Rapid Detection and Quantification of Plant Innate Immunity Response Using Raman Spectroscopy

As global food supplies and security have been challenged by water scarcity and climate variations, the expected increase in food demand will require a corresponding increase in crop productivity and disruptive improvements in agricultural production systems, including implementing strategies to mitigate the degradation of crop yield caused by plant diseases. Several groups have explored the use of Raman spectroscopy for rapid diagnosis of such diseases. Raman spectrum can record molecular vibrations of cellular metabolites without the use of a label or reagents. Differences in the Raman spectra of a diseased sample compared to that of the control sample are as distinguishing as fingerprints, reflecting changes in cellular metabolites following pathogen infection. In a collaborative effort between the Disruptive & Sustainable Technologies for Agricultural Precision (DiSTAP) Interdisciplinary Research Group (IRG) at Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore, and the Temasek Life Sciences Laboratory, the team has developed a rapid Raman spectroscopy-based method for the detection and quantification of early innate immunity responses in Arabidopsis and Choy Sum plants. Spectroscopy had the opportunity to speak to members of the team about this method.

Your recent paper (1) describes a rapid Raman spectroscopy-based method for the detection and quantification of early innate immunity responses in Arabidopsis and Choy Sum plants. Why do you believe that there is a need for such a tool? What sort of information or insight does it provide that currently available methods or tools do not?
Gajendra P Singh, Scientific Director and Principal Investigator at DiSTAP: Bacterial and fungal infections are one of the main causes of crop yield loss. Early detection is the key to limiting the economic and human toll. Existing infection detection technologies are either biochemical assay based which require significant resources and time and hence are cost-prohibitive for farmers or lack necessary accuracy. With a growing population and climate change, it is vital to increase the yield of crops. Hence, there is an urgent need for a tool that can accurately detect early-stage infection in plants in the field. The rapid quantitative Raman spectroscopy-based technology developed at DiSTAP by researchers at the Singapore MIT Alliance for Research and Technology (SMART) and the Temasek Life Sciences Laboratory (TLL) meets this unmet need.

Why these particular plants?
Singh: The vision for the DiSTAP program is to develop disruptive and sustainable technologies for agricultural precision needed for Singapore's agricultural production and the associated global market. One of the main goals of DiSTAP is to help achieve the “30 by 30” goal of the Singapore Government, where 30 percent of Singapore’s nutritional needs will be produced locally by 2030 and green leafy vegetables form an important part of this goal. Hence, we started developing analytical tools for these particular plants, but the technology is currently also being applied and developed for other commercial crops both in urban farms and open fields.

Why did you choose a p-test rather than other mathematical methods, such as chemometrics, to discriminate between plants challenged with flg22 and elf18 elicitors that could be differentiated from mock-treated plants?
Singh: Raman spectra obtained from healthy, and early-stage infected plants look very similar to the naked eye but using statistical methods we can spot the difference. The p-test is commonly used in industry and is a time-tested and simple statistical method that can be used to negate the indifference between the averages of two samples. Hence, we chose the p-test to identify the relevant areas of the Raman spectrum. We have now also developed machine learning-based algorithms, which require large datasets and will be publishing the results in our next paper.

How did you determine if the sample size was adequate to draw meaningful conclusions for your work?
Singh: We performed extensive experiments where spectra were acquired from tens of locations of a plant leaf and also from tens of different plant biological replicates. Data processing revealed that prediction accuracy is not compromised if at least three locations on a plant leaf and at least three biological replicates are selected. We considered it as an adequate sample size that allows for real-time application of the technology.

What benefits did using Raman spectroscopy provide over direct PCR or other biochemical techniques?
Singh: Raman spectroscopy can be applied in the field without disturbing the plant, is accurate and cost-effective, and requires minimal sample preparation and technical supervision of trained technical staff.

