Surface Enhanced Raman Spectroscopy (SERS) based sensing platform with machine learning


Invention Summary:

Surface enhanced Raman spectroscopy (SERS) is a technique that can discriminate analyte molecules from the unique vibrational pattern they produce, which is correlated to their molecular structure. Identifying changes such as single base mutation in DNA, RNA and disease-specific microRNA biomarkers can facilitate early diagnosis of genetic diseases. However, achieving such sensitivity with traditional sequencing methods like PCR and NGS is challenging and very expensive.

Rutgers researchers have developed a direct SERS-based sensing platform using colloidal silver nanoparticle substrates combined with machine learning‐based functional data analysis to identify single base variation in oligonucleotide sequences. This rapid and cost-effective method has successfully demonstrated the sequencing of 22-nucleotide-long DNA and RNA strands, as well as the detection of mutations in disease-specific microRNA sequences. The technology offers an affordable and scalable approach for DNA and RNA mutation detection, making it well-suited for clinical diagnostic applications.

Market Applications:

  • Disease Diagnostic Kits
  • Infectious Disease Monitoring
  • Precision Medicine
  • Genetic Counseling
  • Early Disease Detection (i.e., cancer)
  • Research and Drug Development

Advantages:

  • Inexpensive and rapid analysis compared to standard sequencing methods
  • Highly sensitive platform capable of differentiating between original and mutated sequences
  • Requires minimal technical expertise

Publications: •   https://doi.org/10.1021/acssensors.4c00166

Intellectual Property & Development Status: Provisional application filed. Patent pending. Available for licensing and/or research collaboration. For any business development and other collaborative partnerships, contact:  marketingbd@research.rutgers.edu

Patent Information: