Nanomaterials with unique and powerful optical properties have emerged as promising tools for imaging and sensing small biological molecules present in cells, tissues, and live organisms. Imaging these important molecules is hampered by the lack of sensing tools, limiting our ability to understand how certain biological molecules allow the brain to communicate with the body. This project will develop new experimental and theoretical tools to detect the presence of brain-relevant molecules, with a focus on two molecules called serotonin and oxytocin, which are involved in human mood, emotions, social behavior, and their dysregulation in disorders like depression and autism. The new tools are based on DNA molecules, long molecules composed of repeating subunits, wrapped around long and rigid carbon nanotubes, which will detect serotonin and oxytocin in the sample by emitting light. The key objective is to discover special DNA molecules that will bind strongly to serotonin and oxytocin, bring them close to the nanotube surface, and change the optical signal that the nanotube emits. This objective will be addressed by combining the information obtained in experimental screening of DNA-nanotube systems with the artificial intelligence computer methods and computer simulations. The project will lead to new methods for discovery of useful DNA molecules, which could be applied towards developing tools to detect other biological molecules of interest that currently remain ‘invisible’. The efforts in this project will enrich the training and research experiences of underrepresented students at the University of Texas at El Paso and the University of California, Berkeley and provide the basis for demonstrations on science behind serotonin and oxytocin to middle school student groups.

Sensing and imaging of small molecular analytes within complex biological samples is of high interest but remains a challenge due to the lack of suitable imaging tools. This project will establish methods to systematically develop conjugates of single-walled carbon nanotubes and single stranded DNA for optical sensing of selected analytes. The methods will be established for two analytes, serotonin, and oxytocin brain neuromodulators. To develop new DNA-nanotube optical sensors, unique DNA sequences, which simultaneously bind with high affinity to the analytes on nanotube surfaces and lead to strong optical response of the nanotubes, need to be identified. Yet, discovering these useful DNA sequences has been a challenge, with most research efforts relying on screening-based serendipity. In this project, the research team will combine the high throughput datasets of experimental DNA sequence screening methods, lower throughput spectroscopic measurements, and the artificial intelligence-based methodology to learn and predict DNA sequences that bind with high affinity to analytes on nanotube surfaces and induce high nanotube optical activity. First, the team will develop and validate new methods to: 1) determine short DNA sequences that bind with high affinity to serotonin and oxytocin molecules on nanotube surfaces; and 2) learn and predict DNA sequences that provide high optical response to DNA-nanotube conjugates in the presence of the analytes. Then, the molecular basis of analyte recognition by DNA sequences on nanotube surfaces will be explored with simulations. This project should have a significant impact on many individuals from underrepresented groups at the University of Texas at El Paso and the University of California, Berkeley through several activities that will be organized. The computational chemistry modules will be introduced into physical chemistry laboratory classes, a student-coordinated research event will be organized, and demonstrations on the science behind serotonin and oxytocin will be developed for middle school student groups. Undergraduate students from underrepresented groups will be recruited and trained in the above research.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Project Start
Project End
Budget Start
2021-06-01
Budget End
2024-05-31
Support Year
Fiscal Year
2021
Total Cost
$179,642
Indirect Cost
Name
University of Texas at El Paso
Department
Type
DUNS #
City
El Paso
State
TX
Country
United States
Zip Code
79968