NanosynQ：A DNA Nanosensor For The Field-Deployable, Point-Of-Contact Detection Diagnosis Of Plant Diseases
Rapid and sustainable improvements in agricultural productivity are urgently needed to meet human food requirements and overcome entrenched poverty. New technologies are needed to address challenges facing food security, including those associated with food safety, plant, animal and human health, and transportation. These problems are especially important in the developing world, which not only has the greatest food needs, but also lacks the sophisticated monitoring and analytical tools needed to apply cutting edge approaches to agricultural production and sustainability. In agricultural systems, emerging diseases account for substantial losses in crops and livestock. Controlling these diseases requires rapid, accurate and reliable detection of the presence of disease causing agents as well as the genetic traits that confer host resistance or sensitivity to these diseases. Current technologies for genetic detection are expensive, unreliable, are difficult to deploy (especially in developing regions), and provide only limited information.
To address these issues, we are developing field-deployable, point-of-contact, DNA nanosensors for the rapid identification of DNA-based traits. To do this, we are integrating and deploying two novel technologies developed at MSU. First, we have developed nanosensor-based assays for DNA signatures that are rapid, sensitive, highly specific, inexpensive and adaptable to a range of disease targets. This work is being conducted in collaboration with Dr. Evangelyn Alocilja (http://www.egr.msu.edu/~alocilja/). Second, in collaboration with the laboratory of Dr. Dave Kramer (https://prl.natsci.msu.edu/people/faculty/david-m-kramer/), we are adapting our DNA nanosensor with PhotosynQ (https://www.photosynq.org/), to created field-deployable nanosensors to create sophisticated, portable, globally-connected phenotyping tools and analytics.
PhotosynQ was developed to specifically address fundamental challenges facing agriculture – such as plant health and food security – by enabling locally appropriate agricultural intelligence solutions. Using the PhotosynQ platform and tools, communities will generate actionable data that can guide the management and breeding of plants to improve the productivity and sustainability of agriculture in their region.
As a diagnostic sensor for human and food-borne pathogens, the nanoparticle-based assay we have developed is robust, relying on the specificity of complementary DNA-target + DNA-probe interactions. Our DNA nanosensor method is a highly specific, quantitative assay with detection limits approaching pg/mL sensitivity limits. Since this assay is DNA-based, it has broad applicability across organisms, matrices, and scales. Key elements of the nano-assay include: 1) analytical sensitivity; 2) analytical specificity; 4) speed – total time from sample-to-result is approximately 30 minutes; 5) affordable (lab cost is <1 cent per assay); and 6) highly adaptable to all biologics and chemistries.
One of the key advantages of the PhotosynQ platform is that it allows users to design methods and diagnostic tools for point-of-contact field deployment. With the development of a lab-based, rapid nanobased assay, we are using PhotosynQ to mobilize the assay, putting it in the hands of stakeholders at “the front line.” In parallel to on ongoing refinement of current, developed, DNA-nanosensor assays, we are expanding into additional areas, including the development of methods to detect specific traits (plants + pathogens) that impact food production, security, plant and human health, and environmental features that impact agriculture. These include both DNA and RNA detection methods. Coupled with the photosynthetic and environmental analytical tools underpinning PhotosynQ’s primary function, the addition of gene-based trait analyses not only enhances the breadth of PhotosynQ’s application, but when used in parallel enhance specificity as well as provide information that will ultimately permit users to address questions related to movement, adaptation, and the efficacy of previous actions.