Raman Spectroscopy Applications in Grapevine: Metabolic Analysis of Plants Infected by Two Different Viruses

Detecting novel plant pathogen threats to food system security by integrating the Plant Reactome and remote sensing

Plant diseases constantly threaten crops and food systems, while global connectivity further increases the risks of spreading existing and exotic pathogens. Here, we first explore how an integrative approach involving plant pathway knowledgegraphs, differential gene expression data, and biochemical data informing Raman spectroscopy could be used to detect plant pathways responding to pathogen attacks. The Plant Reactome (https://plantreactome.gramene.org) demonstrates the potential to synthesize knowledgegraphs depicting plant-pathogen interactions, leveraging availability of publicly available OMIC data sets related to major diseases of rice and maize. Plant pathway signatures may then guide the development of drone and satellite remote-sensing methods for early monitoring of disease outbreaks across farms and landscapes. A review of current proximal- and remote-sensing technology demonstrates the potential for actionable early pathogen detection. We furthermore identify knowledge gaps that need to be addressed for developing these tools as components of effective strategies for safeguarding global food security against current and emerging pathogens.

Noninvasive Raman spectroscopy for the detection of rice bacterial leaf blight and bacterial leaf streak

Plant diseases pose significant threats to agricultural yields and are responsible for nearly 20 % of losses in total food production. Therefore, the rapid detection of plant pathogens is critically important for preventing the rapid development of plant diseases and minimizing crop damage. Raman spectroscopy (RS) has been shown to be effective for detecting living biological samples. Compared with traditional detection methods, RS is fast, sensitive, and non-destructive; it also does not require sample labeling. In this study, we used Laser tweezers Raman spectroscopy combined with convolutional neural networks to detect two closely related strains of bacteria, Xanthomonas oryzae pv. oryzae (Xoo) and Xanthomonas oryzae pv. oryzicola (Xoc), exuded from bacteria-infected rice leaves. The accuracy of this technique was 97.5 %. For the application of RS in the field, we used the portable Raman spectrometer to detect mock-inoculated as well as Xoo- and Xoc-infected rice leaves at different disease courses. The identification accuracy via this technique was 87.02 % in the early stage, in which no obvious symptoms were apparent. This method also revealed spectral differences in rice leaves caused by the two bacteria, which could be leveraged for subsequent analysis of the molecular mechanism of infection. Our results indicate that RS is a promising approach for the early detection of bacterial diseases in rice in the field, as well as for in-depth single-cell analysis in laboratory settings.

In-vivo Raman microspectroscopy reveals differential nitrate concentration in different developmental zones in Arabidopsis roots

BACKGROUND

Nitrate (NO3-) is one of the two major forms of inorganic nitrogen absorbed by plant roots, and the tissue nitrate concentration in roots is considered important for optimizing developmental programs. Technologies to quantify the expression levels of nitrate transporters and assimilating enzymes at the cellular level have improved drastically in the past decade. However, a technological gap remains for detecting nitrate at a high spatial resolution. Using extraction-based methods, it is challenging to reliably estimate nitrate concentration from a small volume of cells (i.e., with high spatial resolution), since targeting a small or specific group of cells is physically difficult. Alternatively, nitrate detection with microelectrodes offers subcellular resolution with high cell specificity, but this method has some limitations on cell accessibility and detection speed. Finally, optical nitrate biosensors have very good (in-vivo) sensitivity (below 1 mM) and cellular-level spatial resolution, but require plant transformation, limiting their applicability. In this work, we apply Raman microspectroscopy for high-dynamic range in-vivo mapping of nitrate in different developmental zones of Arabidopsis thaliana roots in-situ.

RESULTS

As a proof of concept, we have used Raman microspectroscopy for in-vivo mapping of nitrate content in roots of Arabidopsis seedlings grown on agar media with different nitrate concentrations. Our results revealed that the root nitrate concentration increases gradually from the meristematic zone (~ 250 µm from the root cap) to the maturation zone (~ 3 mm from the root cap) in roots grown under typical growth conditions used for Arabidopsis, a trend that has not been previously reported. This trend was observed for plants grown in agar media with different nitrate concentrations (0.5-10 mM). These results were validated through destructive measurement of nitrate concentration.