Briefly discuss your findings and their implications for better understanding of the analytical results.
Rajani Sarojam, Principal Investigator at DiSTAP, Principal Investigator at Temasek Life Sciences Laboratory: Our study found that pathogen/elicitor treated plants could be differentiated from healthy plants based on their Raman spectral fingerprints before the manifestation of any disease symptoms. Several characterized Arabidopsis early innate immune response mutants were studied to validate these results. The Raman spectral changes observed between pathogen/elicitor treated plants and mock-treated plants implicated a transient increase in numerous metabolites, most significantly carotenoids, to be involved in the early innate immune response. The differences in the Raman spectra obtained from pathogen-infected plants versus that of the mock infiltrated plants were quantified using statistical methods and mathematical formula, and an Infection response factor (IRF) was developed. IRF potentially can be used as a quantitative measure to detect early immune response in plants to indicate disease onset. The use of IRF as a diagnostic Raman signature for early innate immune response will provide an important tool for the identification of plants with early pathogen infection and facilitate effective disease management.

What are the biggest challenges that you have encountered in developing this method? What options or alternative developments are available to overcome these challenges or to improve your approach?
Rajeev Ram, Principal Investigator at DiSTAP, Professor at Massachusetts Institute of Technology: The most significant challenge in applying Raman spectroscopy in the early detection of plant infection was in the complexity of the plant’s immune response. Unlike our previous work detecting nitrogen deficiency or shade stress, the immune response of the plant involves simultaneous changes in the concentration and structure of many molecules in the leaf. Developing the appropriate mathematical tools to analyze these complex changes took over two years of effort, but today we have a metric that provides the farmer with a yes/no answer on whether the plant being tested is infected. This technique requires approximately 5 minutes to analyze an individual plant, and our goal is to make these measurements even faster to support rapid measurements across an entire facility.

What sort of feedback did you receive from this paper and the study results?
Singh: This research is a pioneering study in the field of accurate and real-time biotic stress detection in commercial crops. It lays down the foundation for the application of quantitative Raman technology in urban farms and open field crops. The reviewers found that this non-invasive early diagnostic method may find wide application for early detection of pathogen infection in agriculture settings and that we proved the effectiveness of Raman spectroscopy in detecting bacterial infections in plants at an early, still asymptomatic stage.

What are your next steps regarding this research? Are there any additional analytical approaches or methods where this tool might be beneficial? Do you think this method will be useful for routine analysis or for routine screening of plants?
Nam-Hai Chua, Co-Lead Principal Investigator at DiSTAP, Temasek Senior Investigator (Emeritus) at Temasek Life Sciences Laboratory: We would like to explore the use of this technique for rapid screening of Arabidopsis mutants compromised in the plant immunity pathway. Rapid screening of mutants followed by their detailed characterization would allow us to identify additional molecular components of the immunity signaling pathway. It is known that a plant’s molecular responses to fungal infection share many common features as those elicited by bacterial infection. We are currently using Raman spectroscopy to characterize the responses of Arabidopsis wild type and mutant plants upon fungal infection, with the aim to obtain Raman signatures specific to infection by fungal pathogens.

  1. P. Joong Chung, G.P. Singh, Huang C-H. Huang, S. Koyyappurath, J.S. Seo, H-Z. Mao, P. Diloknawarit, R.J. Ram, R. Sarojam, and N-H. Chua, Front. Plant Sci. 12, 2254 (2021).

Nam-Hai Chua is Temasek Senior Investigator (Emeritus), Temasek Life Sciences Laboratory (TLL) and Chief Scientific Advisor, Wilmar Intl Ltd. He worked as the Andrew Mellon Professor and Head of the Laboratory of Plant Molecular Biology at The Rockefeller University. He is also the Co-Lead Principal Investigator at DiSTAP, SMART in Singapore.

Gajendra P Singh is the Scientific Director and Principal Investigator at Disruptive & Sustainable Technologies for Agricultural Precision (DiSTAP) Interdisciplinary Research Group (IRG) at Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore

Rajeev Ram is Professor of Electrical Engineering at MIT and also a Principal Investigator at DiSTAP, SMART.

Rajani Sarojam is Principal Investigator at the Temasek Life Sciences Laboratory (TLL) and a Principal Investigator at DiSTAP, SMART.