad host-range necrotrophic fungal pathogen. Although many previous studies have used this system, there is no standardized procedure to make these studies comparable. Here we present an automated image-based method for the analysis of Botrytis-induced disease symptoms with high throughput potential. The method uses detached leaves and time resolved symptom tracking. A full work pipeline, from plant culture to data analysis, is described here.