CONCLUSIONS

We present a methodology based on Raman microspectroscopy for in-vivo label-free mapping of nitrate within small root tissue volumes in Arabidopsis. Measurements are done in-situ without additional sample preparation. Our measurements revealed nitrate concentration changes from lower to higher concentration from tip to mature root tissue. Accumulation of nitrate in the maturation zone tissue shows a saturation behavior. The presented Raman-based approach allows for in-situ non-destructive measurements of Raman-active compounds.

Contemporary applications of vibrational spectroscopy in plant stresses and phenotyping

Plant pathogens, including viruses, bacteria, and fungi, cause massive crop losses around the world. Abiotic stresses, such as drought, salinity and nutritional deficiencies are even more detrimental. Timely diagnostics of plant diseases and abiotic stresses can be used to provide site- and doze-specific treatment of plants. In addition to the direct economic impact, this "smart agriculture" can help minimizing the effect of farming on the environment. Mounting evidence demonstrates that vibrational spectroscopy, which includes Raman (RS) and infrared spectroscopies (IR), can be used to detect and identify biotic and abiotic stresses in plants. These findings indicate that RS and IR can be used for in-field surveillance of the plant health. Surface-enhanced RS (SERS) has also been used for direct detection of plant stressors, offering advantages over traditional spectroscopies. Finally, all three of these technologies have applications in phenotyping and studying composition of crops. Such non-invasive, non-destructive, and chemical-free diagnostics is set to revolutionize crop agriculture globally. This review critically discusses the most recent findings of RS-based sensing of biotic and abiotic stresses, as well as the use of RS for nutritional analysis of foods.

Applicability of metabolomics to improve sustainable grapevine production

Metabolites represent the end product of gene expression, protein interaction and other regulatory mechanisms. The metabolome reflects a biological system's response to genetic and environmental changes, providing a more accurate description of plants' phenotype than the transcriptome or the proteome. Grapevine (Vitis vinifera L.), established for the production of wine grapes, table grapes, and raisins, holds immense agronomical and economic significance not only in the Mediterranean region but worldwide. As all plants, grapevines face the adverse impact of biotic and abiotic stresses that negatively affect multiple stages of grape and wine industry, including plant and berry development pre- and post-harvest, fresh grapes processing and consequently wine quality. In the present review we highlight the applicability of metabolome analysis in the understanding of the mechanisms involved in grapevine response and acclimatization upon the main biotic and abiotic constrains. The metabolome of induced morphogenic processes such as adventitious rooting and somatic embryogenesis is also explored, as it adds knowledge on the physiological and molecular phenomena occurring in the explants used, and on the successfully propagation of grapevines with desired traits. Finally, the microbiome-induced metabolites in grapevine are discussed in view of beneficial applications derived from the plant symbioses.

Using Raman spectroscopy for early detection of resistance-breaking strains of tomato spotted wilt orthotospovirus in tomatoes

Tomato spotted wilt (TSW) disease caused by tomato spotted wilt orthotospovirus (TSWV, Orthotospovirus tomatomaculae) poses a significant threat to specialty and staple crops worldwide by causing over a billion dollars in crop losses annually. Current strategies for TSWV diagnosis heavily rely on nucleic acid or protein-based techniques which require significant technical expertise, and are invasive, time-consuming, and expensive, thereby catalyzing the search for better alternatives. In this study, we explored the potential of Raman spectroscopy (RS) in early detection of TSW in a non-invasive and non-destructive manner. Specifically, we investigated whether RS could be used to detect strain specific TSW symptoms associated with four TSWV strains infecting three differentially resistant tomato cultivars. In the acquired spectra, we observed notable reductions in the intensity of vibrational peaks associated with carotenoids. Using high-performance liquid chromatography (HPLC), we confirmed that TSWV caused a substantial decrease in the concentration of lutein that was detected by RS. Finally, we demonstrated that Partial Least Squares-Discriminant Analysis (PLS-DA) could be used to differentiate strain-specific TSW symptoms across all tested cultivars. These results demonstrate that RS can be a promising solution for early diagnosis of TSW, enabling timely disease intervention and thereby mitigating crop losses inflicted by TSWV.