Plant Methods

Abstract Background Thorough understanding of complex model systems requires the characterisation of processes in different cell types of an organism. This can be achieved with high-throughput spatial transcriptomics at a large scale. However, for plant model systems this is still challenging as suitable transcriptomics methods are sparsely available. Here we present GaST-seq (Grid-assisted, Spatial Transcriptome sequencing), an easy to adopt, micro-scale spatial-transcriptomics workflow that allows to study expression profiles across small areas of plant tissue at a fraction of the cost of existing sequencing-based methods. Results We compare the GaST-seq method with widely used library preparation methods (Illumina TruSeq). In spatial experiments we show that the GaST-seq method is sensitive enough to identify expression differences across a plant organ. We further assess the spatial transcriptome response of Arabidopsis thaliana leaves exposed to the bacterial molecule flagellin-22, and show that with eukaryotic (Albugo laibachii) infection both host and pathogen spatial transcriptomes are obtained. Conclusion We show that our method can be used to identify known, rapidly flagellin-22 elicited genes, plant immune response pathways to bacterial attack and spatial expression patterns of genes associated with these pathways.

The deposition of silicon into epidermal cells of grass species is thought to be an important mechanism that plants use as a defense against pests and environmental stresses. There are a number of techniques available to study the size, density and distribution pattern of silica bodies in grass leaves. However, none of those techniques can provide a high-throughput analysis, especially for a great number of samples. We developed a method utilizing the autofluorescence of silica bodies to investigate their size and distribution, along with the number of carbon inclusions within the silica bodies of perennial grass species Koeleria macrantha. Fluorescence images were analyzed by image software Adobe Photoshop CS5 or ImageJ that remarkably facilitated the quantification of silica bodies in the dry ash. We observed three types of silica bodies or silica body related mineral structures. Silica bodies were detected on both abaxial and adaxial epidermis of K. macrantha leaves, although their sizes, density, and distribution patterns were different. No auto-fluorescence was detected from carbon inclusions. The combination of fluorescence microscopy and image processing software displayed efficient utilization in the identification and quantification of silica bodies in K. macrantha leaf tissues, which should applicable to biological, ecological and geological studies of grasses including forage, turf grasses and cereal crops.

Virus-induced gene silencing (VIGS) is a useful tool for functional characterizations of plant genes. However, the penetrance of VIGS varies depending on the genes to be silenced, and has to be evaluated by examining the transcript levels of target genes. In this report, we report the development of a novel VIGS vector that permits a preliminary assessment of the silencing penetrance. This new vector is based on an attenuated variant of Turnip crinkle virus (TCV) known as CPB that can be readily used in Arabidopsis thaliana to interrogate genes of this model plant. A CPB derivative, designated CPB1B, was produced by inserting a 46 nucleotide section of the Arabidopsis PHYTOENE DESATURASE (PDS) gene into CPB, in antisense orientation. CPB1B induced robust PDS silencing, causing easily visible photobleaching in systemically infected Arabidopsis leaves. More importantly, CPB1B can accommodate additional inserts, derived from other Arabidopsis genes, causing the silencing of two or more genes simultaneously. With photobleaching as a visual marker, we adopted the CPB1B vector to validate the involvement of DICER-LIKE 4 (DCL4) in antiviral defense against TCV. We further revealed the involvement of ARGONAUTE 2 (AGO2) in PDS silencing and antiviral defense against TCV in dcl2drb4 double mutant plants. These results demonstrated that DOUBLE-STRANDED RNA-BINDING PROTEIN 4 (DRB4), whose protein product (DRB4) commonly partners with DCL4 in the antiviral silencing pathway, was dispensable for PDS silencing induced by CPB1B derivative in dcl2drb4 double mutant plants. The CPB1B-based vector developed in this work is a valuable tool with visualizable indicator of the silencing penetrance for interrogating Arabidopsis genes, especially those involved in the RNA silencing pathways.

In general, enzyme activity is estimated from spectrophotometric data, by taking the slope of the linear part of the progress curve describing the rate of change in the substrate or product monitored. As long as the substrate concentrations are sufficiently high to saturate the enzyme and, the velocity of the catalyzed reaction is directly proportional to the enzyme concentration. Under these premises, this velocity can be taken as a measure of the amount of active enzyme present. Estimation of the enzyme activity through linear regression of the data should only be applied when linearity is true, which is often not the case or has not been checked. In this paper, we propose a more elaborate method, based on a kinetic modelling approach, to estimate the in vitro specific enzyme activity from spectrophotometric assay data. As a case study, kinetic models were developed to estimate the activity of the enzymes pyruvate decarboxylase and alcohol dehydrogenase extracted from ‘Jonagold’ apple (Malus x domestica Borkh. cv. ‘Jonagold’). The models are based on Michaelis–Menten and first order kinetics, which describe the reaction mechanism catalyzed by the enzymes. In contrast to the linear regression approach, the models can be used to estimate the enzyme activity regardless of whether linearity is achieved since they integrally take into account the complete progress curve. The use of kinetic models to estimate the enzyme activity can be applied to all other enzymes as long as the underlying reaction mechanism is known. The kinetic models can also be used as a tool to optimize the enzyme assays by systematically studying the effect of the various design parameters.

The study of plant phenotype by deep learning has received increased interest in recent years, which impressive progress has been made in the fields of plant breeding. Deep learning extremely relies on a large amount of training data to extract and recognize target features in the field of plant phenotype classification and recognition tasks. However, for some flower cultivars identification tasks with a huge number of cultivars, it is difficult for traditional deep learning methods to achieve better recognition results with limited sample data. Thus, a method based on metric learning for flower cultivars identification is proposed to solve this problem. We added center loss to the classification network to make inter-class samples disperse and intra-class samples compact, the script of ResNet18, ResNet50, and DenseNet121 were used for feature extraction. To evaluate the effectiveness of the proposed method, a public dataset Oxford 102 Flowers dataset and two novel datasets constructed by us are chosen. For the method of joint supervision of center loss and L2-softmax loss, the test accuracy rate is 91.88%, 97.34%, and 99.82% across three datasets, respectively. Feature distribution observed by T-distributed stochastic neighbor embedding (T-SNE) verifies the effectiveness of the method presented above. An efficient metric learning method has been described for flower cultivars identification task, which not only provides high recognition rates but also makes the feature extracted from the recognition network interpretable. This study demonstrated that the proposed method provides new ideas for the application of a small amount of data in the field of identification, and has important reference significance for the flower cultivars identification research.

After the publication of our article [1], it was brought to our attention that in six places in the article we omitted to use quotation marks to show where the text has been directly used from the cited references.

Grain yield, ear and kernel attributes can assist to understand the performance of maize plant under different environmental conditions and can be used in the variety development process to address farmer’s preferences. These parameters are however still laborious and expensive to measure. A low-cost ear digital imaging method was developed that provides estimates of ear and kernel attributes i.e., ear number and size, kernel number and size as well as kernel weight from photos of ears harvested from field trial plots. The image processing method uses a script that runs in a batch mode on ImageJ; an open source software. Kernel weight was estimated using the total kernel number derived from the number of kernels visible on the image and the average kernel size. Data showed a good agreement in terms of accuracy and precision between ground truth measurements and data generated through image processing. Broad-sense heritability of the estimated parameters was in the range or higher than that for measured grain weight. Limitation of the method for kernel weight estimation is discussed. The method developed in this work provides an opportunity to significantly reduce the cost of selection in the breeding process, especially for resource constrained crop improvement programs and can be used to learn more about the genetic bases of grain yield determinants.

An efficient in vivo transient transfection system using protoplasts is an important tool to study gene expression, metabolic pathways, and multiple mutagenesis parameters in plants. Although rice protoplasts can be isolated from germinated seedlings or cell suspension culture, preparation of those donor tissues can be inefficient, time-consuming, and laborious. Additionally, the lengthy process of protoplast isolation and transfection needs to be completed in a single day. Here we report a protocol for the isolation of protoplasts directly from rice calli, without using seedlings or suspension culture. The method is developed to employ discretionary pause points during protoplast isolation and before transfection. Protoplasts maintained within a sucrose cushion partway through isolation, for completion on a subsequent day, per the first pause point, are referred to as S protoplasts. Fully isolated protoplasts maintained in MMG solution for transfection on a subsequent day, per the second pause point, are referred to as M protoplasts. Both S and M protoplasts, 1 day after initiation of protoplast isolation, had minimal loss of viability and transfection efficiency compared to protoplasts 0 days after isolation. S protoplast viability decreases at a lower rate over time than that of M protoplasts and can be used with added flexibility for transient transfection assays and time-course experiments. The protoplasts produced by this method are competent for transfection of both plasmids and ribonucleoproteins (RNPs). Cas9 RNPs were used to demonstrate the utility of these protoplasts to assay genome editing in vivo. The current study describes a highly effective and accessible method to isolate protoplasts from callus tissue induced from rice seeds. This method utilizes donor materials that are resource-efficient and easy to propagate, permits convenience via pause points, and allows for flexible transfection days after protoplast isolation. It provides an advantageous and useful platform for a variety of in vivo transient transfection studies in rice.

Image processing techniques have been widely used in the analysis of leaf characteristics. Earlier techniques for processing digital RGB color images of plant leaves had several drawbacks, such as inadequate de-noising, and adopting normal-probability statistical estimation models which have few parameters and limited applicability. We confirmed the skewness distribution characteristics of the red, green, blue and grayscale channels of the images of tobacco leaves. Twenty skewed-distribution parameters were computed including the mean, median, mode, skewness, and kurtosis. We used the mean parameter to establish a stepwise regression model that is similar to earlier models. Other models based on the median and the skewness parameters led to accurate RGB-based description and prediction, as well as better fitting of the SPAD value. More parameters improved the accuracy of RGB model description and prediction, and extended its application range. Indeed, the skewed-distribution parameters can describe changes of the leaf color depth and homogeneity. The color histogram of the blade images follows a skewed distribution, whose parameters greatly enrich the RGB model and can describe changes in leaf color depth and homogeneity.