Singapore-MIT Alliance for Research and Technology (SMART)

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      in Environment

      SMART researchers develop method for early detection of bacterial infection in crops

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      Researchers from the Disruptive and Sustainable Technologies for Agricultural Precision (DiSTAP) Interdisciplinary Research Group (IRG) ofSingapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore, and their local collaborators from Temasek Life Sciences Laboratory (TLL), have developed a rapid Raman spectroscopy-based method for detecting and quantifying early bacterial infection in crops. The Raman spectral biomarkers and diagnostic algorithm enable the noninvasive and early diagnosis of bacterial infections in crop plants, which can be critical for the progress of plant disease management and agricultural productivity.

      Due to the increasing demand for global food supply and security, there is a growing need to improve agricultural production systems and increase crop productivity. Globally, bacterial pathogen infection in crop plants is one of the major contributors to agricultural yield losses. Climate change also adds to the problem by accelerating the spread of plant diseases. Hence, developing methods for rapid and early detection of pathogen-infected crops is important to improve plant disease management and reduce crop loss.

      The breakthrough by SMART and TLL researchers offers a faster and more accurate method to detect bacterial infection in crop plants at an earlier stage, as compared to existing techniques. The new results appear in a paper titled “Rapid detection and quantification of plant innate immunity response using Raman spectroscopy” published in the journal Frontiers in Plant Science.

      “The early detection of pathogen-infected crop plants is a significant step to improve plant disease management,” says Chua Nam Hai, DiSTAP co-lead principal investigator, professor, TLL deputy chair, and co-corresponding author. “It will allow the fast and selective removal of pathogen load and curb the further spread of disease to other neighboring crops.”

      Traditionally, plant disease diagnosis involves a simple visual inspection of plants for disease symptoms and severity. “Visual inspection methods are often ineffective, as disease symptoms usually manifest only at relatively later stages of infection, when the pathogen load is already high and reparative measures are limited. Hence, new methods are required for rapid and early detection of bacterial infection. The idea would be akin to having medical tests to identify human diseases at an early stage, instead of waiting for visual symptoms to show, so that early intervention or treatment can be applied,” says MIT Professor Rajeev Ram, who is a DiSTAP principal investigator and co-corresponding author on the paper.

      While existing techniques, such as current molecular detection methods, can detect bacterial infection in plants, they are often limited in their use. Molecular detection methods largely depend on the availability of pathogen-specific gene sequences or antibodies to identify bacterial infection in crops; the implementation is also time-consuming and nonadaptable for on-site field application due to the high cost and bulky equipment required, making it impractical for use in agricultural farms.

      “At DiSTAP, we have developed a quantitative Raman spectroscopy-based algorithm that can help farmers to identify bacterial infection rapidly. The developed diagnostic algorithm makes use of Raman spectral biomarkers and can be easily implemented in cloud-based computing and prediction platforms. It is more effective than existing techniques as it enables accurate identification and early detection of bacterial infection, both of which are crucial to saving crop plants that would otherwise be destroyed,” explains Gajendra Pratap Singh, scientific director and principal investigator at DiSTAP and co-lead author.

      A portable Raman system can be used on farms and provides farmers with an accurate and simple yes-or-no response when used to test for the presence of bacterial infections in crops. The development of this rapid and noninvasive method could improve plant disease management and have a transformative impact on agricultural farms by efficiently reducing agricultural yield loss and increasing productivity.

      “Using the diagnostic algorithm method, we experimented on several edible plants such as choy sum,” says DiSTAP and TLL principal investigator and co-corresponding author Rajani Sarojam. “The results showed that the Raman spectroscopy-based method can swiftly detect and quantify innate immunity response in plants infected with bacterial pathogens. We believe that this technology will be beneficial for agricultural farms to increase their productivity by reducing their yield loss due to plant diseases.”

      The researchers are currently working on the development of high-throughput, custom-made portable or hand-held Raman spectrometers that will allow Raman spectral analysis to be quickly and easily performed on field-grown crops.

      SMART and TLL developed and discovered the diagnostic algorithm and Raman spectral biomarkers. TLL also confirmed and validated the detection method through mutant plants. The research is carried out by SMART and supported by the National Research Foundation of Singapore under its Campus for Research Excellence And Technological Enterprise (CREATE) program.

