Novel imaging-based phenotyping strategies for dissecting crosstalk in plant development

High-throughput image segmentation and machine learning approaches in the plant sciences across multiple scales

Agriculture has benefited greatly from the rise of big data and high-performance computing. The acquisition and analysis of data across biological scales have resulted in strategies modeling inter- actions between plant genotype and environment, models of root architecture that provide insight into resource utilization, and the elucidation of cell-to-cell communication mechanisms that are instrumental in plant development. Image segmentation and machine learning approaches for interpreting plant image data are among many of the computational methodologies that have evolved to address challenging agricultural and biological problems. These approaches have led to contributions such as the accelerated identification of gene that modulate stress responses in plants and automated high-throughput phenotyping for early detection of plant diseases. The continued acquisition of high throughput imaging across multiple biological scales provides opportunities to further push the boundaries of our understandings quicker than ever before. In this review, we explore the current state of the art methodologies in plant image segmentation and machine learning at the agricultural, organ, and cellular scales in plants. We show how the methodologies for segmentation and classification differ due to the diversity of physical characteristics found at these different scales. We also discuss the hardware technologies most commonly used at these different scales, the types of quantitative metrics that can be extracted from these images, and how the biological mechanisms by which plants respond to abiotic/biotic stresses or genotypic modifications can be extracted from these approaches.

High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field

Food scarcity, population growth, and global climate change have propelled crop yield growth driven by high-throughput phenotyping into the era of big data. However, access to large-scale phenotypic data has now become a critical barrier that phenomics urgently must overcome. Fortunately, the high-throughput plant phenotyping platform (HT3P), employing advanced sensors and data collection systems, can take full advantage of non-destructive and high-throughput methods to monitor, quantify, and evaluate specific phenotypes for large-scale agricultural experiments, and it can effectively perform phenotypic tasks that traditional phenotyping could not do. In this way, HT3Ps are novel and powerful tools, for which various commercial, customized, and even self-developed ones have been recently introduced in rising numbers. Here, we review these HT3Ps in nearly 7 years from greenhouses and growth chambers to the field, and from ground-based proximal phenotyping to aerial large-scale remote sensing. Platform configurations, novelties, operating modes, current developments, as well the strengths and weaknesses of diverse types of HT3Ps are thoroughly and clearly described. Then, miscellaneous combinations of HT3Ps for comparative validation and comprehensive analysis are systematically present, for the first time. Finally, we consider current phenotypic challenges and provide fresh perspectives on future development trends of HT3Ps. This review aims to provide ideas, thoughts, and insights for the optimal selection, exploitation, and utilization of HT3Ps, and thereby pave the way to break through current phenotyping bottlenecks in botany.

Bioinformatics-assisted, integrated omics studies on medicinal plants

The immense therapeutic and economic values of medicinal plants have attracted increasing attention from the worldwide researchers. It has been recognized that production of the authentic and high-quality herbal drugs became the prerequisite for maintaining the healthy development of the traditional medicine industry. To this end, intensive research efforts have been devoted to the basic studies, in order to pave a way for standardized authentication of the plant materials, and bioengineering of the metabolic pathways in the medicinal plants. In this paper, the recent advances of omics studies on the medicinal plants were summarized from several aspects, including phenomics and taxonomics, genomics, transcriptomics, proteomics and metabolomics. We proposed a multi-omics data-based workflow for medicinal plant research. It was emphasized that integration of the omics data was important for plant authentication and mechanistic studies on plant metabolism. Additionally, the computational tools for proper storage, efficient processing and high-throughput analyses of the omics data have been introduced into the workflow. According to the workflow, authentication of the medicinal plant materials should not only be performed at the phenomics level but also be implemented by genomic and metabolomic marker-based examination. On the other hand, functional genomics studies, transcriptional regulatory networks and protein-protein interactions will contribute greatly for deciphering the secondary metabolic pathways. Finally, we hope that our work could inspire further efforts on the bioinformatics-assisted, integrated omics studies on the medicinal plants.

Leveraging Breeding Values Obtained from Random Regression Models for Genetic Inference of Longitudinal Traits

Understanding the genetic basis of dynamic plant phenotypes has largely been limited because of a lack of space and labor resources needed to record dynamic traits, often destructively, for a large number of genotypes. However, the recent advent of image-based phenotyping platforms has provided the plant science community with an effective means to nondestructively evaluate morphological, developmental, and physiological processes at regular, frequent intervals for a large number of plants throughout development. The statistical frameworks typically used for genetic analyses (e.g., genome-wide association mapping, linkage mapping, and genomic prediction) in plant breeding and genetics are not particularly amenable for repeated measurements. Random regression (RR) models are routinely used in animal breeding for the genetic analysis of longitudinal traits and provide a robust framework for modeling trait trajectories and performing genetic analysis simultaneously. We recently used a RR approach for genomic prediction of shoot growth trajectories in rice ( L.) from 33,674 single nucleotide polymorphisms. In this study, we have extended this approach for genetic inference by leveraging genomic breeding values derived from RR models for rice shoot growth during early vegetative development. This approach provides improvements over conventional single time point analyses for discovering loci associated with shoot growth trajectories. The RR approach uncovers persistent as well as time-specific transient quantitative trait loci. This methodology can be widely applied to understand the genetic architecture of other complex polygenic traits with repeated measurements.

