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In the current study, we have utilized semi-enclosed, leak-proof, microfluidic paper-based analytical devices (μPAD's) modified with isatin conjugated chitosan as specific colorimetric reagent for the detection of proline. Proline is one of the globally accepted stress biomarker in plants and also one of the prominent amino acid present in wine and some processed food. Quantification of proline is regularly required in agriculture field, food and wine industries. Specific interaction of isatin with proline, uniform film forming ability of chitosan which results in uniform coloration and the presence of leak-proof layer which prevent the diffusion of colorimetric reagent deeper resulted in enhancement of color signal intensity at the reaction zone were utilized. Further, the images of the μPAD's were captured using smartphone with 3D printed imaging box which houses smartphone and μPAD's. This platform utilizes smartphone flash for uniform illumination and ensures constant positioning of μPAD's to capture images. This greatly enhances the sensitivity and accuracy of our platform. Compared to previously published highly sensitive multi-layer, paper-based platform for detection of proline, current method has enhanced detection range (∼7 fold) and has comparable limit of detection of 23.75 μM. Moreover, the developed μPAD's platform has reduced optimum reaction temperature and time compared to previous work. The developed paper based platform was utilized for evaluation of proline content in young Arabidopsis plants which are subjected to water stress for 5 days. The devised paper-based methods have the potential to be applicable for the on-site evaluation of various stresses in plants.

Free proline, termed proline, is a biomarker used for diagnosing drought stress in plants. A previously developed proline–ninhydrin reaction-based paper sensor could quickly and easily detect proline, but it was limited by low sensitivity. In this study, we developed an enclosed multilayer paper-based microfluidic sensor with high sensitivity for the quantitative detection of proline in plants. The multilayer paper-based sensor was manufactured using simple wax printing and origami methods, and contained an internal mixing channel to allow good mixing of the proline with ninhydrin, increasing the proline–ninhydrin reactivity and providing accurate and sensitive proline detection. By preloading ninhydrin onto the sample loading area, uniform coloration of the sensing window was achieved, allowing quantitative analysis of various proline concentrations using a constant reaction time. Only the sensing window and sample loading area were exposed to limit sample evaporation and contamination from the external environment. The LOD of the fabricated sensor was 23 μM, which is approximately 29-fold lower than that of the previously proposed paper sensor (657 μM). Samples were extracted from A. thaliana plants subjected to drought stress for proline detection. The proline concentrations measured using the developed paper sensor and a spectrophotometric method were not statistically significant at a confidence level of 95%. Therefore, the developed sensor can be applied to measure proline concentrations precisely in the field with a low detection limit. The developed paper-based sensor can be used to detect the early stages of drought in plants and thus improve crop productivity.

Small RNAs (sRNAs) are important short-length molecules with regulatory functions essential for plant development and plasticity. High-throughput sequencing of total sRNA populations has revealed that the largest share of sRNA remains uncategorized. To better understand the role of sRNA-mediated cellular regulation, it is necessary to create accurate and comprehensive catalogues of sRNA and their sequence features, a task that currently relies on nontrivial bioinformatic approaches. Although a large number of computational tools have been developed to predict features of sRNA sequences, these tools are mostly dedicated to microRNAs and none integrates the functionalities necessary to describe units from all sRNA pathways thus far discovered in plants. Here, we review the different classes of sRNA found in plants and describe available bioinformatics tools that can help in their detection and categorization.

We developed and characterized a paper-based microfluidic sensor for the on-site diagnosis of drought stress in plants. Proline was used as a biomarker for analyzing drought stress, which was extracted by a colorimetric method using the proline-ninhydrin reaction. Paper was used as the main sensor material for the on-site detection of proline as it is easily transportable and cost-effective. The paper-based sensor was fabricated using wax-printing and origami methods, and the sensor was precoated with ninhydrin to allow for easy and convenient on-site use. Furthermore, a sample-to-ninhydrin ratio of 1:2 was found to confer optimal sensitivity to the drought diagnosis sensor. The concentration of proline in a sample was quantified by red-green-blue analysis to determine the change in green color intensity levels in response to distinct proline concentrations, which were detected by the sensor. The limit of detection of proline using the devised sensor was 657 µM, and the green color intensity level decreased with increasing proline concentration. In addition, the sensor was validated in an experimental drought stress model with Arabidopsis and subjected to drought stress for 21 days, and the amount of proline detected was 10 mM. The devised paper-based microfluidic sensor highlights the possibility of the on-site evaluation of drought stress in plants with potential to be utilized in various agricultural areas in the future.