      SMART was established by MIT and the NRF in 2007. The first entity in CREATE developed by NRF, SMART serves as an intellectual and innovation hub for research interactions between MIT and Singapore, undertaking cutting-edge research projects in areas of interest to both Singapore and MIT. SMART currently comprises an Innovation Center and five IRGs: Antimicrobial Resistance, Critical Analytics for Manufacturing Personalized-Medicine, DiSTAP, Future Urban Mobility, and Low Energy Electronic Systems. SMART research is funded by the NRF under the CREATE program.

      Led by Professor Michael Strano of MIT and Professor Chua Nam Hai of Temasek Lifesciences Laboratory, the DiSTAP program addresses deep problems in food production in Singapore and the world by developing a suite of impactful and novel analytical, genetic, and biomaterial technologies. The goal is to fundamentally change how plant biosynthetic pathways are discovered, monitored, engineered, and ultimately translated to meet the global demand for food and nutrients. Scientists from MIT, TTL, Nanyang Technological University, and National University of Singapore are collaboratively developing new tools for the continuous measurement of important plant metabolites and hormones for novel discovery, deeper understanding and control of plant biosynthetic pathways in ways not yet possible, especially in the context of green leafy vegetables; leveraging these new techniques to engineer plants with highly desirable properties for global food security, including high-yield density production, and drought and pathogen resistance; and applying these technologies to improve urban farming. More

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      in Security

      Researchers design sensors to rapidly detect plant hormones

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      Researchers from the Disruptive and Sustainable Technologies for Agricultural Precision (DiSTAP) interdisciplinary research group of the Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore, and their local collaborators from Temasek Life Sciences Laboratory (TLL) and Nanyang Technological University (NTU), have developed the first-ever nanosensor to enable rapid testing of synthetic auxin plant hormones. The novel nanosensors are safer and less tedious than existing techniques for testing plants’ response to compounds such as herbicide, and can be transformative in improving agricultural production and our understanding of plant growth.

      The scientists designed sensors for two plant hormones — 1-naphthalene acetic acid (NAA) and 2,4-dichlorophenoxyacetic acid (2,4-D) — which are used extensively in the farming industry for regulating plant growth and as herbicides, respectively. Current methods to detect NAA and 2,4-D cause damage to plants, and are unable to provide real-time in vivo monitoring and information.

      Based on the concept of corona phase molecular recognition (​​CoPhMoRe) pioneered by the Strano Lab at SMART DiSTAP and MIT, the new sensors are able to detect the presence of NAA and 2,4-D in living plants at a swift pace, providing plant information in real-time, without causing any harm. The team has successfully tested both sensors on a number of everyday crops including pak choi, spinach, and rice across various planting mediums such as soil, hydroponic, and plant tissue culture.

      Explained in a paper titled “Nanosensor Detection of Synthetic Auxins In Planta using Corona Phase Molecular Recognition” published in the journal ACS Sensors, the research can facilitate more efficient use of synthetic auxins in agriculture and hold tremendous potential to advance plant biology study.

      “Our CoPhMoRe technique has previously been used to detect compounds such as hydrogen peroxide and heavy-metal pollutants like arsenic — but this is the first successful case of CoPhMoRe sensors developed for detecting plant phytohormones that regulate plant growth and physiology, such as sprays to prevent premature flowering and dropping of fruits,” says DiSTAP co-lead principal investigator Michael Strano, the Carbon P. Dubbs Professor of Chemical Engineering at MIT. “This technology can replace current state-of-the-art sensing methods which are laborious, destructive, and unsafe.”

      Of the two sensors developed by the research team, the 2,4-D nanosensor also showed the ability to detect herbicide susceptibility, enabling farmers and agricultural scientists to quickly find out how vulnerable or resistant different plants are to herbicides without the need to monitor crop or weed growth over days. “This could be incredibly beneficial in revealing the mechanism behind how 2,4-D works within plants and why crops develop herbicide resistance,” says DiSTAP and TLL Principal Investigator Rajani Sarojam.