In vivo transport of three radioactive [18F]-fluorinated deoxysucrose analogs by the maize sucrose transporter ZmSUT1

Sucrose transporter (SUT) proteins translocate sucrose across cell membranes; however, mechanistic aspects of sucrose binding by SUTs are not well resolved. Specific hydroxyl groups in sucrose participate in hydrogen bonding with SUT proteins. We previously reported that substituting a radioactive fluorine-18 [¹⁸F] at the C-6' position within the fructosyl moiety of sucrose did not affect sucrose transport by the maize (Zea mays) ZmSUT1 protein. To determine how ¹⁸F substitution of hydroxyl groups at two other positions within sucrose, the C-1' in the fructosyl moiety or the C-6 in the glucosyl moiety, impact sucrose transport, we synthesized 1'-[F¹⁸]fluoro-1'-deoxysucrose and 6-[F¹⁸]fluoro-6-deoxysucrose ([¹⁸F]FDS) analogs. Each [¹⁸F]FDS derivative was independently introduced into wild-type or sut1 mutant plants, which are defective in sucrose phloem loading. All three (1'-, 6'-, and 6-) [¹⁸F]FDS derivatives were efficiently and equally translocated, similarly to carbon-14 [¹⁴C]-labeled sucrose. Hence, individually replacing the hydroxyl groups at these positions within sucrose does not interfere with substrate recognition, binding, or membrane transport processes, and hydroxyl groups at these three positions are not essential for hydrogen bonding between sucrose and ZmSUT1. [¹⁸F]FDS imaging afforded several advantages compared to [¹⁴C]-sucrose detection. We calculated that 1'-[¹⁸F]FDS was transported at approximately a rate of 0.90 ± 0.15 m.h-1 in wild-type leaves, and at 0.68 ± 0.25 m.h-1 in sut1 mutant leaves. Collectively, our data indicated that [¹⁸F]FDS analogs are valuable tools to probe sucrose-SUT interactions and to monitor sucrose transport in plants.

Image Harvest: an open-source platform for high-throughput plant image processing and analysis

High-throughput plant phenotyping is an effective approach to bridge the genotype-to-phenotype gap in crops. Phenomics experiments typically result in large-scale image datasets, which are not amenable for processing on desktop computers, thus creating a bottleneck in the image-analysis pipeline. Here, we present an open-source, flexible image-analysis framework, called Image Harvest (IH), for processing images originating from high-throughput plant phenotyping platforms. Image Harvest is developed to perform parallel processing on computing grids and provides an integrated feature for metadata extraction from large-scale file organization. Moreover, the integration of IH with the Open Science Grid provides academic researchers with the computational resources required for processing large image datasets at no cost. Image Harvest also offers functionalities to extract digital traits from images to interpret plant architecture-related characteristics. To demonstrate the applications of these digital traits, a rice (Oryza sativa) diversity panel was phenotyped and genome-wide association mapping was performed using digital traits that are used to describe different plant ideotypes. Three major quantitative trait loci were identified on rice chromosomes 4 and 6, which co-localize with quantitative trait loci known to regulate agronomically important traits in rice. Image Harvest is an open-source software for high-throughput image processing that requires a minimal learning curve for plant biologists to analyzephenomics datasets.

A year (2014-2015) of plants in Proteomics journal. Progress in wet and dry methodologies, moving from protein catalogs, and the view of classic plant biochemists

The present review is an update of the previous one published in Proteomics 2015 Reviews special issue [Jorrin-Novo, J. V. et al., Proteomics 2015, 15, 1089-1112] covering the July 2014-2015 period. It has been written on the bases of the publications that appeared in Proteomics journal during that period and the most relevant ones that have been published in other high-impact journals. Methodological advances and the contribution of the field to the knowledge of plant biology processes and its translation to agroforestry and environmental sectors will be discussed. This review has been organized in four blocks, with a starting general introduction (literature survey) followed by sections focusing on the methodology (in vitro, in vivo, wet, and dry), proteomics integration with other approaches (systems biology and proteogenomics), biological information, and knowledge (cell communication, receptors, and signaling), ending with a brief mention of some other biological and translational topics to which proteomics has made some contribution.