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Advancing DNA Structural Analysis: A SERS Approach Free from Citrate Interference Combined with Machine Learning.

Ying Zhang1, Xiaoming Lyu1, Yaowen Xing1

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This study presents a new surface-enhanced Raman spectroscopy (SERS) platform using silver nanoparticles to detect DNA structures without interference. This breakthrough enables precise analysis of various DNA conformations for diagnostics and research.

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Area of Science:

  • Biomolecular analysis
  • Spectroscopy
  • Nanotechnology

Background:

  • Surface-enhanced Raman spectroscopy (SERS) is vital for biomolecular analysis.
  • Citrate interference in SERS hinders accurate DNA signal detection.
  • Existing methods struggle with spectral overlap from citrate ions and DNA features.

Purpose of the Study:

  • To develop an ultrasensitive SERS platform for interference-free DNA detection.
  • To enable direct structural characterization of diverse DNA conformations.
  • To overcome spectral interference challenges in SERS-based DNA analysis.

Main Methods:

  • Utilized thiol-modified silver nanoparticles (Ag@SDCNPs) to eliminate citrate interference.
  • Employed calcium ions as aggregating agents and deuterated methanol as an internal standard.
  • Applied machine learning (ML) techniques including LDA, t-SNE, and SVM for spectral classification and predictive modeling.

Main Results:

  • Achieved direct, interference-free detection and structural characterization of various DNA forms (ssDNA, dsDNA, i-motif, hairpin, G-quadruplex, tsDNA).
  • Demonstrated high spectral quality and reproducibility using optimized SERS conditions.
  • Attained classification and prediction accuracies exceeding 99% using ML models.

Conclusions:

  • Established a robust and versatile SERS platform for comprehensive DNA structural analysis.
  • Overcame citrate interference, enabling precise detection of complex DNA structures.
  • The platform holds significant potential for advancing clinical diagnostics and biomedical research through improved DNA analysis.