      “Our research can help the industry gain a better understanding of plant growth dynamics and has the potential to completely change how the industry screens for herbicide resistance, eliminating the need to monitor crop or weed growth over days,” says Mervin Chun-Yi Ang, a research scientist at DiSTAP. “It can be applied across a variety of plant species and planting mediums, and could easily be used in commercial setups for rapid herbicide susceptibility testing, such as urban farms.”

      NTU Professor Mary Chan-Park Bee Eng says, “Using nanosensors for in planta detection eliminates the need for extensive extraction and purification processes, which saves time and money. They also use very low-cost electronics, which makes them easily adaptable for commercial setups.”

      The team says their research can lead to future development of real-time nanosensors for other dynamic plant hormones and metabolites in living plants as well.

      The development of the nanosensor, optical detection system, and image processing algorithms for this study was done by SMART, NTU, and MIT, while TLL validated the nanosensors and provided knowledge of plant biology and plant signaling mechanisms. The research is carried out by SMART and supported by NRF under its Campus for Research Excellence And Technological Enterprise (CREATE) program.

      DiSTAP is one of the five interdisciplinary research roups in SMART. The DiSTAP program addresses deep problems in food production in Singapore and the world by developing a suite of impactful and novel analytical, genetic, and biosynthetic technologies. The goal is to fundamentally change how plant biosynthetic pathways are discovered, monitored, engineered, and ultimately translated to meet the global demand for food and nutrients.

      Scientists from MIT, TTL, NTU, and National University of Singapore (NUS) are collaboratively developing new tools for the continuous measurement of important plant metabolites and hormones for novel discovery, deeper understanding and control of plant biosynthetic pathways in ways not yet possible, especially in the context of green leafy vegetables; leveraging these new techniques to engineer plants with highly desirable properties for global food security, including high yield density production, drought, and pathogen resistance and biosynthesis of high-value commercial products; developing tools for producing hydrophobic food components in industry-relevant microbes; developing novel microbial and enzymatic technologies to produce volatile organic compounds that can protect and/or promote growth of leafy vegetables; and applying these technologies to improve urban farming.

      DiSTAP is led by Michael Strano and Singapore co-lead principal investigator Professor Chua Nam Hai.

      SMART was established by MIT, in partnership with the NRF, in 2007. SMART, the first entity in CREATE, serves as an intellectual and innovation hub for research interactions between MIT and Singapore, undertaking cutting-edge research projects in areas of interest to both. SMART currently comprises an Innovation Center and five interdisciplinary research groups: Antimicrobial Resistance (AMR), Critical Analytics for Manufacturing Personalized-Medicine (CAMP), DiSTAP, Future Urban Mobility (FM), and Low Energy Electronic Systems (LEES). SMART is funded by the NRF. More

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      in Security

      A new way to detect the SARS-CoV-2 Alpha variant in wastewater

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      Researchers from the Antimicrobial Resistance (AMR) interdisciplinary research group at the Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore, alongside collaborators from Biobot Analytics, Nanyang Technological University (NTU), and MIT, have successfully developed an innovative, open-source molecular detection method that is able to detect and quantify the B.1.1.7 (Alpha) variant of SARS-CoV-2. The breakthrough paves the way for rapid, inexpensive surveillance of other SARS-CoV-2 variants in wastewater.

      As the world continues to battle and contain Covid-19, the recent identification of SARS-CoV-2 variants with higher transmissibility and increased severity has made developing convenient variant tracking methods essential. Currently, identified variants include the B.1.17 (Alpha) variant first identified in the United Kingdom and the B.1.617.2 (Delta) variant first detected in India.

      Wastewater surveillance has emerged as a critical public health tool to safely and efficiently track the SARS-CoV-2 pandemic in a non-intrusive manner, providing complementary information that enables health authorities to acquire actionable community-level information. Most recently, viral fragments of SARS-CoV-2 were detected in housing estates in Singapore through a proactive wastewater surveillance program. This information, alongside surveillance testing, allowed Singapore’s Ministry of Health to swiftly respond, isolate, and conduct swab tests as part of precautionary measures.

      However, detecting variants through wastewater surveillance is less commonplace due to challenges in existing technology. Next-generation sequencing for wastewater surveillance is time-consuming and expensive. Tests also lack the sensitivity required to detect low variant abundances in dilute and mixed wastewater samples due to inconsistent and/or low sequencing coverage.

      The method developed by the researchers is uniquely tailored to address these challenges and expands the utility of wastewater surveillance beyond testing for SARS-CoV-2, toward tracking the spread of SARS-CoV-2 variants of concern.

      Wei Lin Lee, research scientist at SMART AMR and first author on the paper adds, “This is especially important in countries battling SARS-CoV-2 variants. Wastewater surveillance will help find out the true proportion and spread of the variants in the local communities. Our method is sensitive enough to detect variants in highly diluted SARS-CoV-2 concentrations typically seen in wastewater samples, and produces reliable results even for samples which contain multiple SARS-CoV-2 lineages.”

      Led by Janelle Thompson, NTU associate professor, and Eric Alm, MIT professor and SMART AMR principal investigator, the team’s study, “Quantitative SARS-CoV-2 Alpha variant B.1.1.7 Tracking in Wastewater by Allele-Specific RT-qPCR” has been published in Environmental Science & Technology Letters. The research explains the innovative, open-source molecular detection method based on allele-specific RT-qPCR that detects and quantifies the B.1.1.7 (Alpha) variant. The developed assay, tested and validated in wastewater samples across 19 communities in the United States, is able to reliably detect and quantify low levels of the B.1.1.7 (Alpha) variant with low cross-reactivity, and at variant proportions down to 1 percent in a background of mixed SARS-CoV-2 viruses.

      Targeting spike protein mutations that are highly predictive of the B.1.1.7 (Alpha) variant, the method can be implemented using commercially available RT-qPCR protocols. Unlike commercially available products that use proprietary primers and probes for wastewater surveillance, the paper details the open-source method and its development that can be freely used by other organizations and research institutes for their work on wastewater surveillance of SARS-CoV-2 and its variants.

      The breakthrough by the research team in Singapore is currently used by Biobot Analytics, an MIT startup and global leader in wastewater epidemiology headquartered in Cambridge, Massachusetts, serving states and localities throughout the United States. Using the method, Biobot Analytics is able to accept and analyze wastewater samples for the B.1.1.7 (Alpha) variant and plans to add additional variants to its analysis as methods are developed. For example, the SMART AMR team is currently developing specific assays that will be able to detect and quantify the B.1.617.2 (Delta) variant, which has recently been identified as a variant of concern by the World Health Organization.

      “Using the team’s innovative method, we have been able to monitor the B.1.1.7 (Alpha) variant in local populations in the U.S. — empowering leaders with information about Covid-19 trends in their communities and allowing them to make considered recommendations and changes to control measures,” says Mariana Matus PhD ’18, Biobot Analytics CEO and co-founder.

      “This method can be rapidly adapted to detect new variants of concern beyond B.1.1.7,” adds MIT’s Alm. “Our partnership with Biobot Analytics has translated our research into real-world impact beyond the shores of Singapore and aid in the detection of Covid-19 and its variants, serving as an early warning system and guidance for policymakers as they trace infection clusters and consider suitable public health measures.”

      The research is carried out by SMART and supported by the National Research Foundation (NRF) Singapore under its Campus for Research Excellence And Technological Enterprise (CREATE) program.

      SMART was established by MIT in partnership with the National Research Foundation of Singapore (NRF) in 2007. SMART is the first entity in CREATE developed by NRF. SMART serves as an intellectual and innovation hub for research interactions between MIT and Singapore, undertaking cutting-edge research projects in areas of interest to both Singapore and MIT. SMART currently comprises an Innovation Center and five IRGs: AMR, Critical Analytics for Manufacturing Personalized-Medicine, Disruptive and Sustainable Technologies for Agricultural Precision, Future Urban Mobility, and Low Energy Electronic Systems.

      The AMR interdisciplinary research group is a translational research and entrepreneurship program that tackles the growing threat of antimicrobial resistance. By leveraging talent and convergent technologies across Singapore and MIT, AMR aims to develop multiple innovative and disruptive approaches to identify, respond to, and treat drug-resistant microbial infections. Through strong scientific and clinical collaborations, its goal is to provide transformative, holistic solutions for Singapore and the world. More