Issue:
The security of our nation’s agricultural sectors face continuous threats from invasive species, which pose substantial risks to American agriculture, food supplies, natural resources, and the economy. Detection at the borders is crucial for preventing their spread and safeguarding native biodiversity and ecosystem integrity, ultimately reducing economic losses, ecological damages, and public health risks. Developing and testing innovative, accurate, detection and inspection systems are keys to addressing a broad range of existing and anticipated invasive species concerning threats.
Objective:
We propose to develop integrated multiplexed sensing platforms to simultaneously detect multiple invasive and/or quarantined pathogens with diverse lifestyles, including viruses, bacteria, fungi, and oomycete pathogens. These platforms build upon a recent pioneering development of a patent-pending LAMP chip for detecting an invasive oomycete pathogen, Phytophthora infestans, by integrating LAMP, nanotechnology, and smartphone technologies.
Value Proposition:
This research is expected to yield a prototype integrated sensing platform that can facilitate the rapid screening and detection of pathogens with high sensitivity, suitable for lab settings and at point of care. Additionally, the multiplexed sensing platform is cost-effective and scalable, with the potential to be expanded for high-throughput detection of pathogens at the borders. Successful development of these tools matched with effective training will lead to improved detection outcomes while reducing inspection bottlenecks that can diminish the economic benefits of trade.
These innovative platforms enable rapid, ultrasensitive, and label-free detection of various pathogens within 30 minutes in both laboratory and point-of-care settings. Furthermore, the LAMP primers are designed to be immobilized on the sensor, allowing the resulting LAMP products to attach to the sensor, thereby significantly reducing the likelihood of carry-over contamination.
Project Lead | Texas A&M AgriLife Research |
Research Team | PI: Junqi Song, PhD Co-PI: Kranthi Mandani, PhD Co-PI: Long Que, PhD |
Budget | $523,879 |
Duration | November 2024 – November 2026 |